Radiomics for the Prediction of Pathological Complete Response to Neoadjuvant Chemoradiation in Locally Advanced Rectal Cancer: A Prospective Observational Trial.
Radiomics for the Prediction of Pathological Complete Response to Neoadjuvant Chemoradiation in Locally Advanced Rectal Cancer: A Prospective Observational Trial.
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DOI：
10.3390/bioengineering10060634
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发表时间：
2023-05-24
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(1)背景资料：越来越多的研究支持放射组学在预测新辅助放化疗（nCRT）的病理完全缓解（pCR）中的作用，以便为局部晚期直肠癌（LARC）患者提供更好的管理。然而，缺乏前瞻性试验的验证阻碍了这些研究的临床应用。本研究的目的是在前瞻性试验中验证用于pCR评估的放射组学模型，以提供对放射组学验证的信息性见解。(2)研究方法：本研究涉及一个回顾性队列的147例连续患者的放射组学模型的开发/验证，和一个前瞻性队列的77例患者从两个机构，以测试其泛化。使用T2加权、弥散加权和动态对比增强MRI构建模型，以了解与pCR的相关性。还评价了医生评价的一致性和病理完全缓解预测的一致性，有和没有放射组学模型的帮助。(3)结果如下：放射组学模型在前瞻性测试队列中优于两位医生的视觉评估，曲线下面积（AUC）为0.84（95%置信区间为0.70-0.94）。在放射组学模型的帮助下，初级医生可以达到与高级肿瘤学家相当的表现。(4)结论：我们已经建立并验证了一个放射组学模型与预处理MRI的pCR预测LARC患者接受nCRT。
(1) Background: An increasing amount of research has supported the role of radiomics for predicting pathological complete response (pCR) to neoadjuvant chemoradiation treatment (nCRT) in order to provide better management of locally advanced rectal cancer (LARC) patients. However, the lack of validation from prospective trials has hindered the clinical adoption of such studies. The purpose of this study is to validate a radiomics model for pCR assessment in a prospective trial to provide informative insight into radiomics validation. (2) Methods: This study involved a retrospective cohort of 147 consecutive patients for the development/validation of a radiomics model, and a prospective cohort of 77 patients from two institutions to test its generalization. The model was constructed using T2-weighted, diffusion-weighted, and dynamic contrast-enhanced MRI to understand the associations with pCR. The consistency of physicians’ evaluations and agreement on pathological complete response prediction were also evaluated, with and without the aid of the radiomics model. (3) Results: The radiomics model outperformed both physicians’ visual assessments in the prospective test cohort, with an area under the curve (AUC) of 0.84 (95% confidence interval of 0.70–0.94). With the aid of the radiomics model, a junior physician could achieve comparable performance as a senior oncologist. (4) Conclusion: We have built and validated a radiomics model with pretreatment MRI for pCR prediction of LARC patients undergoing nCRT.