Li Junjie, Li Xuanle, Chen Yingge, Wang Yunling, Wang Binjie, Zhang Xuefeng, Zhang Na
College of Sciences, Northeastern University, Shenyang, 110819, China.
Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518071, China.
Med Biol Eng Comput. 2025 Apr;63(4):1117-1129. doi: 10.1007/s11517-024-03253-2. Epub 2024 Dec 6.
Predicting tumor biomarkers with high precision is essential for improving the diagnostic accuracy and developing more effective treatment strategies. This paper proposes a machine learning model that utilizes CT images and biopsy whole slide images (WSI) to classify mesothelin expression levels in pancreatic cancer. By combining multimodal learning and stochastic configuration networks, a radiopathomics mesothelin-prediction system named RPMSNet is developed. The system extracts radiomic and pathomic features from CT images and WSI, respectively, and sends them into stochastic configuration networks for the final prediction. Compared to traditional radiomics or pathomics, this system has the capability to capture more comprehensive image features, providing a multidimensional insight into tissue characteristics. The experiments and analyses demonstrate the accuracy and effectiveness of the system, with an area under the curve of 81.03%, an accuracy of 73.67%, a sensitivity of 71.54%, a precision of 76.78%, and a F1-score of 72.61%, surpassing both single-modality and dual-modality models. RPMSNet highlights its potential for early diagnosis and personalized treatment in precision medicine.
高精度预测肿瘤生物标志物对于提高诊断准确性和制定更有效的治疗策略至关重要。本文提出了一种机器学习模型，该模型利用CT图像和活检全切片图像（WSI）对胰腺癌中间皮素的表达水平进行分类。通过结合多模态学习和随机配置网络，开发了一种名为RPMSNet的放射病理学间皮素预测系统。该系统分别从CT图像和WSI中提取放射组学和病理组学特征，并将其送入随机配置网络进行最终预测。与传统的放射组学或病理组学相比，该系统能够捕获更全面的图像特征，为组织特征提供多维度的见解。实验和分析证明了该系统的准确性和有效性，曲线下面积为81.03%，准确率为73.67%，灵敏度为71.54%，精确率为76.78%，F1分数为72.61%，超过了单模态和双模态模型。RPMSNet突出了其在精准医学中早期诊断和个性化治疗的潜力。
