Hyperspectral imaging benchmark based on machine learning for intraoperative brain tumour detection.

Autor: Leon R; Research Institute for Applied Microelectronics, University of Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain. slmartin@iuma.ulpgc.es., Fabelo H; Research Institute for Applied Microelectronics, University of Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain. hfabelo@iuma.ulpgc.es.; Fundación Canaria Instituto de Investigación Sanitaria de Canarias (FIISC), Las Palmas de Gran Canaria, Spain. hfabelo@iuma.ulpgc.es., Ortega S; Research Institute for Applied Microelectronics, University of Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain.; Nofima, Norwegian Institute of Food Fisheries and Aquaculture Research, Tromsø, Norway., Cruz-Guerrero IA; Facultad de Ciencias, Universidad Autónoma de San Luis Potosí, San Luis Potosí, México.; Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA.; Department of Pediatric Plastic and Reconstructive Surgery, Children's Hospital Colorado, Aurora, Colorado, USA., Campos-Delgado DU; Facultad de Ciencias, Universidad Autónoma de San Luis Potosí, San Luis Potosí, México.; Instituto de Investigación en Comunicación Óptica, Universidad Autónoma de San Luis Potosí, San Luis Potosí, México., Szolna A; Department of Neurosurgery, University Hospital Doctor Negrin of Gran Canaria, Las Palmas de Gran Canaria, Spain., Piñeiro JF; Instituto de Investigación en Comunicación Óptica, Universidad Autónoma de San Luis Potosí, San Luis Potosí, México., Espino C; Department of Neurosurgery, University Hospital Doctor Negrin of Gran Canaria, Las Palmas de Gran Canaria, Spain., O'Shanahan AJ; Department of Neurosurgery, University Hospital Doctor Negrin of Gran Canaria, Las Palmas de Gran Canaria, Spain., Hernandez M; Department of Neurosurgery, University Hospital Doctor Negrin of Gran Canaria, Las Palmas de Gran Canaria, Spain., Carrera D; Department of Neurosurgery, University Hospital Doctor Negrin of Gran Canaria, Las Palmas de Gran Canaria, Spain., Bisshopp S; Department of Neurosurgery, University Hospital Doctor Negrin of Gran Canaria, Las Palmas de Gran Canaria, Spain., Sosa C; Department of Neurosurgery, University Hospital Doctor Negrin of Gran Canaria, Las Palmas de Gran Canaria, Spain., Balea-Fernandez FJ; Research Institute for Applied Microelectronics, University of Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain.; Department of Psychology, Sociology and Social Work, University of Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain., Morera J; Department of Neurosurgery, University Hospital Doctor Negrin of Gran Canaria, Las Palmas de Gran Canaria, Spain., Clavo B; Fundación Canaria Instituto de Investigación Sanitaria de Canarias (FIISC), Las Palmas de Gran Canaria, Spain.; Research Unit, University Hospital Doctor Negrin of Gran Canaria, Las Palmas de Gran Canaria, Spain., Callico GM; Research Institute for Applied Microelectronics, University of Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain.
Jazyk: angličtina
Zdroj: NPJ precision oncology [NPJ Precis Oncol] 2023 Nov 14; Vol. 7 (1), pp. 119. Date of Electronic Publication: 2023 Nov 14.
DOI: 10.1038/s41698-023-00475-9
Abstrakt: Brain surgery is one of the most common and effective treatments for brain tumour. However, neurosurgeons face the challenge of determining the boundaries of the tumour to achieve maximum resection, while avoiding damage to normal tissue that may cause neurological sequelae to patients. Hyperspectral (HS) imaging (HSI) has shown remarkable results as a diagnostic tool for tumour detection in different medical applications. In this work, we demonstrate, with a robust k-fold cross-validation approach, that HSI combined with the proposed processing framework is a promising intraoperative tool for in-vivo identification and delineation of brain tumours, including both primary (high-grade and low-grade) and secondary tumours. Analysis of the in-vivo brain database, consisting of 61 HS images from 34 different patients, achieve a highest median macro F1-Score result of 70.2 ± 7.9% on the test set using both spectral and spatial information. Here, we provide a benchmark based on machine learning for further developments in the field of in-vivo brain tumour detection and delineation using hyperspectral imaging to be used as a real-time decision support tool during neurosurgical workflows.
(© 2023. The Author(s).)
Databáze: MEDLINE
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