A Radiomics Approach on Chest CT Distinguishes Primary Lung Cancer from Solitary Lung Metastasis in Colorectal Cancer Patients

Jong Eun Lee; Luu Ngoc Do; Won Gi Jeong; Hyo Jae Lee; Kum Ju Chae; Yun Hyeon Kim; Ilwoo Park

doi:10.3390/jpm12111859

A Radiomics Approach on Chest CT Distinguishes Primary Lung Cancer from Solitary Lung Metastasis in Colorectal Cancer Patients

J Pers Med. 2022 Nov 7;12(11):1859. doi: 10.3390/jpm12111859.

Authors

Jong Eun Lee¹, Luu Ngoc Do², Won Gi Jeong³, Hyo Jae Lee³, Kum Ju Chae⁴, Yun Hyeon Kim¹, Ilwoo Park^{1

2

5

6}

Affiliations

¹ Department of Radiology, Chonnam National University Hospital, Chonnam National University Medical School, Gwangju, Korea.
² Department of Radiology, Chonnam National University, Gwangju, Korea.
³ Department of Radiology, Chonnam National University Hwasun Hospital, Chonnam National University Medical School, Hwasun, Korea.
⁴ Department of Radiology, Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Korea.
⁵ Department of Artificial Intelligence Convergence, Chonnam National University, Gwangju, Korea.
⁶ Department of Data Science, Chonnam National University, Gwangju, Korea.

Abstract

Purpose: This study utilized a radiomics approach combined with a machine learning algorithm to distinguish primary lung cancer (LC) from solitary lung metastasis (LM) in colorectal cancer (CRC) patients with a solitary pulmonary nodule (SPN).

Materials and methods: In a retrospective study, 239 patients who underwent chest computerized tomography (CT) at three different institutions between 2011 and 2019 and were diagnosed as primary LC or solitary LM were included. The data from the first institution were divided into training and internal testing datasets. The data from the second and third institutions were used as an external testing dataset. Radiomic features were extracted from the intra and perinodular regions of interest (ROI). After a feature selection process, Support vector machine (SVM) was used to train models for classifying between LC and LM. The performances of the SVM classifiers were evaluated with both the internal and external testing datasets. The performances of the model were compared to those of two radiologists who reviewed the CT images of the testing datasets for the binary prediction of LC versus LM.

Results: The SVM classifier trained with the radiomic features from the intranodular ROI and achieved the sensitivity/specificity of 0.545/0.828 in the internal test dataset, and 0.833/0.964 in the external test dataset, respectively. The SVM classifier trained with the combined radiomic features from the intra- and perinodular ROIs achieved the sensitivity/specificity of 0.545/0.966 in the internal test dataset, and 0.833/1.000 in the external test data set, respectively. Two radiologists demonstrated the sensitivity/specificity of 0.545/0.966 and 0.636/0.828 in the internal test dataset, and 0.917/0.929 and 0.833/0.929 in the external test dataset, which were comparable to the performance of the model trained with the combined radiomics features.

Conclusion: Our results suggested that the machine learning classifiers trained using radiomics features of SPN in CRC patients can be used to distinguish the primary LC and the solitary LM with a similar level of performance to radiologists.

Keywords: colorectal neoplasms; computed tomography; lung neoplasms; machine learning; solitary pulmonary nodule.

Grants and funding

This study was supported by grants from the Ministry of Education, Republic of Korea (NRF-2022R1I1A3072856) and Chonnam National University Hospital Biomedical Research Institute (BCRI22037).