Abstract：
OBJECTIVE:Radiomics, an emerging tool for medical image analysis, is potential towards precisely characterizing gastric cancer (GC). Whether using one-slice 2D annotation or whole-volume 3D annotation remains a long-time debate, especially for heterogeneous GC. We comprehensively compared 2D and 3D radiomic features' representation and discrimination capacity regarding GC, via three tasks (T LNM, lymph node metastasis's prediction; T LVI, lymphovascular invasion's prediction; T pT, pT4 or other pT stages' classification). METHODS:Four-center 539 GC patients were retrospectively enrolled and divided into the training and validation cohorts. From 2D or 3D regions of interest (ROIs) annotated by radiologists, radiomic features were extracted respectively. Feature selection and model construction procedures were customed for each combination of two modalities (2D or 3D) and three tasks. Subsequently, six machine learning models (Model 2D LNM, Model 3D LNM; Model 2D LVI, Model 3D LVI; Model 2D pT, Model 3D pT) were derived and evaluated to reflect modalities' performances in characterizing GC. Furthermore, we performed an auxiliary experiment to assess modalities' performances when resampling spacing different. RESULTS:Regarding three tasks, the yielded areas under the curve (AUCs) were: Model 2D LNM's 0.712 [95% confidence interval, 0.613-0.811], Model 3D LNM's 0.680 (0.584-0.775); Model 2D LVI's 0.677 (0.595-0.761), Model 3D LVI's 0.615 (0.528-0.703); Model 2D pT's 0.840 (0.793-0.875), Model 3D pT's 0.813 (0.779-0.901). Moreover, the auxiliary experiment indicated that Models 2D are statistically advantageous than Models 3D with different resampling spacings. CONCLUSION:Models constructed with 2D radiomic features revealed comparable performances with those constructed with 3D features in characterizing GC. SIGNIFICANCE:Our work indicated that time-saving 2D annotation would be the better choice in GC, and provided a related reference to further radiomics-based researches.
journal_name
IEEE J Biomed Health Informjournal_title
IEEE journal of biomedical and health informaticsauthors
Meng L,Dong D,Chen X,Fang M,Wang R,Li J,Liu Z,Tian Jdoi
10.1109/JBHI.2020.3002805subject
Has Abstractpub_date
2020-06-16 00:00:00eissn
2168-2194issn
2168-2208journal_volume
PPpub_type
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