4.4 Article

Prognostic Value of Computed Tomography and/or 18F-Fluorodeoxyglucose Positron Emission Tomography Radiomics Features in Locally Advanced Non-small Cell Lung Cancer

Journal

CLINICAL LUNG CANCER
Volume 22, Issue 5, Pages 461-468

Publisher

CIG MEDIA GROUP, LP
DOI: 10.1016/j.cllc.2021.03.015

Keywords

Carcinoma; Chemoradiotherapy; Imaging; Survival analysis; Texture features

Categories

Funding

  1. Radiological Society of North America (RSNA) Research Medical Student Grant
  2. National Institutes of Health (NIH)/National Cancer Institute (NCI) [K12 CA138464]
  3. NIH/National Center for Advancing Translational Sciences [UL1 TR001860]

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In patients with locally advanced non-small cell lung cancer, adding PET radiomics features to conventional factors significantly improves prognostic value, while adding CT features does not.
We compared the prognostic value of computed tomography (CT) and F-18-fluorodeoxyglucose positron emission tomography (PET) radiomics features for patients with locally advanced non-small cell lung cancer treated with chemoradiotherapy. This 39-patient study demonstrated that adding PET radiomics features to conventional factors significantly improved the prognostic value versus conventional factors alone; adding CT radiomics features did not improve accuracy. Introduction: We investigated whether adding computed tomography (CT) and/or F-18-fluorodeoxyglucose (F-18-FDG) PET radiomics features to conventional prognostic factors (CPFs) improves prognostic value in locally advanced nonsmall cell lung cancer (NSCLC). Materials and Methods: We retrospectively identified 39 cases with stage III NSCLC who received chemoradiotherapy and underwent planning CT and staging F-18-FDG PET scans. Seven CPFs were recorded. Feature selection was performed on 48 CT and 49 PET extracted radiomics features. A penalized multivariate Cox proportional hazards model was used to generate models for overall survival based on CPFs alone, CPFs with CT features, CPFs with PET features, and CPFs with CT and PET features. Linear predictors generated and categorized into 2 risk groups for which Kaplan-Meier survival cur ves were calculated. A log-rank test was performed to quantify the discrimination between the groups and calculated the Harrell's C-index to quantify the discriminatory power. A likelihood ratio test was performed to determine whether adding CT and/or PET features to CPFs improved model performance. Results: All 4 models significantly discriminated between the 2 risk groups. The discriminatory power was significantly increased when CPFs were combined with PET features (C-index 0.82; likelihood ratio test P<.01) or with both CT and PET features (0.83; P<.01) compared with CPFs alone (0.68). There was no significant improvement when CPFs were combined with CT features (0.68). Conclusion: Adding PET radiomics features to CPFs yielded a significant improvement in the prognostic value in locally advanced NSCLC; adding CT features did not. (C) 2021 Elsevier Inc. All rights reserved.

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