Abstract
BackgroundWe retrospectively evaluated the capability of radiomic features to predict tumor growth in lung cancer screening and compared the performance of multi-window radiomic features and single window radiomic features.MethodsOne hundred fifty lung nodules among 114 screen-detected, incident lung cancer patients from the National Lung Screening Trial (NLST) were investigated. Volume double time (VDT) was calculated as the difference between continuous two scans and used to define indolent and aggressive lung cancers. Lung nodules were semi-automatically segmented using lung and mediastinal windows separately, and subtracting the mediastinal window region from the lung window region generated the difference region. 364 radiomic features were separately exacted from nodules using the lung window, the mediastinal window and the difference region. Multivariable models were conducted to identify the most predictive features in predicting tumor growth. Clinical information was also obtained from the database.ResultsBased on our definition, 26% of the cases were indolent lung cancer. The tumor growth pattern could be predicted by radiomic models constructed using features obtained in the lung window, the difference region, and by combining features obtained in both the lung window and difference regions with areas under the receiver operator characteristic (AUROCs) of 0.799, 0.819, and 0.846, respectively. The multi-window feature model showed better performance compared to single window features (P < 0.001). Incorporating clinical factors into the multi-window feature models showed improvement, yielding an accuracy of 84.67% and AUROC of 0.855 for distinguishing indolent from aggressive disease.ConclusionsMulti-window CT based radiomics features are valuable predictors of indolent lung cancers and out performed single CT window setting. Combining clinical information improved predicting performance.
Highlights
We retrospectively evaluated the capability of radiomic features to predict tumor growth in lung cancer screening and compared the performance of multi-window radiomic features and single window radiomic features
The National Lung Screening trial (NLST) demonstrated a 20% reduction in lung cancer mortality among high-risk individuals screened with low-dose computerized tomography (LDCT) screening versus those screened with standard chest X-ray [4]
There were totally 39 (26%) nodules classified as indolent lung cancer compared to 111 (74%) nodules classified as aggressive
Summary
We retrospectively evaluated the capability of radiomic features to predict tumor growth in lung cancer screening and compared the performance of multi-window radiomic features and single window radiomic features. Screening and early detection of high-risk individuals, based on age and smoking history, can detect lung cancer at an earlier, more treatable stage, and has been shown to improve lung cancer survival rates [2, 3]. In the NLST, prior studies estimated that 18 to 22.5% of screen-detected cancers would not become symptomatic in a patient’s lifetime and would remain as indolent lung cancer [7]. Overdiagnosis of indolent lung cancer results in additional, unnecessary screening, increased costs, higher levels of radiation exposures, undue stress for patients and their families, and unnecessary morbidity that is sometimes associated with overtreatment. As CT imaging has an important role in the longitudinal clinical management of lung lesions, it is critical to find additional imagingbased biomarkers that could distinguish biologically indolent and aggressive lung cancer at an early stage of development and optimize the scan interval to reduce both overdiagnosis and underdiagnosis
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