Development of a deep learning system for predicting biochemical recurrence in prostate cancer

Background Biochemical recurrence (BCR) occurs in 20%–40% of men with prostate cancer (PCa) who undergo radical prostatectomy. Predicting which patients will experience BCR in advance helps in formulating more targeted prostatectomy procedures. However, current preoperative recurrence prediction mai...

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Veröffentlicht in:BMC cancer Jg. 25; H. 1; S. 232 - 14
Hauptverfasser: Cao, Lu, He, Ruimin, Zhang, Ao, Li, Lingmei, Cao, Wenfeng, Liu, Ning, Zhang, Peisen
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London BioMed Central 10.02.2025
BioMed Central Ltd
Springer Nature B.V
BMC
Schlagworte:
Aged
Algorithms
Artificial intelligence
Biochemical recurrence
Biomedical and Life Sciences
Biomedicine
Biopsy
Cancer
Cancer Research
Cancer therapies
Care and treatment
Data mining
Datasets
Deep Learning
Diagnosis
Diseases
Evaluation
Health aspects
Health Promotion and Disease Prevention
Humans
Machine learning
Male
Medical research
Medicine, Experimental
Medicine/Public Health
Middle Aged
Multi instance learning
Neoplasm Grading
Neoplasm Recurrence, Local - blood
Neoplasm Recurrence, Local - diagnosis
Neoplasm Recurrence, Local - pathology
Neural networks
Neural Networks, Computer
Oncology
Pathology
Patient outcomes
Patients
Predictions
Probability
Prognosis
Prostate cancer
Prostate cancer biopsy
Prostate-Specific Antigen - blood
Prostatectomy
Prostatic Neoplasms - blood
Prostatic Neoplasms - diagnosis
Prostatic Neoplasms - pathology
Prostatic Neoplasms - surgery
Relapse
Statistical analysis
Surgery
Surgical Oncology
China
Taiwan
Prostate cancer biopsy
Deep learning
Biochemical recurrence
Multi instance learning
ISSN:1471-2407, 1471-2407
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Zusammenfassung:Background Biochemical recurrence (BCR) occurs in 20%–40% of men with prostate cancer (PCa) who undergo radical prostatectomy. Predicting which patients will experience BCR in advance helps in formulating more targeted prostatectomy procedures. However, current preoperative recurrence prediction mainly relies on the use of the Gleason grading system, which omits within-grade morphological patterns and subtle histopathological features, leaving a significant amount of prognostic potential unexplored. Methods We collected and selected a total of 1585 prostate biopsy images with tumor regions from 317 patients (5 Whole Slide Images per patient) to develop a deep learning system for predicting BCR of PCa before prostatectomy. The Inception_v3 neural network was employed to train and test models developed from patch-level images. The multiple instance learning method was used to extract whole slide image-level features. Finally, patient-level artificial intelligence models were developed by integrating deep learning -generated pathology features with several machine learning algorithms. Results The BCR prediction system demonstrated great performance in the testing cohort (AUC = 0.911, 95% Confidence Interval: 0.840–0.982) and showed the potential to produce favorable clinical benefits according to Decision Curve Analyses. Increasing the number of WSIs for each patient improves the performance of the prediction system. Additionally, the study explores the correlation between deep learning -generated features and pathological findings, emphasizing the interpretative potential of artificial intelligence models in pathology. Conclusions Deep learning system can use biopsy samples to predict the risk of BCR in PCa, thereby formulating targeted treatment strategies.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:1471-2407
1471-2407
DOI:10.1186/s12885-025-13628-9