Sistema de Salut de Catalunya
Institut Català de la Salut
[Lin S, Keow S, Bikash B, Tan D] Department of Pathology and Laboratory Medicine, University of Western Ontario, London, Ontario, Canada. [Samsoondar JP, Bandari E] Schulich School of Medicine and Dentistry, University of Western Ontario, London, Ontario, Canada. [Sansano I] Servei d’Anatomia Patològica, Vall d'Hebron Hospital Universitari, Barcelona, Spain
Vall d'Hebron Barcelona Hospital Campus
2023-03-14T12:58:24Z
2023-03-14T12:58:24Z
2023-03
Cancer; Image analysis; Thoracic pathology
Càncer; Anàlisi d'imatges; Patologia toràcica
Cáncer; Análisis de imágenes; Patología torácica
Non–small cell lung carcinoma is currently staged based on the size and involvement of other structures. Tumor size may be a surrogate measure of the total number of tumor cells. A recently revised reporting system for adenocarcinoma incorporates high-risk histologic patterns, which may have increased cellular density. Modern digital image analysis tools can be utilized to automate the quantification of cells. In this study, we tested the hypothesis that tumor cellularity can be used as a novel prognostic tool for lung cancer. Digital slides from The Cancer Genome Atlas lung adenocarcinoma (ADC) data set (n = 213) and lung squamous cell carcinoma (SCC) data set (n = 90) were obtained and analyzed using QuPath. The number of tumor cells was normalized with the surface area of the tumor to provide a measure of tumor cell density. Tumor cellularity was calculated by multiplying the size of the tumor with the cell density. Major histologic patterns and grade were compared with the tumor density of the lung ADC and lung SCC cases. The overall and progression-free survival were compared between groups of high and low tumor cellularity. High-grade histologic patterns in the ADC and SCC cases were associated with greater tumor densities compared with low-grade patterns. Cases with lower tumor cellularity had improved overall and progression-free survival compared with cases with higher cellularity. These results support tumor cellularity as a novel prognostic tool for non–small cell lung carcinoma that considers tumor stage and grade elements.
Funding support is gratefully acknowledged from the Megan J. Davey Opportunity Fund used to support a workstation to facilitate the computational analysis in this study and travel costs to present an earlier version of this content at the United States & Canadian Academy of Pathology 111th Annual Meeting (2022).
Article
Published version
English
Pulmons - Càncer - Prognosi; Imatgeria per al diagnòstic; Cèl·lules canceroses; DISEASES::Neoplasms::Neoplasms by Site::Thoracic Neoplasms::Respiratory Tract Neoplasms::Lung Neoplasms::Bronchial Neoplasms::Carcinoma, Bronchogenic::Carcinoma, Non-Small-Cell Lung; ANALYTICAL, DIAGNOSTIC AND THERAPEUTIC TECHNIQUES, AND EQUIPMENT::Diagnosis::Prognosis; INFORMATION SCIENCE::Information Science::Computing Methodologies::Image Processing, Computer-Assisted; ENFERMEDADES::neoplasias::neoplasias por localización::neoplasias torácicas::neoplasias del tracto respiratorio::neoplasias pulmonares::neoplasias de los bronquios::carcinoma broncogénico::carcinoma de pulmón de células no pequeñas; TÉCNICAS Y EQUIPOS ANALÍTICOS, DIAGNÓSTICOS Y TERAPÉUTICOS::diagnóstico::pronóstico; CIENCIA DE LA INFORMACIÓN::Ciencias de la información::metodologías computacionales::procesamiento de imágenes asistido por ordenador
Elsevier
Modern Pathology;36(3)
https://doi.org/10.1016/j.modpat.2022.100055
Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
Articles científics - HVH [3437]
