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 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.展开更多
Purpose: To characterize missed prostate tumors and their cumulative volume with various biopsy regimens to determine optimal biopsy schemes. Methods: We performed 6, 12 and 18-core needle biopsies on 165 and 36-core ...Purpose: To characterize missed prostate tumors and their cumulative volume with various biopsy regimens to determine optimal biopsy schemes. Methods: We performed 6, 12 and 18-core needle biopsies on 165 and 36-core biopsies on 47 autopsy prostates, respectively. The 6-core biopsy included 6 cores from the mid peripheral zone (MPZ), the 12-core biopsy included 6 cores from the MPZ and lateral PZ (LPZ), and the 18-core biopsies included 6 cores from the MPZ, LPZ and central zone (CZ). The 36-core biopsies included 12 cores in each of these 3 areas. We analyzed the sensitivity of biopsies at each site and evaluated the cumulative volume of cancers and tumor foci missed. Results: Whole-mount analysis identified 59 cancers, 110 tumor foci, and a total cumulative tumor volume of 43 cm3. The percentage of tumor foci and corresponding cumulative volume missed with 6, 12, 18 and 36-core biopsies were of 79% and 58%, 64% and 48%, 57% and 26%, and 42% and 17%, respectively (p 0.05). 12-core biopsies from the MPZ and LPZ performed best for clinically significant cancers detection. However, increasing the number of cores over the 6-core biopsy cutoff increased solely the detection of tumor foci 0.5 cm3. Conclusion: Twelve biopsies from the MPZ and LPZ detected most of the clinically significant cancers while missing most of the tumor foci. These missed tumors represented only a small amount of the overall cancer volume.展开更多
Objective To explore the expression of novel protein kinase C ε ( PKCε) in normal prostate ( NP) tissue, benign prostate hyperplasia ( BPH) ,pericancerous ( PC) tissue and prostate cancer ( Pca) ,and study its corre...Objective To explore the expression of novel protein kinase C ε ( PKCε) in normal prostate ( NP) tissue, benign prostate hyperplasia ( BPH) ,pericancerous ( PC) tissue and prostate cancer ( Pca) ,and study its correlation with the grade and stage of Pca. Methods Ten NP slides,ten BPH slides,ten PC slides and 43 Pca展开更多
基金supported by the Natural Science Foundation of Beijing(Z200027)the National Natural Science Foundation of China(62027901,81930053)the Key-Area Research and Development Program of Guangdong Province(2021B0101420005).
文摘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.
文摘Purpose: To characterize missed prostate tumors and their cumulative volume with various biopsy regimens to determine optimal biopsy schemes. Methods: We performed 6, 12 and 18-core needle biopsies on 165 and 36-core biopsies on 47 autopsy prostates, respectively. The 6-core biopsy included 6 cores from the mid peripheral zone (MPZ), the 12-core biopsy included 6 cores from the MPZ and lateral PZ (LPZ), and the 18-core biopsies included 6 cores from the MPZ, LPZ and central zone (CZ). The 36-core biopsies included 12 cores in each of these 3 areas. We analyzed the sensitivity of biopsies at each site and evaluated the cumulative volume of cancers and tumor foci missed. Results: Whole-mount analysis identified 59 cancers, 110 tumor foci, and a total cumulative tumor volume of 43 cm3. The percentage of tumor foci and corresponding cumulative volume missed with 6, 12, 18 and 36-core biopsies were of 79% and 58%, 64% and 48%, 57% and 26%, and 42% and 17%, respectively (p 0.05). 12-core biopsies from the MPZ and LPZ performed best for clinically significant cancers detection. However, increasing the number of cores over the 6-core biopsy cutoff increased solely the detection of tumor foci 0.5 cm3. Conclusion: Twelve biopsies from the MPZ and LPZ detected most of the clinically significant cancers while missing most of the tumor foci. These missed tumors represented only a small amount of the overall cancer volume.
文摘Objective To explore the expression of novel protein kinase C ε ( PKCε) in normal prostate ( NP) tissue, benign prostate hyperplasia ( BPH) ,pericancerous ( PC) tissue and prostate cancer ( Pca) ,and study its correlation with the grade and stage of Pca. Methods Ten NP slides,ten BPH slides,ten PC slides and 43 Pca
