Machine learning based on optimal VOI of multi-sequence MR images to predict lymphovascular invasion in invasive breast cancer.

Objectives: Lymphovascular invasion serves as a crucial prognostic indicator in invasive breast cancer, influencing treatment decisions. We aimed to develop a machine learning model utilizing optimal volumes of interest extracted from multisequence magnetic resonance images to predict lymphovascular invasion in patients with invasive breast cancer. Materials and methods: This study comprised 191 patients postoperatively diagnosed with invasive breast cancer through multi-sequence magnetic resonance imaging. Independent predictors were identified through univariate and multivariate logistic regression analyses, culminating in the construction of a clinical model. Radiomic features were extracted from multi-sequence magnetic resonance imaging images across various volume of interest scales (-2 mm, entire, +2 mm, +4 mm, and +6 mm). Subsequently, various radiomic models were developed using machine learning model algorithms, including logistic regression, support vector machine, k-nearest neighbor, gradient boosting machine, classification and regression tree, and random forest. A hybrid model was then formulated, amalgamating optimal radiomic and clinical models. Results: The area under the curve of the clinical model was 0.757. Among the radiomic models, the most efficient diagnosis was achieved by the k-nearest neighbor-based radiomics-volume of interest (+2 mm), resulting in an area under the curve of 0.780. The hybrid model, integrating the k-nearest neighbor-based radiomics-volume of interest (+2 mm), and the clinical model surpassed the individual clinical and radiomics models, exhibiting a superior area under the curve of 0.864. Conclusion: Utilizing a hybrid approach integrating clinical data and multi-sequence magnetic resonance imaging-derived radiomics models based on the multiscale tumor region volume of interest (+2 mm) proved effective in determining lymphovascular invasion status in patients with invasive breast cancer. This innovative methodology may offer valuable insights for treatment planning and disease management.

CT-Based Sarcopenic Nomogram for Predicting Progressive Disease in Advanced Non-Small-Cell Lung Cancer.

BACKGROUND: It is prudent to identify the risk for progressive disease (PD) in patients with non-small-cell lung cancer (NSCLC) who undergo platinum-based chemotherapy. The present study aimed to develop a CT imaging-based sarcopenic nomogram for predicting the risk of PD prior to chemotherapy treatment. METHODS: We retrospectively enrolled patients with NSCLC who underwent platinum-based chemotherapy. Imaging-based body composition parameters such as skeletal muscle index (SMI) for assessment of sarcopenia were obtained from pre-chemotherapy chest CT images at the level of the eleventh thoracic vertebral body (T11). Sarcopenic nomogram was constructed using multivariate logistic regression and performance of the nomogram was evaluated by discrimination, calibration curve, and decision curve. RESULTS: Sixty (14.7%) of the 408 patients in the study cohort developed PD during chemotherapy. The prediction nomogram for developing PD achieved a moderate efficiency with an area under the curve (AUC) of 0.75 (95% CI: 0.69-0.80) for the training cohort, and 0.76 (95%CI: 0.68-0.84) for the validation cohort, as well as a good performance of consistence (bootstrap for training cohort: 0.75 ± 0.02; validation cohort: 0.74 ± 0.06). Favorable clinical application was observed in the decision curve analysis. CONCLUSION: Our CT-based sarcopenic nomogram showed the potential for an individualized prediction of progression for patients with NSCLC receiving platinum-based chemotherapy.

Prevalence and risk factors of chronic kidney disease and diabetic kidney disease in a central Chinese urban population: a cross-sectional survey.

BACKGROUND: This study was conducted to evaluate and update the current prevalence of and risk factors for chronic kidney disease (CKD) and diabetic kidney disease (DKD) in a central Chinese urban population. METHODS: From December 2017 to June 2018, a total of 5231 subjects were randomly enrolled from 3 communities in 3 districts of Zhengzhou. CKD was defined as estimated glomerular filtration rate (eGFR) < 60 mL/min.1.73m2 or urinary albumin to creatinine ratio ≥ 30 mg/g (albuminuria). Diabetic subjects with systolic blood pressure > 140 mmHg, albuminuria or an eGFR less than 60 mL/min/1.73 m2 were classified as having DKD. Participants completed a questionnaire assessing lifestyle and relevant medical history, and blood and urine specimens were taken. Serum creatinine, uric acid, total cholesterol, triglycerides, low-density lipoprotein, high-density lipoprotein and urinary albumin were assessed. The age- and sex-adjusted prevalences of CKD and DKD were calculated, and risk factors associated with the presence of reduced eGFR, albuminuria, DKD, severity of albuminuria and progression of reduced renal function were analyzed by binary and ordinal logistic regression. RESULTS: The overall adjusted prevalence of CKD was 16.8% (15.8-17.8%) and that of DKD was 3.5% (3.0-4.0%). Decreased renal function was detected in 132 participants (2.9, 95% confidence interval [CI]: 2.5-3.2%), whereas albuminuria was found in 858 participants (14.9, 95% CI: 13.9-15.9%). In all participants with diabetes, the prevalence of reduced eGFR was 6.3% (95% CI = 3.9-8.6%) and that of albuminuria was 45.3% (95% CI = 40.4-50.1%). The overall prevalence of CKD in participants with diabetes was 48.0% (95% CI = 43.1-52.9%). The results of the binary and ordinal logistic regression indicated that the factors independently associated with a higher risk of reduced eGFR and albuminuria were older age, sex, smoking, alcohol consumption, overweight, obesity, diabetes, hypertension, dyslipidemia and hyperuricemia. CONCLUSIONS: Our study shows the current prevalence of CKD and DKD in residents of Central China. The high prevalence suggests an urgent need to implement interventions to relieve the high burden of CKD and DKD in China.

Silencing of long noncoding RNA PVT1 inhibits podocyte damage and apoptosis in diabetic nephropathy by upregulating FOXA1.

The number of patients with diabetic nephropathy (DN) is still on the rise worldwide, and this requires the development of new therapeutic strategies. Recent reports have highlighted genetic factors in the treatment of DN. Herein, we aimed to study the roles of long noncoding RNA (lncRNA) plasmacytoma variant translocation 1 (PVT1) and histone 3 lysine 27 trimethylation (H3K27me3) in DN. A model of DN was established by inducing diabetes in mice with streptozotocin. Mouse podocyte clone 5 (MPC5) podocytes and primary podocytes were cultured in normal and high glucose media to observe cell morphology and to quantify PVT1 expression. The roles of PVT1 and enhancer of zeste homolog 2 (EZH2) were validated via loss-of-function and gain-of-function in vitro experiments to identify the interactions among PVT1, EZH2, and forkhead box A1 (FOXA1). The podocyte damage and apoptosis due to PVT1 and FOXA1 were verified with in vivo experiments. PVT1 was highly expressed in MPC5 and primary podocytes in DN patients and in cultures grown in high glucose medium. A large number of CpG (C-phosphate-G) island sites were predicted at the FOXA1 promoter region, where PVT1 recruited EZH2 to promote the recruitment of H3K27me3. The silencing of PVT1 or the overexpression of FOXA1 relieved the damage and inhibited the apoptosis of podocytes in DN, as was evidenced by the upregulated expression of synaptopodin and podocin, higher expression of Bcl-2, and lower expression of Bax and cleaved caspase-3. The key findings of this study collectively indicate that the suppression of lncRNA PVT1 exerts inhibitory effects on podocyte damage and apoptosis via FOXA1 in DN, which is of clinical significance.

Prevalence and risk factors of chronic kidney disease and diabetic kidney disease in Chinese rural residents: a cross-sectional survey.

We conducted a cross-sectional survey including 23869 participants and aimed to measure the prevalences of and risk factors for chronic kidney disease (CKD) and diabetic kidney disease (DKD) in a Chinese rural population. CKD and DKD status was defined according to the combination of estimated glomerular filtration rate (eGFR) and presence of albuminuria Participant completed a questionnaire involving life-style and relevant medical history, and the blood and urinary specimen were taken. The age- and gender- adjusted prevalences of CKD and DKD were calculated and risk factors associated with the presence of CKD and DKD were analyzed by logistic regression. The overall prevalence of CKD was 16.4% (15.9-16.8%) and of DKD was 2.9% (2.7-3.1%). In participants with diabetes, the overall prevalence of CKD was 35.5% (95% CI = 33.7-37.3%). Factors independently associated with renal damage were age, gender, education, personal income, alcohol consumption, overweight, obesity, diabetes, hypertension and dyslipidemia. Our study shows current prevalences of CKD and DKD in Chinese rural residents. Further researches could identify potential factors explaining the observed differences and implement the interventions to relieve the high burden of CKD and DKD in rural population.