ArticleScientific reports2020
Radiomic features based on Hessian index for prediction of prognosis in head-and-neck cancer patients.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Segmentation Algorithm-Based Safety Analysis of Cardiac Computed Tomography Angiography to Evaluate Doctor-Nurse-Patient Integrated Nursing Management for Cardiac Interventional Surgery.Computational and mathematical methods in medicine · 2022Trial
- Incremental Value of Synthetic MRI Radiomics in Predicting Neoadjuvant Response in Human Epidermal Growth Factor Receptor 2-Positive Breast Cancer.Radiology. Imaging cancer · 2026Article
- Toward New Assessment in Sarcoma Identification and Grading Using Artificial Intelligence Techniques.Diagnostics (Basel, Switzerland) · 2025Article
- A radiomic model leveraging conventional and Hessian matrix-based radiomic features from DCE-MRI for predicting efficacy of neoadjuvant chemotherapy in patients with HER2-low breast cancer.Frontiers in medicine · 2025Article
- 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
- CT radiomic signature predicts survival and chemotherapy benefit in stage I and II HPV-associated oropharyngeal carcinoma.NPJ precision oncology · 2023Article
- Recurrence prediction with local binary pattern-based dosiomics in patients with head and neck squamous cell carcinoma.Physical and engineering sciences in medicine · 2023Article
- Can Persistent Homology Features Capture More Intrinsic Information about Tumors fromMetabolites · 2022Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study demonstrated the usefulness of radiomic features based on the Hessian index of differential topology for the prediction of prognosis prior to treatment in head-and-neck (HN) cancer patients. The Hessian index, which can indicate tumor heterogeneity with convex, concave, and other points (saddle points), was calculated as the number of negative eigenvalues of the Hessian matrix at each voxel on computed tomography (CT) images. Three types of signatures were constructed in a training cohort (n = 126), one type each from CT conventional features, Hessian index features, and combined features from the conventional and index feature sets. The prognostic value of the signatures were evaluated using statistically significant difference (p value, log-rank test) to compare the survival curves of low- and high-risk groups. In a test cohort (n = 68), the p values of the models built with conventional, index, combined features, and clinical variables were 2.95 [Formula: see text] 10
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