Performance and clinical impact of machine learning based lung nodule detection using vessel suppression in melanoma patients
| Autor: | Johannes Boos, Oliver Th Bethge, Gerald Antoch, Lino M Sawicki, Judith Böven, Janina Below, Joel Aissa, Norman-Philipp Hoff, Patric Kröpil, Benedikt Michael Schaarschmidt |
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| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Adult
Male Nodule detection Lung Neoplasms Skin Neoplasms Medizin Computed tomography Machine learning computer.software_genre 030218 nuclear medicine & medical imaging Metastasis Machine Learning 03 medical and health sciences 0302 clinical medicine Multidetector Computed Tomography Medicine Humans Radiology Nuclear Medicine and imaging Diagnosis Computer-Assisted Neoplasm Metastasis Melanoma Aged Retrospective Studies Mean diameter Aged 80 and over Lung medicine.diagnostic_test business.industry Middle Aged medicine.disease medicine.anatomical_structure ROC Curve 030220 oncology & carcinogenesis Radiological weapon Female Artificial intelligence Tomography business computer |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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