Combining Low-Dose Computer-Tomography-Based Radiomics and Serum Metabolomics for Diagnosis of Malignant Nodules in Participants of Lung Cancer Screening Studies

Abstract

Radiomics is an emerging approach to support the diagnosis of pulmonary nodules detected via low-dose computed tomography lung cancer screening. Serum metabolome is a promising source of auxiliary biomarkers that could help enhance the precision of lung cancer diagnosis in CT-based screening. Thus, we aimed to verify whether the combination of these two techniques, which provides local/morphological and systemic/molecular features of disease at the same time, increases the performance of lung cancer classification models. The collected cohort consists of 1086 patients with radiomic and 246 patients with serum metabolomic evaluations. Different machine learning techniques, i.e., random forest and logistic regression were applied for each omics. Next, model predictions were combined with various integration methods to create a final model. The best single omics models were characterized by an AUC of 83% in radiomics and 60% in serum metabolomics. The model integration only slightly increased the performance of the combined model (AUC equal to 85%), which was not statistically significant. We concluded that radiomics itself has a good ability to discriminate lung cancer from benign lesions. However, additional research is needed to test whether its combination with other molecular assessments would further improve the diagnosis of screening-detected lung nodules.

Keywords: classification models; early detection; integration; lung cancer; screening study.

Figure 1

UMAP visualization of patients’ clustering using either radiomic or metabolomic modalities.

Figure 2

Top 3 most differentially changed features in metabolomic (A) and radiomic (B) modalities.

Figure 3

Heatmaps of confusion matrices for prediction models on the MOLTEST-BIS test set for each modality and after integration. (A) Results of logistic regression models. (B) Results of random forest models.

Figure 4

ROC with given AUC for prediction models on the MOLTEST-BIS test set for each modality and after integration. (A) Results of logistic regression models. (B) Results of random forest models.