Toward confident prostate cancer detection using ultrasound: a multi-center study.
Int J Comput Assist Radiol Surg
; 19(5): 841-849, 2024 May.
Article
in En
| MEDLINE
| ID: mdl-38704793
ABSTRACT
PURPOSE:
Deep learning-based analysis of micro-ultrasound images to detect cancerous lesions is a promising tool for improving prostate cancer (PCa) diagnosis. An ideal model should confidently identify cancer while responding with appropriate uncertainty when presented with out-of-distribution inputs that arise during deployment due to imaging artifacts and the biological heterogeneity of patients and prostatic tissue.METHODS:
Using micro-ultrasound data from 693 patients across 5 clinical centers who underwent micro-ultrasound guided prostate biopsy, we train and evaluate convolutional neural network models for PCa detection. To improve robustness to out-of-distribution inputs, we employ and comprehensively benchmark several state-of-the-art uncertainty estimation methods.RESULTS:
PCa detection models achieve performance scores up to 76 % average AUROC with a 10-fold cross validation setup. Models with uncertainty estimation obtain expected calibration error scores as low as 2 % , indicating that confident predictions are very likely to be correct. Visualizations of the model output demonstrate that the model correctly identifies healthy versus malignant tissue.CONCLUSION:
Deep learning models have been developed to confidently detect PCa lesions from micro-ultrasound. The performance of these models, determined from a large and diverse dataset, is competitive with visual analysis of magnetic resonance imaging, the clinical benchmark to identify PCa lesions for targeted biopsy. Deep learning with micro-ultrasound should be further studied as an avenue for targeted prostate biopsy.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Prostatic Neoplasms
/
Deep Learning
Limits:
Humans
/
Male
Language:
En
Journal:
Int J Comput Assist Radiol Surg
Journal subject:
RADIOLOGIA
Year:
2024
Type:
Article
Affiliation country:
Canada