Kunfeng Liu1, Kunwei Li1, Tingfan Wu2, Mingzhu Liang1, Yinghua Zhong1, Xiangyang Yu3, Xin Li2, Chuanmiao Xie4, Lanjun Zhang5, Xueguo Liu6,7. 1. Department of Radiology, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China. 2. Translational Medicine Team, GE Healthcare, Shanghai, China. 3. State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Department of Thoracic Surgery, Sun Yat-sen University Cancer Center, Guangzhou, China. 4. Department of Radiology, Sun Yat-sen University Cancer Center, Guangzhou, China. 5. Department of Thoracic Surgery, Sun Yat-sen University Cancer Center, Guangzhou, China. 6. Department of Radiology, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China. liuxueg@mail.sysu.edu.cn. 7. Department of Radiology, Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, China. liuxueg@mail.sysu.edu.cn.
Abstract
OBJECTIVES: To assess methods to improve the accuracy of prognosis for clinical stage I solid lung adenocarcinoma using radiomics based on different volumes of interests (VOIs). METHODS: This retrospective study included patients with postoperative clinical stage I solid lung adenocarcinoma from two hospitals, center 1 and center 2. Three databases were generated: dataset A (training set from center 1), dataset B (internal test set from center 1), and dataset C (external validation test from center 2). Disease-free survival (DFS) data were collected. CT radiomics models were constructed based on four VOIs: gross tumor volume (GTV), 3 mm external to the tumor border (peritumoral volume [PTV]0~+3), 6 mm crossing tumor border (PTV-3~+3), and 6 mm external to the tumor border (PTV0~+6). The area under the receiver operating characteristic curve (AUC) was used to compare the model accuracies. RESULTS: A total of 334 patients were included (204 and 130 from centers 1 and 2). The model using PTV-3~+3 (AUC 0.81 [95% confidence interval {CI}: 0.75, 0.94], 0.81 [0.63, 0.90] for datasets B and C) outperformed the other three models, GTV (0.73 [0.58, 0.81], 0.73 [0.58, 0.83]), PTV0~+3 (0.76 [0.52, 0.87], 0.75 [0.60, 0.83]), and PTV0~+6 (0.72 [0.60, 0.81], 0.69 [0.59, 0.81]), in datasets B and C, all p < 0.05. CONCLUSIONS: A radiomics model based on a VOI of 6 mm crossing tumor border more accurately predicts prognosis of clinical stage I solid lung adenocarcinoma than that based on VOIs including overall tumor or external rims of 3 mm and 6 mm. KEY POINTS: • Radiomics is a useful approach to improve the accuracy of prognosis for stage I solid adenocarcinoma. • The radiomics model based on VOIs that includes 3 mm within and external to the tumor border (peritumoral volume [PTV]-3~+3) outperformed models that included either only the tumor itself or those that only included the peritumoral volume.
OBJECTIVES: To assess methods to improve the accuracy of prognosis for clinical stage I solid lung adenocarcinoma using radiomics based on different volumes of interests (VOIs). METHODS: This retrospective study included patients with postoperative clinical stage I solid lung adenocarcinoma from two hospitals, center 1 and center 2. Three databases were generated: dataset A (training set from center 1), dataset B (internal test set from center 1), and dataset C (external validation test from center 2). Disease-free survival (DFS) data were collected. CT radiomics models were constructed based on four VOIs: gross tumor volume (GTV), 3 mm external to the tumor border (peritumoral volume [PTV]0~+3), 6 mm crossing tumor border (PTV-3~+3), and 6 mm external to the tumor border (PTV0~+6). The area under the receiver operating characteristic curve (AUC) was used to compare the model accuracies. RESULTS: A total of 334 patients were included (204 and 130 from centers 1 and 2). The model using PTV-3~+3 (AUC 0.81 [95% confidence interval {CI}: 0.75, 0.94], 0.81 [0.63, 0.90] for datasets B and C) outperformed the other three models, GTV (0.73 [0.58, 0.81], 0.73 [0.58, 0.83]), PTV0~+3 (0.76 [0.52, 0.87], 0.75 [0.60, 0.83]), and PTV0~+6 (0.72 [0.60, 0.81], 0.69 [0.59, 0.81]), in datasets B and C, all p < 0.05. CONCLUSIONS: A radiomics model based on a VOI of 6 mm crossing tumor border more accurately predicts prognosis of clinical stage I solid lung adenocarcinoma than that based on VOIs including overall tumor or external rims of 3 mm and 6 mm. KEY POINTS: • Radiomics is a useful approach to improve the accuracy of prognosis for stage I solid adenocarcinoma. • The radiomics model based on VOIs that includes 3 mm within and external to the tumor border (peritumoral volume [PTV]-3~+3) outperformed models that included either only the tumor itself or those that only included the peritumoral volume.
Authors: Jiabi Zhao; Lin Sun; Ke Sun; Tingting Wang; Bin Wang; Yang Yang; Chunyan Wu; Xiwen Sun Journal: Front Oncol Date: 2021-11-09 Impact factor: 6.244