Radiomics analysis to predict pulmonary nodule malignancy using machine learning approaches.

Autor:	Warkentin MT; Prosserman Centre for Population Health Research, Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, Ontario, Canada.; Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada., Al-Sawaihey H; Prosserman Centre for Population Health Research, Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, Ontario, Canada., Lam S; Department of Medicine, University of British Columbia, Vancouver, British Columbia, Canada.; Department of Integrative Oncology, British Columbia Cancer Research Institute, Vancouver, British Columbia, Canada., Liu G; Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.; Department of Medical Oncology and Hematology, Princess Margaret Hospital Cancer Centre, Toronto, Ontario, Canada., Diergaarde B; Department of Human Genetics, University of Pittsburgh School of Public Health, Pittsburgh, Pennsylvania, USA.; Cancer Epidemiology and Prevention Program, UPMC Hillman Cancer Center, Pittsburgh, Pennsylvania, USA., Yuan JM; Cancer Epidemiology and Prevention Program, UPMC Hillman Cancer Center, Pittsburgh, Pennsylvania, USA.; Department of Epidemiology, University of Pittsburgh School of Public Health, Pittsburgh, Pennsylvania, USA., Wilson DO; Department of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA., Atkar-Khattra S; Department of Integrative Oncology, British Columbia Cancer Research Institute, Vancouver, British Columbia, Canada., Grant B; Department of Medical Oncology and Hematology, Princess Margaret Hospital Cancer Centre, Toronto, Ontario, Canada., Brhane Y; Prosserman Centre for Population Health Research, Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, Ontario, Canada., Khodayari-Moez E; Prosserman Centre for Population Health Research, Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, Ontario, Canada., Murison KR; Prosserman Centre for Population Health Research, Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, Ontario, Canada., Tammemagi MC; Cancer Control and Evidence Integration, Cancer Care Ontario, Toronto, Ontario, Canada., Campbell KR; Prosserman Centre for Population Health Research, Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, Ontario, Canada.; Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada., Hung RJ; Prosserman Centre for Population Health Research, Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, Ontario, Canada Rayjean.Hung@lunenfeld.ca.; Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.
Jazyk:	angličtina
Zdroj:	Thorax [Thorax] 2024 Mar 15; Vol. 79 (4), pp. 307-315. Date of Electronic Publication: 2024 Mar 15.
DOI:	10.1136/thorax-2023-220226
Abstrakt:	Background: Low-dose CT screening can reduce lung cancer-related mortality. However, most screen-detected pulmonary abnormalities do not develop into cancer and it often remains challenging to identify malignant nodules, particularly among indeterminate nodules. We aimed to develop and assess prediction models based on radiological features to discriminate between benign and malignant pulmonary lesions detected on a baseline screen. Methods: Using four international lung cancer screening studies, we extracted 2060 radiomic features for each of 16 797 nodules (513 malignant) among 6865 participants. After filtering out low-quality radiomic features, 642 radiomic and 9 epidemiological features remained for model development. We used cross-validation and grid search to assess three machine learning (ML) models (eXtreme Gradient Boosted Trees, random forest, least absolute shrinkage and selection operator (LASSO)) for their ability to accurately predict risk of malignancy for pulmonary nodules. We report model performance based on the area under the curve (AUC) and calibration metrics in the held-out test set. Results: The LASSO model yielded the best predictive performance in cross-validation and was fit in the full training set based on optimised hyperparameters. Our radiomics model had a test-set AUC of 0.93 (95% CI 0.90 to 0.96) and outperformed the established Pan-Canadian Early Detection of Lung Cancer model (AUC 0.87, 95% CI 0.85 to 0.89) for nodule assessment. Our model performed well among both solid (AUC 0.93, 95% CI 0.89 to 0.97) and subsolid nodules (AUC 0.91, 95% CI 0.85 to 0.95). Conclusions: We developed highly accurate ML models based on radiomic and epidemiological features from four international lung cancer screening studies that may be suitable for assessing indeterminate screen-detected pulmonary nodules for risk of malignancy. Competing Interests: Competing interests: None declared. (© Author(s) (or their employer(s)) 2024. No commercial re-use. See rights and permissions. Published by BMJ.)
Databáze:	MEDLINE
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