Multi-channel deep learning model-based myocardial spatial-temporal morphology feature on cardiac MRI cine images diagnoses the cause of LVH.

BACKGROUND: To develop a fully automatic framework for the diagnosis of cause for left ventricular hypertrophy (LVH) via cardiac cine images. METHODS: A total of 302 LVH patients with cine MRI images were recruited as the primary cohort. Another 53 LVH patients prospectively collected or from multi-centers were used as the external test dataset. Different models based on the cardiac regions (Model 1), segmented ventricle (Model 2) and ventricle mask (Model 3) were constructed. The diagnostic performance was accessed by the confusion matrix with respect to overall accuracy. The capability of the predictive models for binary classification of cardiac amyloidosis (CA), hypertrophic cardiomyopathy (HCM) or hypertensive heart disease (HHD) were also evaluated. Additionally, the diagnostic performance of best Model was compared with that of 7 radiologists/cardiologists. RESULTS: Model 3 showed the best performance with an overall classification accuracy up to 77.4% in the external test datasets. On the subtasks for identifying CA, HCM or HHD only, Model 3 also achieved the best performance with AUCs yielding 0.895-0.980, 0.879-0.984 and 0.848-0.983 in the validation, internal test and external test datasets, respectively. The deep learning model showed non-inferior diagnostic capability to the cardiovascular imaging expert and outperformed other radiologists/cardiologists. CONCLUSION: The combined model based on the mask of left ventricular segmented from multi-sequences cine MR images shows favorable and robust performance in diagnosing the cause of left ventricular hypertrophy, which could be served as a noninvasive tool and help clinical decision.

Comparison of MRI and CT-Based Radiomics and Their Combination for Early Identification of Pathological Response to Neoadjuvant Chemotherapy in Locally Advanced Gastric Cancer.

BACKGROUND: Current radiomics for treatment response assessment in gastric cancer (GC) have focused solely on Computed tomography (CT). The importance of multi-parametric magnetic resonance imaging (mp-MRI) radiomics in GC is less clear. PURPOSE: To compare and combine CT and mp-MRI radiomics for pretreatment identification of pathological response to neoadjuvant chemotherapy in GC. STUDY TYPE: Retrospective. POPULATION: Two hundred twenty-five GC patients were recruited and split into training (157) and validation dataset (68) in the ratio of 7:3 randomly. FIELD/SEQUENCE: T2-weighted fast spin echo (fat suppressed T2-weighted imaging [fs-T2WI]), diffusion weighted echo planar imaging (DWI), and fast gradient echo (dynamic contrast enhanced [DCE]) sequences at 3.0T. ASSESSMENT: Apparent diffusion coefficient (ADC) maps were generated from DWI. CT, fs-T2WI, ADC, DCE, and mp-MRI Radiomics score (Radscores) were compared between responders and non-responders. A multimodal nomogram combining CT and mp-MRI Radscores was developed. Patients were followed up for 3-65 months (median 19) after surgery, the overall survival (OS) and progression free survival (PFS) were calculated. STATISTICAL TESTS: A logistic regression classifier was applied to construct the five models. Each model's performance was evaluated using a receiver operating characteristic curve. The association of the nomogram with OS/PFS was evaluated by Kaplan-Meier survival analysis and C-index. A P value <0.05 was considered statistically significant. RESULTS: CT Radscore, mp-MRI Radscore and nomogram were significantly associated with tumor regression grading. The nomogram achieved the highest area under the curves (AUCs) of 0.893 (0.834-0.937) and 0.871 (0.767-0.940) in training and validation datasets, respectively. The C-index was 0.589 for OS and 0.601 for PFS. The AUCs of the mp-MRI model were not significantly different to that of the CT model in training (0.831 vs. 0.770, P = 0.267) and validation dataset (0.797 vs. 0.746, P = 0.137). DATA CONCLUSIONS: mp-MRI radiomics provides similar results to CT radiomics for early identification of pathologic response to neoadjuvant chemotherapy. The multimodal radiomics nomogram further improved the capability. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: 2.

Differentiation of pulmonary sclerosing pneumocytoma from solid malignant pulmonary nodules by radiomic analysis on multiphasic CT.

PURPOSE: To investigate the diagnostic value and feasibility of radiomics-based texture analysis in differentiating pulmonary sclerosing pneumocytoma (PSP) from solid malignant pulmonary nodules (SMPN) on single- and three-phase computed tomography (CT) images. MATERIALS AND METHODS: A total of 25 PSP patients and 35 SMPN patients with pathologically confirmed results were retrospectively included in this study. For each patient, the tumor regions were manually labeled in images acquired at the noncontrast phase (NCP), arterial phase (AP), and venous phase (VP). The least absolute shrinkage and selection operator (LASSO) method was used to select the most useful predictive features extracted from the CT images. The predictive models that discriminate PSP from SMPN based on single-phase CT images (NCP, AP, and VP) or three-phase CT images (Combined model) were developed and validated through fivefold cross-validation using a logistic regression classifier. Model performance was evaluated using receiver operating characteristic (ROC) analysis. The predictive performance was also compared between the Combined model and human readers. RESULTS: Four, five, and five features were selected from NCP, AP, and VP CT images for the development of radiomic models, respectively. The NCP, AP, and VP models exhibited areas under the curve (AUCs) of 0.748 (95% confidence interval [CI], 0.620-0.852), 0.749 (95% CI, 0.620-0.852), and 0.790 (95% CI, 0.665-0.884) in the validation dataset, respectively. The Combined model based on three-phase CT images outperformed the NCP, AP, and VP models (all p < 0.05), yielding an AUC of 0.882 (95% CI, 0.773-0.951) in the validation dataset. The Combined model displayed noninferior performance compared to two senior radiologists; however, it outperformed two junior radiologists (p = 0.004 and 0.001, respectively). CONCLUSION: The Combined model based on radiomic features extracted from three-phase CT images achieved radiologist-level performance and could be used as promising noninvasive tool to differentiate PSP from SMPN.

Chemerin suppresses hepatocellular carcinoma metastasis through CMKLR1-PTEN-Akt axis.

BACKGROUND: Chemerin, a known chemoattractant, participates in multiple biological events. However, its role in cancer remains largely unknown. METHODS: Chemerin expression was evaluated by real-time PCR, western blot and immunohistochemistry. Forced expression, RNAi, immunoprecipitation, etc. were used in function and mechanism study. Mouse models of extrahepatic and intrahepatic metastasis were employed to evaluate the therapeutic potential of chemerin. RESULTS: Chemerin expression was significantly downregulated in hepatocellular carcinoma, and associated with poor prognosis of HCC patients. Forced expression of chemerin inhibited in vitro migration, invasion and in vivo metastasis of HCC cells. Administration of chemerin effectively suppressed extrahepatic and intrahepatic metastases of HCC cells, resulting in prolonged survival of tumour-bearing nude mice. Chemerin upregulated expression and phosphatase activity of PTEN by interfering with PTEN-CMKLR1 interaction, leading to weakened ubiquitination of PTEN and decreased p-Akt (Ser473) level, which was responsible for suppressed migration, invasion and metastasis of HCC cells. Positive correlation between chemerin and PTEN, and reverse correlation between chemerin and p-Akt (Ser473) were also observed in HCC clinical samples and intrahepatic mouse model in vivo. CONCLUSIONS: Our study has revealed the suppressive role and therapeutic potential of chemerin in HCC metastasis, providing both a prognostic marker and drug candidate for HCC.