What benefit can be obtained from magnetic resonance imaging diagnosis with artificial intelligence in prostate cancer compared with clinical assessments?

Abstract

The present study aimed to explore the potential of artificial intelligence (AI) methodology based on magnetic resonance (MR) images to aid in the management of prostate cancer (PCa). To this end, we reviewed and summarized the studies comparing the diagnostic and predictive performance for PCa between AI and common clinical assessment methods based on MR images and/or clinical characteristics, thereby investigating whether AI methods are generally superior to common clinical assessment methods for the diagnosis and prediction fields of PCa. First, we found that, in the included studies of the present study, AI methods were generally equal to or better than the clinical assessment methods for the risk assessment of PCa, such as risk stratification of prostate lesions and the prediction of therapeutic outcomes or PCa progression. In particular, for the diagnosis of clinically significant PCa, the AI methods achieved a higher summary receiver operator characteristic curve (SROC-AUC) than that of the clinical assessment methods (0.87 vs. 0.82). For the prediction of adverse pathology, the AI methods also achieved a higher SROC-AUC than that of the clinical assessment methods (0.86 vs. 0.75). Second, as revealed by the radiomics quality score (RQS), the studies included in the present study presented a relatively high total average RQS of 15.2 (11.0–20.0). Further, the scores of the individual RQS elements implied that the AI models in these studies were constructed with relatively perfect and standard radiomics processes, but the exact generalizability and clinical practicality of the AI models should be further validated using higher levels of evidence, such as prospective studies and open-testing datasets.

The implications of single-cell RNA-seq analysis in prostate cancer: unraveling tumor heterogeneity, therapeutic implications and pathways towards personalized therapy

“…Prostate cancer (PCa) is one of the leading malignant tumors affecting males globally, with concerning trends in both incidence and mortality [ 1 – 3 ]. In China, the situation is particularly challenging, marked by advanced tumor stages, higher metastasis rates, and lower survival rates compared with some Western countries [ 4 – 6 ]. There is an urgent need for early diagnosis and treatment of PCa.…”

Detecting Adverse Pathology of Prostate Cancer With a Deep Learning Approach Based on a 3D Swin‐Transformer Model and Biparametric MRI: A Multicenter Retrospective Study

“…Deep learning has been widely used to assist in the diagnosis and treatment decision-making of PCa. 16,[19][20][21][22] However, deep learning methods for detecting AP presence are lacking, other than one recent study that focused on the detection of ECE. 23 Thus, the aim of this study was to develop and evaluate a deep learning model to detect the presence of AP (i.e., ECE, SVI, or PSM) based on biparametric MRI (bpMRI), which included T2WI, DWI, and apparent diffusion coefficient (ADC) images.…”

“…In contrast to hand‐crafted radiomics, deep learning can automatically learn task‐specific features (eg, tumor anatomic structure, neurovascular bundles, and other invisible characteristics), which are closely associated with the aggressiveness of PCa. Deep learning has been widely used to assist in the diagnosis and treatment decision‐making of PCa 16,19–22 . However, deep learning methods for detecting AP presence are lacking, other than one recent study that focused on the detection of ECE 23 …”