Shan Wu1, Na Zhang1, Zhifeng Wu1, Jialiang Ren2, Linning E3. 1. Department of Radiology, Shanxi Bethune Hospital, 99 Longcheng Street, Taiyuan, Shanxi 030032, China. 2. GE Healthcare China, Beijing, China. 3. Department of Radiology, Shanxi Bethune Hospital, 99 Longcheng Street, Taiyuan, Shanxi 030032, China. Electronic address: elinning@163.com.
Abstract
RATIONALE AND OBJECTIVES: To compare the ability of radiomics models including the perinodular parenchyma and standard nodular radiomics model in lung cancer diagnosis of solid pulmonary nodules smaller than 2 cm. MATERIALS AND METHODS: In this retrospective study, the computed tomography (CT) scans of 206 patients with a lung nodule from a single institution in 2012-2019 were collected. For each nodule, four volumes of interest were defined using the gross tumor volume (GTV) and peritumoral volumes (PTVs) of 5, 10, and 15 mm around the tumor. RESULTS: Radiomics models created from GTV, GTV plus 5 mm of PTV, GTV plus 10 mm of PTV, and GTV plus 15 mm of PTV achieved AUCs of 0.89, 0.81, 0.81, and 0.73, respectively, in the validation cohort for the diagnostic classification of benign and malignant pulmonary nodules. The performance of the models gradually decreased as the PTV increased. Wavelet features were the primary features identified in optimal radiomics signatures (2/3 in R, 4/5 in GTV plus 5 mm PTV, 3/4 in GTV plus 10 mm PTV, 2/3 in GTV plus 15 mm PTV). CONCLUSION: Our study indicated that the radiomics signatures of GTV had a good prediction ability in distinguishing benign and malignant solid pulmonary nodules smaller than 2 cm on CT. However, the radiomics feature of the surrounding parenchyma of the nodule did not enhance the effectiveness of the diagnostic model.
RATIONALE AND OBJECTIVES: To compare the ability of radiomics models including the perinodular parenchyma and standard nodular radiomics model in lung cancer diagnosis of solid pulmonary nodules smaller than 2 cm. MATERIALS AND METHODS: In this retrospective study, the computed tomography (CT) scans of 206 patients with a lung nodule from a single institution in 2012-2019 were collected. For each nodule, four volumes of interest were defined using the gross tumor volume (GTV) and peritumoral volumes (PTVs) of 5, 10, and 15 mm around the tumor. RESULTS: Radiomics models created from GTV, GTV plus 5 mm of PTV, GTV plus 10 mm of PTV, and GTV plus 15 mm of PTV achieved AUCs of 0.89, 0.81, 0.81, and 0.73, respectively, in the validation cohort for the diagnostic classification of benign and malignant pulmonary nodules. The performance of the models gradually decreased as the PTV increased. Wavelet features were the primary features identified in optimal radiomics signatures (2/3 in R, 4/5 in GTV plus 5 mm PTV, 3/4 in GTV plus 10 mm PTV, 2/3 in GTV plus 15 mm PTV). CONCLUSION: Our study indicated that the radiomics signatures of GTV had a good prediction ability in distinguishing benign and malignant solid pulmonary nodules smaller than 2 cm on CT. However, the radiomics feature of the surrounding parenchyma of the nodule did not enhance the effectiveness of the diagnostic model.