Yize Guo’s research while affiliated with Qingdao University and other places

Background Chronic Kidney Disease (CKD) is a common severe complication after radical nephrectomy in patients with renal cancer. The timely and accurate prediction of the long-term progression of renal function post-surgery is crucial for early intervention and ultimately improving patient survival rates. Objective This study aimed to establish a machine learning model to predict the likelihood of long-term renal dysfunction progression after surgery by analyzing patients’ general information in depth. Methods We retrospectively collected data of eligible patients from the Affiliated Hospital of Qingdao University. The primary outcome was upgrading of the Chronic Kidney Disease stage between pre- and 3-year post-surgery. We constructed seven different machine-learning models based on Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (Lightgbm), Gaussian Naive Bayes (GaussianNB), and K-Nearest Neighbors (KNN). The performance of all predictive models was evaluated using the area under the receiver operating characteristic curve (AUC), precision-recall curves, confusion matrices, and calibration curves. Results Among 360 patients with renal cancer who underwent radical nephrectomy included in this study, 185 (51.3%) experienced an upgrade in Chronic Kidney Disease stage 3-year post-surgery. Eleven predictive variables were selected for further construction of the machine learning models. The logistic regression model provided the most accurate prediction, with the highest AUC (0.8154) and an accuracy of 0.787.

Objective This retrospective aims to develop a comprehensive predictive model based on CT radiomic features and clinical parameters, facilitating early preoperative diagnosis of pyonephrosis. Methods Clinical and radiological data from 311 patients treated for upper urinary tract stones with obstructive pyelohydronephrosis, between January 2018 and May 2023, were retrospectively collected. Univariate and multivariate logistic regression analyses were conducted on clinical data to identify independent risk factors for pyonephrosis. A clinical model was developed using logistic regression. The 3D Slicer software was employed to manually delineate the region of interest (ROI) in the preoperative CT images, corresponding to the area of pyelohydronephrosis, for feature extraction. The optimal radiomic features were selected to construct radiomic models and calculate the radiomic score (Radscore). Subsequently, a combined clinical–radiomic model—the nomogram—was established by integrating the Radscore with independent risk factors. Results Univariate and multivariate logistic regression analyses identified cystatin C, Hounsfield Unit (HU) of pyonephrosis, history of ipsilateral urological surgery, and positive urine culture as independent risk factors for pyonephrosis (P < 0.05). Fourteen optimal radiomic features were selected from CT images to construct four radiomic models, with the Naive Bayes model demonstrating the best predictive performance in both training and validation sets. In the training set, the AUCs for the clinical model, radiomic model, and nomogram were 0.902, 0.939, and 0.991, respectively; in the validation set, they were 0.843, 0.874, and 0.959. Both calibration and decision curves showed good agreement between the predicted probabilities of the nomogram and the actual occurrences. Conclusion The nomogram, constructed from CT radiomic features and clinical variables, provides an effective non-invasive predictive tool for pyonephrosis, surpassing both clinical and radiomic models.

Background: Chronic Kidney Disease (CKD) is a common severe complication after radical nephrectomy in patients with renal cancer. The timely and accurate prediction of the long-term progression of renal function post-surgery is crucial for early intervention and ultimately improving patient survival rates. Objective: This study aimed to establish a machine learning model to predict the likelihood of long-term renal dysfunction progression after surgery by analyzing patients’ general information in depth. Methods: We retrospectively collected data of eligible patients from the Affiliated Hospital of Qingdao University. The primary outcome was upgrading of the Chronic Kidney Disease stage between pre- and 3-year post-surgery. We constructed seven different machine-learning models based on Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (Lightgbm), Gaussian Naive Bayes (GaussianNB), and K-Nearest Neighbors (KNN). The performance of all predictive models was evaluated using the area under the receiver operating characteristic curve (AUC), precision-recall curves, confusion matrices, and calibration curves. Results: Among 360 patients with renal cancer who underwent radical nephrectomy included in this study, 185 (51.3%) experienced an upgrade in Chronic Kidney Disease stage 3-year post-surgery. Eleven predictive variables were selected for further construction of the machine learning models. The logistic regression model provided the most accurate prediction, with the highest AUC (0.8154) and an accuracy of 0.787. Conclusion: The logistic regression model can more accurately predict long-term renal dysfunction progression after radical nephrectomy in patients with renal cancer.

Objective: This retrospective aims to develop a comprehensive predictive model based on CT radiomic features and clinical parameters, facilitating early preoperative diagnosis of pyonephrosis. Methods: Clinical and radiological data from 311 patients treated for upper urinary tract stones with obstructive pyelohydronephrosis, between January 2018 and May 2023, were retrospectively collected. Univariate and multivariate logistic regression analyses were conducted on clinical data to identify independent risk factors for pyonephrosis. A clinical model was developed using logistic regression. The 3D Slicer software was employed to manually delineate the region of interest (ROI) in the preoperative CT images, corresponding to the area of pyelohydronephrosis, for feature extraction. The optimal radiomic features were selected to construct radiomic models and calculate the radiomic score (Radscore). Subsequently, a combined clinical-radiomic model—the nomogram—was established by integrating the Radscore with independent risk factors. Results: Univariate and multivariate logistic regression analyses identified cystatin C, Hounsfield Unit (HU) of Pyonephrosis, history of ipsilateral urological surgery, and positive urine culture as independent risk factors for pyonephrosis (P<0.05). Fourteen optimal radiomic features were selected from CT images to construct four radiomic models, with the Naive Bayes model demonstrating the best predictive performance in both training and validation sets. In the training set, the AUCs for the clinical model, radiomic model, and nomogram were 0.902, 0.939, and 0.991, respectively; in the validation set, they were 0.843, 0.874, and 0.959. Both calibration and decision curves showed good agreement between the predicted probabilities of the nomogram and the actual occurrences. Conclusion: The nomogram, constructed from CT radiomic features and clinical variables, provides an effective non-invasive predictive tool for pyonephrosis, surpassing both clinical and radiomic models.

Background The study aimed to evaluate the differences in prostate cancer (PCa) detection rates between novice and experienced resident physicians using free-hand cognitive fusion transperineal prostate biopsy (fTP-Bx) through propensity score matching (PSM). Methods A retrospective analysis was conducted on a cohort of patients who underwent fTP-Bx procedures. The needle biopsies were performed by two groups of surgical doctors with varying levels of prostate biopsy experience (Novice Group and Experienced Group) between March 1, 2023, and March 1, 2024. The PSM method was employed to compare the differences in cancer detection-related parameters between various groups of differing prostate biopsy experience levels. Results In total, 398 patients were included in the study, with 196 in the Experienced Group and 202 in the Novice Group. Prior to PSM, significant differences were observed between the groups in terms of operation duration (p = 0.014) and multiparametric MRI results (mpMRI, p = 0.009). However, after adjusting for confounding factors through PSM, there were no differences in the absolute number of cores involved, percentage of cores involved, clinically significant prostate cancer (csPCa) detection rates, and overall PCa detection rates between the different prostate biopsy experience groups. Despite potential variations in operation duration related to different levels of needle biopsy experience, there were no distinctions observed between novice and experienced doctors in terms of prostate cancer detection, particularly concerning csPCa. Conclusions Novice resident physicians have the ability to complete qualified fTP-Bx.