Background: Lymph node metastasis (LNM) is difficult to precisely predict before surgery in patients with early-T-stage non-small cell lung cancer (NSCLC). This study aimed to develop machine learning (ML)-based predictive models for LNM. Methods: Clinical characteristics and imaging features were retrospectively collected from 1,102 NSCLC ≤ 2 cm patients. A total of 23 variables were included to develop predictive models for LNM by multiple ML algorithms. The models were evaluated by the receiver operating characteristic (ROC) curve for predictive performance and decision curve analysis (DCA) for clinical values. A feature selection approach was used to identify optimal predictive factors. Results: The areas under the ROC curve (AUCs) of the 8 models ranged from 0.784 to 0.899. Some ML-based models performed better than models using conventional statistical methods in both ROC curves and decision curves. The random forest classifier (RFC) model with 9 variables introduced was identified as the best predictive model. The feature selection indicated the top five predictors were tumor size, imaging density, carcinoembryonic antigen (CEA), maximal standardized uptake value (SUV max), and age. Conclusions: By incorporating clinical characteristics and radiographical features, it is feasible to develop ML-based models for the preoperative prediction of LNM in early-T-stage NSCLC, and the RFC model performed best.
BackgroundPrevious studies have shown an association with glutathione S-transferase (GST) gene polymorphisms in patients with non-small cell lung cancer (NSCLC) and treatment response. This study aimed to undertake a literature review and meta-analysis of GST gene polymorphisms, including GSTT1, GSTM1, and GSTP1 IIe105Val, and the treatment response to cisplatin-based chemotherapy in patients with NSCLC.Material/MethodsA literature search was undertaken of the main medical publication databases for publications, up to March 2017, on the association between GSTT1, GSTM1, and GSTP1 IIe105Val polymorphisms and the clinical outcome in patients with NSCLC treated with cisplatin-based chemotherapy. A random fixed-effects model was used to calculate the pooled odds ratio (OR) and 95% confidence interval (CI) to evaluate the associations, considering multiple genetic models. A subgroup analysis according to ethnicity was performed.ResultsTwenty-three published studies were identified that showed that both the null GSTM1 and the GG genotype of GSTP1 IIe105Val were associated with improved treatment response to cisplatin-based chemotherapy (GSTT1 present/null: OR=1.328; 95% CI, 1.074–1.643) (GSTP1 GG + AG vs. AA: OR=0.596; 95% CI, 0.468–0.759). In subgroup analysis, the GSTP1 polymorphism was significantly associated with treatment response in East-Asian patients, but not in Caucasian patients.ConclusionsMeta-analysis showed that the GG genotype of GSTP1 IIe105Val and the null GSTM1 genotype were associated with an improved treatment response to cisplatin-based chemotherapy in patients with NSCLC, especially in East-Asian patients.
Activation of transforming growth factor β1 (TGFB1)/SMAD3 signaling may lead to additional synthesis of collagen type IV (COL4), which is a major contributor to extracellular matrix (ECM) accumulation in diabetic nephropathy (DN). C-peptide can attenuate fibrosis to have unique beneficial effects in DN. However, whether and how C-peptide affects TGFB1/SMAD3-activated COL4 synthesis is unclear. In this study, pathological changes, expression of COL4 a1-a5 chains (), COL4 distribution and protein and TGFB1 and SMAD3 protein were first assessed in a rat model of diabetes. Then, rat mesangial cells were treated with high glucose (HG) and/or C-peptide to investigate the underlying mechanism. expression, COL4 protein and secretion, TGFB1 protein, SMAD3 nuclear translocation and binding of SMAD3 to its cognate sites in the promoters of, and were measured. It was found that C-peptide attenuated glomerular pathological changes and suppressed renal mRNA expression, COL4 protein content and TGFB1 protein content. C-peptide had a dose-dependent effect to inhibit mRNA expression, COL4 protein content and secretion, in HG-stimulated mesangial cells. In addition, the HG-induced increase in TGFB1 protein content was significantly reduced by C-peptide. Although not apparently affecting SMAD3 nuclear translocation, C-peptide prevented SMAD3 from binding to its sites in the , and promoters in HG-stimulated mesangial cells. In conclusion, C-peptide could prevent SMAD3 from binding to its sites in the, and promoters, to inhibit COL4 generation. These results may provide a mechanism for the alleviation of fibrosis in DN by C-peptide.
A 47-year-old man presented with right upper abdominal pain for 1 month. Contrast-enhanced CT revealed hilar bile duct stenosis with dilatation of the intrahepatic bile ducts, and his serum CA19-9 and CA242 levels were significantly elevated. 18 F-FDG and 68 Ga-FAPI PET/CTwere performed for differential diagnosis. 18 F-FDG PET/CT showed only mild FDG uptake in the hepatic hilum. Astonishingly, in 68 Ga-FAPI PET/CT, intense radioactivity was presented on the same region, which indicated massive fibroblasts aggregation in hepatic hilum. The patient was finally diagnosed as portal biliopathy caused by cavernous transformation of the portal vein.
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