Objective To construct prostate cancer(PCa)prediction models based on machine learning algorithms, so as to improve the accuracy of early diagnosis of PCa. Methods A retrospective analysis was performed on the clinical data of 504 patients who underwent prostate biopsy at our hospital during Jan. 2020 and Nov. 2024. Patients' age, body mass index(BMI), history of hypertension, diabetes and smoking, total prostate-specific antigen(tPSA), free prostate-specific antigen(fPSA), f/tPSA, prostate volume(PV), neutrophil count, lymphocyte count, neutrophil-to-lymphocyte ratio(NLR), Prostate Imaging Reporting and Data System(PI-RADS)score, digital rectal examination(DRE)results, and pathological findings were collected. The patients were divided into the training and testing sets at a ratio of 7:3. Ten early diagnosis prediction models of PCa were constructed using 10 supervised machine learning algorithms. Model performance was evaluated and validated using metrics including area under the receiver operating characteristic curve(AUC), accuracy, sensitivity, specificity, calibration curves, and decision curve analysis(DCA). SHAP analysis was used to interpret the models, and to clarify the importance of each feature and the basis for model decisions. Results All models showed good predictive value, with the gradient boosting machine(GBM)model performing the best(AUC=0.905, accuracy=84.1%, sensitivity=90.2%, specificity=80.0%). Calibration curves indicated good calibration and fitting of the GBM model, while DCA demonstrated favorable clinical net benefits. SHAP analysis identified the most significant features affecting PCa occurrence in descending order:tPSA, f/tPSA, PIRADS score, PV, age, and DRE. Conclusion The GBM model exhibits the optimal performance among the 10 models. The importance of features for predicting the occurrence of PCa, from the highest to the lowest, is as follows:tPSA, f/tPSA, PIRADS score, PV, age, and DRE.

Objective:To explore the predictive factors for ureteral stone impaction preoperatively and to construct a nomogram prediction model for impacted ureteral stones.Methods:A retrospective analysis was conducted on the clinical data of 209 patients with ureteral stones treated at The Second Affiliated Hospital of Zhengzhou University from July 2023 to June 2024. There were 164 males（78.5%）and 45 females（21.5%）. The age was 49（47，57）years，and the body mass index（BMI）was 25.10（23.55，27.24）kg/m2. Of the patients，85（40.7%）had comorbid hypertension and 85（40.7%）had comorbid diabetes. Stones were located on the left side in 124 patients（59.3%）and on the right side in 85 patients（40.7%）. Hydronephrosis was present in 169 patients（80.9%），and urine culture was positive in 29 patients（13.9%）. Patients were divided into impacted and non-impacted groups based on the presence or absence of ureteral stone impaction. Univariate and multivariate logistic regression analyses were performed to determine independent predictive factors for impacted ureteral stones. A nomogram model was constructed based on these results. The performance of the predictive model was evaluated using receiver operating characteristic（ROC）curves，calibration plots，and decision curve analysis（DCA）.Results:Among the 209 patients in this study，85（40.7%）experienced ureteral stone impaction. The impacted group had a significantly higher neutrophil-to-lymphocyte ratio（NLR）than the non-impacted group（3.91 ± 2.05 vs. 3.25 ± 2.10， P = 0.024），a higher rate of hydronephrosis［81.2%（69/85）vs. 80.6%（100/124）， P = 0.002］，larger stone surface area［（64.96 ± 39.96）mm2 vs.（51.86 ± 39.80）mm2， P = 0.021］，greater ureteral wall thickness（UWT）［（3.96 ± 1.37）mm vs.（3.06 ± 1.33）mm， P < 0.001］，and a higher ratio of the upper ureter diameter（D1）to the lower ureter diameter（D2）（DDR）（2.87 ± 1.58 vs. 2.00 ± 0.99， P < 0.001）. Univariate analysis showed that NLR，hydronephrosis，stone length，stone surface area，UWT，D1，D2，and DDR were statistically significant（ P < 0.05）. After multivariate logistic regression analysis，the following items were identified as independent predictors of impacted ureteral stones：NLR（ OR = 1.205，95% CI 1.026 - 1.415， P = 0.023），hydronephrosis（ OR = 1.840，95% CI 1.236 - 2.740， P = 0.003），stone length（ OR = 1.587，95% CI 1.142 - 2.206， P = 0.006），ureteral wall thickness（UWT）（ OR = 1.643，95% CI 1.263 - 2.136， P < 0.001），and DDR（ OR = 2.907，95% CI 1.040 - 8.130， P = 0.042）.Based on these independent predictive factors，a nomogram prediction model for impacted ureteral stones was constructed. The area under the ROC curve was 0.797（95% CI 0.737 - 0.858），and the calibration curve showed good consistency. The decision curve suggested that the model had good clinical net benefit. Conclusions:NLR，hydronephrosis，stone length，UWT，and DDR are all independent predictors for impacted ureteral stones. The nomogram model constructed based on these factors has good predictive performance.

Objective:To explore the independent factors influencing extraprostatic extension (EPE) after radical prostatectomy(RP) in patients with clinically localized prostate cancer by utilizing the Surveillance, Epidemiology, and End Results (SEER) database. A nomogram model was developed and externally validated.Methods:Clinical and pathological data of 20 916 clinically localized prostate cancer patients (T 1-2N 0M 0) who underwent RP between 2010 and 2021 were extracted from the SEER database. The mean age was (61.71±7.09) years old, and a total of 17 835 patients (85.3%) were married.There were 2 243 patients (10.7%) with prostate-specific antigen (PSA) <4 ng/ml, 14 831 patients (70.9%) with ≥4 and <10 ng/ml, and 2 965 patients (14.2%) with ≥10 and <20 ng/ml. There were 14 870 patients (71.1%) with clinical staging of stage T 1, and 6 046 patients (28.9%) with T 2. There were 48 patients (0.2%) with pathological staging of stage T 1, 15 794 (75.5%) with T 2, 5 001(23.9%) with T 3, and 73 (0.3%) with T 4 stage after radical surgery.The patients of SEER database were divided into training and internal validation groups in a 7∶3 ratio by using stratified sampling. Additionally, data were collected for 75 clinically localized prostate cancer patients who underwent RP at the Second Affiliated Hospital of Zhengzhou University from September 2019 to September 2024, serving as the external validation group.The mean age was(65.39±7.45) years old. Among them, 73 (97.3%) were married. There were 2 patients (2.7%) with PSA <4 ng/ml, 17 patients (22.7%) with ≥4 and <10 ng/ml, and 34 patients (45.3%) with ≥10 and <20 ng/ml. There were 47 patients (62.7%) with clinical staging of stage T 1, and 28 patients (37.3%) with T 2. There were 7 patients (9.3%) with pathological staging of stage T 1, 48 patients (64.0%)with T 2, 18 patients (24.0%) with T 3, and 2 patients (2.7%) with T 4 stage after radical surgery. All patients were categorized into organ-confined (OC) and EPE groups based on post-surgical pathology. Univariate and multivariate logistic regression analyses, with a stepwise backward selection, were performed on the training group to identify independent risk factors of EPE, which were used to construct a nomogram model. Model performance was assessed using receiver operating characteristic (ROC) curve area under the curve (AUC), calibration curves, and decision curve analysis (DCA) for the training group, internal validation group, and external validation group. Results:EPE was observed in 3 585 cases (24.5%), 1 489 cases (23.8%), and 20 cases (26.7%) in the training, internal validation, and external validation groups, respectively. Logistic regression analyses identified preoperative age ( OR=1.026, P<0.001), PSA levels (≥10 and <20 ng/ml: OR=1.790, P<0.001; ≥20 ng/ml: OR=2.683, P<0.001), tumor maximum diameter (10-20 mm: OR=2.051, P<0.001; >20 mm: OR=3.937, P<0.001), biopsy Gleason score (score 7: OR=1.911, P<0.001; score 8: OR=2.906, P<0.001; score 9: OR = 5.278, P<0.001; score 10: OR=4.421, P=0.003), number of positive biopsy cores (≥4 cores: OR=1.260, P<0.001), and their proportion of total cores ( OR=1.012, P<0.001) as independent predictors of EPE. The nomogram model demonstrated good predictive performance, with AUC of 0.741, 0.748, and 0.724 in the training, internal validation, and external validation groups, respectively. Calibration and DCA curves confirmed the model’s excellent stability and generalizability. Conclusions:Age, PSA levels, maximum tumor diameter, biopsy Gleason score, number of positive biopsy cores, and their proportion of total cores are independent predictors of EPE after RP in clinically localized prostate cancer. The constructed model effectively predicts the risk of EPE occurrence.

Objective:To explore the independent factors influencing extraprostatic extension (EPE) after radical prostatectomy(RP) in patients with clinically localized prostate cancer by utilizing the Surveillance, Epidemiology, and End Results (SEER) database. A nomogram model was developed and externally validated.Methods:Clinical and pathological data of 20 916 clinically localized prostate cancer patients (T 1-2N 0M 0) who underwent RP between 2010 and 2021 were extracted from the SEER database. The mean age was (61.71±7.09) years old, and a total of 17 835 patients (85.3%) were married.There were 2 243 patients (10.7%) with prostate-specific antigen (PSA) <4 ng/ml, 14 831 patients (70.9%) with ≥4 and <10 ng/ml, and 2 965 patients (14.2%) with ≥10 and <20 ng/ml. There were 14 870 patients (71.1%) with clinical staging of stage T 1, and 6 046 patients (28.9%) with T 2. There were 48 patients (0.2%) with pathological staging of stage T 1, 15 794 (75.5%) with T 2, 5 001(23.9%) with T 3, and 73 (0.3%) with T 4 stage after radical surgery.The patients of SEER database were divided into training and internal validation groups in a 7∶3 ratio by using stratified sampling. Additionally, data were collected for 75 clinically localized prostate cancer patients who underwent RP at the Second Affiliated Hospital of Zhengzhou University from September 2019 to September 2024, serving as the external validation group.The mean age was(65.39±7.45) years old. Among them, 73 (97.3%) were married. There were 2 patients (2.7%) with PSA <4 ng/ml, 17 patients (22.7%) with ≥4 and <10 ng/ml, and 34 patients (45.3%) with ≥10 and <20 ng/ml. There were 47 patients (62.7%) with clinical staging of stage T 1, and 28 patients (37.3%) with T 2. There were 7 patients (9.3%) with pathological staging of stage T 1, 48 patients (64.0%)with T 2, 18 patients (24.0%) with T 3, and 2 patients (2.7%) with T 4 stage after radical surgery. All patients were categorized into organ-confined (OC) and EPE groups based on post-surgical pathology. Univariate and multivariate logistic regression analyses, with a stepwise backward selection, were performed on the training group to identify independent risk factors of EPE, which were used to construct a nomogram model. Model performance was assessed using receiver operating characteristic (ROC) curve area under the curve (AUC), calibration curves, and decision curve analysis (DCA) for the training group, internal validation group, and external validation group. Results:EPE was observed in 3 585 cases (24.5%), 1 489 cases (23.8%), and 20 cases (26.7%) in the training, internal validation, and external validation groups, respectively. Logistic regression analyses identified preoperative age ( OR=1.026, P<0.001), PSA levels (≥10 and <20 ng/ml: OR=1.790, P<0.001; ≥20 ng/ml: OR=2.683, P<0.001), tumor maximum diameter (10-20 mm: OR=2.051, P<0.001; >20 mm: OR=3.937, P<0.001), biopsy Gleason score (score 7: OR=1.911, P<0.001; score 8: OR=2.906, P<0.001; score 9: OR = 5.278, P<0.001; score 10: OR=4.421, P=0.003), number of positive biopsy cores (≥4 cores: OR=1.260, P<0.001), and their proportion of total cores ( OR=1.012, P<0.001) as independent predictors of EPE. The nomogram model demonstrated good predictive performance, with AUC of 0.741, 0.748, and 0.724 in the training, internal validation, and external validation groups, respectively. Calibration and DCA curves confirmed the model’s excellent stability and generalizability. Conclusions:Age, PSA levels, maximum tumor diameter, biopsy Gleason score, number of positive biopsy cores, and their proportion of total cores are independent predictors of EPE after RP in clinically localized prostate cancer. The constructed model effectively predicts the risk of EPE occurrence.

Objective:To explore the predictive factors for ureteral stone impaction preoperatively and to construct a nomogram prediction model for impacted ureteral stones.Methods:A retrospective analysis was conducted on the clinical data of 209 patients with ureteral stones treated at The Second Affiliated Hospital of Zhengzhou University from July 2023 to June 2024. There were 164 males（78.5%）and 45 females（21.5%）. The age was 49（47，57）years，and the body mass index（BMI）was 25.10（23.55，27.24）kg/m2. Of the patients，85（40.7%）had comorbid hypertension and 85（40.7%）had comorbid diabetes. Stones were located on the left side in 124 patients（59.3%）and on the right side in 85 patients（40.7%）. Hydronephrosis was present in 169 patients（80.9%），and urine culture was positive in 29 patients（13.9%）. Patients were divided into impacted and non-impacted groups based on the presence or absence of ureteral stone impaction. Univariate and multivariate logistic regression analyses were performed to determine independent predictive factors for impacted ureteral stones. A nomogram model was constructed based on these results. The performance of the predictive model was evaluated using receiver operating characteristic（ROC）curves，calibration plots，and decision curve analysis（DCA）.Results:Among the 209 patients in this study，85（40.7%）experienced ureteral stone impaction. The impacted group had a significantly higher neutrophil-to-lymphocyte ratio（NLR）than the non-impacted group（3.91 ± 2.05 vs. 3.25 ± 2.10， P = 0.024），a higher rate of hydronephrosis［81.2%（69/85）vs. 80.6%（100/124）， P = 0.002］，larger stone surface area［（64.96 ± 39.96）mm2 vs.（51.86 ± 39.80）mm2， P = 0.021］，greater ureteral wall thickness（UWT）［（3.96 ± 1.37）mm vs.（3.06 ± 1.33）mm， P < 0.001］，and a higher ratio of the upper ureter diameter（D1）to the lower ureter diameter（D2）（DDR）（2.87 ± 1.58 vs. 2.00 ± 0.99， P < 0.001）. Univariate analysis showed that NLR，hydronephrosis，stone length，stone surface area，UWT，D1，D2，and DDR were statistically significant（ P < 0.05）. After multivariate logistic regression analysis，the following items were identified as independent predictors of impacted ureteral stones：NLR（ OR = 1.205，95% CI 1.026 - 1.415， P = 0.023），hydronephrosis（ OR = 1.840，95% CI 1.236 - 2.740， P = 0.003），stone length（ OR = 1.587，95% CI 1.142 - 2.206， P = 0.006），ureteral wall thickness（UWT）（ OR = 1.643，95% CI 1.263 - 2.136， P < 0.001），and DDR（ OR = 2.907，95% CI 1.040 - 8.130， P = 0.042）.Based on these independent predictive factors，a nomogram prediction model for impacted ureteral stones was constructed. The area under the ROC curve was 0.797（95% CI 0.737 - 0.858），and the calibration curve showed good consistency. The decision curve suggested that the model had good clinical net benefit. Conclusions:NLR，hydronephrosis，stone length，UWT，and DDR are all independent predictors for impacted ureteral stones. The nomogram model constructed based on these factors has good predictive performance.

Objective:To analyze the eye lens dose and annual effective dose to interventional radiation workers in some hospitals of Xinxiang city from 2020 to 2022, and to ascertain the dose to interventional radiation workers.Methods:By using TLDs, the eye lens dose Hp(3) and annual effective dose Hp(10) were monitored for three consecutive years in six hospitals in Xinxiang city. The lens doses and annual effective doses to intervention radiation workers in different years in different-level hospitals and departments were analyzed. Results:From 2020 to 2022, a total of 117 people were monitored. The left eye lens dose range was 0.12-164.24 mSv, and the right eye lens dose range was 0.07-51.64 mSv. The average annual dose was 8.56 mSv for left eye lens and 4.49 mSv for right eye lens The average annual dose distribution in the MDL-5 mSv range for the left and right eye lens was 60.68% and 73.50%, respectively. 9.41% (11 people) of the left eye lens doses exceeded 20 mSv. The annual effective doses range was 0.11-31.27 mSv, with average annual dose of 2.56 mSv. The proportion of average annual effective doses mainly distributed in the range of MDL to 1.25 mSv was 52.14%, with 2.56% annual effective dose exceeding 20 mSv. There was no significant difference in left and right eye lens dose and annual effective dose between the tertiary hospitals and the secondary hospitals in three years ( P>0.05). Compared with different departments, the cumulative per capita dose in three years was statistically significant (left eye H=11.42, right eye H=13.72, annual effective dose H=25.94, P<0.05). The lens dose and annual effective dose in neurology department were lower than those in cardiology department and comprehensive intervention department ( Zcardiology department=-3.33, -3.78, -4.83, P<0.05; Zcomprehensive intervention department=-2.71, -2.63, -4.39, P<0.05). Conclusions:Most of the annual equivalent dose and annual effective dose to eye lens of the interventional radiation workers in Xinxiang city meet the national limits, but some of them have higher doses and exceed the national limits. It is suggested that the routine and continuous monitoring of eye lens doses to interventional radiologists should be strengthened while routine monitoring of annual effective dose, and attention should be paid to the eye lens and annual effective dose to interventional radiologists in secondary hospitals to improve the awareness of protection.

[Objective] To analyze the characteristics of HLA-A, HLA-B, HLA-DRB1, HLA-C, HLA-DQB1 and HLA-DPB1 allele polymorphisms among cord blood donors in Guangdong population. [Methods] According to HLA high resolution genotyping data of 32 717 samples of cord blood donors from Guangdong Cord Blood Bank from January 2009 to December 2023, the allele frequencies were calculated by direct counting and the haplotype frequencies were calculated by using Arlequin software 3.5.2.2. [Results] A total of 102 HLA-A alleles, 160 HLA-B alleles and 96 HLA-DRB1 alleles were detected in 32 717 samples. Among them, 46 HLA-DPB1 alleles were detected in 5 377 samples, and 66 HLA-C alleles and 35 HLA-DQB1 alleles were detected in 13 310 samples. The most common alleles were HLA-A^*11∶01 (28.96%), HLA-B^*40∶01 (15.23%), HLA-DRB1^*09∶01 (15.72%), HLA-C^*01∶02 (19.40%), HLA-DQB1^*03∶01 (20.85%) and HLA-DPB1^*05∶01 (40.79%). The most common 3 loci haplotype and 6 loci haplotype were HLA-A^*02∶07-B^*46∶01-DRB1^*09∶01 (1.55%), HLA-C^*07∶02-DQB1^*03∶01-DPB1^*05∶01 (1.77%), HLA-DRB1^*09∶01-DQB1^*03∶03-DPB1^*05∶01 (3.31%) and HLA-A^*02∶07-B^*46∶01-C^*01∶02-DRB1^*09∶01-DQB1^*03∶03-DPB1^*05∶01 (0.30%). [Conclusion] In this study, the allele and haplotype frequencies of HLA-A, HLA-B, HLA-DRB1, HLA-C, HLA-DQB1 and HLA-DPB1 were obtained in the cord blood donors in Guangdong, which can provide important reference data for HLA gene related research and the selection of donors for clinical application.

Objective:To investigate the application value of combining Prostate Imaging Reporting and Data System (PI-RADS v2.1) score and Systemic Immune-Inflammation Index (SII) in predicting pathological upgrading in patients with localized prostate cancer after radical prostatectomy(RP).Methods:A retrospective analysis was conducted on clinical data from 76 patients with localized prostate cancer who underwent prostate biopsy and radical prostatectomy at the Second Affiliated Hospital of Zhengzhou University between September 2019 and May 2024. The median age was 68 (65, 71) years. Total prostate-specific antigen (tPSA) was 17.4 (8.4, 30.9) ng/ml, and prostate volume was 43.1 (29.9, 58.9) ml. PI-RADS scores were ≤3 in 22 cases (28.9%) and >3 in 54 cases (71.1%). According to the International Society of Urological Pathology (ISUP) grading of biopsy specimens, 31 patients (40.8%) were classified as Group <3 and 45 patients (59.2%) as Group ≥3. Postoperatively, 25 patients (32.9%) were classified as ISUP Group <3, and 51 patients (67.1%) as Group ≥3. Pathological upgrading was defined as either: ①a higher ISUP grade in postoperative specimens compared to biopsy specimens or; ②benign prostate tissue identified in biopsy specimens but confirmed as prostate cancer postoperatively. Clinical data were compared between the pathological upgrade and non-upgrade groups. Univariate and multivariate logistic regression analyses were performed to identify independent risk factors for pathological upgrading and to construct a nomogram model. Receiver operating characteristic (ROC) curves were used to evaluate the predictive performance of individual indicators (PI-RADS, SII, %PSA, and the proportion of tumor tissue in biopsy specimens) and the combined nomogram model. Internal validation was conducted using cross-validation, and calibration and decision curves were generated to assess the nomogram′s accuracy and clinical net benefit.Results:Among the 76 patients included, 10 (13.2%) experienced pathological downgrading, 36 (47.4%) had consistent grading, and 30 (39.5%) experienced pathological upgrading. The platelet-to-lymphocyte ratio (PLR) [118.2(93.5, 139.1) vs. 95.2(79.3, 116.4), P=0.021], SII [394.8(331.0, 513.6) vs. 338.8(217.2, 407.8), P=0.002], and the number of cases with a PI-RADS score >3 [26 cases(86.7%) vs. 28 cases(60.9%), P=0.015] were significantly higher in the pathological upgrade group than in the non-upgrade group. Conversely, the percentage of positive biopsy cores [35.9%(12.6%, 51.8%) vs. 43.8%(21.0%, 92.1%), P=0.045], the proportion of tumor tissue in biopsy specimens [6.9%(1.3%, 20.1%) vs. 19.3%(9.1%, 58.4%), P<0.01], and the number of cases in ISUP biopsy Group ≥3 [12 cases (40.0%) vs. 33 cases (71.7%), P=0.006] were significantly lower in the upgrade group (all P < 0.05). Univariate and multivariate logistic regression analyses showed that PI-RADS score( OR=17.111, 95% CI 2.388-122.592, P<0.01), SII( OR=1.009, 95% CI 1.001-1.016, P=0.028), %PSA ( OR=0.003, 95% CI 0.002-0.004, P<0.01), and the proportion of tumor tissue in biopsy specimens ( OR=0.899, 95% CI 0.837-0.966, P<0.01) were independent predictors of pathological upgrading. The area under the ROC curve (AUC) for PI-RADS, SII, %PSA, and the proportion of tumor tissue in biopsy specimens were 0.607, 0.711, 0.618, and 0.778, respectively. The combined AUC for %PSA and the proportion of tumor tissue was 0.791, while the combined AUC of the four-indicator nomogram model was 0.914. The DeLong test indicated a statistically significant difference in diagnostic performance between the two models ( P<0.01). Calibration and decision curves demonstrated good accuracy and clinical net benefit for the nomogram model. Conclusions:The PI-RADS v2.1 score and SII have significant predictive value for pathological upgrading after radical prostatectomy in prostate cancer. A nomogram model combining PI-RADS, SII, %PSA, and the proportion of tumor tissue in biopsy specimens shows excellent predictive performance.

Objective:To investigate the application value of combining Prostate Imaging Reporting and Data System (PI-RADS v2.1) score and Systemic Immune-Inflammation Index (SII) in predicting pathological upgrading in patients with localized prostate cancer after radical prostatectomy(RP).Methods:A retrospective analysis was conducted on clinical data from 76 patients with localized prostate cancer who underwent prostate biopsy and radical prostatectomy at the Second Affiliated Hospital of Zhengzhou University between September 2019 and May 2024. The median age was 68 (65, 71) years. Total prostate-specific antigen (tPSA) was 17.4 (8.4, 30.9) ng/ml, and prostate volume was 43.1 (29.9, 58.9) ml. PI-RADS scores were ≤3 in 22 cases (28.9%) and >3 in 54 cases (71.1%). According to the International Society of Urological Pathology (ISUP) grading of biopsy specimens, 31 patients (40.8%) were classified as Group <3 and 45 patients (59.2%) as Group ≥3. Postoperatively, 25 patients (32.9%) were classified as ISUP Group <3, and 51 patients (67.1%) as Group ≥3. Pathological upgrading was defined as either: ①a higher ISUP grade in postoperative specimens compared to biopsy specimens or; ②benign prostate tissue identified in biopsy specimens but confirmed as prostate cancer postoperatively. Clinical data were compared between the pathological upgrade and non-upgrade groups. Univariate and multivariate logistic regression analyses were performed to identify independent risk factors for pathological upgrading and to construct a nomogram model. Receiver operating characteristic (ROC) curves were used to evaluate the predictive performance of individual indicators (PI-RADS, SII, %PSA, and the proportion of tumor tissue in biopsy specimens) and the combined nomogram model. Internal validation was conducted using cross-validation, and calibration and decision curves were generated to assess the nomogram′s accuracy and clinical net benefit.Results:Among the 76 patients included, 10 (13.2%) experienced pathological downgrading, 36 (47.4%) had consistent grading, and 30 (39.5%) experienced pathological upgrading. The platelet-to-lymphocyte ratio (PLR) [118.2(93.5, 139.1) vs. 95.2(79.3, 116.4), P=0.021], SII [394.8(331.0, 513.6) vs. 338.8(217.2, 407.8), P=0.002], and the number of cases with a PI-RADS score >3 [26 cases(86.7%) vs. 28 cases(60.9%), P=0.015] were significantly higher in the pathological upgrade group than in the non-upgrade group. Conversely, the percentage of positive biopsy cores [35.9%(12.6%, 51.8%) vs. 43.8%(21.0%, 92.1%), P=0.045], the proportion of tumor tissue in biopsy specimens [6.9%(1.3%, 20.1%) vs. 19.3%(9.1%, 58.4%), P<0.01], and the number of cases in ISUP biopsy Group ≥3 [12 cases (40.0%) vs. 33 cases (71.7%), P=0.006] were significantly lower in the upgrade group (all P < 0.05). Univariate and multivariate logistic regression analyses showed that PI-RADS score( OR=17.111, 95% CI 2.388-122.592, P<0.01), SII( OR=1.009, 95% CI 1.001-1.016, P=0.028), %PSA ( OR=0.003, 95% CI 0.002-0.004, P<0.01), and the proportion of tumor tissue in biopsy specimens ( OR=0.899, 95% CI 0.837-0.966, P<0.01) were independent predictors of pathological upgrading. The area under the ROC curve (AUC) for PI-RADS, SII, %PSA, and the proportion of tumor tissue in biopsy specimens were 0.607, 0.711, 0.618, and 0.778, respectively. The combined AUC for %PSA and the proportion of tumor tissue was 0.791, while the combined AUC of the four-indicator nomogram model was 0.914. The DeLong test indicated a statistically significant difference in diagnostic performance between the two models ( P<0.01). Calibration and decision curves demonstrated good accuracy and clinical net benefit for the nomogram model. Conclusions:The PI-RADS v2.1 score and SII have significant predictive value for pathological upgrading after radical prostatectomy in prostate cancer. A nomogram model combining PI-RADS, SII, %PSA, and the proportion of tumor tissue in biopsy specimens shows excellent predictive performance.

【Objective】 To establish a nomogram model for predicting the risk of positive prostate biopsy in MRI-negative patients, and to perform the internal validation. 【Methods】 We retrospectively analyzed the clinical data of 197 MRI-negative patients who underwent prostate biopsy at our hospital, analyzed the independent predictors of positive prostate biopsy with univariate and multivariate logistic regression analysis, constructed the nomogram model and conducted internal validation. 【Results】 Multivariate logistic regression analysis showed age (P=0.003), digital rectal examination (DRE)(P=0.005), total prostate-specific antigen (tPSA) (P=0.001) and prostate volume (PV)(P<0.001) were independent risk factors of MRI-negative but prostate biopsy-positive results. The nomogram model based on all variables was established. The area under the receiver operating characteristic (ROC) curve (AUC) was 0.862, which was greater than that of tPSA (AUC=0.739), PV(AUC=0.711) and DRE(AUC=0.666) (all P<0.05). The average absolute error of the model was 1.1% after 500 internal resampling, indicating that the prediction of positive prostate biopsy was consistent with the actual situation. 【Conclusion】 The age, DRE, tPSA and PV were independent predictors of positive prostate biopsy in MRI-negative patients. The nomogram model has a good prediction performance.