Li-Da Chen1, Wei Li1, Meng-Fei Xian2, Xin Zheng1, Yuan Lin3, Bao-Xian Liu1, Man-Xia Lin1, Xin Li4, Yan-Ling Zheng1, Xiao-Yan Xie1, Ming-De Lu1,5, Ming Kuang1,5, Jian-Bo Xu6, Wei Wang7. 1. Department of Medical Ultrasonics, Ultrasomics Artificial Intelligence X-Lab, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-sen University, 58 Zhongshan Road 2, Guangzhou, 510080, People's Republic of China. 2. Department of Medical Ultrasonics, East Division, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, People's Republic of China. 3. Department of Pathology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, People's Republic of China. 4. Research Center of GE Healthcare, Shanghai, People's Republic of China. 5. Department of Hepatobiliary Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, People's Republic of China. 6. Department of Gastrointestinal Surgery, The First Affiliated Hospital of Sun Yat-sen University, 58 Zhongshan Road 2, Guangzhou, 510080, People's Republic of China. xjianb@mail.sysu.edu.cn. 7. Department of Medical Ultrasonics, Ultrasomics Artificial Intelligence X-Lab, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-sen University, 58 Zhongshan Road 2, Guangzhou, 510080, People's Republic of China. wangw73@mail.sysu.edu.cn.
Abstract
OBJECTIVE: To develop a machine learning-based ultrasound (US) radiomics model for predicting tumour deposits (TDs) preoperatively. METHODS: From December 2015 to December 2017, 127 patients with rectal cancer were prospectively enrolled and divided into training and validation sets. Endorectal ultrasound (ERUS) and shear-wave elastography (SWE) examinations were conducted for each patient. A total of 4176 US radiomics features were extracted for each patient. After the reduction and selection of US radiomics features , a predictive model using an artificial neural network (ANN) was constructed in the training set. Furthermore, two models (one incorporating clinical information and one based on MRI radiomics) were developed. These models were validated by assessing their diagnostic performance and comparing the areas under the curve (AUCs) in the validation set. RESULTS: The training and validation sets included 29 (33.3%) and 11 (27.5%) patients with TDs, respectively. A US radiomics ANN model was constructed. The model for predicting TDs showed an accuracy of 75.0% in the validation cohort. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and AUC were 72.7%, 75.9%, 53.3%, 88.0% and 0.743, respectively. For the model incorporating clinical information, the AUC improved to 0.795. Although the AUC of the US radiomics model was improved compared with that of the MRI radiomics model (0.916 vs. 0.872) in the 90 patients with both ultrasound and MRI data (which included both the training and validation sets), the difference was nonsignificant (p = 0.384). CONCLUSIONS: US radiomics may be a potential model to accurately predict TDs before therapy. KEY POINTS: • We prospectively developed an artificial neural network model for predicting tumour deposits based on US radiomics that had an accuracy of 75.0%. • The area under the curve of the US radiomics model was improved than that of the MRI radiomics model (0.916 vs. 0.872), but the difference was not significant (p = 0.384). • The US radiomics-based model may potentially predict TDs accurately before therapy, but this model needs further validation with larger samples.
OBJECTIVE: To develop a machine learning-based ultrasound (US) radiomics model for predicting tumour deposits (TDs) preoperatively. METHODS: From December 2015 to December 2017, 127 patients with rectal cancer were prospectively enrolled and divided into training and validation sets. Endorectal ultrasound (ERUS) and shear-wave elastography (SWE) examinations were conducted for each patient. A total of 4176 US radiomics features were extracted for each patient. After the reduction and selection of US radiomics features , a predictive model using an artificial neural network (ANN) was constructed in the training set. Furthermore, two models (one incorporating clinical information and one based on MRI radiomics) were developed. These models were validated by assessing their diagnostic performance and comparing the areas under the curve (AUCs) in the validation set. RESULTS: The training and validation sets included 29 (33.3%) and 11 (27.5%) patients with TDs, respectively. A US radiomics ANN model was constructed. The model for predicting TDs showed an accuracy of 75.0% in the validation cohort. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and AUC were 72.7%, 75.9%, 53.3%, 88.0% and 0.743, respectively. For the model incorporating clinical information, the AUC improved to 0.795. Although the AUC of the US radiomics model was improved compared with that of the MRI radiomics model (0.916 vs. 0.872) in the 90 patients with both ultrasound and MRI data (which included both the training and validation sets), the difference was nonsignificant (p = 0.384). CONCLUSIONS: US radiomics may be a potential model to accurately predict TDs before therapy. KEY POINTS: • We prospectively developed an artificial neural network model for predicting tumour deposits based on US radiomics that had an accuracy of 75.0%. • The area under the curve of the US radiomics model was improved than that of the MRI radiomics model (0.916 vs. 0.872), but the difference was not significant (p = 0.384). • The US radiomics-based model may potentially predict TDs accurately before therapy, but this model needs further validation with larger samples.
Authors: Jun Li; Shengke Yang; Junjie Hu; Hao Liu; Feng Du; Jie Yin; Sai Liu; Ci Li; Shasha Xing; Jiatian Yuan; Bo Lv; Jun Fan; Shusheng Leng; Xin Zhang; Bing Wang Journal: Oncotarget Date: 2016-04-05
Authors: Jin Yang; Shasha Xing; Jun Li; Shengke Yang; Junjie Hu; Hao Liu; Feng Du; Jie Yin; Sai Liu; Ci Li; Jiatian Yuan; Bo Lv Journal: Oncotarget Date: 2016-11-08
Authors: Khaled Bousabarah; Brian Letzen; Jonathan Tefera; Lynn Savic; Isabel Schobert; Todd Schlachter; Lawrence H Staib; Martin Kocher; Julius Chapiro; MingDe Lin Journal: Abdom Radiol (NY) Date: 2020-06-04