Objective: To compare peritoneal indexes between transperitoneal approach and retroperitioneal approach of robot-assisted partial nephrectomy (RAPN) for dorsal renal tumors via transperitoneal and retroperitoneal approaches，thereby providing reference for clinical decision-making in managing such neoplasms. Methods: The clinical data of renal cancer patients undergoing RAPN performed by the same surgeon at our hospital during 2017 and 2021 were retrospectively analyzed.A total of 80 patients with complete data of dorsal renal tumors were screened and divided into two groups based on the surgical approaches：50 cases in the transperitoneal group and 30 in the retroperitoneal group.The general information，intraoperative data，positive rate of pathological margins，recovery time of gastrointestinal functions，and incidence of complications were compared between the two groups. Results: All operations were successfully completed, and the surgical margins were negative.There were no statistically significant differences in warm ischemia time [17 (15，18) min vs.16 (14，19) min，P=0.772]，operation time [120 (105，149) min vs.124 (108，152) min，P=0.584]，intraoperative blood loss [100 (50，100) mL vs.100 (50，100) mL，P=0.814]，and incidence of postoperative complications (17% vs.24%，P=0.504) between the two groups (P>0.05).The postoperative recovery time of gastrointestinal functions in the retroperitoneal group was significantly shorter than that in the transperitoneal group [2.0 (2.0，3.0) d vs.3.5 (3.0，4.0) d，P<0.001]. Conclusion: The perioperative outcomes of patients undergoing RAPN via the retroperitoneal approach are similar to those via the transperitoneal approach.However，the retroperitoneal approach has an advantage of faster recovery of gastrointestinal functions.

Objective:To investigate the efficacy of a predictive model for differentiating benign and malignant renal tumors based on CT radiomic features and clinical features.Methods:A retrospective study was conducted on 1 395 patients with renal tumors admitted to the First Affiliated Hospital of Zhengzhou University from December 2011 to December 2021, including 842 males and 553 females. The median age was 55 (44, 59) years, and the median tumor diameter was 3.6 (2.7, 4.6) cm. All patients underwent contrast-enhanced CT scaning before surgery, and radiomic features were extracted from non-contrast, arterial, and venous phase images. Prediction models for distinguishing benign and malignant renal tumors were constructed using five machine learning algorithms (logistic regression, support vector machine, neural network, random forest, and extreme gradient boosting), and these models were then ensembled to construct a stacking classifier. All patients underwent partial nephrectomy, and they were divided into a training group (941 cases, December 2011 to June 2020) and a validation group (454 cases, July 2020 to December 2021) based on the date of surgery. A clinical-radiomic model was developed by combining the result of stacking classifier, clinical features and CT report results, and its predictive performance was evaluated in the validation group.Results:The radiomic signature based on the combined features and five machine learning algorithms(AUC 0.835-0.844) showed higher accuracy in predicting benign and malignant renal tumors compared to single phases (AUC 0.744-0.831). After integrating the five machine learning algorithms, the AUC of the three-phase combined radiomic model in the validation group improved to 0.847(95% CI 0.802-0.892). The clinical-radiomic model, incorporating radiomic features, clinical features, and CT report results, achieved a significantly higher AUC in the validation group compared to radiologists [0.919(95% CI 0.889-0.950)vs. 0.835(95% CI 0.786-0.883), P<0.01]. Conclusions:The predictive model integrating CT radiomics features, clinical characteristics, and CT report results demonstrates excellent discriminative ability in distinguishing benign and malignant renal tumors.

Objective:To analyze the clinical features associated with pelvic lymph node metastasis(PLNM)in prostate cancer and to construct a preoperative prediction model for PLNM,thereby reducing unnecessary extended pelvic lymph node dissection(ePLND).Methods:Based on predefined inclusion and exclusion criteria,344 patients who underwent radical prostatectomy and ePLND at the First Affilia-ted Hospital of Zhengzhou University between 2014 and 2024 were retrospectively enrolled,among whom,77 patients(22.4％)were pathologically confirmed to have lymph node-positive disease.The clinical characteristics,MRI reports,and pathological results were collected.The data were then randomly divi-ded into a training cohort(241 cases,70％)and a validation cohort(103 cases,30％).Univariate and multivariate Logistic regression analysis were employed to construct a preoperative prediction model for PLNM.Results:Univariate Logistic regression analysis revealed that total prostate specific antigen(tPSA)(P=0.021),free prostate specific antigen(fPSA)(P=0.002),fPSA to tPSA ratio(fPSA/tPSA)(P=0.011),percentage of positive biopsy cores(P＜0.001),prostate imaging reporting and data system(PI-RADS)score(P=0.004),biopsy Gleason score ≥8(P=0.005),clinical T stage(P＜0.001),and MRI-indicated lymph node involvement(MRI-LNI)(P＜0.001)were significant predictors of PLNM.Multivariate Logistic regression analysis demonstrated that the percentage of positive biopsy cores(OR=91.24,95％CI:13.34-968.68),PI-RADS score(OR=7.64,95％ CI:1.78-138.06),and MRI-LNI(OR=4.67,95％ CI:1.74-13.24)were independent risk factors for PLNM.And a novel nomogram for predicting PLNM was developed by integrating all these three variables.Com-pared with the individual predictors:percentage of positive biopsy cores[area under curve(AUC)=0.806],PI-RADS score(AUC=0.679),and MRI-LNI(AUC=0.768),the multivariate model incor-porating all three variables demonstrated significantly superior predictive performance(AUC=0.883).Consistently,calibration curves and decision curve analyses confirmed that the multivariable model had high predictive accuracy and provided significant net clinical benefit relative to single-variable models.And using a cutoff of 6％,the multiparameter model missed only approximately 5.2％of PLNM cases(4/77),while reducing approximately 53％of ePLND procedures(139/267),demonstrating favorable predictive efficacy.Conclusion:Percentage of positive biopsy cores,PI-RADS score and MRI-LNI are independent risk factors for PLNM.The constructed multivariate model significantly improves predictive efficacy,offering a valuable tool to guide clinical decisions on ePLND.

Objective:To analyze the clinical features associated with pelvic lymph node metastasis(PLNM)in prostate cancer and to construct a preoperative prediction model for PLNM,thereby reducing unnecessary extended pelvic lymph node dissection(ePLND).Methods:Based on predefined inclusion and exclusion criteria,344 patients who underwent radical prostatectomy and ePLND at the First Affilia-ted Hospital of Zhengzhou University between 2014 and 2024 were retrospectively enrolled,among whom,77 patients(22.4％)were pathologically confirmed to have lymph node-positive disease.The clinical characteristics,MRI reports,and pathological results were collected.The data were then randomly divi-ded into a training cohort(241 cases,70％)and a validation cohort(103 cases,30％).Univariate and multivariate Logistic regression analysis were employed to construct a preoperative prediction model for PLNM.Results:Univariate Logistic regression analysis revealed that total prostate specific antigen(tPSA)(P=0.021),free prostate specific antigen(fPSA)(P=0.002),fPSA to tPSA ratio(fPSA/tPSA)(P=0.011),percentage of positive biopsy cores(P＜0.001),prostate imaging reporting and data system(PI-RADS)score(P=0.004),biopsy Gleason score ≥8(P=0.005),clinical T stage(P＜0.001),and MRI-indicated lymph node involvement(MRI-LNI)(P＜0.001)were significant predictors of PLNM.Multivariate Logistic regression analysis demonstrated that the percentage of positive biopsy cores(OR=91.24,95％CI:13.34-968.68),PI-RADS score(OR=7.64,95％ CI:1.78-138.06),and MRI-LNI(OR=4.67,95％ CI:1.74-13.24)were independent risk factors for PLNM.And a novel nomogram for predicting PLNM was developed by integrating all these three variables.Com-pared with the individual predictors:percentage of positive biopsy cores[area under curve(AUC)=0.806],PI-RADS score(AUC=0.679),and MRI-LNI(AUC=0.768),the multivariate model incor-porating all three variables demonstrated significantly superior predictive performance(AUC=0.883).Consistently,calibration curves and decision curve analyses confirmed that the multivariable model had high predictive accuracy and provided significant net clinical benefit relative to single-variable models.And using a cutoff of 6％,the multiparameter model missed only approximately 5.2％of PLNM cases(4/77),while reducing approximately 53％of ePLND procedures(139/267),demonstrating favorable predictive efficacy.Conclusion:Percentage of positive biopsy cores,PI-RADS score and MRI-LNI are independent risk factors for PLNM.The constructed multivariate model significantly improves predictive efficacy,offering a valuable tool to guide clinical decisions on ePLND.

Objective:To investigate the efficacy of a predictive model for differentiating benign and malignant renal tumors based on CT radiomic features and clinical features.Methods:A retrospective study was conducted on 1 395 patients with renal tumors admitted to the First Affiliated Hospital of Zhengzhou University from December 2011 to December 2021, including 842 males and 553 females. The median age was 55 (44, 59) years, and the median tumor diameter was 3.6 (2.7, 4.6) cm. All patients underwent contrast-enhanced CT scaning before surgery, and radiomic features were extracted from non-contrast, arterial, and venous phase images. Prediction models for distinguishing benign and malignant renal tumors were constructed using five machine learning algorithms (logistic regression, support vector machine, neural network, random forest, and extreme gradient boosting), and these models were then ensembled to construct a stacking classifier. All patients underwent partial nephrectomy, and they were divided into a training group (941 cases, December 2011 to June 2020) and a validation group (454 cases, July 2020 to December 2021) based on the date of surgery. A clinical-radiomic model was developed by combining the result of stacking classifier, clinical features and CT report results, and its predictive performance was evaluated in the validation group.Results:The radiomic signature based on the combined features and five machine learning algorithms(AUC 0.835-0.844) showed higher accuracy in predicting benign and malignant renal tumors compared to single phases (AUC 0.744-0.831). After integrating the five machine learning algorithms, the AUC of the three-phase combined radiomic model in the validation group improved to 0.847(95% CI 0.802-0.892). The clinical-radiomic model, incorporating radiomic features, clinical features, and CT report results, achieved a significantly higher AUC in the validation group compared to radiologists [0.919(95% CI 0.889-0.950)vs. 0.835(95% CI 0.786-0.883), P<0.01]. Conclusions:The predictive model integrating CT radiomics features, clinical characteristics, and CT report results demonstrates excellent discriminative ability in distinguishing benign and malignant renal tumors.