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Precision Health Database


Genomics & Health Impact Scan Database|Search|PHGKB

Last data update: Apr 19, 2021. (Total: 39616 Documents since 2012)
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[Artificial intelligence in the diagnosis of prostate cancer].
Popov G V et al. Arkhiv patologii 2021 83(3) 38-45
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
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
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
[Machine learning and multiparametric MRI for early diagnosis of prostate cancer].
Bonekamp D et al. Der Urologe. Ausg. A 2021
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)
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)
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)
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
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
A Systematic Review of Artificial Intelligence in Prostate Cancer.
Van Booven Derek J et al. Research and reports in urology 2021 1331-39
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
[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
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
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
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
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
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)
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
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
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
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
Dr. Answer AI for prostate cancer: Clinical outcome prediction model and service.
Rho Mi Jung et al. PloS one 2020 15(8) e0236553
PSA-based machine learning model improves prostate cancer risk stratification in a screening population.
Perera Marlon et al. World journal of urology 2020 Aug
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
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
Simulated clinical deployment of fully automatic deep learning for clinical prostate MRI assessment.
Schelb Patrick et al. European radiology 2020 Aug
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
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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