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5 results on '"Kunal Nagpal"'

1. Development and validation of a deep learning-based microsatellite instability predictor from prostate cancer whole-slide images

Author

Qiyuan Hu, Abbas A. Rizvi, Geoffery Schau, Kshitij Ingale, Yoni Muller, Rachel Baits, Sebastian Pretzer, Aïcha BenTaieb, Abigail Gordhamer, Roberto Nussenzveig, Adam Cole, Matthew O. Leavitt, Ryan D. Jones, Rohan P. Joshi, Nike Beaubier, Martin C. Stumpe, and Kunal Nagpal

Subjects
Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Abstract Microsatellite instability-high (MSI-H) is a tumor-agnostic biomarker for immune checkpoint inhibitor therapy. However, MSI status is not routinely tested in prostate cancer, in part due to low prevalence and assay cost. As such, prediction of MSI status from hematoxylin and eosin (H&E) stained whole-slide images (WSIs) could identify prostate cancer patients most likely to benefit from confirmatory testing to evaluate their eligibility for immunotherapy and need for Lynch syndrome testing. Prostate biopsies and surgical resections from prostate cancer patients referred to our institution were analyzed. MSI status was determined by next-generation sequencing. Patients sequenced before a cutoff date formed an algorithm development set (n = 4015, MSI-H 1.8%) and a paired validation set (n = 173, MSI-H 19.7%) that consisted of two serial sections from each sample, one stained and scanned internally and the other at an external site. Patients sequenced after the cutoff date formed a temporally independent validation set (n = 1350, MSI-H 2.3%). Attention-based multiple instance learning models were trained to predict MSI-H from H&E WSIs. The predictor achieved area under the receiver operating characteristic curve values of 0.78 (95% CI [0.69–0.86]), 0.72 (95% CI [0.63–0.81]), and 0.72 (95% CI [0.62–0.82]) on the internally prepared, externally prepared, and temporal validation sets, respectively, showing effective predictability and generalization to both external staining/scanning processes and temporally independent samples. While MSI-H status is significantly correlated with Gleason score, the model remained predictive within each Gleason score subgroup.

Published
2024
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2. Deep learning models for histologic grading of breast cancer and association with disease prognosis

Author

Ronnachai Jaroensri, Ellery Wulczyn, Narayan Hegde, Trissia Brown, Isabelle Flament-Auvigne, Fraser Tan, Yuannan Cai, Kunal Nagpal, Emad A. Rakha, David J. Dabbs, Niels Olson, James H. Wren, Elaine E. Thompson, Erik Seetao, Carrie Robinson, Melissa Miao, Fabien Beckers, Greg S. Corrado, Lily H. Peng, Craig H. Mermel, Yun Liu, David F. Steiner, and Po-Hsuan Cameron Chen

Subjects
Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Abstract Histologic grading of breast cancer involves review and scoring of three well-established morphologic features: mitotic count, nuclear pleomorphism, and tubule formation. Taken together, these features form the basis of the Nottingham Grading System which is used to inform breast cancer characterization and prognosis. In this study, we develop deep learning models to perform histologic scoring of all three components using digitized hematoxylin and eosin-stained slides containing invasive breast carcinoma. We first evaluate model performance using pathologist-based reference standards for each component. To complement this typical approach to evaluation, we further evaluate the deep learning models via prognostic analyses. The individual component models perform at or above published benchmarks for algorithm-based grading approaches, achieving high concordance rates with pathologist grading. Further, prognostic performance using deep learning-based grading is on par with that of pathologists performing review of matched slides. By providing scores for each component feature, the deep-learning based approach also provides the potential to identify the grading components contributing most to prognostic value. This may enable optimized prognostic models, opportunities to improve access to consistent grading, and approaches to better understand the links between histologic features and clinical outcomes in breast cancer.

Published
2022
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3. Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancer

Author

Andrew Evans, Martin C. Stumpe, Greg S. Corrado, Robert C. MacDonald, Po-Hsuan, Davis Foote, Jenny L. Smith, Mahul B. Amin, Chen, Fraser Tan, Ellery Wulczyn, Jason D. Hipp, Lily Peng, Kunal Nagpal, Craig H. Mermel, Yun Liu, Arash Mohtashamian, James H. Wren, Ankur R. Sangoi, and Niels Olson

Subjects
FOS: Computer and information sciences, Computer Science - Machine Learning, medicine.medical_specialty, Computer science, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, Medicine (miscellaneous), Health Informatics, urologic and male genital diseases, lcsh:Computer applications to medicine. Medical informatics, Article, Machine Learning (cs.LG), Prostate cancer, Health Information Management, medicine, business.industry, Deep learning, medicine.disease, Computer Science Applications, surgical procedures, operative, Editorial, Tumor morphology, lcsh:R858-859.7, Artificial intelligence, Radiology, business
Abstract

For prostate cancer patients, the Gleason score is one of the most important prognostic factors, potentially determining treatment independent of the stage. However, Gleason scoring is based on subjective microscopic examination of tumor morphology and suffers from poor reproducibility. Here we present a deep learning system (DLS) for Gleason scoring whole-slide images of prostatectomies. Our system was developed using 112 million pathologist-annotated image patches from 1226 slides, and evaluated on an independent validation dataset of 331 slides. Compared to a reference standard provided by genitourinary pathology experts, the mean accuracy among 29 general pathologists was 0.61 on the validation set. The DLS achieved a significantly higher diagnostic accuracy of 0.70 (p = 0.002) and trended towards better patient risk stratification in correlations to clinical follow-up data. Our approach could improve the accuracy of Gleason scoring and subsequent therapy decisions, particularly where specialist expertise is unavailable. The DLS also goes beyond the current Gleason system to more finely characterize and quantitate tumor morphology, providing opportunities for refinement of the Gleason system itself.

Published
2019
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4. Development and Assessment of an Artificial Intelligence–Based Tool for Skin Condition Diagnosis by Primary Care Physicians and Nurse Practitioners in Teledermatology Practices

Author

Vishakha Gupta, Rory Sayres, Ayush Jain, David H. Way, Lily Peng, Yun Liu, Karen DeSalvo, Jay David Hartford, Yuan Liu, Peggy Bui, Clara H. Eng, Greg S. Corrado, Carter Dunn, Dale R. Webster, David Coz, Guilherme de Oliveira Marinho, Yi Gao, Susan Jen Huang, Kunal Nagpal, and Kimberly Kanada

Subjects
Teledermatology, Telemedicine, Randomization, Referral, business.industry, education, Guinea Pigs, MEDLINE, General Medicine, Mobile Applications, Search Engine, Interquartile range, Artificial Intelligence, Medicine, Animals, Humans, Medical history, Artificial intelligence, Medical diagnosis, business, health care economics and organizations, Original Investigation
Abstract

IMPORTANCE: Most dermatologic cases are initially evaluated by nondermatologists such as primary care physicians (PCPs) or nurse practitioners (NPs). OBJECTIVE: To evaluate an artificial intelligence (AI)–based tool that assists with diagnoses of dermatologic conditions. DESIGN, SETTING, AND PARTICIPANTS: This multiple-reader, multiple-case diagnostic study developed an AI-based tool and evaluated its utility. Primary care physicians and NPs retrospectively reviewed an enriched set of cases representing 120 different skin conditions. Randomization was used to ensure each clinician reviewed each case either with or without AI assistance; each clinician alternated between batches of 50 cases in each modality. The reviews occurred from February 21 to April 28, 2020. Data were analyzed from May 26, 2020, to January 27, 2021. EXPOSURES: An AI-based assistive tool for interpreting clinical images and associated medical history. MAIN OUTCOMES AND MEASURES: The primary analysis evaluated agreement with reference diagnoses provided by a panel of 3 dermatologists for PCPs and NPs. Secondary analyses included diagnostic accuracy for biopsy-confirmed cases, biopsy and referral rates, review time, and diagnostic confidence. RESULTS: Forty board-certified clinicians, including 20 PCPs (14 women [70.0%]; mean experience, 11.3 [range, 2-32] years) and 20 NPs (18 women [90.0%]; mean experience, 13.1 [range, 2-34] years) reviewed 1048 retrospective cases (672 female [64.2%]; median age, 43 [interquartile range, 30-56] years; 41 920 total reviews) from a teledermatology practice serving 11 sites and provided 0 to 5 differential diagnoses per case (mean [SD], 1.6 [0.7]). The PCPs were located across 12 states, and the NPs practiced in primary care without physician supervision across 9 states. The NPs had a mean of 13.1 (range, 2-34) years of experience and practiced in primary care without physician supervision across 9 states. Artificial intelligence assistance was significantly associated with higher agreement with reference diagnoses. For PCPs, the increase in diagnostic agreement was 10% (95% CI, 8%-11%; P

Published
2021

5. Reply: ‘The importance of study design in the application of artificial intelligence methods in medicine’

Author

Martin C. Stumpe, Kunal Nagpal, Yun Liu, Craig H. Mermel, and Po-Hsuan Cameron Chen

Subjects
medicine.medical_specialty, Prostate cancer, Medicine (miscellaneous), Health Informatics, lcsh:Computer applications to medicine. Medical informatics, medicine.disease, Computer Science Applications, Health Information Management, Correspondence, medicine, Pathology, lcsh:R858-859.7, Medical physics, Psychology
Published
2019

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