CT-based radiomics scores predict response to neoadjuvant chemotherapy and survival in patients with gastric cancer
CT-based radiomics scores predict response to neoadjuvant chemotherapy and survival in patients with gastric cancer
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基于 CT 的放射组学评分可预测胃癌患者对新辅助化疗的反应和生存
DOI：
10.1186/s12885-020-06970-7
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发表时间：
2020-05-25
期刊：
影响因子：
3.8
通讯作者：
Xu, Jian-Bo
中科院分区：
文献类型：
--
作者：
Sun, Kai-Yu;Hu, Hang-Tong;Xu, Jian-Bo
背景新辅助化疗是可切除胃癌的一种有希望的治疗选择，但患者的反应各不相同。我们的目的是开发和验证放射组学评分（rad_score）来预测新辅助化疗的治疗反应，并研究其在生存分层中的疗效。方法选择106例术前接受新辅助化疗的胃癌患者，其中培训组74例，验证组32例。放射组学特征提取自治疗前门静脉期CT。在特征约简后，通过Randomised Tree算法建立rad_score。通过将rad_score与临床变量整合构建rad_clinical_score，因此仅通过临床变量构建临床评分。这三个分数的区别和临床实用性进行了验证。根据评分阈值（更新术后临床变量）将患者分为两组，并比较其生存率。结果在验证队列中，rad_score对新辅助化疗的疗效有较好的预测作用（AUC [95% CI] =0.82 [0.67，0.98]），优于临床评分（基于术前临床变量），但差异无统计学意义（0.62 [0.42，0.83]，P = 0.09）。rad_clinical_score不能进一步改善rad_score的性能（0.70 [0.51，0.88]，P = 0.16）。根据这些评分的阈值，在整个队列中，高分组的生存率均优于低分组（均P < 0.001）。结论我们开发的rad_score可有效预测胃癌患者对新辅助化疗的治疗反应，并可将胃癌患者分为不同的生存组。我们提出的策略是有用的个性化治疗计划。
Background Neoadjuvant chemotherapy is a promising treatment option for potential resectable gastric cancer, but patients' responses vary. We aimed to develop and validate a radiomics score (rad_score) to predict treatment response to neoadjuvant chemotherapy and to investigate its efficacy in survival stratification. Methods A total of 106 patients with neoadjuvant chemotherapy before gastrectomy were included (training cohort: n = 74; validation cohort: n = 32). Radiomics features were extracted from the pre-treatment portal venous-phase CT. After feature reduction, a rad_score was established by Randomised Tree algorithm. A rad_clinical_score was constructed by integrating the rad_score with clinical variables, so was a clinical score by clinical variables only. The three scores were validated regarding their discrimination and clinical usefulness. The patients were stratified into two groups according to the score thresholds (updated with post-operative clinical variables), and their survivals were compared. Results In the validation cohort, the rad_score demonstrated a good predicting performance in treatment response to the neoadjuvant chemotherapy (AUC [95% CI] =0.82 [0.67, 0.98]), which was better than the clinical score (based on pre-operative clinical variables) without significant difference (0.62 [0.42, 0.83], P = 0.09). The rad_clinical_score could not further improve the performance of the rad_score (0.70 [0.51, 0.88], P = 0.16). Based on the thresholds of these scores, the high-score groups all achieved better survivals than the low-score groups in the whole cohort (all P < 0.001). Conclusion The rad_score that we developed was effective in predicting treatment response to neoadjuvant chemotherapy and in stratifying patients with gastric cancer into different survival groups. Our proposed strategy is useful for individualised treatment planning.