Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancer

By K. Nagpal, Davis Foote, Yun Liu, Po-Hsuan Cameron Chen, Ellery Wulczyn, Fraser Tan, Niels Olson, Jenny L. Smith, Arash Mohtashamian, James H. Wren and 9 more. npj Digital Medicine, 2018

458 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/77ac601c30d06782f870f4f9a53b55be2c413347

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Citations in the corpus

Review of Deep Learning Algorithms and Architectures (2019, background): “Google AI [10] was able to beat the average accuracy of US board certified general pathologists in grading prostate cancer by 70% to 61%.”
The impact of pre- and post-image processing techniques on deep learning frameworks: A comprehensive review for digital pathology image analysis (2020, methodology): “Campanella et al. [110] implemented a recurrent neural network (RNN) to aggregate the classified patches into the WSI label while Nagpal et al. [107] applied a nearest-neighbor classifier that used a summary of the heatmap to classify the entire whole-slide.”
"Hello AI": Uncovering the Onboarding Needs of Medical Practitioners for Human-AI Collaborative Decision-Making (2019, background): “Prostate cancer is one of the most common types of cancer, and the subject of significant attention in recent work on machine learning-powered cancer diagnosis [11, 15, 35, 56, 57, 69].”
Artificial intelligence (AI) and big data in cancer and precision oncology (2020, methodology): “This deep learning system trained > 900 pathology glass slides and their ﬁndings were consistent with those provided by the certiﬁed pathologists [75].”
Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge (2022, unknown): “GG 1 852 (17) 1,814 (33) 24 (12) 48 (24) 48 (14) 53 (25) 247 (33) 65 (20) 3,151 (25)”
Deep neural network models for computational histopathology: A survey (2019, background): “, 2018) in breast cancer and Gleason grades in prostate cancer (Nagpal et al., 2019).”
Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models (2022, background): “Indeed today, Inception-V3 serves as the basic foundation of many of the top medical imaging models currently published, ranging from ophthalmology [5,6] and pathology [7] to dermatology [8].”
Artificial intelligence as the next step towards precision pathology (2020, methodology): “In another study, deep learning model was trained on 1226 HE whole slides from prosta-tectomies [94].”
Deep learning in histopathology: the path to the clinic (2021, background): “Current applications include tumor detection and classification (often by sub-type 23,25–39 ), image segmentation 40–50 , cell detection and counting 51–55 , mitosis detection 56–60 , analysis of kidney transplant biopsies 20 and tumor grading 61–63 among others.”