Your browser doesn't support javascript.
loading
Developing a robust two-step machine learning multiclassification pipeline to predict primary site in head and neck carcinoma from lymph nodes.
Liu, Jiaying; Corti, Anna; Calareso, Giuseppina; Spadarella, Gaia; Licitra, Lisa; Corino, Valentina D A; Mainardi, Luca.
Afiliación
  • Liu J; Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy.
  • Corti A; Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy.
  • Calareso G; Radiology Department, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Milan, Italy.
  • Spadarella G; Postgraduation School in Radiodiagnostics, University of Milan, Italy.
  • Licitra L; Department of Clinical Medicine and Surgery, Federico II University, Naples, Italy.
  • Corino VDA; Head and Neck Cancer Medical Oncology Department, Fondazione IRCCS Instituto Nazionale dei Tumori di Milano, Milan, Italy.
  • Mainardi L; Department of Oncology and Hemato-Oncology, University of Milan, Italy.
Heliyon ; 10(2): e24377, 2024 Jan 30.
Article en En | MEDLINE | ID: mdl-38312621
ABSTRACT
This study aimed to develop a robust multiclassification pipeline to determine the primary tumor location in patients with head and neck carcinoma of unknown primary using radiomics and machine learning techniques. The dataset included 400 head and neck cancer patients with primary tumor in oropharynx, OPC (n = 162), nasopharynx, NPC (n = 137), oral cavity, OC (n = 63), larynx and hypopharynx, HL (n = 38). Two radiomic-based multiclassification pipelines (P1 and P2) were developed. P1 consisted in a direct identification of the primary sites, whereas P2 was based on a two-step

approach:

in the first step, the number of classes was reduced by merging the two minority classes which were reclassified in the second step. Diverse correlation thresholds (0.75, 0.80, 0.85), feature selection methods (sequential forwards/backwards selection, sequential floating forward selection, neighborhood component analysis and minimum redundancy maximum relevance), and classification models (neural network, decision tree, naïve Bayes, bagged trees and support vector machine) were assessed. P2 outperformed P1, with the best results obtained with the support vector machine classifier including radiomic and clinical features (accuracies of 75.3 % (HL), 75.4 % (OC), 71.3 % (OPC), 92.9 % (NPC)). These results indicate that the two-step multiclassification pipeline integrating radiomics and clinical information is a promising approach to predict the tumor site of unknown primary.
Palabras clave
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Heliyon Año: 2024 Tipo del documento: Article País de afiliación: Italia Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Heliyon Año: 2024 Tipo del documento: Article País de afiliación: Italia Pais de publicación: Reino Unido