Shuai Chen1, Shuai Ren1,2, Kai Guo1, Marcus J Daniels3, Zhongqiu Wang4, Rong Chen2. 1. Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, 210029, Jiangsu, China. 2. Department of Diagnostic Radiology and Nuclear Medicine, School of Medicine, University of Maryland, Baltimore, MD, 21201, USA. 3. Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA. 4. Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, 210029, Jiangsu, China. zhongqiuwang0815@163.com.
Abstract
PURPOSE: To develop and validate a CT-based radiomics nomogram in preoperative differential diagnosis of SCNs from mucin-producing PCNs. MATERIAL AND METHODS: A total of 89 patients consisting of 31 SCNs, 30 IPMNs, and 28 MCNs who underwent preoperative CT were analyzed. A total of 710 radiomics features were extracted from each case. Patients were divided into training (n = 63) and validation cohorts (n = 26) with a ratio of 7:3. Least absolute shrinkage and selection operator (LASSO) method and logistic regression analysis were used for feature selection and model construction. A nomogram was created from a comprehensive model consisting of clinical features and the fusion radiomics signature. A decision curve analysis was used for clinical decisions. RESULTS: The radiomics features extracted from CT could assist with the differentiation of SCNs from mucin-producing PCNs in both the training and validation cohorts. The signature of the combination of the plain, late arterial, and venous phases had the largest areas under the curve (AUCs) of 0.960 (95% CI 0.910-1) in the training cohort and 0.817 (95% CI 0.651-0.983) in the validation cohort with good calibration. The value and efficacy of the nomogram was verified using decision curve analysis. CONCLUSION: A comprehensive nomogram incorporating clinical features and fusion radiomics signature can differentiate SCNs from mucin-producing PCNs.
PURPOSE: To develop and validate a CT-based radiomics nomogram in preoperative differential diagnosis of SCNs from mucin-producing PCNs. MATERIAL AND METHODS: A total of 89 patients consisting of 31 SCNs, 30 IPMNs, and 28 MCNs who underwent preoperative CT were analyzed. A total of 710 radiomics features were extracted from each case. Patients were divided into training (n = 63) and validation cohorts (n = 26) with a ratio of 7:3. Least absolute shrinkage and selection operator (LASSO) method and logistic regression analysis were used for feature selection and model construction. A nomogram was created from a comprehensive model consisting of clinical features and the fusion radiomics signature. A decision curve analysis was used for clinical decisions. RESULTS: The radiomics features extracted from CT could assist with the differentiation of SCNs from mucin-producing PCNs in both the training and validation cohorts. The signature of the combination of the plain, late arterial, and venous phases had the largest areas under the curve (AUCs) of 0.960 (95% CI 0.910-1) in the training cohort and 0.817 (95% CI 0.651-0.983) in the validation cohort with good calibration. The value and efficacy of the nomogram was verified using decision curve analysis. CONCLUSION: A comprehensive nomogram incorporating clinical features and fusion radiomics signature can differentiate SCNs from mucin-producing PCNs.
Authors: Konstantin Dmitriev; Arie E Kaufman; Ammar A Javed; Ralph H Hruban; Elliot K Fishman; Anne Marie Lennon; Joel H Saltz Journal: Med Image Comput Comput Assist Interv Date: 2017-09-04
Authors: Sang Youn Kim; Jeong Min Lee; Se Hyung Kim; Kyung-Sook Shin; Young Jun Kim; Su Kyung An; Chang Jin Han; Joon Koo Han; Byung Ihn Choi Journal: AJR Am J Roentgenol Date: 2006-11 Impact factor: 3.959
Authors: Anne Marie Lennon; Christopher L Wolfgang; Marcia Irene Canto; Alison P Klein; Joseph M Herman; Michael Goggins; Elliot K Fishman; Ihab Kamel; Matthew J Weiss; Luis A Diaz; Nickolas Papadopoulos; Kenneth W Kinzler; Bert Vogelstein; Ralph H Hruban Journal: Cancer Res Date: 2014-06-12 Impact factor: 12.701
Authors: E Lopez Hänninen; M Pech; J Ricke; T Denecke; H Amthauer; L Lehmkuhl; M Böhmig; R Röttgen; J Pinkernelle; R Felix; J Langrehr Journal: Acta Radiol Date: 2006-03 Impact factor: 1.990
Authors: José Celso Ardengh; César Vivian Lopes; Eder Rios de Lima-Filho; Rafael Kemp; José Sebastião Dos Santos Journal: Scand J Gastroenterol Date: 2013-11-05 Impact factor: 2.423
Authors: Maria Elena Laino; Angela Ammirabile; Ludovica Lofino; Lorenzo Mannelli; Francesco Fiz; Marco Francone; Arturo Chiti; Luca Saba; Matteo Agostino Orlandi; Victor Savevski Journal: Healthcare (Basel) Date: 2022-08-11