An integrated radiology-pathology machine learning classifier for outcome prediction following radical prostatectomy: Preliminary findings

A Hiremath, G Corredor, L Li, P Leo, C Magi-Galluzzi… - Heliyon, 2024 - cell.com
Objectives To evaluate the added benefit of integrating features from pre-treatment MRI
(radiomics) and digitized post-surgical pathology slides (pathomics) in prostate cancer …

Construction of a preoperative radiologic-risk signature for predicting the pathologic status of prostate cancer at radical prostatectomy

L Qi, CJ Wu, J Zhang, ML Bao, X Yan… - American Journal of …, 2018 - Am Roentgen Ray Soc
OBJECTIVE. We developed a radiologic-risk signature (RRS) that serves as a surrogate for
the pathologic status of prostate cancer (PCA) and investigated its ability to predict disease …

The practical clinical role of machine learning models with different algorithms in predicting prostate cancer local recurrence after radical prostatectomy

C Hu, X Qiao, C Hu, C Cao, X Wang, J Bao - Cancer Imaging, 2024 - Springer
Background The detection of local recurrence for prostate cancer (PCa) patients following
radical prostatectomy (RP) is challenging and can influence the treatment plan. Our aim was …

Improved prediction of PSA recurrence through systems pathology

C Cordon-Cardo, A Kotsianti, MJ Donovan… - Journal of Clinical …, 2004 - ascopubs.org
4591 Improved prediction of PSA recurrence through systems pathology Background:
Current tools in clinical practice which are used to predict PSA recurrence s/p radical …

[HTML][HTML] Radiologist-like artificial intelligence for grade group prediction of radical prostatectomy for reducing upgrading and downgrading from biopsy

L Shao, YE Yan, Z Liu, X Ye, H Xia, X Zhu, Y Zhang… - Theranostics, 2020 - ncbi.nlm.nih.gov
Rationale: To reduce upgrading and downgrading between needle biopsy (NB) and radical
prostatectomy (RP) by predicting patient-level Gleason grade groups (GGs) of RP to avoid …

Clinical application of machine learning models in patients with prostate cancer before prostatectomy

A Guerra, MR Orton, H Wang, M Konidari, K Maes… - Cancer Imaging, 2024 - Springer
Background To build machine learning predictive models for surgical risk assessment of
extracapsular extension (ECE) in patients with prostate cancer (PCa) before radical …

Can we predict pathology without surgery? Weighing the added value of multiparametric MRI and whole prostate radiomics in integrative machine learning models

G Marvaso, LJ Isaksson, M Zaffaroni, MG Vincini… - European …, 2024 - Springer
Objective To test the ability of high-performance machine learning (ML) models employing
clinical, radiological, and radiomic variables to improve non-invasive prediction of the …

[HTML][HTML] Machine learning-based prediction of pathological upgrade from combined transperineal systematic and MRI-targeted prostate biopsy to final pathology: a …

J Zhuang, Y Kan, Y Wang, A Marquis, X Qiu… - Frontiers in …, 2022 - frontiersin.org
Objective: To evaluate the pathological concordance from combined systematic and MRI-
targeted prostate biopsy to final pathology, and to verify the effectiveness of machine …

A nomogram for accurately predicting the pathological upgrading of prostate cancer, based on 68Ga‐PSMA PET/CT

Q Hu, X Hong, L Xu, R Jia - The Prostate, 2022 - Wiley Online Library
Purpose To develop and validate a nomogram for preoperative predicting the pathological
upgrading of prostate cancer (PCa). Methods The prediction model was developed in a …

Assessment of oncological outcomes after radical prostatectomy according to preoperative and postoperative cancer of the prostate risk assessment scores: results …

SR Leyh-Bannurah, P Dell'Oglio, E Zaffuto… - European urology …, 2019 - Elsevier
Abstract Background Among prostate cancer (PCa) patients undergoing radical
prostatectomy (RP) and with virtually identical unfavorable pathological characteristics …