Simon John Christoph Soerensen

All Publications

• Facility-Level Variation in Nephrology Care among Veterans after Urinary Stone Diagnosis. Kidney360 Thomas, K., Ganesan, C., Liu, S., Thomas, I. C., Montez-Rath, M. E., Christoph Soerensen, S. J., Chertow, G. M., Leppert, J. T., Pao, A. C. 2024

  Abstract

  Prevention is a cornerstone for management of recurrent urinary stone disease. Current guidelines recommend metabolic evaluation, lifestyle modification, and medical treatment for patients with urinary stone disease. Nephrologists are uniquely qualified to evaluate stone risk and formulate treatment strategies to reduce that risk. The objective of this study was to determine the frequency of nephrology visits after a urinary stone diagnosis, a key window of opportunity to assess stone risk.We used nationwide data from the United States Veterans Health Administration to identify patients who had an incident stone diagnosis between 2016 and 2018. We examined the proportion of patients who visited a nephrology clinic within 6 months of stone diagnosis.We identified 42,927 Veterans with urinary stone disease. Only 2432 (5.6%) visited a nephrology clinic within six months of the index diagnosis. The proportion of patients who visited a nephrology clinic after their stone diagnosis ranged between 0.7 and 20.7% across 104 VHA sites, with a median of 4.6% (25%, 75% range 3.4-7.0%). The median rate ratio for a nephrology follow-up visit after a stone diagnosis was 1.72. Veterans with chronic kidney disease were significantly more likely to visit a nephrology clinic relative to Veterans without chronic kidney disease (OR 5.19; 95% CI 4.69, 5.74).Nephrologists are infrequently and variably involved in the care of patients after a urinary stone diagnosis, suggesting potential for quality improvement.

  View details for DOI 10.34067/KID.0000000639

  View details for PubMedID 39561010

• Pesticides and prostate cancer incidence and mortality: An environment-wide association study. Cancer Soerensen, S. J., Lim, D. S., Montez-Rath, M. E., Chertow, G. M., Chung, B. I., Rehkopf, D. H., Leppert, J. T. 2024

  Abstract

  Prostate cancer is the most common cancer among men in the United States, yet modifiable risk factors remain elusive. In this study, the authors investigated the potential role of agricultural pesticide exposure in prostate cancer incidence and mortality.For this environment-wide association study (EWAS), linear regression was used to analyze county-level associations between the annual use of 295 distinct pesticides (measured in kg per county) and prostate cancer incidence and mortality rates in the contiguous United States. Data were analyzed in two cohorts: 1997-2001 pesticide use with 2011-2015 outcomes (discovery) and 2002-2006 use with 2016-2020 outcomes (replication). The reported effect sizes highlight how a 1-standard-deviation increase in log-transformed pesticide use (kg per county) corresponds to changes in incidence. Analyses were adjusted for county-level demographics, agricultural data, and multiple testing.Twenty-two pesticides showed consistent, direct associations with prostate cancer incidence across both cohorts. Of these, four pesticides were also associated with prostate cancer mortality. In the replication cohort, each 1-standard-deviation increase in log-transformed pesticide use corresponded to incidence increases per 100,000 individuals (trifluralin, 6.56 [95% confidence interval (CI), 5.04-8.07]; cloransulam-methyl, 6.18 [95% CI, 4.06-8.31]; diflufenzopyr, 3.20 [95% CI, 1.09-5.31]; and thiamethoxam, 2.82 [95% CI, 1.14-4.50]). Limitations included ecological study design, potential unmeasured confounding, and lack of individual-level exposure data.The results of this study suggest a potential link between certain pesticides and increased prostate cancer incidence and mortality. These findings warrant further investigation of these specific pesticides to confirm their role in prostate cancer risk and to develop potential public health interventions.

  View details for DOI 10.1002/cncr.35572

  View details for PubMedID 39492609

• Variation in Urology Care after Urinary Stone Surgery among Veterans at High-risk for Recurrence. Urology Ganesan, C., Shahzad, S. R., Thomas, I. C., Montez-Rath, M. E., Soerensen, S. J., Chertow, G. M., Pao, A. C., Leppert, J. T. 2024

  Abstract

  To determine rates of urology follow-up and implementation of stone prevention measures after stone surgery and to assess variation in care delivery within a large, integrated healthcare system.We used nationwide data from the United States Veterans Health Administration to identify patients who had stone surgery between 2016 and 2018 and who were at higher risk for recurrence. Our cohort included 13,444 Veterans across 90 facilities. We examined the proportion of patients who had a post-operative urology visit or who received a prevention measure (24-hour urine test, serum parathyroid hormone measurement, or prescription of a stone-related medication) within 6 months of stone surgery. We calculated the median odds ratio to quantify facility-level variation in urology care after stone surgery, adjusting for patient- and facility-level characteristics.Within 6 months of stone surgery, 94.2% Veterans had a urology visit, yet only 8.8% completed 24-hour urine testing, 8.4% had a parathyroid hormone measurement, and 31.0% were prescribed a stone-related medication. Implementation of prevention measures varied widely across facilities with the median odds ratio ranging between 1.18 for medication prescriptions and 1.77 for 24-hour urine testing.While most patients have a urology visit after stone surgery, stone prevention measures were implemented infrequently and inconsistently for patients at higher risk for recurrence, indicating an opportunity for quality improvement.

  View details for DOI 10.1016/j.urology.2024.10.012

  View details for PubMedID 39419204

• An Automated Electronic Health Record Score to Estimate Length of Stay and Readmission in Patients Undergoing Radical Cystectomy for Bladder Cancer. The Journal of urology Christoph Soerensen, S. J., Schmidt, B., Thomas, I., Montez-Rath, M. E., Thong, A. E., Prado, K., Shah, J. B., Skinner, E. C., Leppert, J. T. 2024: 101097JU0000000000004262

  Abstract

  PURPOSE: Patients treated with radical cystectomy experience a high rate of postoperative complications and frequent hospital readmissions. We sought to explore the utility of the Care Assessment Needs (CAN) score, derived from electronic health data, to estimate the risk of these adverse clinical outcomes, thereby aiding patient counseling and informed treatment decision-making.MATERIALS AND METHODS: We retrospectively examined data from 982 bladder cancer patients who underwent radical cystectomy between 2013 to 2018 within the national Veterans Health Administration system. We tested for associations between the preoperative CAN score and length of stay, discharge location, and readmission rates.RESULTS: We observed a correlation between higher CAN scores and longer hospital stays (adjusted relative risk = 1.03 [95% CI: 1.02-1.05]). An increased CAN score was also linked to greater odds of discharge to a skilled nursing facility or death (adjusted odds ratio = 1.16 [95% CI: 1.06-1.26]). Furthermore, the score was associated with hospital readmission at both 30 and 90 days post-discharge (adjusted hazard ratio = 1.03 [95% CI: 1.00-1.07] and 1.04 [95% CI: 1.00-1.07], respectively).CONCLUSIONS: The CAN score is associated with the length of hospital stay, discharge to a skilled nursing facility, and readmission within 30 and 90 days following radical cystectomy. These findings highlight the potential of healthcare systems leveraging electronic health records for automatically calculating multi-dimensional tools, like the CAN score, to identify patients at risk of adverse clinical outcomes following radical cystectomy.

  View details for DOI 10.1097/JU.0000000000004262

  View details for PubMedID 39357009

• Trends in pre-biopsy MRI usage for prostate cancer detection, 2007-2022. Prostate cancer and prostatic diseases Soerensen, S. J., Li, S., Langston, M. E., Fan, R. E., Rusu, M., Sonn, G. A. 2024

  Abstract

  Clinical guidelines favor MRI before prostate biopsy due to proven benefits. However, adoption patterns across the US are unclear.This study used the Merative™ Marketscan® Commercial & Medicare Databases to analyze 872,829 prostate biopsies in 726,663 men from 2007-2022. Pre-biopsy pelvic MRI within 90 days was the primary outcome. Descriptive statistics and generalized estimating equations assessed changes over time, urban-rural differences, and state-level variation.Pre-biopsy MRI utilization increased significantly from 0.5% in 2007 to 35.5% in 2022, with faster adoption in urban areas (36.1% in 2022) versus rural areas (28.3% in 2022). Geographic disparities were notable, with higher utilization in California, New York, and Minnesota, and lower rates in the Southeast and Mountain West.The study reveals a paradigm shift in prostate cancer diagnostics towards MRI-guided approaches, influenced by evolving guidelines and clinical evidence. Disparities in access, particularly in rural areas and specific regions, highlight the need for targeted interventions to ensure equitable access to advanced diagnostic techniques.

  View details for DOI 10.1038/s41391-024-00896-y

  View details for PubMedID 39306635

  View details for PubMedCentralID 9084630

• External validation of an artificial intelligence model for Gleason grading of prostate cancer on prostatectomy specimens. BJU international Schmidt, B., Soerensen, S. J., Bhambhvani, H. P., Fan, R. E., Bhattacharya, I., Choi, M. H., Kunder, C. A., Kao, C. S., Higgins, J., Rusu, M., Sonn, G. A. 2024

  Abstract

  To externally validate the performance of the DeepDx Prostate artificial intelligence (AI) algorithm (Deep Bio Inc., Seoul, South Korea) for Gleason grading on whole-mount prostate histopathology, considering potential variations observed when applying AI models trained on biopsy samples to radical prostatectomy (RP) specimens due to inherent differences in tissue representation and sample size.The commercially available DeepDx Prostate AI algorithm is an automated Gleason grading system that was previously trained using 1133 prostate core biopsy images and validated on 700 biopsy images from two institutions. We assessed the AI algorithm's performance, which outputs Gleason patterns (3, 4, or 5), on 500 1-mm2 tiles created from 150 whole-mount RP specimens from a third institution. These patterns were then grouped into grade groups (GGs) for comparison with expert pathologist assessments. The reference standard was the International Society of Urological Pathology GG as established by two experienced uropathologists with a third expert to adjudicate discordant cases. We defined the main metric as the agreement with the reference standard, using Cohen's kappa.The agreement between the two experienced pathologists in determining GGs at the tile level had a quadratically weighted Cohen's kappa of 0.94. The agreement between the AI algorithm and the reference standard in differentiating cancerous vs non-cancerous tissue had an unweighted Cohen's kappa of 0.91. Additionally, the AI algorithm's agreement with the reference standard in classifying tiles into GGs had a quadratically weighted Cohen's kappa of 0.89. In distinguishing cancerous vs non-cancerous tissue, the AI algorithm achieved a sensitivity of 0.997 and specificity of 0.88; in classifying GG ≥2 vs GG 1 and non-cancerous tissue, it demonstrated a sensitivity of 0.98 and specificity of 0.85.The DeepDx Prostate AI algorithm had excellent agreement with expert uropathologists and performance in cancer identification and grading on RP specimens, despite being trained on biopsy specimens from an entirely different patient population.

  View details for DOI 10.1111/bju.16464

  View details for PubMedID 38989669

• Ambient air pollution and urological cancer risk: A systematic review and meta-analysis of epidemiological evidence. Nature communications Li, J., Deng, Z., Soerensen, S. J., Kachuri, L., Cardenas, A., Graff, R. E., Leppert, J. T., Langston, M. E., Chung, B. I. 2024; 15 (1): 5116

  Abstract

  Exposure to ambient air pollution has significant adverse health effects; however, whether air pollution is associated with urological cancer is largely unknown. We conduct a systematic review and meta-analysis with epidemiological studies, showing that a 5 μg/m3 increase in PM2.5 exposure is associated with a 6%, 7%, and 9%, increased risk of overall urological, bladder, and kidney cancer, respectively; and a 10 μg/m3 increase in NO2 is linked to a 3%, 4%, and 4% higher risk of overall urological, bladder, and prostate cancer, respectively. Were these associations to reflect causal relationships, lowering PM2.5 levels to 5.8 μg/m3 could reduce the age-standardized rate of urological cancer by 1.5 ~ 27/100,000 across the 15 countries with the highest PM2.5 level from the top 30 countries with the highest urological cancer burden. Implementing global health policies that can improve air quality could potentially reduce the risk of urologic cancer and alleviate its burden.

  View details for DOI 10.1038/s41467-024-48857-2

  View details for PubMedID 38879581

  View details for PubMedCentralID PMC11180144

• PREDICTORS OF TREATMENT FAILURE AFTER FOCAL HIGH-INTENSITY FOCUSED ULTRASOUND (HIFU) OF LOCALIZED PROSTATE CANCER Soerensen, S., Sommer, E. R., Zhou, S. R., Rusu, M., Fan, R. E., Sonn, G. A. LIPPINCOTT WILLIAMS & WILKINS. 2024: E411-E412

  View details for Web of Science ID 001263885301276

• MUNICIPAL DRINKING WATER CONSTITUENTS AND BLADDER CANCER INCIDENCE: A WATER-WIDE ASSOCIATION STUDY (WWAS) Soerensen, S., Jackson, C., Rehkopf, D. H., Skinner, E. C., Prado, K. B., Chung, B. I., Leppert, J. T. LIPPINCOTT WILLIAMS & WILKINS. 2024: E584

  View details for Web of Science ID 001263885302029

• PHYSICIAN DENSITY AND PROSTATE CANCER MORTALITY IN THE US FROM 2011-2020 Soerensen, S., Montez-Rath, M. E., Chung, B. I., Rehkopf, D. H., Leppert, J. T. LIPPINCOTT WILLIAMS & WILKINS. 2024: E1110-E1111

  View details for Web of Science ID 001263885303468

• PESTICIDES AND PROSTATE CANCER INCIDENCE AND MORTALITY: AN ENVIRONMENTAL WIDE ASSOCIATION STUDY Soerensen, S., Lim, D. S., Montez-Rath, M. E., Chertow, G. M., Chung, B. I., Rehkopf, D. H., Leppert, J. T. LIPPINCOTT WILLIAMS & WILKINS. 2024: E620

  View details for Web of Science ID 001263885302097

• RAPHIA: A deep learning pipeline for the registration of MRI and whole-mount histopathology images of the prostate. Computers in biology and medicine Shao, W., Vesal, S., Soerensen, S. J., Bhattacharya, I., Golestani, N., Yamashita, R., Kunder, C. A., Fan, R. E., Ghanouni, P., Brooks, J. D., Sonn, G. A., Rusu, M. 2024; 173: 108318

  Abstract

  Image registration can map the ground truth extent of prostate cancer from histopathology images onto MRI, facilitating the development of machine learning methods for early prostate cancer detection. Here, we present RAdiology PatHology Image Alignment (RAPHIA), an end-to-end pipeline for efficient and accurate registration of MRI and histopathology images. RAPHIA automates several time-consuming manual steps in existing approaches including prostate segmentation, estimation of the rotation angle and horizontal flipping in histopathology images, and estimation of MRI-histopathology slice correspondences. By utilizing deep learning registration networks, RAPHIA substantially reduces computational time. Furthermore, RAPHIA obviates the need for a multimodal image similarity metric by transferring histopathology image representations to MRI image representations and vice versa. With the assistance of RAPHIA, novice users achieved expert-level performance, and their mean error in estimating histopathology rotation angle was reduced by 51% (12 degrees vs 8 degrees), their mean accuracy of estimating histopathology flipping was increased by 5% (95.3% vs 100%), and their mean error in estimating MRI-histopathology slice correspondences was reduced by 45% (1.12 slices vs 0.62 slices). When compared to a recent conventional registration approach and a deep learning registration approach, RAPHIA achieved better mapping of histopathology cancer labels, with an improved mean Dice coefficient of cancer regions outlined on MRI and the deformed histopathology (0.44 vs 0.48 vs 0.50), and a reduced mean per-case processing time (51 vs 11 vs 4.5 min). The improved performance by RAPHIA allows efficient processing of large datasets for the development of machine learning models for prostate cancer detection on MRI. Our code is publicly available at: https://github.com/pimed/RAPHIA.

  View details for DOI 10.1016/j.compbiomed.2024.108318

  View details for PubMedID 38522253

• Improving Automated Prostate Cancer Detection and Classification Accuracy with Multi-scale Cancer Information Li, C., Bhattacharya, I., Vesal, S., Saunders, S., Soerensen, S., Fan, R. E., Sonn, G. A., Rusu, M., Cao, Xu, Rekik, Cui, Z., Ouyang SPRINGER INTERNATIONAL PUBLISHING AG. 2024: 341-350

  View details for DOI 10.1007/978-3-031-45673-2_34

  View details for Web of Science ID 001109643200034

• Groundwater constituents and the incidence of kidney cancer. Cancer Soerensen, S. J., Montez-Rath, M. E., Cheng, I., Gomez, S. L., Oh, D. L., Jackson, C., Li, J., Rehkopf, D., Chertow, G. M., Langston, M. E., Ganesan, C., Pao, A. C., Chung, B. I., Leppert, J. T. 2023

  Abstract

  Kidney cancer incidence demonstrates significant geographic variation suggesting a role for environmental risk factors. This study sought to evaluate associations between groundwater exposures and kidney cancer incidence.The authors identified constituents from 18,506 public groundwater wells in all 58 California counties measured in 1996-2010, and obtained county-level kidney cancer incidence data from the California Cancer Registry for 2003-2017. The authors developed a water-wide association study (WWAS) platform using XWAS methodology. Three cohorts were created with 5 years of groundwater measurements and 5-year kidney cancer incidence data. The authors fit Poisson regression models in each cohort to estimate the association between county-level average constituent concentrations and kidney cancer, adjusting for known risk factors: sex, obesity, smoking prevalence, and socioeconomic status at the county level.Thirteen groundwater constituents met stringent WWAS criteria (a false discovery rate <0.10 in the first cohort, followed by p values <.05 in subsequent cohorts) and were associated with kidney cancer incidence. The seven constituents directly related to kidney cancer incidence (and corresponding standardized incidence ratios) were chlordane (1.06; 95% confidence interval [CI], 1.02-1.10), dieldrin (1.04; 95% CI, 1.01-1.07), 1,2-dichloropropane (1.04; 95% CI, 1.02-1.05), 2,4,5-TP (1.03; 95% CI, 1.01-1.05), glyphosate (1.02; 95% CI, 1.01-1.04), endothall (1.02; 95% CI, 1.01-1.03), and carbaryl (1.02; 95% CI, 1.01-1.03). Among the six constituents inversely related to kidney cancer incidence, the standardized incidence ratio furthest from the null was for bromide (0.97; 95% CI, 0.94-0.99).This study identified several groundwater constituents associated with kidney cancer. Public health efforts to reduce the burden of kidney cancer should consider groundwater constituents as environmental exposures that may be associated with the incidence of kidney cancer.

  View details for DOI 10.1002/cncr.34898

  View details for PubMedID 37287332

• Biomarkers for the Detection and Surveillance of Renal Cancer. The Urologic clinics of North America Nolazco, J. I., Soerensen, S. J., Chung, B. I. 2023; 50 (2): 191-204

  Abstract

  Renal cell carcinoma (RCC) is a heterogeneous disease characterized by a broad spectrum of disorders in terms of genetics, molecular and clinical characteristics. There is an urgent need for noninvasive tools to stratify and select patients for treatment accurately. In this review, we analyze serum, urinary, and imaging biomarkers that have the potential to detect malignant tumors in patients with RCC. We discuss the characteristics of these numerous biomarkers and their ability to be used routinely in clinical practice. The development of biomarkers continues to evolve with promising prospects.

  View details for DOI 10.1016/j.ucl.2023.01.009

  View details for PubMedID 36948666

• DETECTION OF CLINICALLY SIGNIFICANT PROSTATE CANCER ON MRI: A COMPARISON OF AN ARTIFICIAL INTELLIGENCE MODEL VERSUS RADIOLOGISTS Soerensen, S., Fan, R. E., Bhattacharya, I., Lim, D. S., Ahmadi, S., Li, X., Vesal, S., Rusu, M., Sonn, G. A. LIPPINCOTT WILLIAMS & WILKINS. 2023: E103

  View details for Web of Science ID 000994549500202

• IMPROVING AUTOMATIC DETECTION OF PROSTATE CANCER ON MRI WITH CLINICAL HISTORY Lim, D. S., Kunder, C., Shao, W., Soerensen, S. C., Fan, R. E., Ghanouni, P., To'o, K., Brooks, J. D., Sonn, G. A., Rusu, M. LIPPINCOTT WILLIAMS & WILKINS. 2023: E768

  View details for Web of Science ID 000994549502350

• GROUNDWATER CONSTITUENTS AND THE INCIDENCE OF KIDNEY CANCER Soerensen, S., Montez-Rath, M. E., Cheng, I., Gomez, S., Oh, D. L., Jackson, C., Rehkopf, D., Chertow, G. M., Ganesan, C., Pao, A. C., Leppert, J. T., Chung, B. I. LIPPINCOTT WILLIAMS & WILKINS. 2023: E880

  View details for Web of Science ID 000994549502566

• IMPROVING PROSTATE CANCER DETECTION ON MRI WITH DEEP LEARNING, CLINICAL VARIABLES, AND RADIOMICS Saunders, S., Li, X., Vesal, S., Bhattacharya, I., Soerensen, S. C., Fan, R. E., Rusu, M., Sonn, G. A. LIPPINCOTT WILLIAMS & WILKINS. 2023: E665

  View details for Web of Science ID 000994549502147

• The Association of Tissue Change and Treatment Success During High-intensity Focused Ultrasound Focal Therapy for Prostate Cancer. European urology focus Khandwala, Y. S., Soerensen, S. J., Morisetty, S., Ghanouni, P., Fan, R. E., Vesal, S., Rusu, M., Sonn, G. A. 2022

  Abstract

  BACKGROUND: Tissue preservation strategies have been increasingly used for the management of localized prostate cancer. Focal ablation using ultrasound-guided high-intensity focused ultrasound (HIFU) has demonstrated promising short and medium-term oncological outcomes. Advancements in HIFU therapy such as the introduction of tissue change monitoring (TCM) aim to further improve treatment efficacy.OBJECTIVE: To evaluate the association between intraoperative TCM during HIFU focal therapy for localized prostate cancer and oncological outcomes 12 mo afterward.DESIGN, SETTING, AND PARTICIPANTS: Seventy consecutive men at a single institution with prostate cancer were prospectively enrolled. Men with prior treatment, metastases, or pelvic radiation were excluded to obtain a final cohort of 55 men.INTERVENTION: All men underwent HIFU focal therapy followed by magnetic resonance (MR)-fusion biopsy 12 mo later. Tissue change was quantified intraoperatively by measuring the backscatter of ultrasound waves during ablation.OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Gleason grade group (GG) ≥2 cancer on postablation biopsy was the primary outcome. Secondary outcomes included GG ≥1 cancer, Prostate Imaging Reporting and Data System (PI-RADS) scores ≥3, and evidence of tissue destruction on post-treatment magnetic resonance imaging (MRI). A Student's t - test analysis was performed to evaluate the mean TCM scores and efficacy of ablation measured by histopathology. Multivariate logistic regression was also performed to identify the odds of residual cancer for each unit increase in the TCM score.RESULTS AND LIMITATIONS: A lower mean TCM score within the region of the tumor (0.70 vs 0.97, p=0.02) was associated with the presence of persistent GG ≥2 cancer after HIFU treatment. Adjusting for initial prostate-specific antigen, PI-RADS score, Gleason GG, positive cores, and age, each incremental increase of TCM was associated with an 89% reduction in the odds (odds ratio: 0.11, confidence interval: 0.01-0.97) of having residual GG ≥2 cancer on postablation biopsy. Men with higher mean TCM scores (0.99 vs 0.72, p=0.02) at the time of treatment were less likely to have abnormal MRI (PI-RADS ≥3) at 12 mo postoperatively. Cases with high TCM scores also had greater tissue destruction measured on MRI and fewer visible lesions on postablation MRI.CONCLUSIONS: Tissue change measured using TCM values during focal HIFU of the prostate was associated with histopathology and radiological outcomes 12 mo after the procedure.PATIENT SUMMARY: In this report, we looked at how well ultrasound changes of the prostate during focal high-intensity focused ultrasound (HIFU) therapy for the treatment of prostate cancer predict patient outcomes. We found that greater tissue change measured by the HIFU device was associated with less residual cancer at 1 yr. This tool should be used to ensure optimal ablation of the cancer and may improve focal therapy outcomes in the future.

  View details for DOI 10.1016/j.euf.2022.10.010

  View details for PubMedID 36372735

• A review of artificial intelligence in prostate cancer detection on imaging. Therapeutic advances in urology Bhattacharya, I., Khandwala, Y. S., Vesal, S., Shao, W., Yang, Q., Soerensen, S. J., Fan, R. E., Ghanouni, P., Kunder, C. A., Brooks, J. D., Hu, Y., Rusu, M., Sonn, G. A. 2022; 14: 17562872221128791

  Abstract

  A multitude of studies have explored the role of artificial intelligence (AI) in providing diagnostic support to radiologists, pathologists, and urologists in prostate cancer detection, risk-stratification, and management. This review provides a comprehensive overview of relevant literature regarding the use of AI models in (1) detecting prostate cancer on radiology images (magnetic resonance and ultrasound imaging), (2) detecting prostate cancer on histopathology images of prostate biopsy tissue, and (3) assisting in supporting tasks for prostate cancer detection (prostate gland segmentation, MRI-histopathology registration, MRI-ultrasound registration). We discuss both the potential of these AI models to assist in the clinical workflow of prostate cancer diagnosis, as well as the current limitations including variability in training data sets, algorithms, and evaluation criteria. We also discuss ongoing challenges and what is needed to bridge the gap between academic research on AI for prostate cancer and commercial solutions that improve routine clinical care.

  View details for DOI 10.1177/17562872221128791

  View details for PubMedID 36249889

  View details for PubMedCentralID PMC9554123

• Evaluation of post-ablation mpMRI as a predictor of residual prostate cancer after focal high intensity focused ultrasound (HIFU) ablation. Urologic oncology Khandwala, Y. S., Morisetty, S., Ghanouni, P., Fan, R. E., Soerensen, S. J., Rusu, M., Sonn, G. A. 2022

  Abstract

  PURPOSE: To evaluate the performance of multiparametric magnetic resonance imaging (mpMRI) and PSA testing in follow-up after high intensity focused ultrasound (HIFU) focal therapy for localized prostate cancer.METHODS: A total of 73 men with localized prostate cancer were prospectively enrolled and underwent focal HIFU followed by per-protocol PSA and mpMRI with systematic plus targeted biopsies at 12 months after treatment. We evaluated the association between post-treatment mpMRI and PSA with disease persistence on the post-ablation biopsy. We also assessed post-treatment functional and oncological outcomes.RESULTS: Median age was 69 years (Interquartile Range (IQR): 66-74) and median PSA was 6.9 ng/dL (IQR: 5.3-9.9). Of 19 men with persistent GG ≥ 2 disease, 58% (11 men) had no visible lesions on MRI. In the 14 men with PIRADS 4 or 5 lesions, 7 (50%) had either no cancer or GG 1 cancer at biopsy. Men with false negative mpMRI findings had higher PSA density (0.16 vs. 0.07 ng/mL2, P = 0.01). No change occurred in the mean Sexual Health Inventory for Men (SHIM) survey scores (17.0 at baseline vs. 17.7 post-treatment, P = 0.75) or International Prostate Symptom Score (IPSS) (8.1 at baseline vs. 7.7 at 24 months, P = 0.81) after treatment.CONCLUSIONS: Persistent GG ≥ 2 cancer may occur after focal HIFU. mpMRI alone without confirmatory biopsy may be insufficient to rule out residual cancer, especially in patients with higher PSA density. Our study also validates previously published studies demonstrating preservation of urinary and sexual function after HIFU treatment.

  View details for DOI 10.1016/j.urolonc.2022.07.017

  View details for PubMedID 36058811

• Bridging the gap between prostate radiology and pathology through machine learning. Medical physics Bhattacharya, I., Lim, D. S., Aung, H. L., Liu, X., Seetharaman, A., Kunder, C. A., Shao, W., Soerensen, S. J., Fan, R. E., Ghanouni, P., To'o, K. J., Brooks, J. D., Sonn, G. A., Rusu, M. 2022

  Abstract

  Prostate cancer remains the second deadliest cancer for American men despite clinical advancements. Currently, Magnetic Resonance Imaging (MRI) is considered the most sensitive non-invasive imaging modality that enables visualization, detection and localization of prostate cancer, and is increasingly used to guide targeted biopsies for prostate cancer diagnosis. However, its utility remains limited due to high rates of false positives and false negatives as well as low inter-reader agreements.Machine learning methods to detect and localize cancer on prostate MRI can help standardize radiologist interpretations. However, existing machine learning methods vary not only in model architecture, but also in the ground truth labeling strategies used for model training. We compare different labeling strategies and the effects they have on the performance of different machine learning models for prostate cancer detection on MRI.Four different deep learning models (SPCNet, U-Net, branched U-Net, and DeepLabv3+) were trained to detect prostate cancer on MRI using 75 patients with radical prostatectomy, and evaluated using 40 patients with radical prostatectomy and 275 patients with targeted biopsy. Each deep learning model was trained with four different label types: pathology-confirmed radiologist labels, pathologist labels on whole-mount histopathology images, and lesion-level and pixel-level digital pathologist labels (previously validated deep learning algorithm on histopathology images to predict pixel-level Gleason patterns) on whole-mount histopathology images. The pathologist and digital pathologist labels (collectively referred to as pathology labels) were mapped onto pre-operative MRI using an automated MRI-histopathology registration platform.Radiologist labels missed cancers (ROC-AUC: 0.75 - 0.84), had lower lesion volumes (~68% of pathology lesions), and lower Dice overlaps (0.24 - 0.28) when compared with pathology labels. Consequently, machine learning models trained with radiologist labels also showed inferior performance compared to models trained with pathology labels. Digital pathologist labels showed high concordance with pathologist labels of cancer (lesion ROC-AUC: 0.97 - 1, lesion Dice: 0.75 - 0.93). Machine learning models trained with digital pathologist labels had the highest lesion detection rates in the radical prostatectomy cohort (aggressive lesion ROC-AUC: 0.91 - 0.94), and had generalizable and comparable performance to pathologist label trained-models in the targeted biopsy cohort (aggressive lesion ROC-AUC: 0.87 - 0.88), irrespective of the deep learning architecture. Moreover, machine learning models trained with pixel-level digital pathologist labels were able to selectively identify aggressive and indolent cancer components in mixed lesions on MRI, which is not possible with any human-annotated label type.Machine learning models for prostate MRI interpretation that are trained with digital pathologist labels showed higher or comparable performance with pathologist label-trained models in both radical prostatectomy and targeted biopsy cohort. Digital pathologist labels can reduce challenges associated with human annotations, including labor, time, inter- and intra-reader variability, and can help bridge the gap between prostate radiology and pathology by enabling the training of reliable machine learning models to detect and localize prostate cancer on MRI. This article is protected by copyright. All rights reserved.

  View details for DOI 10.1002/mp.15777

  View details for PubMedID 35633505

• Correlation of 68Ga-RM2 PET with Post-Surgery Histopathology Findings in Patients with Newly Diagnosed Intermediate- or High-Risk Prostate Cancer. Journal of nuclear medicine : official publication, Society of Nuclear Medicine Duan, H., Baratto, L., Fan, R. E., Soerensen, S. J., Liang, T., Chung, B. I., Thong, A. E., Gill, H., Kunder, C., Stoyanova, T., Rusu, M., Loening, A. M., Ghanouni, P., Davidzon, G. A., Moradi, F., Sonn, G. A., Iagaru, A. 2022

  Abstract

  Rationale: 68Ga-RM2 targets gastrin-releasing peptide receptors (GRPR), which are overexpressed in prostate cancer (PC). Here, we compared pre-operative 68Ga-RM2 PET to post-surgery histopathology in patients with newly diagnosed intermediate- or high-risk PC. Methods: Forty-one men, 64.0+/-6.7-year-old, were prospectively enrolled. PET images were acquired 42 - 72 (median+/-SD 52.5+/-6.5) minutes after injection of 118.4 - 247.9 (median+/-SD 138.0+/-22.2)MBq of 68Ga-RM2. PET findings were compared to pre-operative mpMRI (n = 36) and 68Ga-PSMA11 PET (n = 17) and correlated to post-prostatectomy whole-mount histopathology (n = 32) and time to biochemical recurrence. Nine participants decided to undergo radiation therapy after study enrollment. Results: All participants had intermediate (n = 17) or high-risk (n = 24) PC and were scheduled for prostatectomy. Prostate specific antigen (PSA) was 8.8+/-77.4 (range 2.5 - 504) ng/mL, and 7.6+/-5.3 (range 2.5 - 28.0) ng/mL when excluding participants who ultimately underwent radiation treatment. Pre-operative 68Ga-RM2 PET identified 70 intraprostatic foci of uptake in 40/41 patients. Post-prostatectomy histopathology was available in 32 patients in which 68Ga-RM2 PET identified 50/54 intraprostatic lesions (detection rate = 93%). 68Ga-RM2 uptake was recorded in 19 non-enlarged pelvic lymph nodes in 6 patients. Pathology confirmed lymph node metastases in 16 lesions, and follow-up imaging confirmed nodal metastases in 2 lesions. 68Ga-PSMA11 and 68Ga-RM2 PET identified 27 and 26 intraprostatic lesions, respectively, and 5 pelvic lymph nodes each in 17 patients. Concordance between 68Ga-RM2 and 68Ga-PSMA11 PET was found in 18 prostatic lesions in 11 patients, and 4 lymph nodes in 2 patients. Non-congruent findings were observed in 6 patients (intraprostatic lesions in 4 patients and nodal lesions in 2 patients). Both 68Ga-RM2 and 68Ga-PSMA11 had higher sensitivity and accuracy rates with 98%, 89%, and 95%, 89%, respectively, compared to mpMRI at 77% and 77%. Specificity was highest for mpMRI with 75% followed by 68Ga-PSMA11 (67%), and 68Ga-RM2 (65%). Conclusion: 68Ga-RM2 PET accurately detects intermediate- and high-risk primary PC with a detection rate of 93%. In addition, it showed significantly higher specificity and accuracy compared to mpMRI and similar performance to 68Ga-PSMA11 PET. These findings need to be confirmed in larger studies to identify which patients will benefit from one or the other or both radiopharmaceuticals.

  View details for DOI 10.2967/jnumed.122.263971

  View details for PubMedID 35552245

• Dual X-ray Absorptiometry Screening for Men Receiving Androgen Deprivation Therapy-Hiding in Plain (Film) Sight. JAMA network open Kishan, A. U., Kamran, S. C., Soerensen, S. J., Leppert, J. T. 2022; 5 (4): e225439

  View details for DOI 10.1001/jamanetworkopen.2022.5439

  View details for PubMedID 35363273

• Deep Learning Improves Speed and Accuracy of Prostate Gland Segmentations on Magnetic Resonance Imaging for Targeted Biopsy JOURNAL OF UROLOGY Soerensen, S., Fan, R. E., Seetharaman, A., Chen, L., Shao, W., Bhattacharya, I., Kim, Y., Sood, R., Borre, M., Chung, B., To'o, K. J., Rusu, M., Sonn, G. A. 2021; 206 (3): 605-612

  View details for Web of Science ID 000711819100035

• Reply to the Editorial Comment on: Using an Automated Electronic Health Record Score To Estimate Life Expectancy In Men Diagnosed With Prostate Cancer In The Veterans Health Administration. Urology. 2021. Urology Soerensen, S. J., Thomas, I., Schmidt, B., Daskivich, T., Skolarus, T. A., Jackson, C., Osborne, T. F., Chertow, G. M., Brooks, J. D., Rehkopf, D., Leppert, J. T. 2021

  Abstract

  OBJECTIVES: To determine if an automatically calculated electronic health record score can estimate intermediate-term life expectancy in men with prostate cancer to provide guideline concordant care.METHODS: We identified all men (n=36,591) diagnosed with prostate cancer in 2013-2015 in the VHA. Of the 36,591, 35,364 (96.6%) had an available Care Assessment Needs (CAN) score (range: 0-99) automatically calculated in the 30 days prior to the date of diagnosis. It was designed to estimate short-term risks of hospitalization and mortality. We fit unadjusted and multivariable Cox proportional hazards regression models to determine the association between the CAN score and overall survival among men with prostate cancer. We compared CAN score performance to two established comorbidity measures: The Charlson Comorbidity Index and Prostate Cancer Comorbidity Index (PCCI).RESULTS: Among 35,364 men, the CAN score correlated with overall stage, with mean scores of 46.5 (±22.4), 58.0 (±24.4), and 68.1 (±24.3) in localized, locally advanced, and metastatic disease, respectively. In both unadjusted and adjusted models for prostate cancer risk, the CAN score was independently associated with survival (HR=1.23 95%CI 1.22-1.24 & adjusted HR=1.17 95%CI 1.16-1.18 per 5-unit change, respectively). The CAN score (overall C-Index 0.74) yielded better discrimination (AUC=0.76) than PCCI (AUC=0.65) or Charlson Comorbidity Index (AUC=0.66) for 5-year survival.CONCLUSIONS: The CAN score is strongly associated with intermediate-term survival following a prostate cancer diagnosis. The CAN score is an example of how learning health care systems can implement multi-dimensional tools to provide fully automated life expectancy estimates to facilitate patient-centered cancer care.

  View details for DOI 10.1016/j.urology.2021.05.056

  View details for PubMedID 34139251

• Deep Learning Improves Speed and Accuracy of Prostate Gland Segmentations on MRI for Targeted Biopsy. The Journal of urology Soerensen, S. J., Fan, R. E., Seetharaman, A., Chen, L., Shao, W., Bhattacharya, I., Kim, Y., Sood, R., Borre, M., Chung, B. I., To'o, K. J., Rusu, M., Sonn, G. A. 2021: 101097JU0000000000001783

  Abstract

  PURPOSE: Targeted biopsy improves prostate cancer diagnosis. Accurate prostate segmentation on MRI is critical for accurate biopsy. Manual gland segmentation is tedious and time-consuming. We sought to develop a deep learning model to rapidly and accurately segment the prostate on MRI and to implement it as part of routine MR-US fusion biopsy in the clinic.MATERIALS AND METHODS: 905 subjects underwent multiparametric MRI at 29 institutions, followed by MR-US fusion biopsy at one institution. A urologic oncology expert segmented the prostate on axial T2-weighted MRI scans. We trained a deep learning model, ProGNet, on 805 cases. We retrospectively tested ProGNet on 100 independent internal and 56 external cases. We prospectively implemented ProGNet as part of the fusion biopsy procedure for 11 patients. We compared ProGNet performance to two deep learning networks (U-Net and HED) and radiology technicians. The Dice similarity coefficient (DSC) was used to measure overlap with expert segmentations. DSCs were compared using paired t-tests.RESULTS: ProGNet (DSC=0.92) outperformed U-Net (DSC=0.85, p <0.0001), HED (DSC=0.80, p< 0.0001), and radiology technicians (DSC=0.89, p <0.0001) in the retrospective internal test set. In the prospective cohort, ProGNet (DSC=0.93) outperformed radiology technicians (DSC=0.90, p <0.0001). ProGNet took just 35 seconds per case (vs. 10 minutes for radiology technicians) to yield a clinically utilizable segmentation file.CONCLUSIONS: This is the first study to employ a deep learning model for prostate gland segmentation for targeted biopsy in routine urologic clinical practice, while reporting results and releasing the code online. Prospective and retrospective evaluations revealed increased speed and accuracy.

  View details for DOI 10.1097/JU.0000000000001783

  View details for PubMedID 33878887

• Removing Race from eGFR calculations: Implications for Urologic Care. Urology Vilson, F. L., Schmidt, B., White, L., Soerensen, S. J., Ganesan, C., Pao, A. C., Enemchukwu, E., Chertow, G. M., Leppert, J. T. 2021

  Abstract

  Equations estimating the glomerular filtration rate are important clinical tools in detecting and managing kidney disease. Urologists extensively use these equations in clinical decision making. For example, the estimated glomerular function rate is used when considering the type of urinary diversion following cystectomy, selecting systemic chemotherapy in managing urologic cancers, and deciding the type of cross-sectional imaging in diagnosing or staging urologic conditions. However, these equations, while widely accepted, are imprecise and adjust for race which is a social, not a biologic construct. The recent killings of unarmed Black Americans in the US have amplified the discussion of racism in healthcare and has prompted institutions to reconsider the role of race in eGFR equations and raced-based medicine. Urologist should be aware of the consequences of removing race from these equations, potential alternatives, and how these changes may affect Black patients receiving urologic care.

  View details for DOI 10.1016/j.urology.2021.03.018

  View details for PubMedID 33798557

• Automated Detection of Aggressive and Indolent Prostate Cancer on Magnetic Resonance Imaging. Medical physics Seetharaman, A., Bhattacharya, I., Chen, L. C., Kunder, C. A., Shao, W., Soerensen, S. J., Wang, J. B., Teslovich, N. C., Fan, R. E., Ghanouni, P., Brooks, J. D., To'o, K. J., Sonn, G. A., Rusu, M. 2021

  Abstract

  PURPOSE: While multi-parametric Magnetic Resonance Imaging (MRI) shows great promise in assisting with prostate cancer diagnosis and localization, subtle differences in appearance between cancer and normal tissue lead to many false positive and false negative interpretations by radiologists. We sought to automatically detect aggressive cancer (Gleason pattern ≥ 4) and indolent cancer (Gleason pattern 3) on a per-pixel basis on MRI to facilitate the targeting of aggressive cancer during biopsy.METHODS: We created the Stanford Prostate Cancer Network (SPCNet), a convolutional neural network model, trained to distinguish between aggressive cancer, indolent cancer, and normal tissue on MRI. Ground truth cancer labels were obtainedby registering MRI with whole-mount digital histopathology images from patients that underwent radical prostatectomy. Before registration, these histopathology images were automatically annotated to show Gleason patterns on a per-pixel basis. The model was trained on data from 78 patients that underwent radical prostatectomy and 24 patients without prostate cancer. The model was evaluated on a pixel and lesion level in 322 patients, including: 6 patients with normal MRI and no cancer, 23 patients that underwent radical prostatectomy, and 293 patients that underwent biopsy. Moreover, we assessed the ability of our model to detect clinically significant cancer (lesions with an aggressive component) and compared it to the performance of radiologists.RESULTS: Our model detected clinically significant lesions with an Area Under the Receiver Operator Characteristics Curve of 0.75 for radical prostatectomy patients and 0.80 for biopsy patients. Moreover, the model detected up to 18% of lesions missed by radiologists, and overall had a sensitivity and specificity that approached that of radiologists in detecting clinically significant cancer.CONCLUSIONS: Our SPCNet model accurately detected aggressive prostate cancer. Its performance approached that of radiologists, and it helped identify lesions otherwise missed by radiologists. Our model has the potential to assist physicians in specifically targeting the aggressive component of prostate cancers during biopsy or focal treatment.

  View details for DOI 10.1002/mp.14855

  View details for PubMedID 33760269

• Clinically significant prostate cancer detection on MRI with self-supervised learning using image context restoration Bolous, A., Seetharaman, A., Bhattacharya, I., Fan, R. E., Soerensen, S., Chen, L., Ghanouni, P., Sonn, G. A., Rusu, M., Mazurowski, M. A., Drukker, K. SPIE-INT SOC OPTICAL ENGINEERING. 2021

  View details for DOI 10.1117/12.2581557

  View details for Web of Science ID 000672800100052

• ProGNet: Prostate Gland Segmentation on MRI with Deep Learning Soerensen, S., Fan, R., Seetharaman, A., Chen, L., Shao, W., Bhattacharya, I., Borre, M., Chung, B., To'o, K., Sonn, G., Rusu, M., Isgum, Landman, B. A. SPIE-INT SOC OPTICAL ENGINEERING. 2021

  View details for DOI 10.1117/12.2580448

  View details for Web of Science ID 000672800200091

• Weakly Supervised Registration of Prostate MRI and Histopathology Images Shao, W., Bhattacharya, I., Soerensen, S. C., Kunder, C. A., Wang, J. B., Fan, R. E., Ghanouni, P., Brooks, J. D., Sonn, G. A., Rusu, M., DeBruijne, M., Cattin, P. C., Cotin, S., Padoy, N., Speidel, S., Zheng, Y., Essert, C. SPRINGER INTERNATIONAL PUBLISHING AG. 2021: 98-107

  View details for DOI 10.1007/978-3-030-87202-1_10

  View details for Web of Science ID 000712021400010

• 3D Registration of pre-surgical prostate MRI and histopathology images via super-resolution volume reconstruction. Medical image analysis Sood, R. R., Shao, W. n., Kunder, C. n., Teslovich, N. C., Wang, J. B., Soerensen, S. J., Madhuripan, N. n., Jawahar, A. n., Brooks, J. D., Ghanouni, P. n., Fan, R. E., Sonn, G. A., Rusu, M. n. 2021; 69: 101957

  Abstract

  The use of MRI for prostate cancer diagnosis and treatment is increasing rapidly. However, identifying the presence and extent of cancer on MRI remains challenging, leading to high variability in detection even among expert radiologists. Improvement in cancer detection on MRI is essential to reducing this variability and maximizing the clinical utility of MRI. To date, such improvement has been limited by the lack of accurately labeled MRI datasets. Data from patients who underwent radical prostatectomy enables the spatial alignment of digitized histopathology images of the resected prostate with corresponding pre-surgical MRI. This alignment facilitates the delineation of detailed cancer labels on MRI via the projection of cancer from histopathology images onto MRI. We introduce a framework that performs 3D registration of whole-mount histopathology images to pre-surgical MRI in three steps. First, we developed a novel multi-image super-resolution generative adversarial network (miSRGAN), which learns information useful for 3D registration by producing a reconstructed 3D MRI. Second, we trained the network to learn information between histopathology slices to facilitate the application of 3D registration methods. Third, we registered the reconstructed 3D histopathology volumes to the reconstructed 3D MRI, mapping the extent of cancer from histopathology images onto MRI without the need for slice-to-slice correspondence. When compared to interpolation methods, our super-resolution reconstruction resulted in the highest PSNR relative to clinical 3D MRI (32.15 dB vs 30.16 dB for BSpline interpolation). Moreover, the registration of 3D volumes reconstructed via super-resolution for both MRI and histopathology images showed the best alignment of cancer regions when compared to (1) the state-of-the-art RAPSODI approach, (2) volumes that were not reconstructed, or (3) volumes that were reconstructed using nearest neighbor, linear, or BSpline interpolations. The improved 3D alignment of histopathology images and MRI facilitates the projection of accurate cancer labels on MRI, allowing for the development of improved MRI interpretation schemes and machine learning models to automatically detect cancer on MRI.

  View details for DOI 10.1016/j.media.2021.101957

  View details for PubMedID 33550008

• AUTHOR REPLY. Urology Soerensen, S. J., Thomas, I. C., Schmidt, B., Daskivich, T. J., Skolarus, T. A., Jackson, C., Osborne, T. F., Chertow, G. M., Brooks, J. D., Rehkopf, D. H., Leppert, J. T. 2021; 155: 76

  View details for DOI 10.1016/j.urology.2021.05.058

  View details for PubMedID 34489006

• Registration of pre-surgical MRI and histopathology images from radical prostatectomy via RAPSODI. Medical physics Rusu, M., Shao, W., Kunder, C. A., Wang, J. B., Soerensen, S. J., Teslovich, N. C., Sood, R. R., Chen, L. C., Fan, R. E., Ghanouni, P., Brooks, J. D., Sonn, G. A. 2020

  Abstract

  PURPOSE: Magnetic resonance imaging (MRI) has great potential to improve prostate cancer diagnosis, however, subtle differences between cancer and confounding conditions render prostate MRI interpretation challenging. The tissue collected from patients who undergo radical prostatectomy provides a unique opportunity to correlate histopathology images of the prostate with pre-operative MRI to accurately map the extent of cancer from histopathology images onto MRI. We seek to develop an open-source, easy-to-use platform to align pre-surgical MRI and histopathology images of resected prostates in patients who underwent radical prostatectomy to create accurate cancer labels on MRI.METHODS: Here, we introduce RAdiology Pathology Spatial Open-Source multi-Dimensional Integration (RAPSODI), the first open-source framework for the registration of radiology and pathology images. RAPSODI relies on three steps. First, it creates a 3D reconstruction of the histopathology specimen as a digital representation of the tissue before gross sectioning. Second, RAPSODI registers corresponding histopathology and MRI slices. Third, the optimized transforms are applied to the cancer regions outlined on the histopathology images to project those labels onto the pre-operative MRI.RESULTS: We tested RAPSODI in a phantom study where we simulated various conditions, e.g., tissue shrinkage during fixation. Our experiments showed that RAPSODI can reliably correct multiple artifacts. We also evaluated RAPSODI in 157 patients from three institutions that underwent radical prostatectomy and have very different pathology processing and scanning. RAPSODI was evaluated in 907 corresponding histpathology-MRI slices and achieved a Dice coefficient of 0.97±0.01 for the prostate, a Hausdorff distance of 1.99±0.70 mm for the prostate boundary, a urethra deviation of 3.09±1.45 mm, and a landmark deviation of 2.80±0.59 mm between registered histopathology images and MRI.CONCLUSION: Our robust framework successfully mapped the extent of cancer from histopathology slices onto MRI providing labels from training machine learning methods to detect cancer on MRI.

  View details for DOI 10.1002/mp.14337

  View details for PubMedID 32564359

• CorrSigNet: Learning CORRelated Prostate Cancer SIGnatures from Radiology and Pathology Images for Improved Computer Aided Diagnosis Medical Image Computing and Computer Assisted Intervention Bhattacharya, I., et al 2020

  View details for DOI 10.1007/978-3-030-59713-9_31

• ProsRegNet: A deep learning framework for registration of MRI and histopathology images of the prostate. Medical image analysis Shao, W. n., Banh, L. n., Kunder, C. A., Fan, R. E., Soerensen, S. J., Wang, J. B., Teslovich, N. C., Madhuripan, N. n., Jawahar, A. n., Ghanouni, P. n., Brooks, J. D., Sonn, G. A., Rusu, M. n. 2020; 68: 101919

  Abstract

  Magnetic resonance imaging (MRI) is an increasingly important tool for the diagnosis and treatment of prostate cancer. However, interpretation of MRI suffers from high inter-observer variability across radiologists, thereby contributing to missed clinically significant cancers, overdiagnosed low-risk cancers, and frequent false positives. Interpretation of MRI could be greatly improved by providing radiologists with an answer key that clearly shows cancer locations on MRI. Registration of histopathology images from patients who had radical prostatectomy to pre-operative MRI allows such mapping of ground truth cancer labels onto MRI. However, traditional MRI-histopathology registration approaches are computationally expensive and require careful choices of the cost function and registration hyperparameters. This paper presents ProsRegNet, a deep learning-based pipeline to accelerate and simplify MRI-histopathology image registration in prostate cancer. Our pipeline consists of image preprocessing, estimation of affine and deformable transformations by deep neural networks, and mapping cancer labels from histopathology images onto MRI using estimated transformations. We trained our neural network using MR and histopathology images of 99 patients from our internal cohort (Cohort 1) and evaluated its performance using 53 patients from three different cohorts (an additional 12 from Cohort 1 and 41 from two public cohorts). Results show that our deep learning pipeline has achieved more accurate registration results and is at least 20 times faster than a state-of-the-art registration algorithm. This important advance will provide radiologists with highly accurate prostate MRI answer keys, thereby facilitating improvements in the detection of prostate cancer on MRI. Our code is freely available at https://github.com/pimed//ProsRegNet.

  View details for DOI 10.1016/j.media.2020.101919

  View details for PubMedID 33385701

• Risk of Depression after 5 Alpha Reductase Inhibitor Medication: Meta-Analysis. The world journal of men's health Kim, J. H., Shim, S. R., Khandwala, Y. n., Del Giudice, F. n., Sorensen, S. n., Chung, B. I. 2019

  Abstract

  Although five-alpha reductase inhibitor (5-ARI) is one of standard treatment for benign prostatic hyperplasia (BPH) or alopecia, potential complications after 5-ARI have been issues recently. This study aimed to investigate the risk of depression after taking 5-ARI and to quantify the risk using meta-analysis.A total of 209,940 patients including 207,798 in 5-ARI treatment groups and 110,118 in control groups from five studies were included for final analysis. Inclusion criteria for finial analysis incudes clinical outcomes regarding depression risk in BPH or alopecia patients. Overall hazard ratio (HR) and odds ratio (OR) for depression were analyzed. Moderator analysis and sensitivity analysis were performed to determine whether HR or OR could be affected by any variables, including number of patients, age, study type, and control type.The pooled overall HRs for the 5-ARI medication was 1.23 (95% confidence interval [CI], 0.99-1.54) in a random effects model. The pooled overall ORs for the 5-ARI medication was 1.19 (95% CI, 0.95-1.49) in random effects model. The sub-group analysis showed that non-cohort studies had higher values of HR and OR than cohort studies. Moderator analysis using meta-regression showed that there were no variables that affect the significant difference in HR and OR outcomes. However, in sensitivity analysis, HR was significantly increased by age (p=0.040).Overall risk of depression after 5-ARI was significantly not high, however its clinical importance needs validation by further studies. These quantitative results could provide useful information for both clinicians and patients.

  View details for DOI 10.5534/wjmh.190046

  View details for PubMedID 31190484