Establishment and Validation of a Novel MRI Radiomics Feature-Based Prognostic Model to Predict Distant Metastasis in Endemic Nasopharyngeal Carcinoma.

Purpose: We aimed to establish a prognostic model based on magnetic resonance imaging (MRI) radiomics features for individual distant metastasis risk prediction in patients with nasopharyngeal carcinoma (NPC). Methods: Regression analysis was applied to select radiomics features from T1-weighted (T1-w), contrast-enhanced T1-weighted (T1C-w), and T2-weighted (T2-w) MRI scans. All prognostic models were established using a primary cohort of 518 patients with NPC. The prognostic ability of the radiomics, clinical (based on clinical factors), and merged prognostic models (integrating clinical factors with radiomics) were identified using a concordance index (C-index). Models were tested using a validation cohort of 260 NPC patients. Distant metastasis-free survival (DMFS) were calculated by using the Kaplan-Meier method and compared by using the log-rank test. Results: In the primary cohort, seven radiomics prognostic models showed similar discrimination ability for DMFS to the clinical prognostic model (P=0.070-0.708), while seven merged prognostic models displayed better discrimination ability than the clinical prognostic model or corresponding radiomics prognostic models (all P<0.001). In the validation cohort, the C-indices of seven radiomics prognostic models (0.645-0.722) for DMFS prediction were higher than in the clinical prognostic model (0.552) (P=0.016 or <0.001) or in corresponding merged prognostic models (0.605-0.678) (P=0.297 to 0.857), with T1+T1C prognostic model (based on Radscore combinations of T1 and T1C Radiomics models) showing the highest C-index (0.722). In the decision curve analysis of the validation cohort for all prognostic models, the T1+T1C prognostic model displayed the best performance. Conclusions: Radiomics models, especially the T1+T1C prognostic model, provided better prognostic ability for DMFS in patients with NPC.

MRI-based radiomics models can improve prognosis prediction for nasopharyngeal carcinoma with neoadjuvant chemotherapy.

BACKGROUND: The purpose of this study was to explore the prognostic value of imaging features and related models in nasopharyngeal carcinoma (NPC) patients that received neoadjuvant chemotherapy. MATERIALS AND METHODS: We systematically reviewed the data of 110 NPC patients who received radiotherapy and neoadjuvant chemotherapy. The patients were randomly divided into the training cohort (n = 88) and the verification cohort (n = 22). The imaging data collected in this study were screened via Pyramidics and used to construct prediction models based on histology and clinical nomographs. The models' accuracy was evaluated via calibration curves and the consistency index (C-index). In addition, we also explored the correlation between radiomics expression patterns, quantitative histological characteristics, and clinical data and then constructed a model to predict the prognosis of NPC. RESULTS: The models that integrated radiomics contours with all the clinical data were superior to those based on the clinical data alone (C-index 0.746 vs. C-index 0.814, respectively) and the calibration curves showed good consistency. The heat map showed that the radiomics expression pattern and selected histological characteristics were correlated with the clinical stage, T stage, and N stage (p < 0.05), and no radiomics feature was associated with lactate dehydrogenase expression, lymphocyte count, or mononuclear cell count. CONCLUSION: MRI-based radiomics can significantly improve the efficacy of traditional TNM staging and clinical data in predicting the progression-free survival (PFS) of patients with advanced NPC, which may provide an opportunity for precision medicine.

Long non­coding RNA HOXA11­AS promotes cell proliferation and metastasis in human breast cancer.

Breast cancer is one of the most frequently occurring malignancies in female cancers worldwide, however, its detailed mechanism of tumorigenesis remains to be elucidated. Long non-coding RNAs (LncRNAs) have previously been demonstrated to be important in multiple cancers, including breast cancer. The present study aimed to elucidate the molecular mechanism of the effects of the novel Lnc RNA HOXA11­AS, on cell proliferation and metastasis in breast cancer. The data revealed that the relative transcript level of HOXA11­AS was upregulated in vivo and in vitro in models of breast cancer. Knockdown of HOXA11­AS in MDA­MB­231 and MDA­MB­436 breast cancer cell lines inhibited the formation of cell colonies and arrested the cell cycle at the G0/G1 phase. Depletion of HOXA11­AS using two specific short interfering (si)RNAs against HOXA11­AS (siHOXA11­AS­1 and siHOXA11­AS­2) additionally suppressed the cell proliferative rate. Furthermore, transwell assays and wound­healing analysis revealed that siRNA transfection inhibited cell migration and invasion by ~50% in the two cell lines. The results of the present study demonstrated the oncogenic role of HOXA11­AS in breast cancer, providing novel clues for the future clinical diagnosis and treatment of early stage breast cancer patients.