Lan He

PURPOSE: To develop and validate a radiomics nomogram for preoperative prediction of lymph node (LN) metastasis in patients with colorectal cancer (CRC). PATIENTS AND METHODS: The prediction model was developed in a primary cohort that consisted of 326 patients with clinicopathologically confirmed CRC, and data was gathered from January 2007 to April 2010. Radiomic features were extracted from portal venous-phase computed tomography (CT) of CRC. Lasso regression model was used for data dimension reduction, feature selection, and radiomics signature building. Multivariable logistic regression analysis was used to develop the predicting model, we incorporated the radiomics signature, CT-reported LN status, and independent clinicopathologic risk factors, and this was presented with a radiomics nomogram. The performance of the nomogram was assessed with respect to its calibration, discrimination, and clinical usefulness. Internal validation was assessed. An independent validation cohort contained 200 consecutive patients from May 2010 to December 2011. RESULTS: The radiomics signature, which consisted of 24 selected features, was significantly associated with LN status (P < .001 for both primary and validation cohorts). Predictors contained in the individualized prediction nomogram included the radiomics signature, CT-reported LN status, and carcinoembryonic antigen level. Addition of histologic grade to the nomogram failed to show incremental prognostic value. The model showed good discrimination, with a C-index of 0.736 (C-index, 0.759 and 0.766 through internal validation), and good calibration. Application of the nomogram in the validation cohort still gave good discrimination (C-index, 0.778 [95% CI, 0.769 to 0.787]) and good calibration. Decision curve analysis demonstrated that the radiomics nomogram was clinically useful. CONCLUSION: This study presents a radiomics nomogram that incorporates the radiomics signature, CT-reported LN status, and clinical risk factors, which can be conveniently used to facilitate the preoperative individualized prediction of LN metastasis in patients with CRC.

Purpose To develop a radiomics signature to estimate disease-free survival (DFS) in patients with early-stage (stage I–II) non–small cell lung cancer (NSCLC) and assess its incremental value to the traditional staging system and clinical-pathologic risk factors for individual DFS estimation. Materials and Methods Ethical approval by the institutional review board was obtained for this retrospective analysis, and the need to obtain informed consent was waived. This study consisted of 282 consecutive patients with stage IA–IIB NSCLC. A radiomics signature was generated by using the least absolute shrinkage and selection operator, or LASSO, Cox regression model. Association between the radiomics signature and DFS was explored. Further validation of the radiomics signature as an independent biomarker was performed by using multivariate Cox regression. A radiomics nomogram with the radiomics signature incorporated was constructed to demonstrate the incremental value of the radiomics signature to the traditional staging system and other clinical-pathologic risk factors for individualized DFS estimation, which was then assessed with respect to calibration, discrimination, reclassification, and clinical usefulness. Results The radiomics signature was significantly associated with DFS, independent of clinical-pathologic risk factors. Incorporating the radiomics signature into the radiomics-based nomogram resulted in better performance (P < .0001) for the estimation of DFS (C-index: 0.72; 95% confidence interval [CI]: 0.71, 0.73) than with the clinical-pathologic nomogram (C-index: 0.691; 95% CI: 0.68, 0.70), as well as a better calibration and improved accuracy of the classification of survival outcomes (net reclassification improvement: 0.182; 95% CI: 0.02, 0.31; P = .02). Decision curve analysis demonstrated that in terms of clinical usefulness, the radiomics nomogram outperformed the traditional staging system and the clinical-pathologic nomogram. Conclusion The radiomics signature is an independent biomarker for the estimation of DFS in patients with early-stage NSCLC. Combination of the radiomics signature, traditional staging system, and other clinical-pathologic risk factors performed better for individualized DFS estimation in patients with early-stage NSCLC, which might enable a step forward precise medicine. © RSNA, 2016 Online supplemental material is available for this article.

Circadian clocks are cell autonomous, transcriptionally based, molecular mechanisms that confer the selective advantage of anticipation, enabling cells/organs to respond to environmental factors in a temporally appropriate manner. Critical to circadian clock function are 2 transcription factors, CLOCK and BMAL1. The purpose of the present study was to reveal novel physiologic functions of BMAL1 in the heart, as well as to determine the pathologic consequences of chronic disruption of this circadian clock component. To address this goal, we generated cardiomyocyte-specific Bmal1 knockout (CBK) mice. Following validation of the CBK model, combined microarray and in silico analyses were performed, identifying 19 putative direct BMAL1 target genes, which included a number of metabolic (e.g., β-hydroxybutyrate dehydrogenase 1 [Bdh1]) and signaling (e.g., the p85α regulatory subunit of phosphatidylinositol 3-kinase [Pik3r1]) genes. Results from subsequent validation studies were consistent with regulation of Bdh1 and Pik3r1 by BMAL1, with predicted impairments in ketone body metabolism and signaling observed in CBK hearts. Furthermore, CBK hearts exhibited depressed glucose utilization, as well as a differential response to a physiologic metabolic stress (i.e., fasting). Consistent with BMAL1 influencing critical functions in the heart, echocardiographic, gravimetric, histologic, and molecular analyses revealed age-onset development of dilated cardiomyopathy in CBK mice, which was associated with a severe reduction in life span. Collectively, our studies reveal that BMAL1 influences metabolism, signaling, and contractile function of the heart.

// Cuishan Liang 1, 2, * , Yanqi Huang 1, 2, * , Lan He 1, 3 , Xin Chen 4 , Zelan Ma 1, 2 , Di Dong 5 , Jie Tian 5 , Changhong Liang 1 , Zaiyi Liu 1 1 Department of Radiology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China 2 Graduate College, Southern Medical University, Guangzhou, 510515, China 3 School of Medicine, South China University of Technology, Guangzhou, Guangdong, 510006, China 4 Department of Radiology, The Affiliated Guangzhou First People&rsquo; Hospital, Guangzhou Medical University, Guangzhou, 510180, China 5 Key Laboratory of Molecular Imaging, Chinese Academy of Sciences, Beijing, 100190, China * These authors have contributed equally to this work Correspondence to: Zaiyi Liu, email: zyliu@163.com Changhong Liang, email: cjr.lchh@vip.163.com Keywords: colorectal cancer, computed tomography, radiomics signature, predictor, stage Received: December 25, 2015&emsp;&emsp;&emsp;&emsp; Accepted: April 2, 2016&emsp;&emsp;&emsp;&emsp; Published: April 22, 2016 ABSTRACT Objectives: To investigative the predictive ability of radiomics signature for preoperative staging (I-II vs. III-IV) of primary colorectal cancer (CRC). Methods: This study consisted of 494 consecutive patients (training dataset: n=286; validation cohort, n=208) with stage I&ndash;IV CRC. A radiomics signature was generated using LASSO logistic regression model. Association between radiomics signature and CRC staging was explored. The classification performance of the radiomics signature was explored with respect to the receiver operating characteristics(ROC) curve. Results: The 16-feature-based radiomics signature was an independent predictor for staging of CRC, which could successfully categorize CRC into stage I-II and III-IV ( p &lt;0.0001) in training and validation dataset. The median of radiomics signature of stage III-IV was higher than stage I-II in the training and validation dataset. As for the classification performance of the radiomics signature in CRC staging, the AUC was 0.792(95%CI:0.741-0.853) with sensitivity of 0.629 and specificity of 0.874. The signature in the validation dataset obtained an AUC of 0.708(95%CI:0.698-0.718) with sensitivity of 0.611 and specificity of 0.680. Conclusions: A radiomics signature was developed and validated to be a significant predictor for discrimination of stage I-II from III-IV CRC, which may serve as a complementary tool for the preoperative tumor staging in CRC.

Osteoarthritis (OA) is a degenerative disease of middle-aged and elderly people, contributed a higher burden of disease in China and the world. In 2017, under the support of the Rheumatology and Immunology Expert Committee of the Cross-Strait Medical and Health Exchange Association. The objective was to develop an evidence-based diagnosis and treatment guideline for OA in China based on emerging new evidence. The guideline was registered at International Practice Guidelines Registry Platform (IPGRP-2018CN028). The grading of recommendations assessment, development and evaluation (GRADE) approach was used to rate the quality of evidence and the strength of recommendations, and the RIGHT (Reporting Items for Practice Guidelines in Healthcare) checklist was followed to report the guideline. The guideline provides recommendations for the OA diagnosis, disease risks monitoring and evaluate, treatment purpose and physical, medical and surgical interventions. This guideline is intended to serve as a tool for Chinese clinicians for the best decisions-making on diagnosis and treatment of OA.