ArticleThoracic cancer2022
Relapse predictability of topological signature on pretreatment planning CT images of stage I non-small cell lung cancer patients before treatment with stereotactic ablative radiotherapy.
Article in Thoracic cancer, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 6 citations in OpenAlex.
- Computed tomography-based prediction of early recurrence risks with estimating individual times to recurrence for lung cancer patients prior to radiotherapy.Physics and imaging in radiation oncology · 2026Article
- Radiomics model integrating MRI and ECV enhances prediction accuracy for progression in high-grade glioma.NPJ precision oncology · 2026Article
- Magnetic resonance-based imaging biopsy with signatures including topological Betti number features for prediction of primary brain metastatic sites.Physical and engineering sciences in medicine · 2023Article
- Can Persistent Homology Features Capture More Intrinsic Information about Tumors fromMetabolites · 2022Article
- Relapse predictability of topological signature on pretreatment planning CT images of stage I non-small cell lung cancer patients before treatment with stereotactic ablative radiotherapy.Thoracic cancer · 2022Article
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Authors and funding
6 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThis study aimed to explore the predictability of topological signatures linked to the locoregional relapse (LRR) and distant metastasis (DM) on pretreatment planning computed tomography images of stage I non-small cell lung cancer (NSCLC) patients before treatment with stereotactic ablative radiotherapy (SABR).
methodsWe divided 125 primary stage I NSCLC patients (LRR: 34, DM: 22) into training (n = 60) and test datasets (n = 65), and the training dataset was augmented to 260 cases using a synthetic minority oversampling technique. The relapse predictabilities of the conventional wavelet-based features (WF), topology-based features [BF, Betti number (BN) map features; iBF, inverted BN map features], and their combined features (BWF, iBWF) were compared. The patients were stratified into high-risk and low-risk groups using the medians of the radiomics scores in the training dataset.
resultsFor the LRR in the test, the iBF, iBWF, and WF showed statistically significant differences (p < 0.05), and the highest nLPC was obtained for the iBF. For the DM in the test, the iBWF showed a significant difference and the highest nLPC.
conclusionThe iBF indicated the potential of improving the LRR and DM prediction of stage I NSCLC patients prior to undergoing SABR.
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