Dongsheng Gu1,2, Yabin Hu3,4, Hui Ding4, Jingwei Wei1,2, Ke Chen5, Hao Liu6, Mengsu Zeng7, Jie Tian8,9,10,11. 1. Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, No. 95 East Zhongguancun Road, Beijing, 100190, China. 2. University of Chinese Academy of Sciences, Beijing, 100049, China. 3. Department of Radiology, Zhongshan Hospital, Fudan University and Shanghai Institute of Medical Imaging, 180 Fenglin Rd., Shanghai, 200032, China. 4. Department of Radiology, Affiliated Hospital (Laoshan hospital) of Qingdao University, Qingdao, 266061, Shandong, China. 5. Department of Pathology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China. 6. Department of Radiology, Central Hospital of ZiBo, Shandong, 255036, China. 7. Department of Radiology, Zhongshan Hospital, Fudan University and Shanghai Institute of Medical Imaging, 180 Fenglin Rd., Shanghai, 200032, China. zengmengsu@outlook.com. 8. Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, No. 95 East Zhongguancun Road, Beijing, 100190, China. tian@ieee.org. 9. University of Chinese Academy of Sciences, Beijing, 100049, China. tian@ieee.org. 10. Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University, Beijing, 100191, China. tian@ieee.org. 11. Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, Shanxi, 710126, China. tian@ieee.org.
Abstract
OBJECTIVE: To develop and validate a radiomics-based nomogram for preoperatively predicting grade 1 and grade 2/3 tumors in patients with pancreatic neuroendocrine tumors (PNETs). METHODS: One hundred thirty-eight patients derived from two institutions with pathologically confirmed PNETs (104 in the training cohort and 34 in the validation cohort) were included in this retrospective study. A total of 853 radiomic features were extracted from arterial and portal venous phase CT images respectively. Minimum redundancy maximum relevance and random forest methods were adopted for the significant radiomic feature selection and radiomic signature construction. A fusion radiomic signature was generated by combining both the single-phase signatures. The nomogram based on a comprehensive model incorporating the clinical risk factors and the fusion radiomic signature was established, and decision curve analysis was applied for clinical use. RESULTS: The fusion radiomic signature has significant association with histologic grade (p < 0.001). The nomogram integrating independent clinical risk factor tumor margin and fusion radiomic signature showed strong discrimination with an area under the curve (AUC) of 0.974 (95% CI 0.950-0.998) in the training cohort and 0.902 (95% CI 0.798-1.000) in the validation cohort with good calibration. Decision curve analysis verified the clinical usefulness of the predictive nomogram. CONCLUSION: We proposed a comprehensive nomogram consisting of tumor margin and fusion radiomic signature as a powerful tool to predict grade 1 and grade 2/3 PNET preoperatively and assist the clinical decision-making for PNET patients. KEY POINTS: • Radiomic signature has strong discriminatory ability for the histologic grade of PNETs. • Arterial and portal venous phase CT imaging are complementary for the prediction of PNET grading. • The comprehensive nomogram outperformed clinical factors in assisting therapy strategy in PNET patients.
OBJECTIVE: To develop and validate a radiomics-based nomogram for preoperatively predicting grade 1 and grade 2/3 tumors in patients with pancreatic neuroendocrine tumors (PNETs). METHODS: One hundred thirty-eight patients derived from two institutions with pathologically confirmed PNETs (104 in the training cohort and 34 in the validation cohort) were included in this retrospective study. A total of 853 radiomic features were extracted from arterial and portal venous phase CT images respectively. Minimum redundancy maximum relevance and random forest methods were adopted for the significant radiomic feature selection and radiomic signature construction. A fusion radiomic signature was generated by combining both the single-phase signatures. The nomogram based on a comprehensive model incorporating the clinical risk factors and the fusion radiomic signature was established, and decision curve analysis was applied for clinical use. RESULTS: The fusion radiomic signature has significant association with histologic grade (p < 0.001). The nomogram integrating independent clinical risk factor tumor margin and fusion radiomic signature showed strong discrimination with an area under the curve (AUC) of 0.974 (95% CI 0.950-0.998) in the training cohort and 0.902 (95% CI 0.798-1.000) in the validation cohort with good calibration. Decision curve analysis verified the clinical usefulness of the predictive nomogram. CONCLUSION: We proposed a comprehensive nomogram consisting of tumor margin and fusion radiomic signature as a powerful tool to predict grade 1 and grade 2/3 PNET preoperatively and assist the clinical decision-making for PNET patients. KEY POINTS: • Radiomic signature has strong discriminatory ability for the histologic grade of PNETs. • Arterial and portal venous phase CT imaging are complementary for the prediction of PNET grading. • The comprehensive nomogram outperformed clinical factors in assisting therapy strategy in PNET patients.
Authors: Rachel B Ger; Carlos E Cardenas; Brian M Anderson; Jinzhong Yang; Dennis S Mackin; Lifei Zhang; Laurence E Court Journal: J Vis Exp Date: 2018-01-08 Impact factor: 1.355
Authors: Jochen Paul Steinacker; Nora Steinacker-Stanescu; Thomas Ettrich; Marko Kornmann; Katharina Kneer; Ambros Beer; Meinrad Beer; Stefan Andreas Schmidt Journal: Visc Med Date: 2020-04-07
Authors: Usman Mahmood; Aditya Apte; Christopher Kanan; David D B Bates; Giuseppe Corrias; Lorenzo Manneli; Jung Hun Oh; Yusuf Emre Erdi; John Nguyen; Joseph O'Deasy; Amita Shukla-Dave Journal: J Med Imaging (Bellingham) Date: 2021-06-29