Dataset Information

Deep learning for automatic Gleason pattern classification for grade group determination of prostate biopsies.

ABSTRACT: Histopathologic grading of prostate cancer using Gleason patterns (GPs) is subject to a large inter-observer variability, which may result in suboptimal treatment of patients. With the introduction of digitization and whole-slide images of prostate biopsies, computer-aided grading becomes feasible. Computer-aided grading has the potential to improve histopathological grading and treatment selection for prostate cancer. Automated detection of GPs and determination of the grade groups (GG) using a convolutional neural network. In total, 96 prostate biopsies from 38 patients are annotated on pixel-level. Automated detection of GP 3 and GP???4 in digitized prostate biopsies is performed by re-training the Inception-v3 convolutional neural network (CNN). The outcome of the CNN is subsequently converted into probability maps of GP???3 and GP???4, and the GG of the whole biopsy is obtained according to these probability maps. Differentiation between non-atypical and malignant (GP???3) areas resulted in an accuracy of 92% with a sensitivity and specificity of 90 and 93%, respectively. The differentiation between GP???4 and GP???3 was accurate for 90%, with a sensitivity and specificity of 77 and 94%, respectively. Concordance of our automated GG determination method with a genitourinary pathologist was obtained in 65% (??=?0.70), indicating substantial agreement. A CNN allows for accurate differentiation between non-atypical and malignant areas as defined by GPs, leading to a substantial agreement with the pathologist in defining the GG.

SUBMITTER: Lucas M

PROVIDER: S-EPMC6611751 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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Deep learning for automatic Gleason pattern classification for grade group determination of prostate biopsies.

Lucas Marit M Jansen Ilaria I Savci-Heijink C Dilara CD Meijer Sybren L SL de Boer Onno J OJ van Leeuwen Ton G TG de Bruin Daniel M DM Marquering Henk A HA

Virchows Archiv : an international journal of pathology 20190516 1

Histopathologic grading of prostate cancer using Gleason patterns (GPs) is subject to a large inter-observer variability, which may result in suboptimal treatment of patients. With the introduction of digitization and whole-slide images of prostate biopsies, computer-aided grading becomes feasible. Computer-aided grading has the potential to improve histopathological grading and treatment selection for prostate cancer. Automated detection of GPs and determination of the grade groups (GG) using a ...[more]

PMID: 31098801

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Project description:PURPOSE:To our knowledge the ideal methodology of quantifying secondary Gleason pattern 4 in men with Grade Group 2/Gleason score 3 + 4 = 7 on biopsy remains unknown. We compared various methods of Gleason pattern 4 quantification and evaluated associations with adverse pathology findings at radical prostatectomy. MATERIALS AND METHODS:A total of 457 men with Grade Group 2 prostate cancer on biopsy subsequently underwent radical prostatectomy at our institution. Only patients with 12 or more reviewed cores were included in analysis. We evaluated 3 methods of quantifying Gleason pattern 4, including the maximum percent of Gleason pattern 4 in any single core, the overall percent of Gleason pattern 4 (Gleason pattern 4 mm/total cancer mm) and the total length of Gleason pattern 4 in mm across all cores. Adverse pathology features at radical prostatectomy were defined as Gleason score 4 + 3 = 7 or greater (Grade Group 3 or greater), and any extraprostatic extension, seminal vesical invasion and/or lymph node metastasis. A training/test set approach and multivariable logistic regression were used to determine whether Gleason pattern 4 quantification methods could aid in predicting adverse pathology. RESULTS:On multivariable analysis all Gleason pattern 4 quantification methods were significantly associated with an increased risk of adverse pathology (p <0.0001) and an increased AUC beyond the base model. The largest AUC increase was 0.044 for the total length of Gleason pattern 4 (AUC 0.728, 95% CI 0.663-0.793). Decision curve analysis demonstrated an increased clinical net benefit with the addition of Gleason pattern 4 quantification to the base model. The total length of Gleason pattern 4 clearly provided the largest net benefit. CONCLUSIONS:Our findings support the inclusion of Gleason pattern 4 quantification in the pathology reports and risk prediction models of patients with Grade Group 2/Gleason score 3 + 4 = 7 prostate cancer. The total length of Gleason pattern 4 across all cores provided the strongest benefit to predict adverse pathology features.

Project description:BackgroundPattern of emphysema at chest CT, scored visually by using the Fleischner Society system, is associated with physiologic impairment and mortality risk.PurposeTo determine whether participant-level emphysema pattern could predict impairment and mortality when classified by using a deep learning method.Materials and MethodsThis retrospective analysis of Genetic Epidemiology of COPD (COPDGene) study participants enrolled between 2007 and 2011 included those with baseline CT, visual emphysema scores, and survival data through 2018. Participants were partitioned into nonoverlapping sets of 2407 for algorithm training, 100 for validation and parameter tuning, and 7143 for testing. A deep learning algorithm using convolutional neural network and long short-term memory architectures was trained to classify pattern of emphysema according to Fleischner criteria. Deep learning scores were compared with visual scores and clinical parameters including pulmonary function tests. Cox proportional hazard models were used to evaluate relationships between emphysema scores and survival. The algorithm was also tested by using CT and clinical data in 1962 participants enrolled in the Evaluation of COPD Longitudinally to Identify Predictive Surrogate End-points (ECLIPSE) study.ResultsA total of 7143 COPDGene participants (mean age ± standard deviation, 59.8 years ± 8.9; 3734 men and 3409 women) were evaluated. Deep learning emphysema classifications were associated with impaired pulmonary function tests, 6-minute walk distance, and St George's Respiratory Questionnaire at univariate analysis (P < .001 for each). Testing in the ECLIPSE cohort showed similar associations (P < .001). In the COPDGene test cohort, deep learning emphysema classification improved the fit of linear mixed models in the prediction of these clinical parameters compared with visual scoring (P < .001). Compared with participants without emphysema, mortality was greater in participants classified by the deep learning algorithm as having any grade of emphysema (adjusted hazard ratios were 1.5, 1.7, 2.9, 5.3, and 9.7, respectively, for trace, mild, moderate, confluent, and advanced destructive emphysema; P < .05).ConclusionDeep learning automation of the Fleischner grade of emphysema at chest CT is associated with clinical measures of pulmonary insufficiency and the risk of mortality.© RSNA, 2019Online supplemental material is available for this article.

Dataset Information

Deep learning for automatic Gleason pattern classification for grade group determination of prostate biopsies.

Publications

Deep learning for automatic Gleason pattern classification for grade group determination of prostate biopsies.

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