Differentiation of non-small cell lung cancer and histoplasmosis pulmonary nodules: insights from radiomics model performance compared with clinician observers

Abstract

Background: Histoplasmosis pulmonary nodules often present in computed tomography (CT) imaging with characteristics suspicious for lung cancer. This presents a work-up decision issue for clinicians in regions where histoplasmosis is an endemic fungal infection, when a nodule suspicious for lung cancer is detected.We hypothesize the application of radiomic features extracted from pulmonary nodules and perinodular parenchyma could accurately distinguish between suspicious histoplasmosis lung nodules and non-small cell lung cancer (NSCLC).Methods: A retrospective clinical cohort of pulmonary nodules with a confirmed diagnosis of histoplasmosis or NSCLC was collected from the University of Iowa Hospitals and Clincs. Radiomic features were extracted describing characteristics of the nodule and perinodular parenchyma regions and used to build a machine learning tool. These cases were assessed by four expert clinicians who gave a blinded risk prediction for NSCLC. Tool and observer performance were assessed by calculating the area under the curve for the receiver operating characteristic (AUC-ROC) and interclass correlation coefficient (ICC).Results: A cohort of 71 subjects with confirmed histopathology (40 NSCLC, 31 histoplasmosis) were case-matched based on age, sex, and smoking history. Superior performance (AUC-ROC =0.89) was demonstrated using leave-one-subject out validation in the tool that incorporated radiomics from the nodule and perinodular parenchyma region extended to 100% nodule diameter. Observers had perfect intrarepeatability (ICC =1.0) and demonstrated fair inter-reader variability (ICC =0.52). Conclusions:Radiomics have potential utility in the challenging task of differentiation between lung cancer and histoplasmosis. Expert clinician readers have high intra-repeatability but demonstrated interreader variability which could provide context for a supplemental radiomics-based tool.

A review on advances in 18F-FDG PET/CT radiomics standardisation and application in lung disease management

“…SUV max , a metabolic PET parameter, might barely differentiate these two conditions as to vast aggregation of activated macrophages and lymphocytes promotes high 18 F-FDG avidity in both conditions [ 100 , 101 ]. Addressing these issue, a research team demonstrated that personalised and distinctive diagnosis of solid active pulmonary TB and solid LC might be performed using the radiomics nomogram [ 102 ].…”

Mobile Low-Dose Computed Tomographic (LDCT) Scanning combined with Remote Reading: a feasible approach to lung cancer screening among rural population

“…General characteristics of the two groups were almost similar except that participants undergoing mobile LDCT screening were a bit older. Age is considered to be an important factor involved in the number of nodules (35,36). The probability of developing lung cancer also increased with aging (37).…”

Early-stage lung cancer detection from radiomics to deep learning in thoracic CT images: a narrative review with contemporary clinical recommendations

“…For this model, its predictions showed that the AUC reached 0.75 (followed up for 2 years) and 0.83 (1 year follow-up), and this method was superior to the traditional image evaluation and CADaided analysis (33). In addition, radiomics analyses are also used to differentiate lung cancer from benign diseases, such as nodular granuloma and infectious nodules, in order to improve the diagnostic accuracy of pulmonary nodules (34)(35)(36). These studies further confirm the application value of radiomics analyses in the determination of pulmonary nodules' benign and malignant properties and also lay the foundation for the subsequent clinical analysis of the pulmonary nodules' properties.…”