Academic Journal
Benchmarking Eliminative Radiomic Feature Selection for Head and Neck Lymph Node Classification
| Τίτλος: | Benchmarking Eliminative Radiomic Feature Selection for Head and Neck Lymph Node Classification |
|---|---|
| Συγγραφείς: | Zoltan R. Bardosi, Daniel Dejaco, Matthias Santer, Marcel Kloppenburg, Stephanie Mangesius, Gerlig Widmann, Ute Ganswindt, Gerhard Rumpold, Herbert Riechelmann, Wolfgang Freysinger |
| Πηγή: | Cancers, Vol 14, Iss 477, p 477 (2022) |
| Στοιχεία εκδότη: | MDPI AG |
| Έτος έκδοσης: | 2022 |
| Συλλογή: | Directory of Open Access Journals: DOAJ Articles |
| Θεματικοί όροι: | head and neck squamous carcinoma, lymph nodes, extracapsular spread, computed-tomography, radiomics, feature extraction, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282 |
| Περιγραφή: | In head and neck squamous cell carcinoma (HNSCC) pathologic cervical lymph nodes (LN) remain important negative predictors. Current criteria for LN-classification in contrast-enhanced computed-tomography scans (contrast-CT) are shape-based; contrast-CT imagery allows extraction of additional quantitative data (“features”). The data-driven technique to extract, process, and analyze features from contrast-CTs is termed “radiomics”. Extracted features from contrast-CTs at various levels are typically redundant and correlated. Current sets of features for LN-classification are too complex for clinical application. Effective eliminative feature selection (EFS) is a crucial preprocessing step to reduce the complexity of sets identified. We aimed at exploring EFS-algorithms for their potential to identify sets of features, which were as small as feasible and yet retained as much accuracy as possible for LN-classification. In this retrospective cohort-study, which adhered to the STROBE guidelines, in total 252 LNs were classified as “non-pathologic” ( n = 70), “pathologic” ( n = 182) or “pathologic with extracapsular spread” ( n = 52) by two experienced head-and-neck radiologists based on established criteria which served as a reference. The combination of sparse discriminant analysis and genetic optimization retained up to 90% of the classification accuracy with only 10% of the original numbers of features. From a clinical perspective, the selected features appeared plausible and potentially capable of correctly classifying LNs. Both the identified EFS-algorithm and the identified features need further exploration to assess their potential to prospectively classify LNs in HNSCC. |
| Τύπος εγγράφου: | article in journal/newspaper |
| Γλώσσα: | English |
| Relation: | https://www.mdpi.com/2072-6694/14/3/477; https://doaj.org/toc/2072-6694; https://doaj.org/article/2a1c834a99a4460a980961e75fa0802c |
| DOI: | 10.3390/cancers14030477 |
| Διαθεσιμότητα: | https://doi.org/10.3390/cancers14030477 https://doaj.org/article/2a1c834a99a4460a980961e75fa0802c |
| Αριθμός Καταχώρησης: | edsbas.B02F5968 |
| Βάση Δεδομένων: | BASE |
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