A multimodal model fusing multiphase contrast-enhanced CT and clinical characteristics for predicting lymph node metastases of pancreatic cancer. (7th September 2022)

• Record Type: Journal Article
• Authors: Lu, Qian; Zhou, Chenjie; Zhang, Haojie; Liang, Lidu; Zhang, Qifan; Chen, Xuemin; Xu, Xiaowu; Zhao, Guodong; Ma, Jianhua; Gao, Yi; Peng, Qing; Li, Shulong
• Is Part Of: Physics in medicine & biology. Volume 67:Number 17(2022)
• Publication Date: 2022-09-07
• Subjects: pancreatic cancer -- lymph node metastasis -- multimodal model -- kernelled support tensor machine -- evidential reasoning; Biophysics -- Periodicals; Medical physics -- Periodicals; 610.153
• DOI: 10.1088/1361-6560/ac858e
• Languages: English
• ISSNs: 0031-9155

Abstract

Abstract: Objective. To develop a multimodal model that combines multiphase contrast-enhanced computed tomography (CECT) imaging and clinical characteristics, including experts' experience, to preoperatively predict lymph node metastasis (LNM) in pancreatic cancer patients. Methods. We proposed a new classifier fusion strategy (CFS) based on a new evidential reasoning (ER) rule (CFS-nER) by combining nomogram weights into a previous ER rule-based CFS. Three kernelled support tensor machine-based classifiers with plain, arterial, and venous phases of CECT as the inputs, respectively, were constructed. They were then fused based on the CFS-nER to construct a fusion model of multiphase CECT. The clinical characteristics were analyzed by univariate and multivariable logistic regression to screen risk factors, which were used to construct correspondent risk factor-based classifiers. Finally, the fusion model of the three phases of CECT and each risk factor-based classifier were fused further to construct the multimodal model based on our CFS-nER, named MMM-nER. This study consisted of 186 patients diagnosed with pancreatic cancer from four clinical centers in China, 88 (47.31%) of whom had LNM. Results. The fusion model of the three phases of CECT performed better overall than single and two-phase fusion models; this implies that the three considered phases of CECT were supplementary and complemented one another. The MMM-nER further improved the predictive performance, which implies that our MMM-nER can complement the supplementary information between CECT and clinical characteristics. The MMM-nER had better predictive performance than based on previous classifier fusion strategies, which presents the advantage of our CFS-nER. Conclusion. We proposed a new CFS-nER, based on which the fusion model of the three phases of CECT and MMM-nER were constructed and performed better than all compared methods. MMM-nER achieved an encouraging performance, implying that it can assist clinicians in noninvasively and preoperatively evaluating the lymph node status of pancreatic cancer.