A recent Radiology journal study assesses the power of a fully automated deep learning (DL) model to produce deterministic outputs for identifying clinically significant prostate cancer (csPCa). Study: Fully Automated Deep Learning Model to Detect Clinically Significant Prostate Cancer at MRI. Image Credit: Antonio Marca / Shutterstock.
com Using machine learning to diagnose prostate cancer Prostate cancer is the second most common cancer affecting men throughout the world. To diagnose csPCa, multiparametric magnetic resonance imaging (MRI) is commonly used. A standardized reporting and interpretation approach involves the use of the prostate imaging reporting and data system (PI-RADS), which requires a high level of expertise.
Nevertheless, using PI-RADS to classify lesions is susceptible to intra- and inter-observer variation. Classic machine learning or DL can be used to detect csPCa by training a model on specific regions of interest that are informed by MRI scans. An alternative approach is to obtain predictions for each voxel by training a segmentation model.
These machine-learning approaches require a radiologist or pathologist to annotate the lesions at the model development stage, as well as the retraining and re-evaluation stages after clinical implementation. As a result, implementing these approaches is associated with high costs that also limit the data set's size. About the study The researchers of the current study were interested in developing a DL model to pre.