Contrast-Enhanced CT-Based Deep Learning Radiomics Nomogram for the Survival Prediction in Gallbladder Cancer Postoperative

Fan-Xiu Meng; Jian-Xin Zhang; Ya-Rong Guo; Ling-Jie Wang; He-Zhao Zhang; Wen-Hao Shao; Jun Xu

doi:10.1016/j.acra.2023.11.027

Contrast-Enhanced CT-Based Deep Learning Radiomics Nomogram for the Survival Prediction in Gallbladder Cancer Postoperative

Acad Radiol. 2023 Dec 6:S1076-6332(23)00663-3. doi: 10.1016/j.acra.2023.11.027. Online ahead of print.

Authors

Fan-Xiu Meng¹, Jian-Xin Zhang², Ya-Rong Guo³, Ling-Jie Wang⁴, He-Zhao Zhang⁵, Wen-Hao Shao⁶, Jun Xu⁷

Affiliations

¹ Faculty of Graduate Studies, Shanxi Medical University, Taiyuan, 030000, China (F.X.M., W.H.S.); Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, 030032, China (F.X.M.).
² Department of Medical Imaging, Shanxi Province Cancer Hospital/ Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, 030013, China (J.X.Z.).
³ Department of Oncology, First Hospital of Shanxi Medical University, Taiyuan, 030000, China (Y.R.G.).
⁴ Department of CT Imaging, First Hospital of Shanxi Medical University, Taiyuan, 030000, China (L.J.W.).
⁵ Department of Hepatopancreatobiliary Surgery, First Hospital of Shanxi Medical University, Taiyuan, 030000, China (J.X., H.Z.Z.).
⁶ Faculty of Graduate Studies, Shanxi Medical University, Taiyuan, 030000, China (F.X.M., W.H.S.).
⁷ Department of Hepatopancreatobiliary Surgery, First Hospital of Shanxi Medical University, Taiyuan, 030000, China (J.X., H.Z.Z.). Electronic address: junxuty@163.com.

PMID: 38061942
DOI: 10.1016/j.acra.2023.11.027

Abstract

Rationale and objectives: An accurate prognostic model is essential for the development of treatment strategies for gallbladder cancer (GBC). This study proposes an integrated model using clinical features, radiomics, and deep learning based on contrast-enhanced computed tomography (CT) images for survival prediction in patients with GBC after surgical resection.

Methods: A total of 167 patients with GBC who underwent surgical resection at two medical institutions were retrospectively enrolled. After obtaining the pre-treatment CT images, the tumor lesions were manually segmented, and handcrafted radiomics features were extracted. A clinical prognostic signature and radiomics signature were built using machine learning algorithms based on the optimal clinical features or handcrafted radiomics features, respectively. Subsequently, a DenseNet121 model was employed for transfer learning on the radiomics image data and as the basis for the deep learning signature. Finally, we used logistic regression on the three signatures to obtain the unified multimodal model for comprehensive interpretation and analysis.

Results: The integrated model performed better than the other models, exhibiting the highest area under the curve (AUC) of 0.870 in the test set, and the highest concordance index (C-index) of 0.736 in predicting patient survival rates. A Kaplan-Meier analysis demonstrated that patients in high-risk group had a lower survival probability compared to those in low-risk group (log-rank p < 0.05).

Conclusion: The nomogram is useful for predicting the survival of patients with GBC after surgical resection, helping in the identification of high-risk patients with poor prognosis and ultimately facilitating individualized management of patients with GBC.

Keywords: Deep learning; Gallbladder cancer; Nomogram; Radiomics; Survival prediction model.