Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu, China.
The First People's Hospital of Yuhang District, Hangzhou, China.
J Xray Sci Technol. 2020;28(2):285-297. doi: 10.3233/XST-190614.
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.
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.
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.
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.
探讨 CT 影像组学特征术前鉴别直肠黏液腺癌（MA）和非黏液腺癌（NMA）的价值，并与常规 CT 值进行比较。
回顾性纳入 225 例经组织学证实的直肠 MA 或 NMA 患者。从增强后 CT 图像的整个肿瘤体积中提取影像组学特征。采用最大相关性最小冗余（mRMR）和 LASSO 回归模型，从 155 例训练队列中选择最佳特征并构建影像组学模型。然后，使用 70 例验证队列验证模型的预测性能，并采用受试者工作特征（ROC）分析方法。同时，两名放射科医生测量了两个队列中肿瘤的增强前后 CT 值及差值（D 值）。绘制 ROC 曲线评估诊断效能。
163 例经病理证实为 NMA，62 例为 MA。影像组学特征由 19 个选定的特征组成，在训练和验证队列中均具有良好的判别性能。ROC 曲线下面积（AUC）在训练队列中分别为 0.93（95%置信区间[CI]：0.89-0.98）和验证队列中为 0.93（95% CI：0.87-0.99）。MA 组肿瘤增强前后 CT 值及其差值（D 值）（分别为 31±7.0、51±12.6 和 20±9.3）均低于 NMA 组（分别为 37±5.6、69±13.3 和 32±11.7）。与影像组学特征相比，使用三组常规 CT 值的诊断效能相对较低，AUC 分别为 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）。
本研究表明，CT 影像组学特征可作为一种无创生物标志物，用于术前鉴别直肠 MA 与 NMA，其准确性优于常规 CT 值。
