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

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.

Detection rate of clinically insignificant prostate cancer ncreases with repeat prostate biopsies

To analyze if clinically insignificant prostate cancer （CIPC） is more frequently detected with repeat prostate biopsies, we retrospectively analyzed the records of 2146 men diagnosed with prostate cancer after one or more prostate biopsies. The patients were divided into five groups according to the number of prostate biopsies obtained, e.g, group I had one biopsy, group 2 had two biopsies and group 3 had three biopsies. Of the 2146 patients diagnosed with prostate cancer, 1956 （91,1%）, 142 （6.6%）, 38 （1.8%）, 9 （0.4%） and 1 （0.1%） men were in groups 1, 2, 3, 4 and 5, respectively. Groups 4 and 5 were excluded because of the small sample sizes. The remaining three groups （groups 1, 2 and 3） were statistically analyzed. There were no differences in age or prostate-specific antigen level among the three groups. CIPC was detected in 201 （10.3%）, 28 （19.7%） and 9 （23.7%） patients in groups 1, 2 and 3, respectively （P〈O.O01）. A multivariate analysis showed that the number of biopsies was an independent predictor to detect ClPC （0R=2.688 for group 2; 0R=4.723 for group 3）. In conclusion, patients undergoing multiple prostate biopsies are more likely to be diagnosed with CIPC than those who only undergo one biopsy. However, the risk still exists that the patient could have clinically significant prostate cancer. Therefore, when counseling patients with regard to serial repeat biopsies, the possibility of prostate cancer overdiagnosis and overtreatment must be balanced with the continued risk of clinically significant disease.