1
|
Gu W, Chen Y, Zhu H, Chen H, Yang Z, Mo S, Zhao H, Chen L, Nakajima T, Yu X, Ji S, Gu Y, Chen J, Tang W. Development and validation of CT-based radiomics deep learning signatures to predict lymph node metastasis in non-functional pancreatic neuroendocrine tumors: a multicohort study. EClinicalMedicine 2023; 65:102269. [PMID: 38106556 PMCID: PMC10725026 DOI: 10.1016/j.eclinm.2023.102269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Revised: 09/26/2023] [Accepted: 09/26/2023] [Indexed: 12/19/2023] Open
Abstract
Background Lymph node status is an important factor for the patients with non-functional pancreatic neuroendocrine tumors (NF-PanNETs) with respect to the surgical methods, prognosis, recurrence. Our aim is to develop and validate a combination model based on contrast-enhanced CT images to predict the lymph node metastasis (LNM) in NF-PanNETs. Methods Retrospective data were gathered for 320 patients with NF-PanNETs who underwent curative pancreatic resection and CT imaging at two institutions (Center 1, n = 236 and Center 2, n = 84) between January 2010 and March 2022. RDPs (Radiomics deep learning signature) were developed based on ten machine-learning techniques. These signatures were integrated with the clinicopathological factors into a nomogram for clinical applications. The evaluation of the model's performance was conducted through the metrics of the area under the curve (AUC). Findings The RDPs showed excellent performance in both centers with a high AUC for predicting LNM and disease-free survival (DFS) in Center 1 (AUC, 0.88; 95% CI: 0.84-0.92; DFS, p < 0.05) and Center 2 (AUC, 0.91; 95% CI: 0.85-0.97; DFS, p < 0.05). The clinical factors of vascular invasion, perineural invasion, and tumor grade were associated with LNM (p < 0.05). The combination nomogram showed better prediction capability for LNM (AUC, 0.93; 95% CI: 0.89-0.96). Notably, our model maintained a satisfactory predictive ability for tumors at the 2-cm threshold, demonstrating its effectiveness across different tumor sizes in Center 1 (≤2 cm: AUC, 0.90 and >2 cm: AUC, 0.86) and Center 2 (≤2 cm: AUC, 0.93 and >2 cm: AUC, 0.91). Interpretation Our RDPs may have the potential to preoperatively predict LNM in NF-PanNETs, address the insufficiency of clinical guidelines concerning the 2-cm threshold for tumor lymph node dissection, and provide precise therapeutic strategies. Funding This work was supported by JSPS KAKENHI Grant Number JP22K20814; the Rare Tumor Research Special Project of the National Natural Science Foundation of China (82141104) and Clinical Research Special Project of Shanghai Municipal Health Commission (202340123).
Collapse
Affiliation(s)
- Wenchao Gu
- Department of Diagnostic and Interventional Radiology, University of Tsukuba, Faculty of Medicine, Ibaraki, Tsukuba, Japan
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Diagnostic Radiology and Nuclear Medicine, Gunma University Graduate School of Medicine, Maebashi, Japan
| | - Yingli Chen
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Haibin Zhu
- Key Laboratory of Carcinogenesis and Translational Research, Department of Radiology, Peking University Cancer Hospital and Institute, Beijing, China
| | - Haidi Chen
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
- Center for Neuroendocrine Tumors, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
- Shanghai Pancreatic Cancer Institute, Shanghai, China
- Pancreatic Cancer Institute, Fudan University, Shanghai, China
| | - Zongcheng Yang
- Department of Stomatology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, PR China
| | - Shaocong Mo
- Department of Digestive Diseases, Huashan Hospital, Fudan University, Shanghai, China
| | - Hongyue Zhao
- Department of Nuclear Medicine, The First Affiliated Hospital of Harbin Medical University, China
| | - Lei Chen
- Department of Radiology, Minhang Branch, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Takahito Nakajima
- Department of Diagnostic and Interventional Radiology, University of Tsukuba, Faculty of Medicine, Ibaraki, Tsukuba, Japan
| | - XianJun Yu
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
- Center for Neuroendocrine Tumors, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
- Shanghai Pancreatic Cancer Institute, Shanghai, China
- Pancreatic Cancer Institute, Fudan University, Shanghai, China
| | - Shunrong Ji
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
- Center for Neuroendocrine Tumors, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
- Shanghai Pancreatic Cancer Institute, Shanghai, China
- Pancreatic Cancer Institute, Fudan University, Shanghai, China
| | - YaJia Gu
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Jie Chen
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
- Center for Neuroendocrine Tumors, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Head & Neck Tumors and Neuroendocrine Tumors, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Wei Tang
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| |
Collapse
|