Skip to content
Open access

Real‐world assessment of a deep learning neural network algorithm for prostate cancer detection in MRI using true de novo data

Aug 2026 · Medical Physics (Lancaster) · Vol 53 · 0 citations · 51 references
Medicine

TL;DR

Using true external data sets paints a much clearer picture of real‐world clinical performance of an algorithm using true external image data sets.

Abstract

Deep‐learning neural network algorithms for detecting prostate cancer in MRI have proliferated in the literature. However, out of 30+ studies published since the PROSTATEx challenge, no studies tested the performance of their algorithm against using true external image data sets (studies came from an outside institution that did not supply any training data to the algorithm) while validating against MR‐US fusion biopsy or whole‐mount prostatectomy. Using true external data sets paints a much clearer picture of real‐world clinical performance of an algorithm.

Read PDF

Similar papers

Review Open access Sep 2026

Deep Learning Methods for Breast Cancer Detection, Classification, and Segmentation Using MRI Scans: A Systematic Review

This systematic review synthesises peer-reviewed studies published between 2014 and 2025 that exclusively applied DL techniques to breast MRI for cancer classification, prediction, or segmentation and demonstrates that DL models consistently achieve high diagnostic performance and have the potential to enhance radiolog...

Qais Al-Azzam, W. Balachandran, Ziad Hunaiti · 0 citations
Open access Jul 2026

Deep learning-based detection of pediatric brain tumor presence on MRI

The CNN demonstrates strong potential as a diagnostic aid for detection brain tumors using pediatric MRI images, and its integration into clinical environments could contribute to more objective interpretation, optimize diagnostic workflows, and provide imaging follow-up before and after treatment.

Estefania Reyes Soto, Silvia Hidalgo Tobón, Dulce Judith Almanza Aranda et al. · 0 citations
Open access Aug 2026

Overcoming clinical data constraints and class imbalance: enhancing brain tumor diagnosis with deep learning on real-world MRI datasets

Improvements in detection accuracy and interpretability reveal a critical trade-off: while macro-accuracy reaches 97.1%, Metastatic tumor recall remains at 75% — underscoring that clinical AI must prioritize equitable error distribution over aggregate metrics.

Akbar Hojjati Najafabadi, Parastoo Namdarian, Hossein loghmani · 0 citations
Jul 2026

Preoperative prediction of positive surgical margins in prostate cancer using multimodal deep learning model: a multicenter study.

The MDESM model, based on a feature disentanglement and multimodal fusion strategy, demonstrates the potential to effectively combine MRI and clinical data to achieve accurate PSM prediction and offers a promising tool for preoperative risk stratification and surgical planning in prostate cancer.

Xu Fu, J. Bao, Xiao-Meng Qiao et al. · 0 citations
Open access Aug 2026

Toward Autonomous Prostate Cancer Clinical Significance Determination from Spectral/Statistics Features in Bi-Parametric MRI

This study developed, applied, and tested new automation tools to expedite the spectral/statistics approach, and resulted in sufficiently high AUCs and a reduction in processing time, warranting future applications in large patient cohorts.

Rulon Mayer, Yuan Yuan, J. Udupa et al. · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.