Zengrui Zhao1, Yun Bian2, Hui Jiang3, Xu Fang2, Jing Li2, Kai Cao2, Chao Ma2, Li Wang2, Jianming Zheng3, Xiaodong Yue4, Huiran Zhang5, Xiangxue Wang6, Anant Madabhushi6, Jun Xu7, Gang Jin8, Jianping Lu9. 1. Jiangsu Key Laboratory of Big Data Analysis Technique, School of Automation, Nanjing University of Information Science and Technology, 219 Ning Liu Road, Nanjing, Jiangsu Province 210044, China. 2. Department of Radiology, Changhai Hospital, Changhai Road 168, Shanghai 200434, China. 3. Department of Pathology, Changhai Hospital, Shanghai, China. 4. Shanghai Institute for Advanced Communication and Data Science, School of Computer Engineering and Science, Shanghai University, Shanghai, China. 5. Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China. 6. Department of Biomedical Engineering, Case Western Reserve University, Ohio. 7. Jiangsu Key Laboratory of Big Data Analysis Technique, School of Automation, Nanjing University of Information Science and Technology, 219 Ning Liu Road, Nanjing, Jiangsu Province 210044, China. Electronic address: xujung@gmail.com. 8. Department of Pancreatic Surgery, Changhai Hospital, Changhai Road 168, Shanghai 200434, China. Electronic address: Jingang20191001@163.com. 9. Department of Radiology, Changhai Hospital, Changhai Road 168, Shanghai 200434, China. Electronic address: cjr.lujianping@vip.163.com.
Abstract
RATIONALE AND OBJECTIVES: Tumor grading of nonfunctional pancreatic neuroendocrine tumors (NF-pNETs) determines the choice of clinical treatment and management. The pathological grade of pancreatic neuroendocrine tumors is usually assessed on postoperative specimens. The goal of our study is to establish a tumor grade (G) prediction model for preoperative G1/2 NF-pNETs using radiomics for multislice spiral CT image analysis. MATERIALS AND METHODS: This retrospective study included a primary cohort of 59 patients and an independent validation cohort of 40 consecutive patients; their multislice spiral CT images were collected from October 2012 to October 2016 and October 2016 to June 2018, respectively. All 99 patients were diagnosed with clinicopathologically confirmed NF-pNETs. Most significant radiomic features were selected using the minimum redundancy and maximum relevance algorithm. Support vector machine classifier with a radial basis function-based predictive model was subsequently developed for clinical use. RESULTS: A total of 585 radiomics features were extracted from every phase for each patient. Six of these radiomics features were identified as most discriminant features for G1 and G2 tumors and used to construct the tumor grade prediction model. The prediction model resulted in the area under the curve values of 0.968 (95% CI: 0.900-0.991) and 0.876 (95% CI: 0.700-0.963) for the training cohort and validation cohort, respectively. Sensitivity and specificity were 96.4% and 83.9%, and 90.9% and 88.9% for the training and validation cohorts, respectively. The decision curves indicated that if the threshold probability is above 0.1, using the rad-score in the current study on G1/2 NF-pNETs is more beneficial than the treat-all-patients scheme or the treat-none scheme. CONCLUSION: Radiomics developed with a combination of nonenhanced and portal venous phases can achieve favorable predictive accuracy for histological grade for G1/G2 NF-pNETs.
RATIONALE AND OBJECTIVES:Tumor grading of nonfunctional pancreatic neuroendocrine tumors (NF-pNETs) determines the choice of clinical treatment and management. The pathological grade of pancreatic neuroendocrine tumors is usually assessed on postoperative specimens. The goal of our study is to establish a tumor grade (G) prediction model for preoperative G1/2 NF-pNETs using radiomics for multislice spiral CT image analysis. MATERIALS AND METHODS: This retrospective study included a primary cohort of 59 patients and an independent validation cohort of 40 consecutive patients; their multislice spiral CT images were collected from October 2012 to October 2016 and October 2016 to June 2018, respectively. All 99 patients were diagnosed with clinicopathologically confirmed NF-pNETs. Most significant radiomic features were selected using the minimum redundancy and maximum relevance algorithm. Support vector machine classifier with a radial basis function-based predictive model was subsequently developed for clinical use. RESULTS: A total of 585 radiomics features were extracted from every phase for each patient. Six of these radiomics features were identified as most discriminant features for G1 and G2 tumors and used to construct the tumor grade prediction model. The prediction model resulted in the area under the curve values of 0.968 (95% CI: 0.900-0.991) and 0.876 (95% CI: 0.700-0.963) for the training cohort and validation cohort, respectively. Sensitivity and specificity were 96.4% and 83.9%, and 90.9% and 88.9% for the training and validation cohorts, respectively. The decision curves indicated that if the threshold probability is above 0.1, using the rad-score in the current study on G1/2 NF-pNETs is more beneficial than the treat-all-patients scheme or the treat-none scheme. CONCLUSION: Radiomics developed with a combination of nonenhanced and portal venous phases can achieve favorable predictive accuracy for histological grade for G1/G2 NF-pNETs.
Authors: Femke C R Staal; Else A Aalbersberg; Daphne van der Velden; Erica A Wilthagen; Margot E T Tesselaar; Regina G H Beets-Tan; Monique Maas Journal: Eur Radiol Date: 2022-07-26 Impact factor: 7.034
Authors: Calogero Casà; Antonio Piras; Andrea D'Aviero; Francesco Preziosi; Silvia Mariani; Davide Cusumano; Angela Romano; Ivo Boskoski; Jacopo Lenkowicz; Nicola Dinapoli; Francesco Cellini; Maria Antonietta Gambacorta; Vincenzo Valentini; Gian Carlo Mattiucci; Luca Boldrini Journal: Ther Adv Gastrointest Endosc Date: 2022-03-16