Predicting malignant potential of subsolid nodules: can radiomics preempt longitudinal follow up CT?

Abstract

Background To assess if radiomics can differentiate benign and malignant subsolid lung nodules (SSNs) on baseline or follow up chest CT examinations. If radiomics can differentiate between benign and malignant subsolid lung nodules, the clinical implications are shorter follow up CT imaging and early recognition of lung adenocarcinoma on imaging. Materials and methods The IRB approved retrospective study included 36 patients (mean age 69 ± 8 years; 5 males, 31 females) with 108 SSNs (31benign, 77 malignant) who underwent follow up chest CT for evaluation of indeterminate SSN. All SSNs were identified on both baseline and follow up chest CT. DICOM CT images were deidentified and exported into the open access 3D Slicer software (version 4.7) to obtain radiomic features. Logistic regression analyses and receiver operating characteristic (ROC) curves for various quantitative parameters were generated with SPSS statistical software. Results Only 2/92 radiomic features (cluster shade and surface volume ratio) enabled differentiation between malignant and benign SSN on baseline chest CT ( P = 0.01 and 0.03) with moderate accuracy [AUC 0.624 (0.505–0.743)]. On follow-up CT, 52/92 radiomic features were significantly different between benign and malignant SSN (P: 0.04 - < 0.0001) with improved accuracy [AUC: 0.708 (0.605–0.811), P = 0.04 - < 0.0001]. Radiomics of benign SSN were stable over time, whereas 63/92 radiomic features of malignant SSNs changed significantly between the baseline and follow up chest CT (P: 0.04 - < 0.0001). Conclusions Temporal changes in radiomic features of subsolid lung nodules favor malignant etiology over benign. The change in radiomics features of subsolid lung nodules can allow shorter follow up CT imaging and early recognition of lung adenocarcinoma on imaging. Radiomic features have limited application in differentiating benign and early malignant SSN on baseline chest CT.

Comparison of Radiomic Models Based on Low-Dose and Standard-Dose CT for Prediction of Adenocarcinomas and Benign Lesions in Solid Pulmonary Nodules

“…Radiomics, via high-throughput extraction of features from imaging data, has been applied to risk prediction, diagnostic discrimination, and disease progression, and improves decision-making in oncology (9)(10)(11). In recent years, a large number of studies build radiomic models using either LDCT (12)(13)(14)(15)(16)(17) or standard-dose CT data (18)(19)(20) to predict malignancy of solitary pulmonary nodules, however, one key question that remains unanswered is whether the performance of LDCT-based radiomic model and underlying significant features are equivalent to that of standard-dose CT.…”

CT Radiomics in Colorectal Cancer: Detection of KRAS Mutation Using Texture Analysis and Machine Learning

“…Our work deals with this problem using traditional machine learning techniques (which can be trained reliably with much less samples) and handcrafted features, extracted only from the tumour area, thus avoiding pixels in the non-cancerous tissue that may distort the analysis. However, other studies consulted, such as Digumarthy et al, 2019 [30], used 36 patients, even lower than the current study.…”

Solid Indeterminate Nodules with a Radiological Stability Suggesting Benignity: A Texture Analysis of Computed Tomography Images Based on the Kurtosis and Skewness of the Nodule Volume Density Histogram

“…Radiomics (the study of extracting computerised, algorithm-based features to quantify the phenotypic characteristics of lesions using medical images) [22, 23] has been used to construct predictive models that relate image characteristics to tumour characteristics. Its four quantitative descriptive characteristics are morphological, statistical, regional, and model-based [23].…”

“…Given the high morbidity and mortality associated with lung cancer, differentiating benign nodules from malignant nodules is crucial [22]. Alpert et al [32] evaluated nodules with a lepidic pattern.…”