Pretreatment MRI radiomics analysis allows for reliable prediction of local recurrence in non-metastatic T4 nasopharyngeal carcinoma.

To identify a radiomics signature to predict local recurrence in patients with non-metastatic T4 nasopharyngeal carcinoma (NPC). A total of 737 patients from Sun Yat-sen University Cancer Center (training cohort: n = 360; internal validation cohort: n = 120) and Wuzhou Red Cross Hospital (external validation cohort: n = 257) underwent feature extraction from the largest axial area of the tumor on pretreatment magnetic resonance imaging scans. Feature selection was based on the prognostic performance and feature stability in the training cohort. Radscores were generated using the Cox proportional hazards regression model with the selected features in the training cohort and then validated in the internal and external validation cohorts. We also constructed a nomogram for predicting local recurrence-free survival (LRFS). Eleven features were selected to construct the Radscore, which was significantly associated with LRFS. For the training, internal validation, and external validation cohorts, the Radscore (C-index: 0.741 vs. 0.753 vs. 0.730) outperformed clinical prognostic variables (C-index for primary gross tumor volume: 0.665 vs. 0.672 vs. 0.577; C-index for age: 0.571 vs. 0.629 vs. 0.605) in predicting LRFS. The generated radiomics nomogram, which integrated the Radscore and clinical variables, exhibited a satisfactory prediction performance (C-index: 0.810 vs. 0.807 vs. 0.753). The nomogram-defined high-risk group had a shorter LRFS than did the low-risk group (5-year LRFS: 73.6% vs. 95.3%, P < .001; 79.6% vs 95.8%, P = .006; 85.7% vs 96.7%, P = .005). The Radscore can reliably predict LRFS in patients with non-metastatic T4 NPC, which might guide individual treatment decisions. FUND: This study was funded by the Health & Medical Collaborative Innovation Project of Guangzhou City, China.

Zhang LL ，Huang MY ，Li Y ，Liang JH ，Gao TS ，Deng B ，Yao JJ ，Lin L ，Chen FP ，Huang XD ，Kou J ，Li CF ，Xie CM ，Lu Y ，Sun Y ... -  《EBioMedicine》

Development and validation of a gene expression-based signature to predict distant metastasis in locoregionally advanced nasopharyngeal carcinoma: a retrospective, multicentre, cohort study.

Gene expression patterns can be used as prognostic biomarkers in various types of cancers. We aimed to identify a gene expression pattern for individual distant metastatic risk assessment in patients with locoregionally advanced nasopharyngeal carcinoma. In this multicentre, retrospective, cohort analysis, we included 937 patients with locoregionally advanced nasopharyngeal carcinoma from three Chinese hospitals: the Sun Yat-sen University Cancer Center (Guangzhou, China), the Affiliated Hospital of Guilin Medical University (Guilin, China), and the First People's Hospital of Foshan (Foshan, China). Using microarray analysis, we profiled mRNA gene expression between 24 paired locoregionally advanced nasopharyngeal carcinoma tumours from patients at Sun Yat-sen University Cancer Center with or without distant metastasis after radical treatment. Differentially expressed genes were examined using digital expression profiling in a training cohort (Guangzhou training cohort; n=410) to build a gene classifier using a penalised regression model. We validated the prognostic accuracy of this gene classifier in an internal validation cohort (Guangzhou internal validation cohort, n=204) and two external independent cohorts (Guilin cohort, n=165; Foshan cohort, n=158). The primary endpoint was distant metastasis-free survival. Secondary endpoints were disease-free survival and overall survival. We identified 137 differentially expressed genes between metastatic and non-metastatic locoregionally advanced nasopharyngeal carcinoma tissues. A distant metastasis gene signature for locoregionally advanced nasopharyngeal carcinoma (DMGN) that consisted of 13 genes was generated to classify patients into high-risk and low-risk groups in the training cohort. Patients with high-risk scores in the training cohort had shorter distant metastasis-free survival (hazard ratio [HR] 4·93, 95% CI 2·99-8·16; p<0·0001), disease-free survival (HR 3·51, 2·43-5·07; p<0·0001), and overall survival (HR 3·22, 2·18-4·76; p<0·0001) than patients with low-risk scores. The prognostic accuracy of DMGN was validated in the internal and external cohorts. Furthermore, among patients with low-risk scores in the combined training and internal cohorts, concurrent chemotherapy improved distant metastasis-free survival compared with those patients who did not receive concurrent chemotherapy (HR 0·40, 95% CI 0·19-0·83; p=0·011), whereas patients with high-risk scores did not benefit from concurrent chemotherapy (HR 1·03, 0·71-1·50; p=0·876). This was also validated in the two external cohorts combined. We developed a nomogram based on the DMGN and other variables that predicted an individual's risk of distant metastasis, which was strengthened by adding Epstein-Barr virus DNA status. The DMGN is a reliable prognostic tool for distant metastasis in patients with locoregionally advanced nasopharyngeal carcinoma and might be able to predict which patients benefit from concurrent chemotherapy. It has the potential to guide treatment decisions for patients at different risk of distant metastasis. The National Natural Science Foundation of China, the National Science & Technology Pillar Program during the Twelfth Five-year Plan Period, the Natural Science Foundation of Guang Dong Province, the National Key Research and Development Program of China, the Innovation Team Development Plan of the Ministry of Education, the Health & Medical Collaborative Innovation Project of Guangzhou City, China, and the Program of Introducing Talents of Discipline to Universities.

Tang XR ，Li YQ ，Liang SB ，Jiang W ，Liu F ，Ge WX ，Tang LL ，Mao YP ，He QM ，Yang XJ ，Zhang Y ，Wen X ，Zhang J ，Wang YQ ，Zhang PP ，Sun Y ，Yun JP ，Zeng J ，Li L ，Liu LZ ，Liu N ，Ma J ... -  《-》

Development and validation of a magnetic resonance imaging-based model for the prediction of distant metastasis before initial treatment of nasopharyngeal carcinoma: A retrospective cohort study.

We aimed to identify a magnetic resonance imaging (MRI)-based model for assessment of the risk of individual distant metastasis (DM) before initial treatment of nasopharyngeal carcinoma (NPC). This retrospective cohort analysis included 176 patients with NPC. Using the PyRadiomics platform, we extracted the imaging features of primary tumors in all patients who did not exhibit DM before treatment. Subsequently, we used minimum redundancy-maximum relevance and least absolute shrinkage and selection operator algorithms to select the strongest features and build a logistic model for DM prediction. The independent statistical significance of multiple clinical variables was tested using multivariate logistic regression analysis. In total, 2780 radiomic features were extracted. A DM MRI-based model (DMMM) comprising seven features was constructed for the classification of patients into high- and low-risk groups in a training cohort and validated in an independent cohort. Overall survival was significantly shorter in the high-risk group than in the low-risk group (P < 0·001). A radiomics nomogram based on radiomic features and clinical variables was developed for DM risk assessment in each patient, and it showed a significant predictive ability in the training [area under the curve (AUC), 0·827; 95% confidence interval (CI), 0.754-0.900] and validation (AUC, 0.792; 95% CI, 0.633-0.952) cohorts. DMMM can serve as a visual prognostic tool for DM prediction in NPC, and it can improve treatment decisions by aiding in the differentiation of patients with high and low risks of DM. FUND: This research received financial support from the National Natural Science Foundation of China (81571664, 81871323, 81801665, 81771924, 81501616, 81671851, and 81527805); the National Natural Science Foundation of Guangdong Province (2018B030311024); the Science and Technology Planning Project of Guangdong Province (2016A020216020); the Scientific Research General Project of Guangzhou Science Technology and Innovation Commission (201707010328); the China Postdoctoral Science Foundation (2016M600145); and the National Key R&D Program of China (2017YFA0205200, 2017YFC1308700, and 2017YFC1309100).

Zhang L ，Dong D ，Li H ，Tian J ，Ouyang F ，Mo X ，Zhang B ，Luo X ，Lian Z ，Pei S ，Dong Y ，Huang W ，Liang C ，Liu J ，Zhang S ... -  《EBioMedicine》

MRI-based radiomics signature is a quantitative prognostic biomarker for nasopharyngeal carcinoma.

Ming X ，Oei RW ，Zhai R ，Kong F ，Du C ，Hu C ，Hu W ，Zhang Z ，Ying H ，Wang J ... -  《-》

Proposal of a Pretreatment Nomogram for Predicting Local Recurrence after Intensity-Modulated Radiation Therapy in T4 Nasopharyngeal Carcinoma: A Retrospective Review of 415 Chinese Patients.

Zhang LL ，Li YY ，Hu J ，Zhou GQ ，Chen L ，Li WF ，Lin AH ，Ma J ，Qi ZY ，Sun Y ... -  《-》