Department of Radiology, Tianjin Medical Imaging Institute, Tianjin First Central Hospital, School of Medicine, Nankai University, Fukang Road No. 24, Nankai District, 300192, Tianjin, China.
Department of Biomedical Engineering, Guangzhou Medical University, Xinzao Road No. 1, Panyu District, Guangzhou, 511436, China.
Eur Radiol. 2022 Nov;32(11):7755-7766. doi: 10.1007/s00330-022-08859-4. Epub 2022 May 24.
To establish and validate a CT radiomics model for prediction of induction chemotherapy (IC) response and progression-free survival (PFS) among patients with locally advanced hypopharyngeal carcinoma (LAHC).
One hundred twelve patients with LAHC (78 in training cohort and 34 in validation cohort) who underwent contrast-enhanced CT (CECT) scans prior to IC were enrolled. Least absolute shrinkage and selection operator (LASSO) was used to select the crucial radiomic features in the training cohort. Radiomics signature and clinical data were used to build a radiomics nomogram to predict individual response to IC. Kaplan-Meier analysis and log-rank test were used to evaluate ability of radiomics signature in progression-free survival risk stratification.
The radiomics signature consisted of 6 selected features from the arterial and venous phases of CECT images and demonstrated good performance in predicting the IC response in both two cohorts. The radiomics nomogram showed good discriminative performance, and the C-index of nomogram was 0.899 (95% confidence interval (CI), 0.831-0.967) and 0.775 (95% CI, 0.591-0.959) in the training and validation cohorts, respectively. Survival analysis indicated that low-risk and high-risk groups defined by the value of radiomics signature had significant difference in PFS (3-year PFS 66.4% vs 29.7%, p < 0.001).
Multiparametric CT-based radiomics model could be useful for predicting treatment response and PFS in patients with LAHC who underwent IC.
• CT radiomics can predict IC response and progression-free survival in hypopharyngeal carcinoma. • We combined significant radiomics signature with clinical predictors to establish a nomogram to predict individual response to IC. • Radiomics signature could divide patients into the high-risk and low-risk groups based on the PFS.
建立并验证 CT 放射组学模型，以预测局部晚期下咽癌（LAHC）患者接受诱导化疗（IC）后的反应和无进展生存期（PFS）。
共纳入 112 例接受 IC 治疗前进行增强 CT（CECT）扫描的 LAHC 患者（训练队列 78 例，验证队列 34 例）。在训练队列中使用最小绝对收缩和选择算子（LASSO）选择关键的放射组学特征。利用放射组学特征和临床数据建立放射组学列线图，以预测个体对 IC 的反应。Kaplan-Meier 分析和对数秩检验用于评估放射组学特征在无进展生存风险分层中的能力。
放射组学特征由 CECT 动脉期和静脉期的 6 个选定特征组成，在两个队列中均能很好地预测 IC 反应。放射组学列线图具有良好的判别性能，列线图的 C 指数在训练队列和验证队列中分别为 0.899（95%置信区间，0.831-0.967）和 0.775（95%置信区间，0.591-0.959）。生存分析表明，根据放射组学特征值定义的低危组和高危组在 PFS 方面有显著差异（3 年 PFS 66.4%与 29.7%，p<0.001）。
多参数 CT 放射组学模型可用于预测接受 IC 治疗的 LAHC 患者的治疗反应和 PFS。
