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CT-measured body composition radiomics predict lymph node metastasis in localized pancreatic ductal adenocarcinoma.
| Content Provider | Europe PMC |
|---|---|
| Author | Gu, Qianbiao He, Mengqing He, Yaqiong Dai, Anqi Liu, Jianbin Chen, Xiang Liu, Peng |
| Abstract | BackgroundTo explored the value of CT-measured body composition radiomics in preoperative evaluation of lymph node metastasis (LNM) in localized pancreatic ductal adenocarcinoma (LPDAC).MethodsWe retrospectively collected patients with LPDAC who underwent surgical resection from January 2016 to June 2022. According to whether there was LNM after operation, the patients were divided into LNM group and non-LNM group in both male and female patients. The patient’s body composition was measured by CT images at the level of the L3 vertebral body before surgery, and the radiomics features of adipose tissue and muscle were extracted. Multivariate logistic regression (forward LR) analyses were used to determine the predictors of LNM from male and female patient, respectively. Sexual dimorphism prediction signature using adipose tissue radiomics features, muscle tissue radiomics features and combined signature of both were developed and compared. The model performance is evaluated on discrimination and validated through a leave-one-out cross-validation method.ResultsA total of 196 patients (mean age, 60 years ± 9 [SD]; 117 men) were enrolled, including 59 LNM in male and 36 LNM in female. Both male and female CT-measured body composition radiomics signatures have a certain predictive power on LNM of LPDAC. Among them, the female adipose tissue signature showed the highest performance (area under the ROC curve (AUC), 0.895), and leave one out cross validation (LOOCV) indicated that the signature could accurately classify 83.5% of cases; The prediction efficiency of the signature can be further improved after adding the muscle radiomics features (AUC, 0.924, and the accuracy of the LOOCV was 87.3%); The abilities of male adipose tissue and muscle tissue radiomics signatures in predicting LNM of LPDAC was similar, AUC was 0.735 and 0.773, respectively, and the accuracy of LOOCV was 62.4% and 68.4%, respectively.ConclusionsCT-measured body composition Radiomics strategy showed good performance for predicting LNM in LPDAC, and has sexual dimorphism. It may provide a reference for individual treatment of LPDAC and related research about body composition in the future.Supplementary InformationThe online version contains supplementary material available at 10.1007/s12672-023-00624-3. |
| Related Links | https://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC9898483&blobtype=pdf |
| Journal | Discover. Oncology [Discov Oncol] |
| Volume Number | 14 |
| DOI | 10.1007/s12672-023-00624-3 |
| PubMed Central reference number | PMC9898483 |
| Issue Number | 1 |
| PubMed reference number | 36735166 |
| e-ISSN | 27306011 |
| Language | English |
| Publisher | Springer US |
| Publisher Date | 2023-02-03 |
| Publisher Place | New York |
| Access Restriction | Open |
| Rights License | Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. © The Author(s) 2023 |
| Subject Keyword | Body composition Radiomics analysis Computed tomography Lymph node metastasis Pancreatic ductal adenocarcinoma |
| Content Type | Text |
| Resource Type | Article |
| Subject | Endocrinology Endocrinology, Diabetes and Metabolism Oncology Cancer Research Endocrine and Autonomic Systems |
