S Park1, L C Chu1, R H Hruban2, B Vogelstein3, K W Kinzler4, A L Yuille5, D F Fouladi1, S Shayesteh1, S Ghandili1, C L Wolfgang6, R Burkhart6, J He6, E K Fishman1, S Kawamoto7. 1. The Russell H. Morgan Department of Radiology and Radiological Science, School of Medicine, Johns Hopkins University, JHOC 3140E, 601N. Caroline Street, 21287 Baltimore, USA. 2. Department of Pathology, The Sol Goldman Pancreatic Cancer Research Center, School of Medicine, Johns Hopkins University, 21287 Baltimore, USA; Sidney Kimmel Comprehensive Cancer Center, School of Medicine, Johns Hopkins University, 21287 Baltimore, USA. 3. Sidney Kimmel Comprehensive Cancer Center, School of Medicine, Johns Hopkins University, 21287 Baltimore, USA; Johns Hopkins University, School of Medicine, Ludwig Center for Cancer Genetics and Therapeutics, 21231 Baltimore, USA; Howard Hughes Medical Institute Investigator, Johns Hopkins University School of Medicine, 21231 Baltimore, USA. 4. Sidney Kimmel Comprehensive Cancer Center, School of Medicine, Johns Hopkins University, 21287 Baltimore, USA; Johns Hopkins University, School of Medicine, Ludwig Center for Cancer Genetics and Therapeutics, 21231 Baltimore, USA. 5. Johns Hopkins University, School of Arts and Sciences, Department of Computer Science, 21218 Baltimore, USA; Johns Hopkins University, School of Arts and Sciences, Department of Cognitive Science, 21218 Baltimore, USA. 6. Johns Hopkins University, School of Medicine, Department of Surgery, 21287 Baltimore, USA. 7. The Russell H. Morgan Department of Radiology and Radiological Science, School of Medicine, Johns Hopkins University, JHOC 3140E, 601N. Caroline Street, 21287 Baltimore, USA. Electronic address: skawamo1@jhmi.edu.
Abstract
PURPOSE: The purpose of this study was to determine whether computed tomography (CT)-based machine learning of radiomics features could help distinguish autoimmune pancreatitis (AIP) from pancreatic ductal adenocarcinoma (PDAC). MATERIALS AND METHODS: Eighty-nine patients with AIP (65 men, 24 women; mean age, 59.7±13.9 [SD] years; range: 21-83 years) and 93 patients with PDAC (68 men, 25 women; mean age, 60.1±12.3 [SD] years; range: 36-86 years) were retrospectively included. All patients had dedicated dual-phase pancreatic protocol CT between 2004 and 2018. Thin-slice images (0.75/0.5mm thickness/increment) were compared with thick-slices images (3 or 5mm thickness/increment). Pancreatic regions involved by PDAC or AIP (areas of enlargement, altered enhancement, effacement of pancreatic duct) as well as uninvolved parenchyma were segmented as three-dimensional volumes. Four hundred and thirty-one radiomics features were extracted and a random forest was used to distinguish AIP from PDAC. CT data of 60 AIP and 60 PDAC patients were used for training and those of 29 AIP and 33 PDAC independent patients were used for testing. RESULTS: The pancreas was diffusely involved in 37 (37/89; 41.6%) patients with AIP and not diffusely in 52 (52/89; 58.4%) patients. Using machine learning, 95.2% (59/62; 95% confidence interval [CI]: 89.8-100%), 83.9% (52:67; 95% CI: 74.7-93.0%) and 77.4% (48/62; 95% CI: 67.0-87.8%) of the 62 test patients were correctly classified as either having PDAC or AIP with thin-slice venous phase, thin-slice arterial phase, and thick-slice venous phase CT, respectively. Three of the 29 patients with AIP (3/29; 10.3%) were incorrectly classified as having PDAC but all 33 patients with PDAC (33/33; 100%) were correctly classified with thin-slice venous phase with 89.7% sensitivity (26/29; 95% CI: 78.6-100%) and 100% specificity (33/33; 95% CI: 93-100%) for the diagnosis of AIP, 95.2% accuracy (59/62; 95% CI: 89.8-100%) and area under the curve of 0.975 (95% CI: 0.936-1.0). CONCLUSIONS: Radiomic features help differentiate AIP from PDAC with an overall accuracy of 95.2%.
PURPOSE: The purpose of this study was to determine whether computed tomography (CT)-based machine learning of radiomics features could help distinguish autoimmune pancreatitis (AIP) from pancreatic ductal adenocarcinoma (PDAC). MATERIALS AND METHODS: Eighty-nine patients with AIP (65 men, 24 women; mean age, 59.7±13.9 [SD] years; range: 21-83 years) and 93 patients with PDAC (68 men, 25 women; mean age, 60.1±12.3 [SD] years; range: 36-86 years) were retrospectively included. All patients had dedicated dual-phase pancreatic protocol CT between 2004 and 2018. Thin-slice images (0.75/0.5mm thickness/increment) were compared with thick-slices images (3 or 5mm thickness/increment). Pancreatic regions involved by PDAC or AIP (areas of enlargement, altered enhancement, effacement of pancreatic duct) as well as uninvolved parenchyma were segmented as three-dimensional volumes. Four hundred and thirty-one radiomics features were extracted and a random forest was used to distinguish AIP from PDAC. CT data of 60 AIP and 60 PDACpatients were used for training and those of 29 AIP and 33 PDAC independent patients were used for testing. RESULTS: The pancreas was diffusely involved in 37 (37/89; 41.6%) patients with AIP and not diffusely in 52 (52/89; 58.4%) patients. Using machine learning, 95.2% (59/62; 95% confidence interval [CI]: 89.8-100%), 83.9% (52:67; 95% CI: 74.7-93.0%) and 77.4% (48/62; 95% CI: 67.0-87.8%) of the 62 test patients were correctly classified as either having PDAC or AIP with thin-slice venous phase, thin-slice arterial phase, and thick-slice venous phase CT, respectively. Three of the 29 patients with AIP (3/29; 10.3%) were incorrectly classified as having PDAC but all 33 patients with PDAC (33/33; 100%) were correctly classified with thin-slice venous phase with 89.7% sensitivity (26/29; 95% CI: 78.6-100%) and 100% specificity (33/33; 95% CI: 93-100%) for the diagnosis of AIP, 95.2% accuracy (59/62; 95% CI: 89.8-100%) and area under the curve of 0.975 (95% CI: 0.936-1.0). CONCLUSIONS: Radiomic features help differentiate AIP from PDAC with an overall accuracy of 95.2%.
Authors: J-Matthias Löhr; Ulrich Beuers; Miroslav Vujasinovic; Domenico Alvaro; Jens Brøndum Frøkjær; Frank Buttgereit; Gabriele Capurso; Emma L Culver; Enrique de-Madaria; Emanuel Della-Torre; Sönke Detlefsen; Enrique Dominguez-Muñoz; Piotr Czubkowski; Nils Ewald; Luca Frulloni; Natalya Gubergrits; Deniz Guney Duman; Thilo Hackert; Julio Iglesias-Garcia; Nikolaos Kartalis; Andrea Laghi; Frank Lammert; Fredrik Lindgren; Alexey Okhlobystin; Grzegorz Oracz; Andrea Parniczky; Raffaella Maria Pozzi Mucelli; Vinciane Rebours; Jonas Rosendahl; Nicolas Schleinitz; Alexander Schneider; Eric Fh van Bommel; Caroline Sophie Verbeke; Marie Pierre Vullierme; Heiko Witt Journal: United European Gastroenterol J Date: 2020-06-18 Impact factor: 4.623
Authors: Yucheng Zhang; Edrise M Lobo-Mueller; Paul Karanicolas; Steven Gallinger; Masoom A Haider; Farzad Khalvati Journal: Front Artif Intell Date: 2020-10-05