PREDICT-GTN 1: Can we improve the FIGO scoring system in gestational trophoblastic neoplasia?

Gestational trophoblastic neoplasia (GTN) patients are treated according to the eight-variable International Federation of Gynaecology and Obstetrics (FIGO) scoring system, that aims to predict first-line single-agent chemotherapy resistance. FIGO is imperfect with one-third of low-risk patients developing disease resistance to first-line single-agent chemotherapy. We aimed to generate simplified models that improve upon FIGO. Logistic regression (LR) and multilayer perceptron (MLP) modelling (n = 4191) generated six models (M1-6). M1, all eight FIGO variables (scored data); M2, all eight FIGO variables (scored and raw data); M3, nonimaging variables (scored data); M4, nonimaging variables (scored and raw data); M5, imaging variables (scored data); and M6, pretreatment hCG (raw data) + imaging variables (scored data). Performance was compared to FIGO using true and false positive rates, positive and negative predictive values, diagnostic odds ratio, receiver operating characteristic (ROC) curves, Bland-Altman calibration plots, decision curve analysis and contingency tables. M1-6 were calibrated and outperformed FIGO on true positive rate and positive predictive value. Using LR and MLP, M1, M2 and M4 generated small improvements to the ROC curve and decision curve analysis. M3, M5 and M6 matched FIGO or performed less well. Compared to FIGO, most (excluding LR M4 and MLP M5) had significant discordance in patient classification (McNemar's test P < .05); 55-112 undertreated, 46-206 overtreated. Statistical modelling yielded only small gains over FIGO performance, arising through recategorisation of treatment-resistant patients, with a significant proportion of under/overtreatment as the available data have been used a priori to allocate primary chemotherapy. Streamlining FIGO should now be the focus.     

Silencing KLF16 inhibits oral squamous cell carcinoma cell proliferation by arresting the cell cycle and inducing apoptosis

Krüppel-like factor 16 (KLF16), a member of the Krüppel-like factor (KLF) family, has been extensively investigated in multiple cancer types. However, the role of KLF16 in oral squamous cell carcinoma (OSCC) remains unknown. Thus, we conducted this study to investigate its related mechanism. KLF16 expression in OSCC cell lines was quantified by western blotting. Then, OECM1 and OC3 cells were divided into Blank, siCtrl, siKLF16#1 and siKLF16#2 groups. Subsequently, cell proliferation was detected using 3-[4,5-dimethylthiazol-2-yl]-2,5 diphenyl tetrazolium bromide (MTT) assays, cell migration and invasion were detected with wound healing and Transwell assays, and cell cycle distribution and cell apoptosis were detected via flow cytometry. KLF16, p21, CDK4, Cyclin D1 and p-Rb expression was detected by western blotting. Finally, xenograft models were established in nude mice to observe the in vivo effects of KLF16 on OSCC. KLF16 protein expression was upregulated in OSCC cells. Compared to the cells in the Blank group, the OECM1 and OC3 cells in the siKLF16#1 group and siKLF16#2 group exhibited a sharp decrease in proliferation but a remarkable increase in apoptosis. Moreover, the proportion of cells in the G0/G1 phase notably increased and that in the S phase decreased, with evident decreases in cell invasion and migration. Moreover, KLF16, cyclin-dependent kinase 4 (CDK4), Cyclin D1 and p-Rb protein expression was upregulated, but p21 expression was downregulated. The mice in the siKLF16#1 and siKLF16#2 xenograft model groups exhibited slower tumour growth and smaller tumours with evident downregulation of Ki67 expression compared to the mice in the Blank group. KLF16 expression was upregulated in OSCC cells, and interfering with KLF16 led to cell cycle arrest, inhibited OSCC cell growth and promoted cell apoptosis.     

A Priori Subcell Limiting Approach for the FR/CPR Method on Unstructured Meshes

A priori subcell limiting approach is developed for high-order flux reconstruction/correction procedure via reconstruction (FR/CPR) methods on two-dimensional unstructured quadrilateral meshes. Firstly, a modified indicator based on modal energy coefficients is proposed to detect troubled cells, where discontinuities exist. Then, troubled cells are decomposed into nonuniform subcells and each subcell has one solution point. A second-order finite difference shock-capturing scheme based on nonuniform nonlinear weighted (NNW) interpolation is constructed to perform the calculation on troubled cells while smooth cells are calculated by the CPR method. Numerical investigations show that the proposed subcell limiting strategy on unstructured quadrilateral meshes is robust in shock-capturing.     

Nipple leiomyoma: A rare neoplasm with a broad spectrum of histologic appearances

Cutaneous leiomyomas are rare benign smooth‐muscle tumors. These lesions are distinguished based on their cell of origin and are subclassified as pilar leiomyoma, angioleiomyoma, and genital‐type leiomyoma. Nipple leiomyoma is the least common genital‐type leiomyoma, arising from the dartoic muscle cell of the nipple. Histologic examination of the lesion is necessary for definitive diagnosis, and these uncommon tumors can pose a diagnostic challenge. We describe herein a series of six nipple leiomyomas with a spectrum of histologic appearances.     

Radiomics model based on preoperative gadoxetic acid-enhanced MRI for predicting liver failure

BACKGROUND Postoperative liver failure is the most severe complication in cirrhotic patients with hepatocellular carcinoma(HCC) after major hepatectomy. Current available clinical indexes predicting postoperative residual liver function are not sufficiently accurate.AIM To determine a radiomics model based on preoperative gadoxetic acid-enhanced magnetic resonance imaging for predicting liver failure in cirrhotic patients with HCC after major hepatectomy.METHODS For this retrospective study, a radiomics-based model was developed based on preoperative hepatobiliary phase gadoxetic acid-enhanced magnetic resonance images in 101 patients with HCC between June 2012 and June 2018. Sixty-one radiomic features were extracted from hepatobiliary phase images and selected by the least absolute shrinkage and selection operator method to construct a radiomics signature. A clinical prediction model, and radiomics-based model incorporating significant clinical indexes and radiomics signature were built using multivariable logistic regression analysis. The integrated radiomics-based model was presented as a radiomics nomogram. The performances of clinical prediction model, radiomics signature, and radiomics-based model for predicting post-operative liver failure were determined using receiver operating characteristics curve, calibration curve, and decision curve analyses.RESULTS Five radiomics features from hepatobiliary phase images were selected to construct the radiomics signature. The clinical prediction model, radiomics signature, and radiomics-based model incorporating indocyanine green clearance rate at 15 min and radiomics signature showed favorable performance for predicting postoperative liver failure(area under the curve: 0.809-0.894). The radiomics-based model achieved the highest performance for predicting liver failure(area under the curve: 0.894; 95%CI: 0.823-0.964). The integrated discrimination improvement analysis showed a significant improvement in the accuracy of liver failure prediction when radiomics signature was added to the clinical prediction model(integrated discrimination improvement = 0.117, P =0.002). The calibration curve and an insignificant Hosmer-Lemeshow test statistic(P = 0.841) demonstrated good calibration of the radiomics-based model. The decision curve analysis showed that patients would benefit more from a radiomics-based prediction model than from a clinical prediction model and radiomics signature alone.CONCLUSION A radiomics-based model of preoperative gadoxetic acid–enhanced MRI can be used to predict liver failure in cirrhotic patients with HCC after major hepatectomy.     

Prevalence of HER2 Expression and Its Correlation with Clinicopathological Parameters in Gastric or Gastroesophageal Junction Adenocarcinoma in North-East Indian Population

Objective: Human epidermal growth factor receptor 2 (erbb2/HER2) overexpression, has now been implicatedin advanced gastric and gastroesophageal junction cancers. The study was conducted to determine the rate of HER2positivity in patients with locally advanced or metastatic gastric and gastroesophageal adenocarcinoma in North-EastIndia and to assess the impact of various demographic and clinical parameters on HER2 positivity. Methods: A total of68 patients of age >18 years of gastric and gastroesophageal adenocarcinoma diagnosed on histopathological examinationfrom September 2016 to February 2018 at Dr B Borooah Cancer Institute, Assam were enrolled for the observational(epidemiological) study. All patients were subjected to the HER2 immunohistochemistry test using a FDA-approved,standardized test kit. HER2 expression was correlated with various demographic and clinicopathological parameters.Results: The overall rate of HER2 positivity in the population studied was 56% (n=38). The rate was non-significantlyhigher in male, older age group (>60 years) and Hindu population. Similarly, HER2 positivity rate was higher in patientswith well differentiated histology and was more common in patients with stage II and III diseases, but neither of theassociations is statistically significant. HER2 positivity rate was significantly higher in proximal and in GEJ tumours(56% versus 44%, P=0.002). Conclusion: HER2 overexpression was evident in 56% of the North-East Indian patientswith locally advanced and metastatic gastric and gastroesophageal adenocarcinoma. The overexpression correlatedsignificantly with primary tumour site. Routine testing of gastric and gastroesophageal tumours for HER2 expressionis recommended to provide a therapeutic advantage in Indian patients.