Department of Radiation Oncology, China Medical University Hospital, Taichung, Taiwan, R.O.C.;
Department of Family Medicine, Changhua Christian Hospital, Changhua, Taiwan, R.O.C.
In Vivo. 2021 May-Jun;35(3):1857-1863. doi: 10.21873/invivo.12448.
Preservation of organ function is important in cancer treatment. The 'watch-and-wait' strategy is an important approach in management of esophageal cancer. However, clinical imaging cannot accurately evaluate the presence or absence of residual tumor after neoadjuvant chemoradiation. As a result, using radiomics to predict complete pathological response in esophageal cancer has gained in popularity in recent years. Given that the characteristics of patients and sites vary considerably, a meta-analysis is needed to investigate the predictive power of radiomics in esophageal cancer.
PRISMA guidelines were used to conduct this study. PubMed, Cochrane, and Embase were searched for literature review. The quality of the selected studies was evaluated by the radiomics quality score. I score and Cochran's Q test were used to evaluate heterogeneity between studies. A funnel plot was used for evaluation of publication bias.
A total of seven articles were collected for this meta-analysis. The pooled area under the receiver operating characteristics curve of the seven selected articles for predicting pathological complete response in eosphageal cancer patient was quite high, achieving a pooled value of 0.813 (95% confidence intervaI=0.761-0.866). The radiomics quality score ranged from -2 to 16 (maximum score: 36 points). Three out of the seven studies used machine learning algorithms, while the others used traditional biostatistics methods. One of the seven studies used morphology class features, while four studies used first-order features, and five used second-order features.
Using radiomics to predict complete pathological response after neoadjuvant chemoradiotherapy in esophageal cancer is feasible. In the future, prospective, multicenter studies should be carried out for predicting pathological complete response in patients with esophageal cancer.
在癌症治疗中，保持器官功能很重要。“观察等待”策略是食管癌管理的重要方法。然而，临床影像学无法准确评估新辅助放化疗后肿瘤是否残留。因此，近年来，利用放射组学预测食管癌完全病理缓解的方法越来越受到关注。鉴于患者和肿瘤部位的特征差异较大，需要进行荟萃分析以研究放射组学在食管癌中的预测能力。
本研究采用 PRISMA 指南进行。检索了 PubMed、Cochrane 和 Embase 以进行文献综述。通过放射组学质量评分评估所选研究的质量。使用 I 评分和 Cochran's Q 检验评估研究之间的异质性。使用漏斗图评估发表偏倚。
共纳入 7 篇文章进行荟萃分析。这 7 篇选定文章预测食管癌患者病理完全缓解的受试者工作特征曲线下面积的汇总值相当高，达到 0.813（95%置信区间=0.761-0.866）。放射组学质量评分为-2 至 16 分（最高得分为 36 分）。7 项研究中的 3 项使用了机器学习算法，而其他研究则使用了传统的生物统计学方法。7 项研究中的 1 项使用了形态学特征，4 项研究使用了一阶特征，5 项研究使用了二阶特征。
使用放射组学预测新辅助放化疗后食管癌的完全病理缓解是可行的。未来应开展前瞻性、多中心研究，以预测食管癌患者的病理完全缓解。
