ArticleMagma (New York, N.Y.)2023
Topology-based radiomic features for prediction of parotid gland cancer malignancy grade in magnetic resonance images.
Article in Magma (New York, N.Y.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed, 7 citations in OpenAlex.
- Preoperative identification of tumor deposits in rectal cancer using a transformer-based multimodal fusion model: a multicenter retrospective study.BMC medical imaging · 2026Article
- MRI-based radiomics for predicting histology in malignant salivary gland tumors: methodology and "proof of principle".Scientific reports · 2024Article
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Authors and funding
5 authors at 5 institutions in 2 countries.
Funding
Abstract
purposeThe malignancy grades of parotid gland cancer (PGC) have been assessed for a decision of treatment policies. Therefore, we have investigated the feasibility of topology-based radiomic features for the prediction of parotid gland cancer (PGC) malignancy grade in magnetic resonance (MR) images. MATERIALS AND
methodsTwo-dimensional T1- and T2-weighted MR images of 39 patients with PGC were selected for this study. Imaging properties of PGC can be quantified using the topology, which could be useful for assessing the number of the k-dimensional holes or heterogeneity in PGC regions using invariants of the Betti numbers. Radiomic signatures were constructed from 41,472 features obtained after a harmonization using an elastic net model. PGC patients were stratified using a logistic classification into low/intermediate- and high-grade malignancy groups. The training data were increased by four times to avoid the overfitting problem using a synthetic minority oversampling technique. The proposed approach was assessed using a 4-fold cross-validation test.
resultsThe highest accuracy of the proposed approach was 0.975 for the validation cases, whereas that of the conventional approach was 0.694.
conclusionThis study indicated that topology-based radiomic features could be feasible for the noninvasive prediction of the malignancy grade of PGCs.
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