Objective To construct a radiomics model to improve the discriminatory ability of 18 F-PSMA-1007 PET/CT for focal prostate cancer.Methods We retrospectively collected data from 74 patients diagnosed with prostate cancer by biopsy at The First Affiliated Hospital of Xi'an Jiaotong University between July 2020 and April 2024.These patients had focal radionuclide accumulation observed on 18F-PSMA-1007 PET/CT,with the median age of 71 years.Among them,42 patients had a Gleason score＜8 and 32 patients had a Gleason score ≥8.An external validation set was randomly selected based on the timing of examination,while the remaining patients were randomly divided into training and test sets at a 7∶3 ratio.Region of interest(ROI)were semi-automatically drawn on registered images,manually adjusted,and symmetrically shifted to contralateral non-tumor tissue.We made variance and correlation analyses to choose features,and built models with Logistic regression and compared the results with those of visual evaluation.Receiver operating characteristics(ROC)curves were drawn to compare model performance,and subgroup analysis was performed to identify optimal features for distinguishing tumor tissue,based on Gleason score,serum total prostate specific antigen(tPSA)levels,and lesion location.Results A total of eight features were selected.The area under the curve(AUC)for visual evaluation,testing set,and external validation set were 0.858,0.933,and 0.891,respectively.The sensitivity was 0.757,0.800 and 0.917;the specificity was 0.960,0.800 and 0.792,respectively.Subgroup analysis showed that the radiomic features 10percentile and skewness had a high value in tumor differentiation.In tumor tissues,the 10percentile values were higher than in non-tumor tissues across all groups(P-values were 0.012,0.002,＜0.001,＜0.001,＜0.001,and＜0.001).When tPSA≤10 ng/mL and Gleason score ≥8,there was no statistically significant difference in skewness between tumor and non-tumor tissues(P=0.08).When tPSA ≥20 ng/mL,the skewness of non-tumor tissue was slightly higher than that of tumor tissue,but the difference was not statistically significant(P-values were 0.285 and 0.791).When the tumor was located in the posterior part of the prostate(left posterior and right posterior),the skewness was significantly higher in tumor tissue than in non-tumor tissue(P-values＜0.001 for both).Conclusion The radiomics model had better sensitivity and accuracy than visual evaluation in distinguishing focal prostate cancer tumors from non-tumor tissues,but visual evaluation had higher specificity.Skewness and 10percentile had a high value in differential diagnosis.

Objective The application of 18F-PSMA-1007 PET/CT in the precise diagnosis and treatment of prostate cancer(PCa)is gradually increasing.This study aimed to explore the predictive value of 18F-PSMA-1007 PET/CT for positive surgical margins after radical prostatectomy(RP).Methods A total of 173 PCa patients who were pathologically confirmed and underwent RP in our hospital from July 2020 to August 2023 were retrospectively recruited.All patients underwent 18F-PSMA-1007 PET/CT whole-body imaging before surgery.According to the postoperative pathological results,they were divided into a negative surgical margin group with 83 cases and a positive surgical margin group with 90 cases.The differences in age,SUVmax,SUVmean,preoperative total prostate specific antigen(tPSA),prostate specific antigen density(PSAD),prostate volume,Gleason score,and clinical T staging between the groups were compared,and the risk factors were further analyzed by Logistic regression.Results The preoperative tPSA(Z=-3.252,P=0.001),SUVmax(Z=2.531,P=0.011),and clinical T staging(P=0.018)were all lower in the negative surgical margin group than in the positive surgical margin group.PSAD(OR=3.492,95％CI:1.095-11.133,P=0.035),SUVmax(OR=1.036,95％CI:1.003-1.070,P=0.034),and clinical T staging(OR=3.364,95％CI:1.117-10.133,P=0.045)were independent risk factors for positive surgical margins.Conclusion 18F-PSMA-1007 PET/CT has certain predictive value for positive surgical margins after RP in PCa patients.High PSAD,high SUVmax and high clinical T stage were independent risk factors for predicting positive margin after PR surgery,thus providing some reference for the clinical treatment strategy of PCa patients.

Objective To include patients with clinical data matched by propensity scores and to explore the value of 18F-fluorode-oxyglucose(18F-FDG)PET/CT metabolic parameters and radiomics in differentiating benign and malignant solitary pulmonary nod-ule(SPN).Methods A total of 54 patients with SPN(27 benign and 27 malignant)were retrospectively selected,all of them under-went 18F-FDG PET/CT scans.Then the metabolic parameters were analyzed,and the metabolic parameters model was established.After delineating the lesion,imaging features were selected through variance and correlation analysis.The logistic regression was used to build the model,and balance accuracy(bACC)was used to compare the performance of the models.The correlation between meta-bolic parameters and radiomics features was analyzed.Results The maximum standardized uptake value(SUVmax),total lesion uptake(TLU),and coefficient of variation(COV)of malignant were higher than those of benign(P＜0.05).SUVmax and COV had positive predictive value for malignant lesions[odds ratio(OR)＞1,P＜0.05].There was no statistical difference between the performance of the metabolic parameters model and the radiomics model(P＞0.05).There was a strong correlation between radiomics features and metabolic parameters.Conclusion After propensity score matching,metabolic parameters and radiomics show no statistical difference in differentiating benign from malignant SPN.

Objective The application of 18F-PSMA-1007 PET/CT in the precise diagnosis and treatment of prostate cancer(PCa)is gradually increasing.This study aimed to explore the predictive value of 18F-PSMA-1007 PET/CT for positive surgical margins after radical prostatectomy(RP).Methods A total of 173 PCa patients who were pathologically confirmed and underwent RP in our hospital from July 2020 to August 2023 were retrospectively recruited.All patients underwent 18F-PSMA-1007 PET/CT whole-body imaging before surgery.According to the postoperative pathological results,they were divided into a negative surgical margin group with 83 cases and a positive surgical margin group with 90 cases.The differences in age,SUVmax,SUVmean,preoperative total prostate specific antigen(tPSA),prostate specific antigen density(PSAD),prostate volume,Gleason score,and clinical T staging between the groups were compared,and the risk factors were further analyzed by Logistic regression.Results The preoperative tPSA(Z=-3.252,P=0.001),SUVmax(Z=2.531,P=0.011),and clinical T staging(P=0.018)were all lower in the negative surgical margin group than in the positive surgical margin group.PSAD(OR=3.492,95％CI:1.095-11.133,P=0.035),SUVmax(OR=1.036,95％CI:1.003-1.070,P=0.034),and clinical T staging(OR=3.364,95％CI:1.117-10.133,P=0.045)were independent risk factors for positive surgical margins.Conclusion 18F-PSMA-1007 PET/CT has certain predictive value for positive surgical margins after RP in PCa patients.High PSAD,high SUVmax and high clinical T stage were independent risk factors for predicting positive margin after PR surgery,thus providing some reference for the clinical treatment strategy of PCa patients.

Objective To construct a radiomics model to improve the discriminatory ability of 18 F-PSMA-1007 PET/CT for focal prostate cancer.Methods We retrospectively collected data from 74 patients diagnosed with prostate cancer by biopsy at The First Affiliated Hospital of Xi'an Jiaotong University between July 2020 and April 2024.These patients had focal radionuclide accumulation observed on 18F-PSMA-1007 PET/CT,with the median age of 71 years.Among them,42 patients had a Gleason score＜8 and 32 patients had a Gleason score ≥8.An external validation set was randomly selected based on the timing of examination,while the remaining patients were randomly divided into training and test sets at a 7∶3 ratio.Region of interest(ROI)were semi-automatically drawn on registered images,manually adjusted,and symmetrically shifted to contralateral non-tumor tissue.We made variance and correlation analyses to choose features,and built models with Logistic regression and compared the results with those of visual evaluation.Receiver operating characteristics(ROC)curves were drawn to compare model performance,and subgroup analysis was performed to identify optimal features for distinguishing tumor tissue,based on Gleason score,serum total prostate specific antigen(tPSA)levels,and lesion location.Results A total of eight features were selected.The area under the curve(AUC)for visual evaluation,testing set,and external validation set were 0.858,0.933,and 0.891,respectively.The sensitivity was 0.757,0.800 and 0.917;the specificity was 0.960,0.800 and 0.792,respectively.Subgroup analysis showed that the radiomic features 10percentile and skewness had a high value in tumor differentiation.In tumor tissues,the 10percentile values were higher than in non-tumor tissues across all groups(P-values were 0.012,0.002,＜0.001,＜0.001,＜0.001,and＜0.001).When tPSA≤10 ng/mL and Gleason score ≥8,there was no statistically significant difference in skewness between tumor and non-tumor tissues(P=0.08).When tPSA ≥20 ng/mL,the skewness of non-tumor tissue was slightly higher than that of tumor tissue,but the difference was not statistically significant(P-values were 0.285 and 0.791).When the tumor was located in the posterior part of the prostate(left posterior and right posterior),the skewness was significantly higher in tumor tissue than in non-tumor tissue(P-values＜0.001 for both).Conclusion The radiomics model had better sensitivity and accuracy than visual evaluation in distinguishing focal prostate cancer tumors from non-tumor tissues,but visual evaluation had higher specificity.Skewness and 10percentile had a high value in differential diagnosis.

Objective To include patients with clinical data matched by propensity scores and to explore the value of 18F-fluorode-oxyglucose(18F-FDG)PET/CT metabolic parameters and radiomics in differentiating benign and malignant solitary pulmonary nod-ule(SPN).Methods A total of 54 patients with SPN(27 benign and 27 malignant)were retrospectively selected,all of them under-went 18F-FDG PET/CT scans.Then the metabolic parameters were analyzed,and the metabolic parameters model was established.After delineating the lesion,imaging features were selected through variance and correlation analysis.The logistic regression was used to build the model,and balance accuracy(bACC)was used to compare the performance of the models.The correlation between meta-bolic parameters and radiomics features was analyzed.Results The maximum standardized uptake value(SUVmax),total lesion uptake(TLU),and coefficient of variation(COV)of malignant were higher than those of benign(P＜0.05).SUVmax and COV had positive predictive value for malignant lesions[odds ratio(OR)＞1,P＜0.05].There was no statistical difference between the performance of the metabolic parameters model and the radiomics model(P＞0.05).There was a strong correlation between radiomics features and metabolic parameters.Conclusion After propensity score matching,metabolic parameters and radiomics show no statistical difference in differentiating benign from malignant SPN.

Objective:To evaluate the value of prostate specific membrane antigen (PSMA) PET/CT-based radiomics models in differentiation between prostate cancer and benign prostatic hyperplasia (BPH).Methods:Data from 50 patients with prostate cancer (age: (70.0±8.8) years) and 25 patients with BPH (age: (66.9±9.4) years) who underwent 18F-PSMA-1007 PET/CT imaging and prostate biopsy in the First Affiliated Hospital of Xi′an Jiaotong University from May 2020 to September 2022 were retrospectively collected. Patients were divided into the training set ( n=53) and test set ( n=22) in the ratio of 7∶3 by using random seed number. The ROIs were delineated based on PET and CT images, and radiomics features were extracted respectively. Feature selection was performed using the minimum redundancy and maximum relevance (mRMR) and the least absolute shrinkage and selection operator (LASSO) algorithm. PET and PET/CT radiomics models were generated using logistic regression. ROC curve analysis was employed for model evaluation. In addition, comparisons of the 2 radiomics models with parameters including the ratio of free prostate specific antigen (fPSA)/total prostate specific antigen (tPSA), PET metabolic parameters, as well as prostate cancer molecular imaging standardize evaluation (PROMISE) were conducted (Delong test). Results:A total of 7 features were included in the PET radiomics model, and 3 CT-based features and 4 PET-based features were included in the PET/CT radiomics model. The AUCs of PET and PET/CT radiomics models in the training set and test set were 0.941, 0.914 and 0.965, 0.914, respectively, which were higher than those of fPSA/tPSA (0.719 and 0.710), SUV max(0.748 and 0.800), peak of SUV (SUV peak, 0.722 and 0.771), metabolic tumor volume (MTV, 0.640 and 0.595), total lesion uptake (TLU, 0.525 and 0.476) and PROMISE (0.644 and 0.667)[ z values for the training set: from -6.26 to -3.13, all P<0.01; z values for the test set: from -3.16 to -1.08, P>0.05 (fPSA/tPSA, SUV max, SUV peak) or P<0.05 (MTV, TLU, PROMISE)]. The differential diagnostic accuracy, sensitivity and specificity of PET and PET/CT radiomics models in the test set were 86.36%(19/22), 13/15, 6/7 and 90.91%(20/22), 15/15, 5/7, respectively. Conclusion:Compared with the clinical and PET parameters, PSMA PET/CT-based radiomics model can further improve the efficiency of differential diagnosis between prostate cancer and BPH.