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直肠癌更优疗效评估工具的迫切需求

The Crying Need for a Better Response Assessment in Rectal Cancer.

机构信息

Tumor Biology and Tumor Bank Laboratory, CHU Bordeaux, F-33600, Pessac, France.

BRIC (BoRdeaux Institute of onCology), UMR1312, INSERM, University of Bordeaux, F-33000, Bordeaux, France.

出版信息

Curr Treat Options Oncol. 2023 Nov;24(11):1507-1523. doi: 10.1007/s11864-023-01125-9. Epub 2023 Sep 13.

Abstract

Since total neoadjuvant treatment achieves almost 30% pathologic complete response, organ preservation has been increasingly debated for good responders after neoadjuvant treatment for patients diagnosed with rectal cancer. Two organ preservation strategies are available: a watch and wait strategy and a local excision strategy including patients with a near clinical complete response. A major issue is the selection of patients according to the initial tumor staging or the response assessment. Despite modern imaging improvement, identifying complete response remains challenging. A better selection could be possible by radiomics analyses, exploiting numerous image features to feed data characterization algorithms. The subsequent step is to include baseline and/or pre-therapeutic MRI, PET-CT, and CT radiomics added to the patients' clinicopathological data, inside machine learning (ML) prediction models, with predictive or prognostic purposes. These models could be further improved by the addition of new biomarkers such as circulating tumor biomarkers, molecular profiling, or pathological immune biomarkers.

摘要

由于新辅助治疗后病理完全缓解率达到近 30%,因此对于新辅助治疗后反应良好的直肠患者,人们越来越多地讨论器官保留问题。目前有两种器官保留策略:一种是观察等待策略,另一种是局部切除策略,包括接近临床完全缓解的患者。一个主要问题是根据初始肿瘤分期或反应评估来选择患者。尽管现代影像学有所改善,但确定完全缓解仍然具有挑战性。通过放射组学分析,利用大量图像特征来为数据特征描述算法提供信息,可能会有更好的选择。随后,可以将基线和/或治疗前 MRI、PET-CT 和 CT 放射组学以及患者的临床病理数据添加到机器学习 (ML) 预测模型中,以进行预测或预后。通过添加新的生物标志物,如循环肿瘤生物标志物、分子谱分析或病理免疫生物标志物,可以进一步改进这些模型。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b84/10643426/6c1f89c547f7/11864_2023_1125_Fig1_HTML.jpg

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