OBJECTIVE To investigate the value of CT-based radiomics signature for preoperatively discriminating mucinous adenocarcinoma (MA) from nomucinous adenocarcinoma (NMA) in rectal cancer and compare with conventional CT values. METHOD A total of 225 patients with histologically confirmed MA or NMA of rectal cancer were retrospectively enrolled. Radiomics features were computed from the entire tumor volume segmented from the post-contrast phase CT images. The maximum relevance and minimum redundancy (mRMR) and LASSO regression model were performed to select the best preforming features and build the radiomics models using a training cohort of 155 cases. Then, predictive performance of the models was validated using a validation cohort of 70 cases and receiver operating characteristics (ROC) analysis method. Meanwhile, CT values in post- and pre-contrast phase, as well as their difference (D-values) of tumors in two cohorts were measured by two radiologists. ROC curves were also calculated to assess diagnostic efficacies. RESULTS One hundred and sixty-three patients were confirmed by pathology as NMA and 62 cases were MA. The radiomics signature comprised 19 selected features and showed good discrimination performance in both the training and validation cohorts. The areas under ROC curves (AUC) are 0.93 (95% confidence interval [CI]: 0.89-0.98) in training cohort and 0.93 (95% CI: 0.87-0.99) in validation cohort, respectively. Three sets of CT values of MA in pre- and post-contrast phase, and their difference (D-value) (31±7.0, 51±12.6 and 20±9.3, respectively) were lower than those of NMA (37±5.6, 69±13.3 and 32±11.7, respectively). Comparing to the radiomics signature, using three sets of conventional CT values yielded relatively low diagnostic performance with AUC of 0.84 (95% CI: 0.78-0.88), 0.75 (95% CI: 0.69-0.81) and 0.78 (95% CI: 0.72-0.83), respectively. CONCLUSION This study demonstrated that CT radiomics features could be utilized as a noninvasive biomarker to identify MA patients from NMA of rectal cancer preoperatively, which is more accurate than using the conventional CT values.

中文翻译：

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多中心CT图像的放射线学分析，用于区分直肠癌中的黏液性腺癌和黏液性腺癌，并与常规CT值进行比较。

目的探讨基于CT的放射学特征在术前鉴别直肠癌中黏液腺癌（MA）与黏液腺癌（NMA）的价值，并与常规CT值进行比较。方法回顾性分析225例经组织学证实为直肠癌的MA或NMA患者。根据对比后阶段CT图像分割出的整个肿瘤体积计算放射组学特征。进行了最大相关性和最小冗余度（mRMR）和LASSO回归模型，以选择最佳的预成型特征，并使用155例训练队列建立了放射学模型。然后，使用70个案例的验证队列和接收者操作特征（ROC）分析方法验证了模型的预测性能。与此同时，两名放射科医生测量了对比后和对比前阶段的CT值，以及两个队列中肿瘤的差值（D值）。还计算了ROC曲线以评估诊断效率。结果经病理证实为NMA的患者为163例，MA为62例。放射学特征包括19个选定特征，并且在训练和验证队列中均表现出良好的辨别性能。在训练队列中，ROC曲线下的面积（AUC）分别为0.93（95％置信区间[CI]：0.89-0.98）和在验证队列中为0.93（95％CI：0.87-0.99）。对比前后MA的三组CT值及其差（D值）（分别为31±7.0、51±12.6和20±9.3）均低于NMA（37±5.6，分别为69±13.3和32±11.7）。与放射学特征相比，使用三组常规CT值产生的诊断性能相对较低，AUC为0.84（95％CI：0.78-0.88），0.75（95％CI：0.69-0.81）和0.78（95％CI：0.72） -0.83）。结论本研究表明，CT放射学特征可作为一种非侵入性生物标记物，用于在术前从直肠癌NMA识别MA患者，比使用常规CT值更准确。