Dr. Lowe joined the School of Medicine faculty in 2019. She received her undergraduate degree in Biology from MIT and her medical degree at UCSD, prior to residency and cytology fellowship at UCLA. In 2011, she joined the faculty at Brigham and Women's Hospital where she developed a research focus on Circulating Tumor Cells (CTCs) and the application of CTC technology to improve clinical diagnostics. Clinically, her interests focus on Cytopathology and Genitourinary Pathology.
- Anatomic and Clinical Pathology
- Fine Needle Aspiration Biopsy
- Genitourinary Pathology
Associate Professor - Med Center Line, Pathology
Specialty Certification, Ultrasound Guided Fine Needle Aspiration Biopsy Training, College of American Pathologists (2019)
Board Certification: Cytopathology, American Board of Pathology (2011)
Fellowship: UCLA Pathology Residency and Fellowship (2011) CA
Board Certification, Anatomic Pathology/Clinical Pathology, American Board of Pathology (2010)
Residency: UCLA Pathology Residency and Fellowship (2010) CA
Medical Education: University of California San Diego School of Medicine Registrar (2006) CA
- Multiplexed fluorescence in situ hybridization-based detection of circulating tumor cells: A novel liquid-based technology to facilitate accurate and early identification of non-small cell lung cancer patients. Cancer cytopathology 2020
A lab-on-a-disc platform enables serial monitoring of individual CTCs associated with tumor progression during EGFR-targeted therapy for patients with NSCLC.
2020; 10 (12): 5181–94
Rationale: Unlike traditional biopsy, liquid biopsy, which is a largely non-invasive diagnostic and monitoring tool, can be performed more frequently to better track tumors and mutations over time and to validate the efficiency of a cancer treatment. Circulating tumor cells (CTCs) are considered promising liquid biopsy biomarkers; however, their use in clinical settings is limited by high costs and a low throughput of standard platforms for CTC enumeration and analysis. In this study, we used a label-free, high-throughput method for CTC isolation directly from whole blood of patients using a standalone, clinical setting-friendly platform. Methods: A CTC-based liquid biopsy approach was used to examine the efficacy of therapy and emergent drug resistance via longitudinal monitoring of CTC counts, DNA mutations, and single-cell-level gene expression in a prospective cohort of 40 patients with epidermal growth factor receptor (EGFR)-mutant non-small cell lung cancer. Results: The change ratio of the CTC counts was associated with tumor response, detected by CT scan, while the baseline CTC counts did not show association with progression-free survival or overall survival. We achieved a 100% concordance rate for the detection of EGFR mutation, including emergence of T790M, between tumor tissue and CTCs. More importantly, our data revealed the importance of the analysis of the epithelial/mesenchymal signature of individual pretreatment CTCs to predict drug responsiveness in patients. Conclusion: The fluid-assisted separation technology disc platform enables serial monitoring of CTC counts, DNA mutations, as well as unbiased molecular characterization of individual CTCs associated with tumor progression during targeted therapy.
View details for DOI 10.7150/thno.44693
View details for PubMedID 32373206
View details for PubMedCentralID PMC7196290
Use of Deep Learning to Develop and Analyze Computational Hematoxylin and Eosin Staining of Prostate Core Biopsy Images for Tumor Diagnosis.
JAMA network open
2020; 3 (5): e205111
Histopathological diagnoses of tumors from tissue biopsy after hematoxylin and eosin (H&E) dye staining is the criterion standard for oncological care, but H&E staining requires trained operators, dyes and reagents, and precious tissue samples that cannot be reused.To use deep learning algorithms to develop models that perform accurate computational H&E staining of native nonstained prostate core biopsy images and to develop methods for interpretation of H&E staining deep learning models and analysis of computationally stained images by computer vision and clinical approaches.This cross-sectional study used hundreds of thousands of native nonstained RGB (red, green, and blue channel) whole slide image (WSI) patches of prostate core tissue biopsies obtained from excess tissue material from prostate core biopsies performed in the course of routine clinical care between January 7, 2014, and January 7, 2017, at Brigham and Women's Hospital, Boston, Massachusetts. Biopsies were registered with their H&E-stained versions. Conditional generative adversarial neural networks (cGANs) that automate conversion of native nonstained RGB WSI to computational H&E-stained images were then trained. Deidentified whole slide images of prostate core biopsy and medical record data were transferred to Massachusetts Institute of Technology, Cambridge, for computational research. Results were shared with physicians for clinical evaluations. Data were analyzed from July 2018 to February 2019.Methods for detailed computer vision image analytics, visualization of trained cGAN model outputs, and clinical evaluation of virtually stained images were developed. The main outcome was interpretable deep learning models and computational H&E-stained images that achieved high performance in these metrics.Among 38 patients who provided samples, single core biopsy images were extracted from each whole slide, resulting in 102 individual nonstained and H&E dye-stained image pairs that were compared with matched computationally stained and unstained images. Calculations showed high similarities between computationally and H&E dye-stained images, with a mean (SD) structural similarity index (SSIM) of 0.902 (0.026), Pearson correlation coefficient (PCC) of 0.962 (0.096), and peak signal to noise ratio (PSNR) of 22.821 (1.232) dB. A second cGAN performed accurate computational destaining of H&E-stained images back to their native nonstained form, with a mean (SD) SSIM of 0.900 (0.030), PCC of 0.963 (0.011), and PSNR of 25.646 (1.943) dB compared with native nonstained images. A single blind prospective study computed approximately 95% pixel-by-pixel overlap among prostate tumor annotations provided by 5 board certified pathologists on computationally stained images, compared with those on H&E dye-stained images. This study also used the first visualization and explanation of neural network kernel activation maps during H&E staining and destaining of RGB images by cGANs. High similarities between kernel activation maps of computationally and H&E-stained images (mean-squared errors <0.0005) provide additional mathematical and mechanistic validation of the staining system.These findings suggest that computational H&E staining of native unlabeled RGB images of prostate core biopsy could reproduce Gleason grade tumor signatures that were easily assessed and validated by clinicians. Methods for benchmarking, visualization, and clinical validation of deep learning models and virtually H&E-stained images communicated in this study have wide applications in clinical informatics and oncology research. Clinical researchers may use these systems for early indications of possible abnormalities in native nonstained tissue biopsies prior to histopathological workflows.
View details for DOI 10.1001/jamanetworkopen.2020.5111
View details for PubMedID 32432709
- A lab-on-a-disc platform enables serial monitoring of individual CTCs associated with tumor progression during EGFR-targeted therapy for patients with NSCLC THERANOSTICS 2020; 10 (12): 5181–94
Malignancy risk for solitary and multiple nodules in Hurthle cell-predominant thyroid fine-needle aspirations: A multi-institutional study
Hürthle cell metaplasia is common in hyperplastic nodules, particularly within the setting of lymphocytic thyroiditis (LT). The Bethesda System for Reporting Thyroid Cytopathology indicates that it is acceptable to classify Hürthle cell-predominant fine-needle aspiration (HC FNA) specimens as atypia of undetermined significance (AUS) rather than suspicious for a Hürthle cell neoplasm (HUR) within the setting of multiple nodules or known LT. The goal of the current study was to address whether this approach is justified.HC FNA specimens were identified and correlated with ultrasound and surgical pathology reports if available. Multinodularity was determined based on findings on macroscopic examination if imaging results were unavailable.A total of 698 HC FNA specimens were identified, including 576 resected nodules, 455 of which (79%) were benign. The overall risk of malignancy for HUR was 27%, whereas the risk of malignancy for AUS was 10%. The mean size of the benign nodules was 2.1 cm on surgical resection specimens, with multiple nodules noted in 293 cases (64%) and histologic LT noted in 116 cases (25%). The mean size of the malignant nodules was 2.8 cm, with multiple nodules and histologic LT noted in 74 cases (61%) and 22 cases (18%), respectively. The malignancy rate did not differ between solitary or multiple nodules (P = .52) or in the presence or absence of LT (P = .12). However, size did significantly differ between malignant and benign nodules (P < 0.01).The malignancy rate did not differ significantly in the presence of multiple nodules or LT, although the latter demonstrated a statistical trend. A diagnosis of AUS over HUR based solely on the presence of multinodularity is not warranted.
View details for DOI 10.1002/cncy.22213
View details for Web of Science ID 000497737300001
View details for PubMedID 31751003
Integration of rare cell capture technology into cytologic evaluation of cerebrospinal fluid specimens from patients with solid tumors and suspected leptomeningeal metastasis.
Journal of the American Society of Cytopathology
INTRODUCTION: Dissemination of tumor to the leptomeninges, subarachnoid space, and cerebrospinal fluid (CSF) is termed leptomeningeal metastasis (LM) and occurs in approximately 5% of patients with solid tumors. LM is associated with dismal clinical prognosis, and routine cytologic and radiologic methods for diagnosing LM have limited sensitivity. The CellSearch immunomagnetic rare cell capture assay is FDA-approved to detect circulating tumor cells (CTCs) in peripheral blood, but whether it may have a role in identifying CSF CTCs is still unclear.MATERIAL AND METHODS: CSF specimens from 20 patients with clinically suspected solid tumor LM collected from 2 institutions between October 2016 and January 2019 were evaluated with routine CSF cytology and underwent concurrent CTC testing with the CellSearch assay (Menarini-Silicon Biosystems, Huntingdon Valley, PA). The results of CTC testing were compared to routine CSF cytology and radiologic studies for detecting LM.RESULTS: The CellSearch assay achieved a sensitivity of 88.9% and specificity of 100% for detecting LM (using a threshold of 1 CTC/mL of CSF as the definition of a positive CTC result). One patient with negative CSF cytology but positive CTCs developed positive cytology 37 days later.CONCLUSIONS: In this proof-of-principle pilot study, we demonstrate that the CellSearch assay can be successfully integrated with the routine CSF cytologic workflow to aid in the diagnosis of solid tumor LM. Importantly, CTCs detected by this rare cell capture assay are found in a subset of patients with non-positive routine CSF cytology, which may have significant implications for patient management.
View details for DOI 10.1016/j.jasc.2019.09.001
View details for PubMedID 31606331