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Can Persistent Homology Features Capture More Intrinsic Information About Tumors from F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography Images of Head and Neck Cancer Patients?

Overview
Journal Metabolites
Publisher MDPI
Date 2022 Oct 27
PMID 36295874
Authors
Affiliations
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Abstract

This study hypothesized that persistent homology (PH) features could capture more intrinsic information about the metabolism and morphology of tumors from F-fluorodeoxyglucose positron emission tomography (PET)/computed tomography (CT) images of patients with head and neck (HN) cancer than other conventional features. PET/CT images and clinical variables of 207 patients were selected from the publicly available dataset of the Cancer Imaging Archive. PH images were generated from persistent diagrams obtained from PET/CT images. The PH features were derived from the PH PET/CT images. The signatures were constructed in a training cohort from features from CT, PET, PH-CT, and PH-PET images; clinical variables; and the combination of features and clinical variables. Signatures were evaluated using statistically significant differences (-value, log-rank test) between survival curves for low- and high-risk groups and the C-index. In an independent test cohort, the signature consisting of PH-PET features and clinical variables exhibited the lowest log-rank -value of 3.30 × 10 and C-index of 0.80, compared with log-rank -values from 3.52 × 10 to 1.15 × 10 and C-indices from 0.34 to 0.79 for other signatures. This result suggests that PH features can capture the intrinsic information of tumors and predict prognosis in patients with HN cancer.

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References
1.
Mroz E, Patel K, Rocco J . Intratumor heterogeneity could inform the use and type of postoperative adjuvant therapy in patients with head and neck squamous cell carcinoma. Cancer. 2020; 126(9):1895-1904. PMC: 7160035. DOI: 10.1002/cncr.32742. View

2.
Nitsch J, Sack J, Halle M, Moltz J, Wall A, Rutherford A . MRI-based radiomic feature analysis of end-stage liver disease for severity stratification. Int J Comput Assist Radiol Surg. 2021; 16(3):457-466. PMC: 7946682. DOI: 10.1007/s11548-020-02295-9. View

3.
Vallieres M, Kay-Rivest E, Perrin L, Liem X, Furstoss C, Aerts H . Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer. Sci Rep. 2017; 7(1):10117. PMC: 5579274. DOI: 10.1038/s41598-017-10371-5. View

4.
Kodama T, Arimura H, Shirakawa Y, Ninomiya K, Yoshitake T, Shioyama Y . 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. Thorac Cancer. 2022; 13(15):2117-2126. PMC: 9346172. DOI: 10.1111/1759-7714.14483. View

5.
Li Y, Lu L, Xiao M, Dercle L, Huang Y, Zhang Z . CT Slice Thickness and Convolution Kernel Affect Performance of a Radiomic Model for Predicting EGFR Status in Non-Small Cell Lung Cancer: A Preliminary Study. Sci Rep. 2018; 8(1):17913. PMC: 6297245. DOI: 10.1038/s41598-018-36421-0. View