AUTHOR=Kang Le , Niu Yulin , Huang Rui , Lin Stefan (YUJIE) , Tang Qianlong , Chen Ailin , Fan Yixin , Lang Jinyi , Yin Gang , Zhang Peng 

TITLE=Predictive Value of a Combined Model Based on Pre-Treatment and Mid-Treatment MRI-Radiomics for Disease Progression or Death in Locally Advanced Nasopharyngeal Carcinoma

JOURNAL=Frontiers in Oncology

VOLUME=Volume 11 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2021.774455

DOI=10.3389/fonc.2021.774455

ISSN=2234-943X

ABSTRACT=Purpose：A combined model was established based on the MRI-radiomics of pre-and-mid-treatment to assess the risk of disease progression or death in locally advanced nasopharyngeal carcinoma. Materials and Methods：A total of 243 patients were analyzed. We extracted 10400 radiomics features from the primary nasopharyngeal tumors and largest metastatic lymph nodes on the axial contrast-enhanced T1 weighted and T2 weighted in pre-and-mid-treatment MRI, respectively. We used the SMOTE algorithm, center & scale & box-cox, Pearson correlation coefficient, and LASSO regression to construct the pre-and-mid-treatment MRI-radiomics prediction model, respectively, and the risk score named P score and M score were calculated. Finally, univariate and multivariate analyses were used for P score, M score, and clinical data to built the combined model and grouped the patients into two by risk levels, namely high and low. Result: A combined model of pre-and-mid-treatment MRI-radiomics successfully categorized patients into high-and low-risk groups. The log-rank test showed that the high-and low-risk groups had good prognostic performance in PFS (P＜0.0001，HR：19.71，95%CI：12.77-30.41), which was better than TNM stage(P=0.004，HR：1.913，95%CI：1.250-2.926), and also had an excellent predictive effect in LRFS, DMFS and OS. Conclusion:Risk grouping of LA-NPC using a combined model of pre-and-mid-treatment MRI-radiomics can better predict disease progression or death.