Integration of MRI-Based Radiomics Features, Clinicopathological Characteristics, and Blood Parameters: A Nomogram Model for Predicting Clinical Outcome in Nasopharyngeal Carcinoma

Frontiers in Oncology

Ke-Zhen Li¹  Zhu Xu¹  Cheng Luo²  Xin Lai²  Zeng-Yi Fang³  Zi-Fei Wu⁴  Ming-Quan Gao⁴  Chuan Wu⁴  Yu-Rou Che⁴  Wei-Dong Wang⁴  Jie-Ke Liu⁴  Li-Ping Luo⁴  Si-Ming Li⁴  Peng Zhou⁴  Mei Wang⁴  Yi-Yao Zhang⁴  Man Yang⁴ 

[1] Department of Oncology, School of Clinical Medicine, Southwest Medical University, Luzhou, China;Department of Radiation Oncology, Sichuan Cancer Hospital and Institute, Chengdu, China;Radiation Oncology, Key Laboratory of Sichuan Province, Chengdu, China;School of Medicine, University of Electronic Science and Technology of China, Chengdu, China;

关键词: radiomics;    progression-free survival;    nasopharyngeal carcinoma;    Ki-67;    blood parameters;   

DOI  :  10.3389/fonc.2022.815952

来源: DOAJ

【 摘 要 】

PurposeThis study aimed to develop a nomogram model based on multiparametric magnetic resonance imaging (MRI) radiomics features, clinicopathological characteristics, and blood parameters to predict the progression-free survival (PFS) of patients with nasopharyngeal carcinoma (NPC).MethodsA total of 462 patients with pathologically confirmed nonkeratinizing NPC treated at Sichuan Cancer Hospital were recruited from 2015 to 2019 and divided into training and validation cohorts at a ratio of 7:3. The least absolute shrinkage and selection operator (LASSO) algorithm was used for radiomics feature dimension reduction and screening in the training cohort. Rad-score, age, sex, smoking and drinking habits, Ki-67, monocytes, monocyte ratio, and mean corpuscular volume were incorporated into a multivariate Cox proportional risk regression model to build a multifactorial nomogram. The concordance index (C-index) and decision curve analysis (DCA) were applied to estimate its efficacy.ResultsNine significant features associated with PFS were selected by LASSO and used to calculate the rad-score of each patient. The rad-score was verified as an independent prognostic factor for PFS in NPC. The survival analysis showed that those with lower rad-scores had longer PFS in both cohorts (p < 0.05). Compared with the tumor–node–metastasis staging system, the multifactorial nomogram had higher C-indexes (training cohorts: 0.819 vs. 0.610; validation cohorts: 0.820 vs. 0.602). Moreover, the DCA curve showed that this model could better predict progression within 50% threshold probability.ConclusionA nomogram that combined MRI-based radiomics with clinicopathological characteristics and blood parameters improved the ability to predict progression in patients with NPC.

【 授权许可】

Unknown