Development of CT-Based Imaging Signature for Preoperative Prediction of Invasive Behavior in Pancreatic Solid Pseudopapillary Neoplasm
| Autor: | Pan Liang, Jianbo Gao, Wenpeng Huang, Yi-Jing Han, Liming Li, Siyun Liu |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Cancer Research
diagnosis invasiveness Feature selection Logistic regression Spearman's rank correlation coefficient 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Radiomics Lasso regression Medicine RC254-282 Original Research Univariate analysis business.industry Neoplasms. Tumors. Oncology. Including cancer and carcinogens computed tomography Regression Oncology Feature (computer vision) radiomics 030220 oncology & carcinogenesis Nuclear medicine business pancreatic solid pseudopapillary neoplasm |
| Zdroj: | Frontiers in Oncology, Vol 11 (2021) Frontiers in Oncology |
| Popis: | PurposeIt is challenging for traditional CT signs to predict invasiveness of pancreatic solid pseudopapillary neoplasm (pSPN). We aim to develop and evaluate CT-based radiomics signature to preoperatively predict invasive behavior in pSPN.MethodsEighty-five patients who had pathologically confirmed pSPN and preoperative contrasted-enhanced CT imaging in our hospital were retrospectively analyzed (invasive: 24; non-invasive: 61). 1316 radiomics features were separately extracted from delineated 2D or 3D ROIs in arterial and venous phases. 200% (SMOTE) was used to generate balanced dataset (invasive: 72, non-invasive: 96) for each phase, which was for feature selection and modeling. The model was internally validated in the original dataset. Inter-observer consistency analysis, spearman correlation, univariate analysis, LASSO regression and backward stepwise logical regression were mainly applied to screen the features, and 6 logistic regression models were established based on multi-phase features from 2D or 3D segmentations. The ROC analysis and Delong’s test were mainly used for model assessment and AUC comparison.ResultsIt retained 11, 8, 7 and 7 features to construct 3D-arterial, 3D-venous, 2D-arterial and 2D-venous model. Based on 3D ROIs, the arterial model (AUC: 0.914) performed better than venous (AUC: 0.815) and the arterial-venous combined model was slightly improved (AUC: 0.918). Based on 2D ROIs, the arterial model (AUC: 0.814) performed better than venous (AUC:0.768), while the arterial-venous combined model (AUC:0.893) performed better than any single-phase model. In addition, the 3D arterial model performed better than the best combined 2D model. The Delong’s test showed that the significant difference of model AUC existed in arterial models in original dataset (p = 0.019) while not in arterial-venous combined model (p=0.49) as comparing 2D and 3D ROIs.ConclusionThe arterial radiomics model constructed by 3D-ROI feature is potential to predict the invasiveness of pSPN preoperatively. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|