Mark A Laurie

All Publications

• Optimizing cystoscopy and TURBT: enhanced imaging and artificial intelligence. Nature reviews. Urology Shkolyar, E., Zhou, S. R., Carlson, C. J., Chang, S., Laurie, M. A., Xing, L., Bowden, A. K., Liao, J. C. 2024

  Abstract

  Diagnostic cystoscopy in combination with transurethral resection of the bladder tumour are the standard for the diagnosis, surgical treatment and surveillance of bladder cancer. The ability to inspect the bladder in its current form stems from a long chain of advances in imaging science and endoscopy. Despite these advances, bladder cancer recurrence and progression rates remain high after endoscopic resection. This stagnation is a result of the heterogeneity of cancer biology as well as limitations in surgical techniques and tools, as incomplete resection and provider-specific differences affect cancer persistence and early recurrence. An unmet clinical need remains for solutions that can improve tumour delineation and resection. Translational advances in enhanced cystoscopy technologies and artificial intelligence offer promising avenues to overcoming the progress plateau.

  View details for DOI 10.1038/s41585-024-00904-9

  View details for PubMedID 38982304

  View details for PubMedCentralID 6889816

• Bladder Cancer and Artificial Intelligence: Emerging Applications. The Urologic clinics of North America Laurie, M. A., Zhou, S. R., Islam, M. T., Shkolyar, E., Xing, L., Liao, J. C. 2024; 51 (1): 63-75

  Abstract

  Bladder cancer is a common and heterogeneous disease that poses a significant burden to the patient and health care system. Major unmet needs include effective early detection strategy, imprecision of risk stratification, and treatment-associated morbidities. The existing clinical paradigm is imprecise, which results in missed tumors, suboptimal therapy, and disease progression. Artificial intelligence holds immense potential to address many unmet needs in bladder cancer, including early detection, risk stratification, treatment planning, quality assessment, and outcome prediction. Despite recent advances, extensive work remains to affirm the efficacy of artificial intelligence as a decision-making tool for bladder cancer management.

  View details for DOI 10.1016/j.ucl.2023.07.002

  View details for PubMedID 37945103

• Explainable deep learning for tumor dynamic modeling and overall survival prediction using Neural-ODE. NPJ systems biology and applications Laurie, M., Lu, J. 2023; 9 (1): 58

  Abstract

  While tumor dynamic modeling has been widely applied to support the development of oncology drugs, there remains a need to increase predictivity, enable personalized therapy, and improve decision-making. We propose the use of Tumor Dynamic Neural-ODE (TDNODE) as a pharmacology-informed neural network to enable model discovery from longitudinal tumor size data. We show that TDNODE overcomes a key limitation of existing models in its ability to make unbiased predictions from truncated data. The encoder-decoder architecture is designed to express an underlying dynamical law that possesses the fundamental property of generalized homogeneity with respect to time. Thus, the modeling formalism enables the encoder output to be interpreted as kinetic rate metrics, with inverse time as the physical unit. We show that the generated metrics can be used to predict patients' overall survival (OS) with high accuracy. The proposed modeling formalism provides a principled way to integrate multimodal dynamical datasets in oncology disease modeling.

  View details for DOI 10.1038/s41540-023-00317-1

  View details for PubMedID 37980358

  View details for PubMedCentralID PMC10657412

• Stroma-mediated breast cancer cell proliferation indirectly drives chemoresistance by accelerating tumor recovery between chemotherapy cycles. Cancer research Miroshnychenko, D., Miti, T., Kumar, P., Miller, A., Laurie, M., Giraldo, N., Bui, M. M., Altrock, P. M., Basanta, D., Marusyk, A. 2023

  Abstract

  The ability of tumors to survive therapy reflects both cell-intrinsic and microenvironmental mechanisms. Across many cancers, including triple-negative breast cancer (TNBC), a high stroma/tumor ratio correlates with poor survival. In many contexts, this correlation can be explained by the direct reduction of therapy sensitivity induced by stroma-produced paracrine factors. We sought to explore whether this direct effect contributes to the link between stroma and poor responses to chemotherapies. In vitro studies with panels of TNBC cell line models and stromal isolates failed to detect a direct modulation of chemoresistance. At the same time, consistent with prior studies, fibroblast-produced secreted factors stimulated treatment-independent enhancement of tumor cell proliferation. Spatial analyses indicated that proximity to stroma is often associated with enhanced tumor cell proliferation in vivo. These observations suggested an indirect link between stroma and chemoresistance, where stroma-augmented proliferation potentiates the recovery of residual tumors between chemotherapy cycles. To evaluate this hypothesis, a spatial agent-based model of stroma impact on proliferation/death dynamics was developed that was quantitatively parameterized using inferences from histological analyses and experimental studies. The model demonstrated that the observed enhancement of tumor cell proliferation within stroma-proximal niches could enable tumors to avoid elimination over multiple chemotherapy cycles. Therefore, this study supports the existence of an indirect mechanism of environment-mediated chemoresistance that might contribute to the negative correlation between stromal content and poor therapy outcomes.

  View details for DOI 10.1158/0008-5472.CAN-23-0398

  View details for PubMedID 37791818

• Efficient Augmented Intelligence Framework for Bladder Lesion Detection. JCO clinical cancer informatics Eminaga, O., Lee, T. J., Laurie, M., Ge, T. J., La, V., Long, J., Semjonow, A., Bogemann, M., Lau, H., Shkolyar, E., Xing, L., Liao, J. C. 2023; 7: e2300031

  Abstract

  Development of intelligence systems for bladder lesion detection is cost intensive. An efficient strategy to develop such intelligence solutions is needed.We used four deep learning models (ConvNeXt, PlexusNet, MobileNet, and SwinTransformer) covering a variety of model complexity and efficacy. We trained these models on a previously published educational cystoscopy atlas (n = 312 images) to estimate the ratio between normal and cancer scores and externally validated on cystoscopy videos from 68 cases, with region of interest (ROI) pathologically confirmed to be benign and cancerous bladder lesions (ie, ROI). The performance measurement included specificity and sensitivity at frame level, frame sequence (block) level, and ROI level for each case.Specificity was comparable between four models at frame (range, 30.0%-44.8%) and block levels (56%-67%). Although sensitivity at the frame level (range, 81.4%-88.1%) differed between the models, sensitivity at the block level (100%) and ROI level (100%) was comparable between these models. MobileNet and PlexusNet were computationally more efficient for real-time ROI detection than ConvNeXt and SwinTransformer.Educational cystoscopy atlas and efficient models facilitate the development of real-time intelligence system for bladder lesion detection.

  View details for DOI 10.1200/CCI.23.00031

  View details for PubMedID 37774313

• Tumor detection under cystoscopy with transformer-augmented deep learning algorithm. Physics in medicine and biology Jia, X., Shkolyar, E., Laurie, M. A., Eminaga, O., Liao, J. C., Xing, L. 2023; 68 (16)

  Abstract

  Objective.Accurate tumor detection is critical in cystoscopy to improve bladder cancer resection and decrease recurrence. Advanced deep learning algorithms hold the potential to improve the performance of standard white-light cystoscopy (WLC) in a noninvasive and cost-effective fashion. The purpose of this work is to develop a cost-effective, transformer-augmented deep learning algorithm for accurate detection of bladder tumors in WLC and to assess its performance on archived patient data.Approach.'CystoNet-T', a deep learning-based bladder tumor detector, was developed with a transformer-augmented pyramidal CNN architecture to improve automated tumor detection of WLC. CystoNet-T incorporated the self-attention mechanism by attaching transformer encoder modules to the pyramidal layers of the feature pyramid network (FPN), and obtained multi-scale activation maps with global features aggregation. Features resulting from context augmentation served as the input to a region-based detector to produce tumor detection predictions. The training set was constructed by 510 WLC frames that were obtained from cystoscopy video sequences acquired from 54 patients. The test set was constructed based on 101 images obtained from WLC sequences of 13 patients.Main results.CystoNet-T was evaluated on the test set with 96.4 F1 and 91.4 AP (Average Precision). This result improved the benchmark of Faster R-CNN and YOLO by 7.3 points in F1 and 3.8 points in AP. The improvement is attributed to the strong ability of global attention of CystoNet-T and better feature learning of the pyramids architecture throughout the training. The model was found to be particularly effective in highlighting the foreground information for precise localization of the true positives while favorably avoiding false alarmsSignificance.We have developed a deep learning algorithm that accurately detects bladder tumors in WLC. Transformer-augmented AI framework promises to aid in clinical decision-making for improved bladder cancer diagnosis and therapeutic guidance.

  View details for DOI 10.1088/1361-6560/ace499

  View details for PubMedID 37548023

• Real-time Detection of Bladder Cancer Using Augmented Cystoscopy with Deep Learning: a Pilot Study. Journal of endourology Chang, T. C., Shkolyar, E., Del Giudice, F., Eminaga, O., Lee, T., Laurie, M., Seufert, C., Jia, X., Mach, K. E., Xing, L., Liao, J. C. 2023

  Abstract

  Detection of bladder tumors under white light cystoscopy (WLC) is challenging yet impactful on treatment outcomes. Artificial intelligence (AI) holds the potential to improve tumor detection; however, its application in the real-time setting remains unexplored. AI has been applied to previously recorded images for post hoc analysis. In this study, we evaluate the feasibility of real-time AI integration during clinic cystoscopy and transurethral resection of bladder tumor (TURBT) on live, streaming video.Patients undergoing clinic flexible cystoscopy and TURBT were prospectively enrolled. A real-time alert device system (real-time CystoNet) was developed and integrated with standard cystoscopy towers. Streaming videos were processed in real time to display alert boxes in sync with live cystoscopy. The per-frame diagnostic accuracy was measured.Real-time CystoNet was successfully integrated in the operating room during TURBT and clinic cystoscopy in 50 consecutive patients. There were 55 procedures that met the inclusion criteria for analysis including 21 clinic cystoscopies and 34 TURBTs. For clinic cystoscopy, real-time CystoNet achieved per-frame tumor specificity of 98.8% with a median error rate of 3.6% (range: 0 - 47%) frames per cystoscopy. For TURBT, the per-frame tumor sensitivity was 52.9% and the per-frame tumor specificity was 95.4% with an error rate of 16.7% for cases with pathologically confirmed bladder cancers.The current pilot study demonstrates the feasibility of using a real-time AI system (real-time CystoNet) during cystoscopy and TURBT to generate active feedback to the surgeon. Further optimization of CystoNet for real-time cystoscopy dynamics may allow for clinically useful AI-augmented cystoscopy.

  View details for DOI 10.1089/end.2023.0056

  View details for PubMedID 37432899

• Conceptual Framework and Documentation Standards of Cystoscopic Media Content for Artificial Intelligence. Journal of biomedical informatics Eminaga, O., Jiyong Lee, T., Ge, J., Shkolyar, E., Laurie, M., Long, J., Graham Hockman, L., Liao, J. C. 2023: 104369

  Abstract

  The clinical documentation of cystoscopy includes visual and textual materials. However, the secondary use of visual cystoscopic data for educational and research purposes remains limited due to inefficient data management in routine clinical practice.A conceptual framework was designed to document cystoscopy in a standardized manner with three major sections: data management, annotation management, and utilization management. A Swiss-cheese model was proposed for quality control and root cause analyses. We defined the infrastructure required to implement the framework with respect to FAIR (findable, accessible, interoperable, reusable) principles. We applied two scenarios exemplifying data sharing for research and educational projects to ensure compliance with FAIR principles.The framework was successfully implemented while following FAIR principles. The cystoscopy atlas produced from the framework could be presented in an educational web portal; a total of 68 full-length qualitative videos and corresponding annotation data were sharable for artificial intelligence projects covering frame classification and segmentation problems at case, lesion, and frame levels.Our study shows that the proposed framework facilitates the storage of visual documentation in a standardized manner and enables FAIR data for education and artificial intelligence research.

  View details for DOI 10.1016/j.jbi.2023.104369

  View details for PubMedID 37088456

• PlexusNet: A neural network architectural concept for medical image classification. Computers in biology and medicine Eminaga, O., Abbas, M., Shen, J., Laurie, M., Brooks, J. D., Liao, J. C., Rubin, D. L. 2023; 154: 106594

  Abstract

  State-of-the-art (SOTA) convolutional neural network models have been widely adapted in medical imaging and applied to address different clinical problems. However, the complexity and scale of such models may not be justified in medical imaging and subject to the available resource budget. Further increasing the number of representative feature maps for the classification task decreases the model explainability. The current data normalization practice is fixed prior to model development and discounting the specification of the data domain. Acknowledging these issues, the current work proposed a new scalable model family called PlexusNet; the block architecture and model scaling by the network's depth, width, and branch regulate PlexusNet's architecture. The efficient computation costs outlined the dimensions of PlexusNet scaling and design. PlexusNet includes a new learnable data normalization algorithm for better data generalization. We applied a simple yet effective neural architecture search to design PlexusNet tailored to five clinical classification problems that achieve a performance noninferior to the SOTA models ResNet-18 and EfficientNet B0/1. It also does so with lower parameter capacity and representative feature maps in ten-fold ranges than the smallest SOTA models with comparable performance. The visualization of representative features revealed distinguishable clusters associated with categories based on latent features generated by PlexusNet. The package and source code are at https://github.com/oeminaga/PlexusNet.git.

  View details for DOI 10.1016/j.compbiomed.2023.106594

  View details for PubMedID 36753979

• Potential of educational cystoscopy atlas for augmented intelligence Eminaga, O., Laurie, M., Lee, T., Jia, X., Liao, J. C. 2023

  View details for DOI 10.1117/12.2650920

• Flat lesion detection of white light cystoscopy with deep learning Jia, X., Shkolyar, E., Eminaga, O., Laurie, M., Zhou, Z., Lee, T., Islam, M., Meng, M. Q., Liao, J. C., Xing, L. 2023

  View details for DOI 10.1117/12.2650583

• Sequential modeling for cystoscopic image classification Laurie, M., Eminaga, O., Shkolyar, E., Jia, X., Lee, T., Long, J., Islam, M., Lau, H., Xing, L., Liao, J. C. 2023

  View details for DOI 10.1117/12.2649334

• An Efficient Framework for Video Documentation of Bladder Lesions for Cystoscopy: A Proof-of-Concept Study. Journal of medical systems Eminaga, O., Ge, T. J., Shkolyar, E., Laurie, M. A., Lee, T. J., Hockman, L., Jia, X., Xing, L., Liao, J. C. 2022; 46 (11): 73

  Abstract

  Processing full-length cystoscopy videos is challenging for documentation and research purposes. We therefore designed a surgeon-guided framework to extract short video clips with bladder lesions for more efficient content navigation and extraction. Screenshots of bladder lesions were captured during transurethral resection of bladder tumor, then manually labeled according to case identification, date, lesion location, imaging modality, and pathology. The framework used the screenshot to search for and extract a corresponding 10-seconds video clip. Each video clip included a one-second space holder with a QR barcode informing the video content. The success of the framework was measured by the secondary use of these short clips and the reduction of storage volume required for video materials. From 86 cases, the framework successfully generated 249 video clips from 230 screenshots, with 14 erroneous video clips from 8 screenshots excluded. The HIPPA-compliant barcodes provided information of video contents with a 100% data completeness. A web-based educational gallery was curated with various diagnostic categories and annotated frame sequences. Compared with the unedited videos, the informative short video clips reduced the storage volume by 99.5%. In conclusion, our framework expedites the generation of visual contents with surgeon's instruction for cystoscopy and potential incorporation of video data towards applications including clinical documentation, education, and research.

  View details for DOI 10.1007/s10916-022-01862-8

  View details for PubMedID 36190581

• Spontaneous cell fusions as a mechanism of parasexual recombination in tumour cell populations NATURE ECOLOGY & EVOLUTION Miroshnychenko, D., Baratchart, E., Ferrall-Fairbanks, M. C., Vander Velde, R., Laurie, M. A., Bui, M. M., Tan, A., Altrock, P. M., Basanta, D., Marusyk, A. 2021; 5 (3): 379–91

  Abstract

  The initiation and progression of cancers reflect the underlying process of somatic evolution, in which the diversification of heritable phenotypes provides a substrate for natural selection, resulting in the outgrowth of the most fit subpopulations. Although somatic evolution can tap into multiple sources of diversification, it is assumed to lack access to (para)sexual recombination-a key diversification mechanism throughout all strata of life. On the basis of observations of spontaneous fusions involving cancer cells, the reported genetic instability of polypoid cells and the precedence of fusion-mediated parasexual recombination in fungi, we asked whether cell fusions between genetically distinct cancer cells could produce parasexual recombination. Using differentially labelled tumour cells, we found evidence of low-frequency, spontaneous cell fusions between carcinoma cells in multiple cell line models of breast cancer both in vitro and in vivo. While some hybrids remained polyploid, many displayed partial ploidy reduction, generating diverse progeny with heterogeneous inheritance of parental alleles, indicative of partial recombination. Hybrid cells also displayed elevated levels of phenotypic plasticity, which may further amplify the impact of cell fusions on the diversification of phenotypic traits. Using mathematical modelling, we demonstrated that the observed rates of spontaneous somatic cell fusions may enable populations of tumour cells to amplify clonal heterogeneity, thus facilitating the exploration of larger areas of the adaptive landscape (relative to strictly asexual populations), which may substantially accelerate a tumour's ability to adapt to new selective pressures.

  View details for DOI 10.1038/s41559-020-01367-y

  View details for Web of Science ID 000608672700001

  View details for PubMedID 33462489

• Optical biopsy of penile cancer with in vivo confocal laser endomicroscopy. Urologic oncology Shkolyar, E. n., Laurie, M. A., Mach, K. E., Trivedi, D. R., Zlatev, D. V., Chang, T. C., Metzner, T. J., Leppert, J. T., Kao, C. S., Liao, J. C. 2019

  Abstract

  Surgical management of penile cancer depends on accurate margin assessment and staging. Advanced optical imaging technologies may improve penile biopsy and organ-sparing treatment. We evaluated the feasibility of confocal laser endomicroscopy for intraoperative assessment of benign and malignant penile tissue.With institutional review board approval, 11 patients were recruited, 9 with suspected penile cancer, and 2 healthy controls. Confocal laser endomicroscopy using a 2.6-mm fiber-optic probe was performed at 1 or 2 procedures on all subjects, for 13 imaging procedures. Fluorescein was administered intravenously approximately 3 minutes prior to imaging for contrast. Video sequences from in vivo (n = 12) and ex vivo (n = 6) imaging were obtained of normal glans, suspicious lesions, and surgical margins. Images were processed, annotated, characterized, and correlated with standard hematoxylin and eosin histopathology.No adverse events related to imaging were reported. Distinguishing features of benign and malignant penile tissue could be identified by confocal laser endomicroscopy. Normal skin had cells of uniform size and shape, with distinct cytoplasmic membranes consistent with squamous epithelium. Malignant lesions were characterized by disorganized, crowded cells of various size and shape, lack of distinct cytoplasmic membranes, and hazy, moth-eaten appearance. The transition from normal to abnormal squamous epithelium could be identified.We report the initial feasibility of intraoperative confocal laser endomicroscopy for penile cancer optical biopsy. Pending further evaluation, confocal laser endomicroscopy could serve as an adjunct or replacement to conventional frozen section pathology for management of penile cancer.

  View details for DOI 10.1016/j.urolonc.2019.08.018

  View details for PubMedID 31537485