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Public Health Genomics and Precision Health Knowledge Base (v7.3)
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[Artificial intelligence in the diagnosis of prostate cancer].
Popov G V et al. Arkhiv patologii 2021 83(3) 38-45
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Radiomic and Genomic Machine Learning Method Performance for Prostate Cancer Diagnosis: Systematic Literature Review.
Castaldo Rossana et al. Journal of medical Internet research 2021 23(4) e22394
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Artificial Intelligence in Magnetic Resonance Imaging-based Prostate Cancer Diagnosis: Where Do We Stand in 2021?
Suarez-Ibarrola Rodrigo et al. European urology focus 2021
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An independent assessment of an artificial intelligence system for prostate cancer detection shows strong diagnostic accuracy.
Perincheri Sudhir et al. Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc 2021
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[Machine learning and multiparametric MRI for early diagnosis of prostate cancer].
Bonekamp D et al. Der Urologe. Ausg. A 2021
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Optimized Identification of High-Grade Prostate Cancer by Combining Different PSA Molecular Forms and PSA Density in a Deep Learning Model.
Gentile Francesco et al. Diagnostics (Basel, Switzerland) 2021 11(2)
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A Multi-Center, Multi-Vendor Study to Evaluate the Generalizability of a Radiomics Model for Classifying Prostate cancer: High Grade vs. Low Grade.
Castillo T Jose M et al. Diagnostics (Basel, Switzerland) 2021 11(2)
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Artificial Intelligence and Machine Learning in Prostate Cancer Patient Management-Current Trends and Future Perspectives.
Tataru Octavian Sabin et al. Diagnostics (Basel, Switzerland) 2021 11(2)
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Computerized tumor multinucleation index (MuNI) is prognostic in p16+ oropharyngeal carcinoma: A multi-site validation study.
Koyuncu Can F et al. The Journal of clinical investigation 2021
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Application of a novel machine learning framework for predicting non-metastatic prostate cancer-specific mortality in men using the Surveillance, Epidemiology, and End Results (SEER) database.
Lee Changhee et al. The Lancet. Digital health 2021 Feb
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A Systematic Review of Artificial Intelligence in Prostate Cancer.
Van Booven Derek J et al. Research and reports in urology 2021 1331-39
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Application of a novel machine learning framework for predicting non-metastatic prostate cancer-specific mortality in men using the Surveillance, Epidemiology, and End Results (SEER) database
C Lee et al, Lancet Digital Health ,February 3, 2021
WeGleNet: A weakly-supervised convolutional neural network for the semantic segmentation of Gleason grades in prostate histology images.
Silva-Rodríguez Julio et al. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society 2021 Jan 88101846
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[Application of artificial intelligence combined with multi-parametric MRI in the early diagnosis of prostate cancer].
Tang Jian-Er et al. Zhonghua nan ke xue = National journal of andrology 2020 Sep 26(9) 783-787
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Supervised machine learning enables non-invasive lesion characterization in primary prostate cancer with [ 68 Ga]Ga-PSMA-11 PET/MRI.
Papp L et al. European journal of nuclear medicine and molecular imaging 2020 Dec
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An artificial intelligence algorithm for prostate cancer diagnosis in whole slide images of core needle biopsies: a blinded clinical validation and deployment study.
Pantanowitz Liron et al. The Lancet. Digital health 2020 Aug 2(8) e407-e416
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Use of the MyProstateScore (MPS) Test to Rule Out Clinically-Significant Cancer: Validation of a Straightforward Clinical Testing Approach.
Tosoian Jeffrey J et al. The Journal of urology 2020 Oct 101097JU0000000000001430
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Machine-Learning Models for Multicenter Prostate Cancer Treatment Plans.
Syed Khajamoinuddin et al. Journal of computational biology : a journal of computational molecular cell biology 2020 Sep
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Analysis of Bone Scans in Various Tumor Entities Using a Deep-Learning-Based Artificial Neural Network Algorithm-Evaluation of Diagnostic Performance.
Wuestemann Jan et al. Cancers 2020 Sep 12(9)
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Digital Biopsy with Fluorescence Confocal Microscope for Effective Real-time Diagnosis of Prostate Cancer: A Prospective, Comparative Study.
Rocco Bernardo et al. European urology oncology 2020 Sep
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The utility of a deep learning-based algorithm for bone scintigraphy in patient with prostate cancer.
Aoki Yuki et al. Annals of nuclear medicine 2020 Sep
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T1 and T2 MR fingerprinting measurements of prostate cancer and prostatitis correlate with deep learning-derived estimates of epithelium, lumen, and stromal composition on corresponding whole mount histopathology.
Shiradkar Rakesh et al. European radiology 2020 Sep
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Deep Learning in Radiation Oncology Treatment Planning for Prostate Cancer: A Systematic Review.
Almeida Gonçalo et al. Journal of medical systems 2020 Aug 44(10) 179
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Dr. Answer AI for prostate cancer: Clinical outcome prediction model and service.
Rho Mi Jung et al. PloS one 2020 15(8) e0236553
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PSA-based machine learning model improves prostate cancer risk stratification in a screening population.
Perera Marlon et al. World journal of urology 2020 Aug
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Artificial intelligence assistance significantly improves Gleason grading of prostate biopsies by pathologists.
Bulten Wouter et al. Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc 2020 Aug
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Multicenter Multireader Evaluation of an Artificial Intelligence-Based Attention Mapping System for the Detection of Prostate Cancer With Multiparametric MRI.
Mehralivand Sherif et al. AJR. American journal of roentgenology 2020 Aug 1-10
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Simulated clinical deployment of fully automatic deep learning for clinical prostate MRI assessment.
Schelb Patrick et al. European radiology 2020 Aug
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Voxel-based supervised machine learning of peripheral zone prostate cancer using noncontrast multiparametric MRI.
Gholizadeh Neda et al. Journal of applied clinical medical physics 2020 Aug
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Machine learning-based analysis of [ 18 F]DCFPyL PET radiomics for risk stratification in primary prostate cancer.
Cysouw Matthijs C F et al. European journal of nuclear medicine and molecular imaging 2020 Jul
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Page last reviewed:
Oct 1, 2020
Page last updated:
Apr 19, 2021
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