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Radiomics analysis to predict pulmonary nodule malignancy using machine learning approaches.
Warkentin, Matthew T; Al-Sawaihey, Hamad; Lam, Stephen; Liu, Geoffrey; Diergaarde, Brenda; Yuan, Jian-Min; Wilson, David O; Atkar-Khattra, Sukhinder; Grant, Benjamin; Brhane, Yonathan; Khodayari-Moez, Elham; Murison, Kiera R; Tammemagi, Martin C; Campbell, Kieran R; Hung, Rayjean J.
Afiliação
  • Warkentin MT; Prosserman Centre for Population Health Research, Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, Ontario, Canada.
  • Al-Sawaihey H; Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.
  • Lam S; Prosserman Centre for Population Health Research, Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, Ontario, Canada.
  • Liu G; Department of Medicine, University of British Columbia, Vancouver, British Columbia, Canada.
  • Diergaarde B; Department of Integrative Oncology, British Columbia Cancer Research Institute, Vancouver, British Columbia, Canada.
  • Yuan JM; Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.
  • Wilson DO; Department of Medical Oncology and Hematology, Princess Margaret Hospital Cancer Centre, Toronto, Ontario, Canada.
  • Atkar-Khattra S; Department of Human Genetics, University of Pittsburgh School of Public Health, Pittsburgh, Pennsylvania, USA.
  • Grant B; Cancer Epidemiology and Prevention Program, UPMC Hillman Cancer Center, Pittsburgh, Pennsylvania, USA.
  • Brhane Y; Cancer Epidemiology and Prevention Program, UPMC Hillman Cancer Center, Pittsburgh, Pennsylvania, USA.
  • Khodayari-Moez E; Department of Epidemiology, University of Pittsburgh School of Public Health, Pittsburgh, Pennsylvania, USA.
  • Murison KR; Department of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
  • Tammemagi MC; Department of Integrative Oncology, British Columbia Cancer Research Institute, Vancouver, British Columbia, Canada.
  • Campbell KR; Department of Medical Oncology and Hematology, Princess Margaret Hospital Cancer Centre, Toronto, Ontario, Canada.
  • Hung RJ; Prosserman Centre for Population Health Research, Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, Ontario, Canada.
Thorax ; 79(4): 307-315, 2024 Mar 15.
Article em En | MEDLINE | ID: mdl-38195644
ABSTRACT

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.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Contexto em Saúde: 6_ODS3_enfermedades_notrasmisibles Problema de saúde: 6_other_respiratory_diseases / 6_trachea_bronchus_lung_cancer Assunto principal: Nódulos Pulmonares Múltiplos / Neoplasias Pulmonares Tipo de estudo: Prognostic_studies / Risk_factors_studies / Screening_studies Limite: Humans País/Região como assunto: America do norte Idioma: En Revista: Thorax Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Canadá

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Contexto em Saúde: 6_ODS3_enfermedades_notrasmisibles Problema de saúde: 6_other_respiratory_diseases / 6_trachea_bronchus_lung_cancer Assunto principal: Nódulos Pulmonares Múltiplos / Neoplasias Pulmonares Tipo de estudo: Prognostic_studies / Risk_factors_studies / Screening_studies Limite: Humans País/Região como assunto: America do norte Idioma: En Revista: Thorax Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Canadá
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