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In Journal of the National Cancer Institute

BACKGROUND : Magnetic resonance imaging (MRI) images are crucial unstructured data for prognostic evaluation in nasopharyngeal carcinoma (NPC). We developed and validated a prognostic system based on the MRI features and clinical data of locoregionally advanced NPC (LA-NPC) patients to distinguish low-risk patients with LA-NPC, for whom concurrent chemoradiotherapy (CCRT) is sufficient.

METHODS : This multicenter, retrospective study included 3444 patients with LA-NPC from January 1, 2010, to January 31, 2017. A three-dimensional convolutional neural network was used to learn the image features from pretreatment MRI images. An eXtreme Gradient Boosting model was trained with the MRI features and clinical data to assign an overall score to each patient. Comprehensive evaluations were implemented to assess the performance of the predictive system. We applied the overall score to distinguish high-risk patients from low-risk patients. The clinical benefit of induction chemotherapy (IC) was analyzed in each risk group by survival curves.

RESULTS : We constructed a prognostic system displaying a concordance index of 0.776 (95% CI = 0.746-0.806) for the internal validation cohort and 0.757 (95% CI = 0.695-0.819), 0.719 (95% CI = 0.650-0.789) and 0.746 (95% CI = 0.699-0.793) for the three external validation cohorts, which presented a statistically significant improvement compared to the conventional tumor-node-metastasis (TNM) staging system. In the high-risk group, patients who received IC plus CCRT had better outcomes than patients who received CCRT alone, while there was no statistically significant difference in the low-risk group.

CONCLUSIONS : The proposed framework can capture more complex and heterogeneous information to predict the prognosis of patients with LA-NPC and potentially contribute to clinical decision making.

Qiang Mengyun, Li Chaofeng, Sun Yuyao, Sun Ying, Ke Liangru, Xie Chuanmiao, Zhang Tao, Zou Yujian, Qiu Wenze, Gao Mingyong, Li Yingxue, Li Xiang, Zhan Zejiang, Liu Kuiyuan, Chen Xi, Liang Chixiong, Chen Qiuyan, Mai Haiqiang, Xie Guotong, Guo Xiang, Lv Xing