Horio Yuki, Ikeda Jota, Matsumoto Kentaro, Okada Shinichiro, Nagano Kentaro, Kusunoki Kurando, Kuwahara Ryuichi, Kimura Kei, Kataoka Kozo, Beppu Naohito, Uchino Motoi, Ikeda Masataka, Okadome Takeshi, Yamakado Koichiro, Ikeuchi Hiroki
Department of Gastroenterological Surgery, Hyogo Medical University, Nishinomiya, Hyogo 663-8501, Japan.
Department of Radiology, Hyogo Medical University, Nishinomiya, Hyogo 663-8501, Japan.
Oncol Lett. 2024 Jul 3;28(3):421. doi: 10.3892/ol.2024.14553. eCollection 2024 Sep.
The radiological diagnosis of Crohn's disease (CD)-related anorectal cancer is difficult; it is often found in advanced stages and has a poor prognosis because of the difficulty of curative surgery. However, there are no studies on predicting the diagnosis of CD-related cancer. The present study aimed to develop a predictive model to diagnose CD cancerous lesions more accurately in a way that can be interpreted by clinicians. Patients with CD who developed anorectal CD lesions at Hyogo Medical University (Nishinomiya, Japan) between March 2009 and June 2022 were included in the present study. T2-weighted and T1-weighted magnetic resonance (MR) images were utilized for our analysis. Images of anorectal lesions were segmented using open-source 3D Slicer software, and radiomic features were extracted using PyRadiomics. Six machine learning models were investigated and compared: i) Support vector machine; ii) naive Bayes; iii) random forest; iv) light gradient boosting machine; v) extremely randomized trees; vi) and regularized greedy forest (RGF). SHapley Additive exPlanations (SHAP) values were calculated to assess the extent to which each radiomic feature contributed to the model's predictions compared to baseline, represented as the average of the model's predictions for all test data. The T2-weighted images of 28 patients with anorectal cancer and 40 non-cancer patients were analyzed and the contrast-enhanced T1-weighted images of 22 cancer and 40 non-cancer patients. The model with the highest area under the curve (AUC) was the RGF-based model constructed using T2-weighted image features, achieving an AUC of 0.944 (accuracy, 0.862; recall, 0.830). The SHAP-based model explanation suggested a strong association between the diagnosis of CD-related anorectal cancer and features such as complex lesion texture; greater pixel separation within the same coronal cross-section; larger, randomly distributed clumps of pixels with the same signal intensity; and a more spherical lesion shape on T2-weighted images. The MRI radiomics-based RGF model demonstrated outstanding performance in predicting CD-related anorectal cancer. These results may affect the diagnosis and surveillance strategies of CD-related colorectal cancer.
克罗恩病（CD）相关的肛管癌的放射学诊断具有挑战性；由于根治性手术困难，这种癌症往往在晚期才被发现，且预后较差。然而，目前尚无关于预测CD相关癌症诊断的研究。本研究旨在开发一种预测模型，以便临床医生能够以可解释的方式更准确地诊断CD癌性病变。本研究纳入了2009年3月至2022年6月期间在日本西宫市兵库医科大学发生肛管CD病变的CD患者。我们利用T2加权和T1加权磁共振（MR）图像进行分析。使用开源的3D Slicer软件对肛管病变图像进行分割，并使用PyRadiomics提取放射组学特征。研究并比较了六种机器学习模型：i）支持向量机；ii）朴素贝叶斯；iii）随机森林；iv）轻梯度提升机；v）极端随机树；vi）正则化贪婪森林（RGF）。计算了SHapley加性解释（SHAP）值，以评估每个放射组学特征相对于基线对模型预测的贡献程度，基线表示为模型对所有测试数据预测的平均值。分析了28例肛管癌患者和40例非癌患者的T2加权图像，以及22例癌症患者和40例非癌患者的对比增强T1加权图像。曲线下面积（AUC）最高的模型是基于T2加权图像特征构建的RGF模型，AUC为0.944（准确率，0.862；召回率，0.830）。基于SHAP的模型解释表明，CD相关肛管癌的诊断与病变纹理复杂、同一冠状面内像素分离度更高、具有相同信号强度的更大且随机分布的像素团块以及T2加权图像上更球形的病变形状等特征之间存在密切关联。基于MRI放射组学的RGF模型在预测CD相关肛管癌方面表现出色。这些结果可能会影响CD相关结直肠癌的诊断和监测策略。
