Uniqueness of radiomic features in non‐small cell lung cancer

• Published in: Journal of applied clinical medical physics 2022-12, Vol.23 (12), p.e13787-n/a
• Main Authors: Ge, Gary, Zhang, Jie
• Format: Article
• Language: English
• Subjects: Algorithms; Cancer therapies; Carcinoma, Non-Small-Cell Lung - diagnostic imaging; Carcinoma, Non-Small-Cell Lung - pathology; Carcinoma, Non-Small-Cell Lung - radiotherapy; concordance correlation coefficient; feature uniqueness; Heart; Humans; Hypotheses; Lung cancer; Lung Neoplasms - diagnostic imaging; Lung Neoplasms - pathology; Medical screening; NSCLC; Patients; Radiation therapy; radiomic features; Radiomics; Reproducibility; Reproducibility of Results; Retrospective Studies; Statistical analysis; Tomography, X-Ray Computed - methods
• ISSN: 1526-9914; 1526-9914
• DOI: 10.1002/acm2.13787

Purpose The uniqueness of radiomic features, combined with their reproducibility, determines the reliability of radiomic studies. This study is to test the hypothesis that radiomic features extracted from a defined region of interest (ROI) are unique to the underlying structure (e.g., tumor). Approach Two cohorts of non‐small cell lung cancer (NSCLC) patients were retrospectively retrieved from a GE and a Siemens CT scanner. The lung nodules (ROI) were delineated manually and radiomic features were extracted using IBEX. The same ROI was then translocated randomly to four other tissue regions of the same set of images: adipose, heart, lung beyond nodule, and muscle for radiomic feature extraction. Coefficient of variation (CV) within different ROIs and concordance correlation coefficient (CCC) between lung nodule and a given tissue region were calculated to test to determine feature uniqueness. The radiomic features were considered nonunique when (1) the CV  0.85 for over 50% of patients; and (2) the CCC > 0.85 appeared in ≥2 tissue regions beyond the defined region. Results In total, 14 patients from GE and 18 patients from Siemens are analyzed. The results show that 12 features fall below the 10% CV threshold for over 50% of patients in the GE cohort and 29 features in the Siemens cohort. According to CCC, 18 radiomic features in GE and 16 features in Siemens are identified as nonunique, with 11 overlapping features. Combining CV and CCC, 9 of 123 calculated features (7.3%) are identified as nonunique to a defined ROI. Conclusions Radiomic feature uniqueness should be considered to improve the reliability of radiomics study.