Donglai Chen1, Yunlang She1, Tingting Wang2, Huikang Xie3, Jian Li4, Gening Jiang1, Yongbing Chen5, Lei Zhang1, Dong Xie1, Chang Chen1. 1. Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China. 2. Department of Radiology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China. 3. Department of Pathology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China. 4. Department of Thoracic Surgery, Affiliated Hospital of Zunyi Medical College, Zunyi Medical College, Zunyi, China. 5. Department of Thoracic Surgery, The Second Affiliated Hospital of Soochow University, Medical College of Soochow University, Suzhou, China.
Abstract
OBJECTIVES: As evidence has proven that sublobar resection is oncologically contraindicated by tumour spread through air spaces (STAS), its preoperative recognition is vital in customizing surgical strategies. We aimed to assess the value of radiomics in predicting STAS in stage I lung adenocarcinoma. METHODS: We retrospectively reviewed the patients with stage I lung adenocarcinoma, who accepted curative resection in our institution between January 2011 and December 2013. Using 'PyRadiomics' package, 88 radiomics features were extracted from computed tomography (CT) images and a prediction model was consequently constructed using Naïve Bayes machine-learning approach. The accuracy of the model was assessed through receiver operating curve analysis, and the performance of the model was validated both internally and externally. RESULTS: A total of 233 patients were included as the training cohort with 69 (29.6%) patients being STAS (+). Patients with STAS had worse recurrence-free survival and overall survival (P < 0.001). After feature extraction, 5 most contributing radiomics features were selected out to develop a Naïve Bayes model. In the internal validation, the model exhibited good performance with an area under the curve value of 0.63 (0.55-0.71). External validation was conducted on a test cohort with 112 patients and produced an area under the curve value of 0.69. CONCLUSIONS: CT-based radiomics is valuable in preoperatively predicting STAS in stage I lung adenocarcinoma, which may aid surgeons in determining the optimal surgical approach.
OBJECTIVES: As evidence has proven that sublobar resection is oncologically contraindicated by tumour spread through air spaces (STAS), its preoperative recognition is vital in customizing surgical strategies. We aimed to assess the value of radiomics in predicting STAS in stage I lung adenocarcinoma. METHODS: We retrospectively reviewed the patients with stage I lung adenocarcinoma, who accepted curative resection in our institution between January 2011 and December 2013. Using 'PyRadiomics' package, 88 radiomics features were extracted from computed tomography (CT) images and a prediction model was consequently constructed using Naïve Bayes machine-learning approach. The accuracy of the model was assessed through receiver operating curve analysis, and the performance of the model was validated both internally and externally. RESULTS: A total of 233 patients were included as the training cohort with 69 (29.6%) patients being STAS (+). Patients with STAS had worse recurrence-free survival and overall survival (P < 0.001). After feature extraction, 5 most contributing radiomics features were selected out to develop a Naïve Bayes model. In the internal validation, the model exhibited good performance with an area under the curve value of 0.63 (0.55-0.71). External validation was conducted on a test cohort with 112 patients and produced an area under the curve value of 0.69. CONCLUSIONS: CT-based radiomics is valuable in preoperatively predicting STAS in stage I lung adenocarcinoma, which may aid surgeons in determining the optimal surgical approach.
Authors: Qingpeng Zeng; Bingzhi Wang; Jiagen Li; Jun Zhao; Yousheng Mao; Yushun Gao; Qi Xue; Shugeng Gao; Nan Sun; Jie He Journal: Cancer Manag Res Date: 2020-09-08 Impact factor: 3.989
Authors: Massimiliano Bassi; Andrea Russomando; Jacopo Vannucci; Andrea Ciardiello; Miriam Dolciami; Paolo Ricci; Angelina Pernazza; Giulia D'Amati; Carlo Mancini Terracciano; Riccardo Faccini; Sara Mantovani; Federico Venuta; Cecilia Voena; Marco Anile Journal: Transl Lung Cancer Res Date: 2022-04