ArticleProstate cancer and prostatic diseases2018
Development and validation of a novel automated Gleason grade and molecular profile that define a highly predictive prostate cancer progression algorithm-based test.
Article in Prostate cancer and prostatic diseases, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed, 43 citations in OpenAlex.
- From molecular mechanisms of prostate cancer to translational applications: based on multi-omics fusion analysis and intelligent medicine.Health information science and systems · 2024Review
- Harnessing artificial intelligence for prostate cancer management.Cell reports. Medicine · 2024Review
- Article
- Development and validation of an AI-enabled digital breast cancer assay to predict early-stage breast cancer recurrence within 6 years.Breast cancer research : BCR · 2022Article
- Multimodal machine learning in precision health: A scoping review.NPJ digital medicine · 2022Article
- A Deep Learning Approach to Diagnostic Classification of Prostate Cancer Using Pathology-Radiology Fusion.Journal of magnetic resonance imaging : JMRI · 2021Article
- Novel immune-related genes in the tumor microenvironment with prognostic value in breast cancer.BMC cancer · 2021Article
- Current and future applications of machine and deep learning in urology: a review of the literature on urolithiasis, renal cell carcinoma, and bladder and prostate cancer.World journal of urology · 2020Review
- Identification of FPR3 as a Unique Biomarker for Targeted Therapy in the Immune Microenvironment of Breast Cancer.Frontiers in pharmacology · 2020Article
- Bioinformatic profiling of prognosis-related genes in the breast cancer immune microenvironment.Aging · 2019Article
- Artificial intelligence in neuropathology: deep learning-based assessment of tauopathy.Laboratory investigation; a journal of technical methods and pathology · 2019Article
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Authors and funding
10 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundPostoperative risk assessment remains an important variable in the effective treatment of prostate cancer. There is an unmet clinical need for a test with the potential to enhance the Gleason grading system with novel features that more accurately reflect a personalized prediction of clinical failure.
methodsA prospectively designed retrospective study utilizing 892 patients, post radical prostatectomy, followed for a median of 8 years. In training, using digital image analysis to combine microscopic pattern analysis/machine learning with biomarkers, we evaluated Precise Post-op model results to predict clinical failure in 446 patients. The derived prognostic score was validated in 446 patients. Eligible subjects required complete clinical-pathologic variables and were excluded if they had received neoadjuvant treatment including androgen deprivation, radiation or chemotherapy prior to surgery. No patients were enrolled with metastatic disease prior to surgery. Evaluate the assay using time to event concordance index (C-index), Kaplan-Meier, and hazards ratio.
resultsIn the training cohort (n = 306), the Precise Post-op test predicted significant clinical failure with a C-index of 0.82, [95% CI: 0.76-0.86], HR:6.7, [95% CI: 3.59-12.45], p < 0.00001. Results were confirmed in validation (n = 284) with a C-index 0.77 [95% CI: 0.72-0.81], HR = 5.4, [95% CI: 2.74-10.52], p < 0.00001. By comparison, a clinical feature base model had a C-index of 0.70 with a HR = 3.7. The Post-Op test also re-classified 58% of CAPRA-S intermediate risk patients as low risk for clinical failure.
conclusionsPrecise Post-op tissue-based test discriminates low from intermediate high risk prostate cancer disease progression in the postoperative setting. Guided by machine learning, the test enhances traditional Gleason grading with novel features that accurately reflect the biology of personalized risk assignment.
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