期刊
FRONTIERS IN ONCOLOGY
卷 11, 期 -, 页码 -出版社
FRONTIERS MEDIA SA
DOI: 10.3389/fonc.2021.726865
关键词
segmentation; radiomics; non-small cell lung cancer; machine learning; prognosis
类别
资金
- French Ministry of Research
- INCa through a grant in the PRINCE project (PRTK-2015) [R16063NN]
- DGOS through a grant in the PRINCE project (PRTK-2015) [R16063NN]
- Marie Sklodowska-Curie Actions, EU's Horizon 2020 Programme [766276]
- POPEYE project under ANR (Agence National de la Recherche) [ANR-19-PERM-0007 POPEYE]
- POPEYE project under ERA PerMed [POPEYE T11EPA4-00055]
- Agence Nationale de la Recherche (ANR) [ANR-19-PERM-0007] Funding Source: Agence Nationale de la Recherche (ANR)
This study found that by extracting radiomic features from tumor volumes and combining with clinical variables for machine learning, similar prognostic accuracy can be achieved even when skipping the time-consuming tumor delineation step, although with significant performance loss.
Background The aim of this work was to investigate the ability of building prognostic models in non-small cell lung cancer (NSCLC) using radiomic features from positron emission tomography and computed tomography with 2-deoxy-2-[fluorine-18]fluoro-d-glucose (F-18-FDG PET/CT) images based on a rough volume of interest (VOI) containing the tumor instead of its accurate delineation, which is a significant time-consuming bottleneck of radiomics analyses. Methods A cohort of 138 patients with stage II-III NSCLC treated with radiochemotherapy recruited retrospectively (n = 87) and prospectively (n = 51) was used. Two approaches were compared: firstly, the radiomic features were extracted from the delineated primary tumor volumes in both PET (using the automated fuzzy locally adaptive Bayesian, FLAB) and CT (using a semi-automated approach with 3D Slicer (TM)) components. Both delineations were carried out within previously manually defined rough VOIs containing the tumor and the surrounding tissues, which were exploited for the second approach: the same features were extracted from this alternative VOI. Both sets for features were then combined with the clinical variables and processed through the same machine learning (ML) pipelines using the retrospectively recruited patients as the training set and the prospectively recruited patients as the testing set. Logistic regression (LR), random forest (RF), and support vector machine (SVM), as well as their consensus through averaging the output probabilities, were considered for feature selection and modeling for overall survival (OS) prediction as a binary classification (either median OS or 6 months OS). The resulting models were compared in terms of balanced accuracy, sensitivity, and specificity. Results Overall, better performance was achieved using the features from delineated tumor volumes. This was observed consistently across ML algorithms and for the two clinical endpoints. However, the loss of performance was not significant, especially when a consensus of the three ML algorithms was considered (0.89 vs. 0.88 and 0.78 vs. 0.77). Conclusion Our findings suggest that it is feasible to achieve similar levels of prognostic accuracy in radiomics-based modeling by relying on a faster and easier VOI definition, skipping a time-consuming tumor delineation step, thus facilitating automation of the whole radiomics workflow. The associated cost is a loss of performance in the resulting models, although this loss can be greatly mitigated when a consensus of several models is relied upon.
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