Researchers assess the power of a fully automated deep learning model to identify of clinically significant prostate cancer.
By Dr. Priyom Bose, Ph.D.Reviewed by Benedette Cuffari, M.Sc.Aug 11 2024 A recent Radiology journal study assesses the power of a fully automated deep learning model to produce deterministic outputs for identifying clinically significant prostate cancer .
A standardized reporting and interpretation approach involves the use of the prostate imaging reporting and data system , which requires a high level of expertise. Nevertheless, using PI-RADS to classify lesions is susceptible to intra- and inter-observer variation. About the study The researchers of the current study were interested in developing a DL model to predict the presence of csPCa without prior information on the tumor's location. They utilized patient-level labels clarifying the presence or absence of csPCa and compared the model's predictions with radiologists' predictions.
Related StoriesFour radiologists' PI-RADS ratings informed the external test set and were used for the internal test set. The DeLong test and receiver operating characteristic curves were used to evaluate radiologist performance. The tumor localization was shown using gradient-weighted class activation maps .
For the image + clinical + radiologist model, the true-positive rate was the highest, and the false-positive rate was the lowest. For pathologically proven cases, the radiologist's TPR was the highest, and the image + clinical model's FPR was the lowest. For the external dataset, the image + radiologist model showed the highest AUC and TPR and the lowest FPR.
Deep Learning Prostate Prostate Cancer Biopsy Imaging Machine Learning Magnetic Resonance Imaging Radiology Tumor
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