Association between Contrast-Enhanced Computed Tomography Radiomic Features, Genomic Alterations and Prognosis in Advanced Lung Adenocarcinoma Patients.
Lisa RinaldiElena Guerini RoccoGianluca SpitaleriSara RaimondiIlaria AttiliAlberto RanghieroGiulio CammarataMarta MinottiGiuliana Lo PrestiFrancesca De PianoFederica BellerbaGianluigi FunicelliStefania VolpeSerena MoraCristiana FodorCristiano RampinelliMassimo BarberisFilippo De MarinisBarbara Alicja Jereczek-FossaRoberto OrecchiaStefania Maria Rita RizzoFrancesca BottaPublished in: Cancers (2023)
Non-invasive methods to assess mutational status, as well as novel prognostic biomarkers, are warranted to foster therapy personalization of patients with advanced non-small cell lung cancer (NSCLC). This study investigated the association of contrast-enhanced Computed Tomography (CT) radiomic features of lung adenocarcinoma lesions, alone or integrated with clinical parameters, with tumor mutational status ( EGFR , KRAS , ALK alterations) and Overall Survival (OS). In total, 261 retrospective and 48 prospective patients were enrolled. A Radiomic Score (RS) was created with LASSO-Logistic regression models to predict mutational status. Radiomic, clinical and clinical-radiomic models were trained on retrospective data and tested (Area Under the Curve, AUC) on prospective data. OS prediction models were trained and tested on retrospective data with internal cross-validation (C-index). RS significantly predicted each alteration at training (radiomic and clinical-radiomic AUC 0.95-0.98); validation performance was good for EGFR (AUC 0.86), moderate for KRAS and ALK (AUC 0.61-0.65). RS was also associated with OS at univariate and multivariable analysis, in the latter with stage and type of treatment. The validation C-index was 0.63, 0.79, and 0.80 for clinical, radiomic, and clinical-radiomic models. The study supports the potential role of CT radiomics for non-invasive identification of gene alterations and prognosis prediction in patients with advanced lung adenocarcinoma, to be confirmed with independent studies.
Keyphrases
- contrast enhanced
- computed tomography
- advanced non small cell lung cancer
- magnetic resonance imaging
- small cell lung cancer
- magnetic resonance
- diffusion weighted
- epidermal growth factor receptor
- positron emission tomography
- end stage renal disease
- newly diagnosed
- ejection fraction
- chronic kidney disease
- big data
- diffusion weighted imaging
- image quality
- cross sectional
- stem cells
- prognostic factors
- bone marrow
- electronic health record
- mass spectrometry
- body composition
- machine learning
- tyrosine kinase
- gene expression
- genome wide
- lymph node metastasis
- copy number