Joel Aissa1, Benedikt Michael Schaarschmidt2, Janina Below3, Oliver Th Bethge4, Judith Böven4, Lino Morris Sawicki4, Norman-Philipp Hoff3, Patric Kröpil4, Gerald Antoch4, Johannes Boos4. 1. University Dusseldorf, Medical Faculty, Department of Diagnostic and Interventional Radiology, Germany. Electronic address: Joel.Aissa@med.uni-duesseldorf.de. 2. University Dusseldorf, Medical Faculty, Department of Diagnostic and Interventional Radiology, Germany. Electronic address: Benedikt.Schaarschmidt@med.uni-duesseldorf.de. 3. University Dusseldorf, Medical Faculty, Clinic of Dermatology, Moorenstr. 5, D-40225 Dusseldorf, Germany. 4. University Dusseldorf, Medical Faculty, Department of Diagnostic and Interventional Radiology, Germany.
Abstract
PURPOSE: To evaluate performance and the clinical impact of a novel machine learning based vessel-suppressing computer-aided detection (CAD) software in chest computed tomography (CT) of patients with malignant melanoma. MATERIALS AND METHODS: We retrospectively included consecutive malignant melanoma patients with a chest CT between 01/2015 and 01/2016. Machine learning based CAD software was used to reconstruct additional vessel-suppressed axial images. Three radiologists independently reviewed a maximum of 15 lung nodules per patient. Vessel-suppressed reconstructions were reviewed independently and results were compared. Follow-up CT examinations and clinical follow-up were used to assess the outcome. Impact of additional nodules on clinical management was assessed. RESULTS: In 46 patients, vessel-suppressed axial images led to the detection of additional nodules in 25/46 (54.3%) patients. CT or clinical follow up was available in 25/25 (100%) patients with additionally detected nodules. 2/25 (8%) of these patients developed new pulmonary metastases. None of the additionally detected nodules were found to be metastases. None of the lung nodules detected by the radiologists was missed by the CAD software. The mean diameter of the 92 additional nodules was 1.5 ± 0.8 mm. The additional nodules did not affect therapeutic management. However, in 14/46 (30.4%) of patients the additional nodules might have had an impact on the radiological follow-up recommendations. CONCLUSION: Machine learning based vessel suppression led to the detection of significantly more lung nodules in melanoma patients. Radiological follow-up recommendations were altered in 30% of the patients. However, all lung nodules turned out to be non-malignant on follow-up.
PURPOSE: To evaluate performance and the clinical impact of a novel machine learning based vessel-suppressing computer-aided detection (CAD) software in chest computed tomography (CT) of patients with malignant melanoma. MATERIALS AND METHODS: We retrospectively included consecutive malignant melanomapatients with a chest CT between 01/2015 and 01/2016. Machine learning based CAD software was used to reconstruct additional vessel-suppressed axial images. Three radiologists independently reviewed a maximum of 15 lung nodules per patient. Vessel-suppressed reconstructions were reviewed independently and results were compared. Follow-up CT examinations and clinical follow-up were used to assess the outcome. Impact of additional nodules on clinical management was assessed. RESULTS: In 46 patients, vessel-suppressed axial images led to the detection of additional nodules in 25/46 (54.3%) patients. CT or clinical follow up was available in 25/25 (100%) patients with additionally detected nodules. 2/25 (8%) of these patients developed new pulmonary metastases. None of the additionally detected nodules were found to be metastases. None of the lung nodules detected by the radiologists was missed by the CAD software. The mean diameter of the 92 additional nodules was 1.5 ± 0.8 mm. The additional nodules did not affect therapeutic management. However, in 14/46 (30.4%) of patients the additional nodules might have had an impact on the radiological follow-up recommendations. CONCLUSION: Machine learning based vessel suppression led to the detection of significantly more lung nodules in melanomapatients. Radiological follow-up recommendations were altered in 30% of the patients. However, all lung nodules turned out to be non-malignant on follow-up.
Authors: Ramandeep Singh; Mannudeep K Kalra; Fatemeh Homayounieh; Chayanin Nitiwarangkul; Shaunagh McDermott; Brent P Little; Inga T Lennes; Jo-Anne O Shepard; Subba R Digumarthy Journal: Quant Imaging Med Surg Date: 2021-04
Authors: Baptiste Vasey; Stephan Ursprung; Benjamin Beddoe; Elliott H Taylor; Neale Marlow; Nicole Bilbro; Peter Watkinson; Peter McCulloch Journal: JAMA Netw Open Date: 2021-03-01