Q Weng1, L Zhou2, H Wang1, J Hui1, M Chen1, P Pang3, L Zheng1, M Xu1, Z Wang1, J Ji4. 1. Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, Lishui, 323000, China. 2. Department of Radiology, Lishui People's Hospital, Lishui, 323000, China. 3. GE Healthcare, Hangzhou 310000, China. 4. Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, Lishui, 323000, China. Electronic address: lschrjjs@163.com.
Abstract
AIM: A nomogram model was developed to predict the histological subtypes of lung invasive adenocarcinomas (IAs) and minimally invasive adenocarcinomas (MIAs) that manifest as part-solid ground-glass nodules (GGNs). MATERIALS AND METHODS: This retrospective study enrolled 119 patients with histopathologically confirmed part-solid GGNs assigned to the training (n=83) or testing cohorts (n=36). Radiomic features were extracted based on the unenhanced computed tomography (CT) images. R software was applied to process the qualitative and quantitative data. The CT features model, radiomic signature model, and combined prediction model were constructed and compared. RESULTS: A total of 396 radiomic features were extracted from the preoperative CT images, four features including MaxIntensity, RMS, ZonePercentage, and LongRunEmphasis_angle0_offset7 were indicated to be the best discriminators to establish the radiomic signature model. The performance of the model was satisfactory in both the training and testing set with areas under the curve (AUCs) of 0.854 (95% confidence interval [CI]: 0.774 to 0.934) and 0.813 (95% CI: 0.670 to 0.955), respectively. The CT morphology of the lesion shape and diameter of the solid component were confirmed to be a significant feature for building the CT features model, which had an AUC of 0.755 (95% CI: 0.648 to 0.843). A nomogram that integrated lesion shape and radiomic signature was constructed, which contributed an AUC of 0.888 (95% CI: 0.82 to 0.955). CONCLUSIONS: The radiomic signature could provide an important reference for differentiating IAs from MIAs, and could be significantly enhanced by the addition of CT morphology. The nomogram may be highly informative for making clinical decisions.
AIM: A nomogram model was developed to predict the histological subtypes of lung invasive adenocarcinomas (IAs) and minimally invasive adenocarcinomas (MIAs) that manifest as part-solid ground-glass nodules (GGNs). MATERIALS AND METHODS: This retrospective study enrolled 119 patients with histopathologically confirmed part-solid GGNs assigned to the training (n=83) or testing cohorts (n=36). Radiomic features were extracted based on the unenhanced computed tomography (CT) images. R software was applied to process the qualitative and quantitative data. The CT features model, radiomic signature model, and combined prediction model were constructed and compared. RESULTS: A total of 396 radiomic features were extracted from the preoperative CT images, four features including MaxIntensity, RMS, ZonePercentage, and LongRunEmphasis_angle0_offset7 were indicated to be the best discriminators to establish the radiomic signature model. The performance of the model was satisfactory in both the training and testing set with areas under the curve (AUCs) of 0.854 (95% confidence interval [CI]: 0.774 to 0.934) and 0.813 (95% CI: 0.670 to 0.955), respectively. The CT morphology of the lesion shape and diameter of the solid component were confirmed to be a significant feature for building the CT features model, which had an AUC of 0.755 (95% CI: 0.648 to 0.843). A nomogram that integrated lesion shape and radiomic signature was constructed, which contributed an AUC of 0.888 (95% CI: 0.82 to 0.955). CONCLUSIONS: The radiomic signature could provide an important reference for differentiating IAs from MIAs, and could be significantly enhanced by the addition of CT morphology. The nomogram may be highly informative for making clinical decisions.
Authors: Pranjal Vaidya; Kaustav Bera; Philip A Linden; Amit Gupta; Prabhakar Shantha Rajiah; David R Jones; Matthew Bott; Harvey Pass; Robert Gilkeson; Frank Jacono; Kevin Li-Chun Hsieh; Gong-Yau Lan; Vamsidhar Velcheti; Anant Madabhushi Journal: Front Oncol Date: 2022-05-30 Impact factor: 5.738
Authors: Ke Sun; Shouyu Chen; Jiabi Zhao; Bin Wang; Yang Yang; Yin Wang; Chunyan Wu; Xiwen Sun Journal: Front Oncol Date: 2021-12-21 Impact factor: 6.244