Journal Article

Radiomics signature based on robust features derived from diffusion data for differentiation between benign and malignant solitary pulmonary lesions

Li, Xinchun, Zhou, Jiaxuan, Ding, Ruolin, Wan, Qi, Liu, Jieqiong, Wen, Yu, Zhao, Kangyan, Fang, Hanzhen

2024

Overview

Background Classifying and characterizing pulmonary lesions are critical for clinical decision-making process to identify optimal therapeutic strategies. The purpose of this study was to develop and validate a radiomics nomogram for distinguishing between benign and malignant pulmonary lesions based on robust features derived from diffusion images. Material and methods The study was conducted in two phases. In the first phase, we prospectively collected 30 patients with pulmonary nodule/mass who underwent twice EPI-DWI scans. The robustness of features between the two scans was evaluated using the concordance correlation coefficient (CCC) and dynamic range (DR). In the second phase, 139 patients who underwent pulmonary DWI were randomly divided into training and test sets in a 7:3 ratio. Maximum relevance minimum redundancy, least absolute shrinkage and selection operator, and logistic regression were used for feature selection and construction of radiomics signatures. Nomograms were established incorporating clinical features, radiomics signatures, and ADC (0, 800) . The diagnostic efficiency of different models was evaluated using the area under the curve (AUC) and decision curve analysis. Results Among the features extracted from DWI and ADC images, 42.7% and 37.4% were stable (both CCC and DR ≥ 0.85). The AUCs for distinguishing pulmonary lesions in the test set for clinical model, ADC, ADC radiomics signatures, and DWI radiomics signatures were 0.694, 0.802, 0.885, and 0.767, respectively. The nomogram exhibited the best differentiation performance (AUC = 0.923). The decision curve showed that the nomogram consistently outperformed ADC value and clinical model in lesion differentiation. Conclusion Our study demonstrates the robustness of radiomics features derived from lung DWI. The ADC radiomics nomogram shows superior clinical net benefits compared to conventional clinical models or ADC values alone in distinguishing solitary pulmonary lesions, offering a promising tool for noninvasive, precision diagnosis in lung cancer.

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: Area Under Curve / Artificial intelligence in Cancer imaging and diagnosis / Cancer Research / Correlation coefficients / Decision analysis / Differentiation / Diffusion weighted imaging / Drunk driving / Feature selection / Health aspects / Humans / Imaging / Lesions / Lung / Lung cancer / Lung Neoplasms - diagnostic imaging / Magnetic resonance imaging / Medical imaging / Medical imaging equipment / Medicine / Medicine & Public Health / Nomograms / Nuclear Medicine / Oncology / Open source software / Patients / Pulmonary lesions / Radiology / Radiomics / Redundancy / Regression analysis / Reproducibility / Research Article / Robustness / Signatures / Solitary pulmonary lesion / Standard scores / Test sets / Tuberculosis