期刊
ANNALS OF TRANSLATIONAL MEDICINE
卷 9, 期 10, 页码 -出版社
AME PUBLISHING COMPANY
DOI: 10.21037/atm-21-25
关键词
Pancreatic neuroendocrine neoplasms (pNENs); deep learning radiomics (DLR); survival analysis
资金
- National Natural Science Foundation of China [81571750, 81771908, 81971684]
- Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions [2019SHIBS0003]
- Tencent Rhinoceros Birds-Scientific Research Foundation for Young Teachers of Shenzhen University
- 2020 SKY Imaging Research Fund of the Chinese International Medical Foundation [Z-2014-07-2003-07]
- Shenzhen Science and Technology Project [JCYJ20200109114014533]
- SZU Top Ranking Project, Shenzhen University [860/000002100108]
- Guangdong Basic and Applied Basic Research Foundation [2020A1515010571]
- Guangzhou Science and Technology Planning Project [201903010073]
- Guangdong College Students' Science and Technology Innovation Cultivation Project [pdjh2020a0497]
By using radiomics and deep learning radiomics, a preoperative recurrence prediction model for pancreatic neuroendocrine neoplasms patients after radical surgery was successfully established, allowing for optimized risk evaluation of pNEN recurrence.
Background: To establish and validate a prediction model for pancreatic neuroendocrine neoplasms (pNENs) recurrence after radical surgery with preoperative computed tomography (CT) images.& nbsp; Methods: We retrospectively collected data from 74 patients with pathologically confirmed pNENs (internal group: 56 patients, Hospital I; external validation group: 18 patients, Hospital II). Using the internal group, models were trained with CT findings evaluated by radiologists, radiomics, and deep learning radiomics (DLR) to predict 5-year pNEN recurrence. Radiomics and DLR models were established for arterial (A), venous (V), and arterial and venous (A&V) contrast phases. The model with the optimal performance was further combined with clinical information, and all patients were divided into high-and low-risk groups to analyze survival with the Kaplan-Meier method.& nbsp; Results: In the internal group, the areas under the curves (AUCs) of DLR-A, DLR-V, and DLR-A&V models were 0.80, 0.58, and 0.72, respectively. The corresponding radiomics AUCs were 0.74, 0.68, and 0.70. The AUC of the CT findings model was 0.53. The DLR-A model represented the optimum; added clinical information improved the AUC from 0.80 to 0.83. In the validation group, the AUCs of DLR-A, DLR-V, and DLR-A&V models were 0.77, 0.48, and 0.64, respectively, and those of radiomics-A, radiomics-V, and radiomics-A&V models were 0.56, 0.52, and 0.56, respectively. The AUC of the CT findings model was 0.52. In the validation group, the comparison between the DLR-A and the random models showed a trend of significant difference (P=0.058). Recurrence-free survival differed significantly between high-and low-risk groups (P=0.003).& nbsp; Conclusions: Using DLR, we successfully established a preoperative recurrence prediction model for pNEN patients after radical surgery. This allows a risk evaluation of pNEN recurrence, optimizing clinical decision-making.
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