Purpose: To identify MRI-based radiomics as prognostic factors in patients with advanced nasopharyngeal carcinoma (NPC).Experimental Design: One-hundred and eighteen patients (training cohort: n ¼ 88; validation cohort: n ¼ 30) with advanced NPC were enrolled. A total of 970 radiomics features were extracted from T2-weighted (T2-w) and contrast-enhanced T1-weighted (CET1-w) MRI. Least absolute shrinkage and selection operator (LASSO) regression was applied to select features for progression-free survival (PFS) nomograms. Nomogram discrimination and calibration were evaluated. Associations between radiomics features and clinical data were investigated using heatmaps.Results: The radiomics signatures were significantly associated with PFS. A radiomics signature derived from joint CET1-w and T2-w images showed better prognostic performance than signatures derived from CET1-w or T2-w images alone. One radiomics nomogram combined a radiomics signature from joint CET1-w and T2-w images with the TNM staging system. This nomogram showed a significant improvement over the TNM staging system in terms of evaluating PFS in the training cohort (C-index, 0.761 vs. 0.514; P < 2.68 Â 10 À9 ). Another radiomics nomogram integrated the radiomics signature with all clinical data, and thereby outperformed a nomogram based on clinical data alone (C-index, 0.776 vs. 0.649; P < 1.60 Â 10 À7 ). Calibration curves showed good agreement. Findings were confirmed in the validation cohort. Heatmaps revealed associations between radiomics features and tumor stages. Conclusions: Multiparametric MRI-based radiomics nomograms provided improved prognostic ability in advanced NPC. These results provide an illustrative example of precision medicine and may affect treatment strategies.

Accurate lung nodule segmentation from computed tomography (CT) images is of great importance for image-driven lung cancer analysis. However, the heterogeneity of lung nodules and the presence of similar visual characteristics between nodules and their surroundings make it difficult for robust nodule segmentation. In this study, we propose a data-driven model, termed the Central Focused Convolutional Neural Networks (CF-CNN), to segment lung nodules from heterogeneous CT images. Our approach combines two key insights: 1) the proposed model captures a diverse set of nodule-sensitive features from both 3-D and 2-D CT images simultaneously; 2) when classifying an image voxel, the effects of its neighbor voxels can vary according to their spatial locations. We describe this phenomenon by proposing a novel central pooling layer retaining much information on voxel patch center, followed by a multi-scale patch learning strategy. Moreover, we design a weighted sampling to facilitate the model training, where training samples are selected according to their degree of segmentation difficulty. The proposed method has been extensively evaluated on the public LIDC dataset including 893 nodules and an independent dataset with 74 nodules from Guangdong General Hospital (GDGH). We showed that CF-CNN achieved superior segmentation performance with average dice scores of 82.15% and 80.02% for the two datasets respectively. Moreover, we compared our results with the inter-radiologists consistency on LIDC dataset, showing a difference in average dice score of only 1.98%.

Cancer cells are known to execute reprogramed metabolism of glucose, amino acids, and lipids. Here, we report a significant role of cholesterol metabolism in cancer metastasis. By employing label-free Raman spectromicroscopy, we found an aberrant accumulation of cholesteryl ester in human pancreatic cancer specimens and cell lines, mediated by acyl-CoA cholesterol acyltransferase-1 (ACAT-1) enzyme. Expression of ACAT-1 showed a correlation with poor patient survival. Abrogation of cholesterol esterification, either by an ACAT-1 inhibitor or by shRNA knockdown, significantly suppressed tumor growth and metastasis in an orthotopic mouse model of pancreatic cancer. Mechanically, ACAT-1 inhibition increased intracellular free cholesterol level, which was associated with elevated endoplasmic reticulum stress and caused apoptosis. Collectively, our results demonstrate a new strategy for treating metastatic pancreatic cancer by inhibiting cholesterol esterification.

<b><i>Background:</i></b> Radiomics has emerged as a new approach that can help identify imaging information associated with tumor pathophysiology. We developed and validated a radiomics nomogram for preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC). <b><i>Methods:</i></b> Two hundred and eight patients with pathologically confirmed HCC (training cohort: <i>n</i> = 146; validation cohort: <i>n</i> = 62) who underwent preoperative gadoxetic acid-enhanced magnetic resonance (MR) imaging were included. Least absolute shrinkage and selection operator logistic regression was applied to select features and construct signatures derived from MR images. Univariate and multivariate analyses were used to identify the significant clinicoradiological variables and radiomics signatures associated with MVI, which were then incorporated into the predictive nomogram. The performance of the radiomics nomogram was evaluated by its calibration, discrimination, and clinical utility. <b><i>Results:</i></b> Higher α-fetoprotein level (<i>p</i> = 0.046), nonsmooth tumor margin (<i>p</i> = 0.003), arterial peritumoral enhancement (<i>p &#x3c;</i> 0.001), and the radiomics signatures of hepatobiliary phase (HBP) T1-weighted images (<i>p &#x3c;</i> 0.001) and HBP T1 maps (<i>p &#x3c;</i> 0.001) were independent risk factors of MVI. The predictive model that incorporated the clinicoradiological factors and the radiomic features derived from HBP images outperformed the combination of clinicoradiological factors in the training cohort (area under the curves [AUCs] 0.943 vs. 0.850; <i>p</i> = 0.002), though the validation did not have a statistical significance (AUCs 0.861 vs. 0.759; <i>p</i> = 0.111). The nomogram based on the model exhibited C-index of 0.936 (95% CI 0.895–0.976) and 0.864 (95% CI 0.761–0.967) in the training and validation cohort, fitting well in calibration curves (<i>p</i> &#x3e; 0.05). Decision curve analysis further confirmed the clinical usefulness of the nomogram. <b><i>Conclusions:</i></b> The nomogram incorporating clinicoradiological risk factors and radiomic features derived from HBP images achieved satisfactory preoperative prediction of the individualized risk of MVI in patients with HCC.