Ultrasound-Based Radiomics for Predicting the WHO/ISUP Grading of Clear-Cell Renal Cell Carcinoma.

OBJECTIVE: To explore the performance of ultrasound image-based radiomics in predicting World Health Organization (WHO)/International Society of Urological Pathology (ISUP) grading of clear-cell renal cell carcinoma (ccRCC). METHODS: A retrospective study was conducted via histopathological examination on participants with ccRCC from January 2021 to August 2023. Participants were randomly allocated to a training set and a validation set in a 3:1 ratio. The maximum cross-sectional image of the lesion on the preoperative ultrasound image was obtained, with the region of interest (ROI) delineated manually. Radiomic features were computed from the ROIs and subsequently normalized using Z-scores. Wilcoxon test and least absolute shrinkage and selection operator (LASSO) regression were applied for feature reduction and model development. The performance of the model was estimated by indicators including area under the curve (AUC), sensitivity and specificity. RESULTS: A total of 336 participants (median age, 57 y; 106 women) with ccRCC were finally included, of whom 243 had low-grade tumors (grade 1-2) and 93 had high-grade tumors (grade 3-4). A total of 1163 radiomic features were extracted from the ROIs for model construction and 117 informative radiomics features selected by Wilcoxon test were submitted to LASSO. Our ultrasound-based radiomics model included 51 features and achieved AUCs of 0.90 and 0.79 for the training and validation sets, respectively. Within the training set, the sensitivity and specificity measured 0.75 and 0.92, respectively, whereas in the validation set, the sensitivity and specificity measured 0.65 and 0.84, respectively. In the subgroup analysis, for the training and validation sets Philips AUCs were 0.91 and 0.75, Toshiba AUCs were 0.82 and 0.90, and General Electric AUCs were 0.95 and 0.82, respectively. CONCLUSION: Ultrasound-based radiomics can effectively predict the WHO/ISUP grading of ccRCC.

Enhanced machine learning approaches for OSA patient screening: model development and validation study.

Age, gender, body mass index (BMI), and mean heart rate during sleep were found to be risk factors for obstructive sleep apnea (OSA), and a variety of methods have been applied to predict the occurrence of OSA. This study aimed to develop and evaluate OSA prediction models using simple and accessible parameters, combined with multiple machine learning algorithms, and integrate them into a cloud-based mobile sleep medicine management platform for clinical use. The study data were obtained from the clinical records of 610 patients who underwent polysomnography (PSG) at the Sleep Medicine Center of the Second Affiliated Hospital of Fujian Medical University between January 2021 and December 2022. The participants were randomly divided into a training-test group (80%) and an independent validation group (20%). The logistic regression, artificial neural network, naïve Bayes, support vector machine, random forest, and decision tree algorithms were used with age, gender, BMI, and mean heart rate during sleep as predictors to build a risk prediction model for moderate-to-severe OSA. To evaluate the performance of the models, we calculated the area under the receiver operating curve (AUROC), accuracy, recall, specificity, precision, and F1-score for the independent validation set. In addition, the calibration curve, decision curve, and clinical impact curve were generated to determine clinical usefulness. Age, gender, BMI, and mean heart rate during sleep were significantly associated with OSA. The artificial neural network model had the best efficacy compared with the other prediction algorithms. The AUROC, accuracy, recall, specificity, precision, F1-score, and Brier score were 80.4% (95% CI 76.7-84.1%), 69.9% (95% CI 69.8-69.9%), 86.5% (95% CI 81.6-91.3%), 61.5% (95% CI 56.6-66.4%), 53.2% (95% CI 47.7-58.7%), 65.9% (95% CI 60.2-71.5%), and 0.165, respectively, for the artificial neural network model. The AUROCs for the LR, NB, SVM, RF, and DT models were 80.2%, 79.7%, 79.2%, 78.4%, and 70.4%, respectively. The six models based on four simple and easily accessible parameters effectively predicted moderate-to-severe OSA in patients with PSG screening, with the artificial neural network model having the best performance. These models can provide a reliable tool for early OSA diagnosis, and their integration into a cloud-based mobile sleep medicine management platform could improve clinical decision making.

A study on the impact of marital status on the survival status of prostate cancer patients based on propensity score matching.

Marital status is an independent prognostic factor for survival in many types of cancers, but its prognostic impact on patients with prostate cancer (PCa) has not been established. The aim of this study was to explore the independent prognostic factors of PCa and to investigate the effect of marital status on survival outcomes in patients with different stratified by PCa. Using the surveillance, epidemiology, and end results (SEER) database, we collected data on 584,655 PCa patients diagnosed between 1975 and 2019. Marital status was classified as married, divorced, widowed, and single. We used the Kaplan-Meier analysis and single multivariate Cox proportional hazards regression analysis to determine the effect of marital status on overall survival (OS) and cancer-specific survival (CSS). In addition, we performed subgroup analyses for different ages, Gleason score and PSA values, and performed a 1:1 propensity score matching (PSM) to reduce the impact of confounding factors to obtain more accurate matching results. According to our findings, marital status was an independent prognostic factor for the survival of PCa patients and a better prognosis of married patients. Moreover, we also found that factors such as age, TNM stage, Gleason score, and PSA concentration were also considered as important predictors for the prognosis of PCa. The above findings can facilitate early detection and treatment of high-risk PCa patients, prolong their life and reduce family burden.