选择新辅助放化疗后保留器官策略的候选者：整合 MRI 放射组学和病理组学的模型的开发和验证。
Selecting Candidates for Organ-Preserving Strategies After Neoadjuvant Chemoradiotherapy for Rectal Cancer: Development and Validation of a Model Integrating MRI Radiomics and Pathomics.
机构信息
Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Chaoyang District, Beijing, China.
Thorough Images, Chaoyang District, Beijing, China.
出版信息
J Magn Reson Imaging. 2022 Oct;56(4):1130-1142. doi: 10.1002/jmri.28108. Epub 2022 Feb 10.
BACKGROUND
Histopathologic evaluation after surgery is the gold standard to evaluate treatment response to neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal cancer (LARC). However, it cannot be used to guide organ-preserving strategies due to poor timeliness.
PURPOSE
To develop and validate a multiscale model incorporating radiomics and pathomics features for predicting pathological good response (pGR) of down-staging to stage ypT0-1N0 after nCRT.
STUDY TYPE
Retrospective.
POPULATION
A total of 153 patients (median age, 55 years; 109 men; 107 training group; 46 validation group) with clinicopathologically confirmed LARC.
FIELD STRENGTH/SEQUENCE: A 3.0-T; fast spin echo T -weighted and single-shot EPI diffusion-weighted images.
ASSESSMENT
The differences in clinicoradiological variables between pGR and non-pGR groups were assessed. Pretreatment and posttreatment radiomics signatures, and pathomics signature were constructed. A multiscale pGR prediction model was established. The predictive performance of the model was evaluated and compared to that of the clinicoradiological model.
STATISTICAL TESTS
The χ test, Fisher's exact test, t-test, the minimum redundancy maximum relevance algorithm, the least absolute shrinkage and selection operator logistic regression algorithm, regression analysis, receiver operating characteristic curve (ROC) analysis, Delong method. P < 0.05 indicated a significant difference.
RESULTS
Pretreatment radiomics signature (odds ratio [OR] = 2.53; 95% CI: 1.58-4.66), posttreatment radiomics signature (OR = 9.59; 95% CI: 3.04-41.46), and pathomics signature (OR = 3.14; 95% CI: 1.40-8.31) were independent factors for predicting pGR. The multiscale model presented good predictive performance with areas under the curve (AUC) of 0.93 (95% CI: 0.88-0.98) and 0.90 (95% CI: 0.78-1.00) in the training and validation groups, those were significantly higher than that of the clinicoradiological model with AUCs of 0.69 (95% CI: 0.55-0.82) and 0.68 (95% CI: 0.46-0.91) in both groups.
DATA CONCLUSION
A model incorporating radiomics and pathomics features effectively predicted pGR after nCRT in patients with LARC.
EVIDENCE LEVEL
3 TECHNICAL EFFICACY: Stage 4.
背景
手术后的组织病理学评估是评价局部晚期直肠癌（LARC）新辅助放化疗（nCRT）治疗反应的金标准。然而，由于时效性差，它不能用于指导保留器官的策略。
目的
开发和验证一种多尺度模型，该模型纳入放射组学和病理组学特征，以预测 nCRT 后降期至ypT0-1N0 的病理完全缓解（pGR）。
研究类型
回顾性。
人群
共纳入 153 名经临床病理证实的 LARC 患者（中位年龄 55 岁；109 名男性；107 名训练组；46 名验证组）。
场强/序列：3.0T；快速自旋回波 T1 加权和单次激发 EPI 扩散加权图像。
评估
评估 pGR 组和非 pGR 组之间的临床影像学变量差异。构建预处理和后处理放射组学特征和病理组学特征。建立多尺度 pGR 预测模型。评估模型的预测性能，并与临床影像学模型进行比较。
统计学检验
卡方检验、Fisher 确切检验、t 检验、最小冗余最大相关性算法、最小绝对收缩和选择算子逻辑回归算法、回归分析、受试者工作特征曲线（ROC）分析、Delong 法。P<0.05 表示差异有统计学意义。
结果
预处理放射组学特征（比值比 [OR] = 2.53；95%置信区间：1.58-4.66）、后处理放射组学特征（OR = 9.59；95%置信区间：3.04-41.46）和病理组学特征（OR = 3.14；95%置信区间：1.40-8.31）是预测 pGR 的独立因素。多尺度模型在训练组和验证组的曲线下面积（AUC）分别为 0.93（95%置信区间：0.88-0.98）和 0.90（95%置信区间：0.78-1.00），预测性能良好，明显高于临床影像学模型在两组中的 AUC 分别为 0.69（95%置信区间：0.55-0.82）和 0.68（95%置信区间：0.46-0.91）。
数据结论
该模型结合了放射组学和病理组学特征，可有效预测 LARC 患者 nCRT 后的 pGR。
证据水平
3 级技术功效：4 级。
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