دورية أكاديمية
Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancer.
العنوان: | Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancer. |
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المؤلفون: | Nagpal K; 1Google AI Healthcare, Google, Mountain View, CA USA., Foote D; 1Google AI Healthcare, Google, Mountain View, CA USA., Liu Y; 1Google AI Healthcare, Google, Mountain View, CA USA., Chen PC; 1Google AI Healthcare, Google, Mountain View, CA USA., Wulczyn E; 1Google AI Healthcare, Google, Mountain View, CA USA., Tan F; 1Google AI Healthcare, Google, Mountain View, CA USA., Olson N; 2Laboratory Department, Naval Medical Center San Diego, San Diego, CA USA., Smith JL; 2Laboratory Department, Naval Medical Center San Diego, San Diego, CA USA., Mohtashamian A; 2Laboratory Department, Naval Medical Center San Diego, San Diego, CA USA., Wren JH; 3Henry M. Jackson Foundation, Bethesda, MD USA., Corrado GS; 1Google AI Healthcare, Google, Mountain View, CA USA., MacDonald R; 1Google AI Healthcare, Google, Mountain View, CA USA., Peng LH; 1Google AI Healthcare, Google, Mountain View, CA USA., Amin MB; 4Department of Pathology and Laboratory Medicine, University of Tennessee Health Science Center, Memphis, TN USA., Evans AJ; 5Department of Pathology, Laboratory Medicine and Pathology, University Health Network and University of Toronto, Toronto, ON Canada., Sangoi AR; 6Department of Pathology, El Camino Hospital, Mountain View, CA USA., Mermel CH; 1Google AI Healthcare, Google, Mountain View, CA USA., Hipp JD; 1Google AI Healthcare, Google, Mountain View, CA USA., Stumpe MC; Present Address: AI and Data Science, Tempus Labs Inc, Chicago, United States. |
المصدر: | NPJ digital medicine [NPJ Digit Med] 2019 Jun 07; Vol. 2, pp. 48. Date of Electronic Publication: 2019 Jun 07 (Print Publication: 2019). |
نوع المنشور: | Journal Article |
اللغة: | English |
بيانات الدورية: | Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101731738 Publication Model: eCollection Cited Medium: Internet ISSN: 2398-6352 (Electronic) Linking ISSN: 23986352 NLM ISO Abbreviation: NPJ Digit Med Subsets: PubMed not MEDLINE |
أسماء مطبوعة: | Original Publication: [London] ; New York : Nature Publishing Group, [2018]- |
مستخلص: | 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. Competing Interests: Competing interestsK.N., D.F., Y.L., P.-H.C.C., E.W., F.T., G.S.C., R.M.D., L.H.P., C.H.M., J.D.H. and M.C.S. are employees of Google LLC and own Alphabet stock. |
التعليقات: | Erratum in: NPJ Digit Med. 2019 Nov 19;2:113. (PMID: 31754638) Comment in: Diagn Pathol. 2019 Dec 27;14(1):138. (PMID: 31881972) |
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فهرسة مساهمة: | Keywords: Prostate cancer |
تواريخ الأحداث: | Date Created: 20190716 Latest Revision: 20231012 |
رمز التحديث: | 20240628 |
مُعرف محوري في PubMed: | PMC6555810 |
DOI: | 10.1038/s41746-019-0112-2 |
PMID: | 31304394 |
قاعدة البيانات: | MEDLINE |
تدمد: | 2398-6352 |
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DOI: | 10.1038/s41746-019-0112-2 |