Ranking the most influential predictors of CT-based radiomics feature values in metastatic lung adenocarcinoma. Issue 155 (October 2022)

• Record Type: Journal Article
• Authors: Crombé, Amandine; Lafon, Mathilde; Nougaret, Stéphanie; Kind, Michèle; Cousin, Sophie
• Is Part Of: European journal of radiology. Issue 155(2022)
• Publication Date: 2022-10
• Subjects: Lung adenocarcinoma -- Radiomics -- Machine-learning -- Response to treatment -- Mutational status -- Bias; ALK anaplastic lymphoma kinase -- BRAF B1 v-raf murine sarcoma viral -- CI confidence interval -- CE contrast enhanced -- CPI check point inhibitors -- CT computed tomography -- EGFR epidermal growth factor receptor -- GLCM gray-level co-occurrence matrix -- GLRLM gray-level run length matrix -- GLZLM gray-level zone length matrix -- IBSI international biomarker standardization initiative -- KRAS Kirsten rat sarcoma viral -- kVp kilovoltage peak -- MAE mean absolute error -- MLR multivariate linear regression -- NGLDM neighborhood gray-level different matrix -- NSCLC non small cell lung cancer -- PET (18F-Fluorodeoxuglucose) positron emission tomography -- R2 R-squared -- RF radiomics feature -- RMSE root mean squared error -- RTL radiomics target lesion -- RECIST response evaluation criteria in solid tumors -- TKI tyrosine kinase inhibitors -- VOI volume of interest -- WHO-PS World health organization performance status; Medical radiology -- Periodicals; Radiology -- Periodicals; Radiologie médicale -- Périodiques; Medical radiology; Periodicals; 616.075705
• DOI: 10.1016/j.ejrad.2022.110472
• Languages: English
• ISSNs: 0720-048X

Abstract

Graphical abstract: Highlights: Volume was the most important contributor to the value of most RFs. Tumor anatomic location and injection phase influenced the value of most RFs. Mutations & treatment response remained independently associated with most RFs. Abstract: Purpose: To investigate which acquisition, post-processing, tumor, and patient characteristics contribute the most to the value of radiomics features (RFs) in lung adenocarcinoma in order to better understand and order the potential sources of bias in radiomics studies in a multivariate setting. Methods: This single-center retrospective study included all consecutive patients with newly-diagnosed lung adenocarcinoma treated between December 2016 and September 2018 who had pre-treatment contrast-enhanced CT-scan showing ≥ 2 target lesions per response evaluation criteria in solid tumors (RECIST) v1.1. All measurable lesions were manually segmented; 49 RFs were extracted using LIFEx v7.0.0. Afterwards, we reverted the usual radiomics approach (i.e., predicting a clinical outcome base on multiple RFs). To do so, for each RF, random forests and linear regression algorithms were trained using cross-validation to predict the RF value depending on the following variables: patient, mutational status, phase of CT-scan acquisition, discretization (binsize), lesion location, lesion volume, and best response obtained during the first line of treatment (partial response per RECIST vs other). The most important contributors to the value of reproducible RFs (intra-class correlation coefficient > 0.80) according to the best random forests model (selected via R-squared) were ranked. Results: 101 patients (median age: 62.3) were included, with a median of 5 target lesions per patient (range: 2–10) providing 466 segmented lesions. Twenty-nine RFs were reproducible. The most important predictors of the reproducible RFs values were, in order: tumor volume, binsize, tumor location, CT-scan phase, KRAS mutation, and treatment response (average importance: 61.7%, 57.4%, 8.1%, 3.3%, 3%, and 2.7%, respectively). The treatment response and KRAS and EGFR/ROS1/ALK mutational status remained independently correlated with the RF value for 64.3%, 32.1%, and 50% reproducible RFs, respectively. Conclusion: Tumor volume, location, acquisition and post-processing parameters should systematically be incorporated in radiomics-based modeling; however, most reproducible RFs do have significant relationships with mutational status and treatment response.