Background Preoperative detection of lymph node (LN) metastasis is critical for planning treatments in colon cancer (CC). The clinical diagnostic criteria based on the size of the LNs are not sensitive to determine metastasis using CT images. In this retrospective study, we investigated the potential value of CT texture features to diagnose LN metastasis using preoperative CT data and patient characteristics by developing quantitative prediction models. Methods A total of 390 CC patients, undergone surgical resection, were enrolled in this monocentric study. 390 histologically validated LNs were collected from patients and randomly separated into training (312 patients, 155 metastatic and 157 normal LNs) and test cohorts (78 patients, 39 metastatic and 39 normal LNs). Six patient characteristics and 146 quantitative CT imaging features were analyzed and key variables were determined using either exhaustive search or least absolute shrinkage algorithm. Two kernel-based support vector machine classifiers (patient-characteristic model and radiomic-derived model), generated with 10-fold cross-validation, were compared with the clinical model that utilizes long-axis diameter for diagnosis of metastatic LN. The performance of the models was evaluated on the test cohort by computing accuracy, sensitivity, specificity, and area under the receiver operating curve (AUC). Results The clinical model had an overall diagnostic accuracy of 64.87%; specifically, accuracy of 65.38% and 62.82%, sensitivity of 83.87% and 84.62%, and specificity of 47.13% and 41.03% for training and test cohorts, respectively. The patient-demographic model obtained accuracy of 67.31% and 73.08%, the sensitivity of 62.58% and 69.23%, and specificity of 71.97% and 76.23% for training and test cohorts, respectively. Besides, the radiomic-derived model resulted in an accuracy of 81.09% and 79.49%, sensitivity of 83.87% and 74.36%, and specificity of 78.34% and 84.62% for training and test cohorts, respectively. Furthermore, the diagnostic performance of the radiomic-derived model was significantly higher than clinical and patient-demographic models ( p < 0.02) according to the DeLong method. Conclusions The texture of the LNs provided characteristic information about the histological status of the LNs. The radiomic-derived model leveraging LN texture provides better preoperative diagnostic accuracy for the detection of metastatic LNs compared to the clinically accepted diagnostic criteria and patient-demographic model.

中文翻译：

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使用机器学习对结肠癌患者淋巴结转移进行术前评估：一项初步研究

背景 淋巴结 (LN) 转移的术前检测对于规划结肠癌 (CC) 治疗至关重要。基于 LN 大小的临床诊断标准对使用 CT 图像确定转移不敏感。在这项回顾性研究中，我们通过开发定量预测模型，使用术前 CT 数据和患者特征研究了 CT 纹理特征在诊断 LN 转移方面的潜在价值。方法 共有 390 例接受手术切除的 CC 患者参加了这项单中心研究。从患者中收集了 390 个经组织学验证的 LN，并随机分为训练组（312 名患者，155 个转移性 LN 和 157 个正常 LN）和测试组（78 名患者，39 个转移性和 39 个正常 LN）。分析了 6 个患者特征和 146 个定量 CT 成像特征，并使用穷举搜索或最小绝对收缩算法确定了关键变量。两个基于内核的支持向量机分类器（患者特征模型和放射组学衍生模型）通过 10 倍交叉验证生成，与利用长轴直径诊断转移性 LN 的临床模型进行了比较。通过计算准确性、灵敏度、特异性和受试者工作曲线下面积 (AUC) 来评估模型的性能。结果临床模型总体诊断准确率为64.87%；具体而言，训练和测试队列的准确度分别为 65.38% 和 62.82%，灵敏度为 83.87% 和 84.62%，特异性分别为 47.13% 和 41.03%。患者人口统计学模型的准确率分别为 67.31% 和 73.08%，敏感性为 62.58% 和 69.23%，特异性分别为 71.97% 和 76.23%。此外，放射组学衍生模型的准确率分别为 81.09% 和 79.49%，灵敏度分别为 83.87% 和 74.36%，特异性分别为 78.34% 和 84.62%。此外，根据 DeLong 方法，放射组学衍生模型的诊断性能显着高于临床和患者人口统计学模型 (p < 0.02)。结论 LN 的质地提供了有关 LN 组织学状态的特征信息。