Miao Shidi, Sun Mengzhuo, Zhang Beibei, Jiang Yuyang, Xuan Qifan, Wang Guopeng, Wang Mingxuan, Jiang Yuxin, Wang Qiujun, Liu Zengyao, Ding Xuemei, Wang Ruitao
School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, China.
Department of Internal Medicine, Harbin Medical University Cancer Hospital, Harbin Medical University, Harbin, China.
Eur Radiol. 2025 Feb 17. doi: 10.1007/s00330-025-11450-2.
This study proposes a multimodal deep learning (DL) approach to investigate the impact of tumors and visceral fat on occult peritoneal metastasis in colorectal cancer (CRC) patients.
We developed a DL model named Multi-scale Feature Fusion Network (MSFF-Net) based on ResNet18, which extracted features of tumors and visceral fat from the longest diameter tumor section and the third lumbar vertebra level (L3) in preoperative CT scans of CRC patients. Logistic regression analysis was applied to patients' clinical data that integrated with DL features. A random forest (RF) classifier was established to evaluate the MSFF-Net's performance on internal and external test sets and compare it with radiologists' performance.
The model incorporating fat features outperformed the single tumor modality in the internal test set. Combining clinical information with DL provided the best diagnostic performance for predicting peritoneal metastasis in CRC patients. The AUCs were 0.941 (95% CI: [0.891, 0.986], p = 0.03) for the internal test set and 0.911 (95% CI: [0.857, 0.971], p = 0.013) for the external test set. CRC patients with peritoneal metastasis had a higher visceral adipose tissue index (VATI) compared to those without. Maximum tumor diameter and VATI were identified as independent prognostic factors for peritoneal metastasis. Grad-CAM decision regions corresponded with the independent prognostic factors identified by logistic regression analysis.
The study confirms the network features of tumors and visceral fat significantly enhance predictive performance for peritoneal metastasis in CRC. Visceral fat is a meaningful imaging biomarker for peritoneal metastasis's early detection in CRC patients.
Question Current research on predicting colorectal cancer with peritoneal metastasis mainly focuses on single-modality analysis, while studies based on multimodal imaging information are relatively scarce. Findings The Multi-scale Feature Fusion Network, constructed based on ResNet18, can utilize CT images of tumors and visceral fat to detect occult peritoneal metastasis in colorectal cancer. Clinical relevance This study identified independent prognostic factors for colorectal cancer peritoneal metastasis and combines them with tumor and visceral fat network features, aiding early diagnosis and accurate prognostic assessment.
本研究提出一种多模态深度学习（DL）方法，以探究肿瘤和内脏脂肪对结直肠癌（CRC）患者隐匿性腹膜转移的影响。
我们基于ResNet18开发了一个名为多尺度特征融合网络（MSFF-Net）的DL模型，该模型从CRC患者术前CT扫描的最长径肿瘤切片和第三腰椎水平（L3）提取肿瘤和内脏脂肪的特征。将逻辑回归分析应用于整合了DL特征的患者临床数据。建立随机森林（RF）分类器以评估MSFF-Net在内部和外部测试集上的性能，并将其与放射科医生的性能进行比较。
在内部测试集中，纳入脂肪特征的模型优于单一肿瘤模态。将临床信息与DL相结合为预测CRC患者的腹膜转移提供了最佳诊断性能。内部测试集的AUC为0.941（95%CI：[0.891,0.986]，p = 0.03），外部测试集的AUC为0.911（95%CI：[0.857,0.971]，p = 0.013）。与无腹膜转移的CRC患者相比，有腹膜转移的患者内脏脂肪组织指数（VATI）更高。最大肿瘤直径和VATI被确定为腹膜转移的独立预后因素。Grad-CAM决策区域与逻辑回归分析确定的独立预后因素相对应。
该研究证实肿瘤和内脏脂肪的网络特征显著提高了CRC患者腹膜转移的预测性能。内脏脂肪是CRC患者腹膜转移早期检测的有意义的影像生物标志物。
问题目前关于预测伴有腹膜转移的结直肠癌的研究主要集中在单模态分析，而基于多模态影像信息的研究相对较少。发现基于ResNet18构建的多尺度特征融合网络可以利用肿瘤和内脏脂肪的CT图像检测结直肠癌中的隐匿性腹膜转移。临床意义本研究确定了结直肠癌腹膜转移的独立预后因素，并将其与肿瘤和内脏脂肪网络特征相结合，有助于早期诊断和准确的预后评估。
