Sergei Bedrikovetski1, Nagendra N Dudi-Venkata2, Gabriel Maicas3, Hidde M Kroon4, Warren Seow5, Gustavo Carneiro3, James W Moore2, Tarik Sammour2. 1. Discipline of Surgery, Faculty of Health and Medical Science, School of Medicine, University of Adelaide, Adelaide, South Australia, Australia; Colorectal Unit, Department of Surgery, Royal Adelaide Hospital, Adelaide, South Australia, Australia. Electronic address: sergei.bedrikovetski@gmail.com. 2. Discipline of Surgery, Faculty of Health and Medical Science, School of Medicine, University of Adelaide, Adelaide, South Australia, Australia; Colorectal Unit, Department of Surgery, Royal Adelaide Hospital, Adelaide, South Australia, Australia. 3. Australian Institute for Machine Learning, School of Computer Science, University of Adelaide, Adelaide, South Australia, Australia. 4. Colorectal Unit, Department of Surgery, Royal Adelaide Hospital, Adelaide, South Australia, Australia. 5. Discipline of Surgery, Faculty of Health and Medical Science, School of Medicine, University of Adelaide, Adelaide, South Australia, Australia.
Abstract
PURPOSE: Accurate clinical diagnosis of lymph node metastases is of paramount importance in the treatment of patients with abdominopelvic malignancy. This review assesses the diagnostic performance of deep learning algorithms and radiomics models for lymph node metastases in abdominopelvic malignancies. METHODOLOGY: Embase (PubMed, MEDLINE), Science Direct and IEEE Xplore databases were searched to identify eligible studies published between January 2009 and March 2019. Studies that reported on the accuracy of deep learning algorithms or radiomics models for abdominopelvic malignancy by CT or MRI were selected. Study characteristics and diagnostic measures were extracted. Estimates were pooled using random-effects meta-analysis. Evaluation of risk of bias was performed using the QUADAS-2 tool. RESULTS: In total, 498 potentially eligible studies were identified, of which 21 were included and 17 offered enough information for a quantitative analysis. Studies were heterogeneous and substantial risk of bias was found in 18 studies. Almost all studies employed radiomics models (n = 20). The single published deep-learning model out-performed radiomics models with a higher AUROC (0.912 vs 0.895), but both radiomics and deep-learning models outperformed the radiologist's interpretation in isolation (0.774). Pooled results for radiomics nomograms amongst tumour subtypes demonstrated the highest AUC 0.895 (95 %CI, 0.810-0.980) for urological malignancy, and the lowest AUC 0.798 (95 %CI, 0.744-0.852) for colorectal malignancy. CONCLUSION: Radiomics models improve the diagnostic accuracy of lymph node staging for abdominopelvic malignancies in comparison with radiologist's assessment. Deep learning models may further improve on this, but data remain limited.
PURPOSE: Accurate clinical diagnosis of lymph node metastases is of paramount importance in the treatment of patients with abdominopelvic malignancy. This review assesses the diagnostic performance of deep learning algorithms and radiomics models for lymph node metastases in abdominopelvic malignancies. METHODOLOGY: Embase (PubMed, MEDLINE), Science Direct and IEEE Xplore databases were searched to identify eligible studies published between January 2009 and March 2019. Studies that reported on the accuracy of deep learning algorithms or radiomics models for abdominopelvic malignancy by CT or MRI were selected. Study characteristics and diagnostic measures were extracted. Estimates were pooled using random-effects meta-analysis. Evaluation of risk of bias was performed using the QUADAS-2 tool. RESULTS: In total, 498 potentially eligible studies were identified, of which 21 were included and 17 offered enough information for a quantitative analysis. Studies were heterogeneous and substantial risk of bias was found in 18 studies. Almost all studies employed radiomics models (n = 20). The single published deep-learning model out-performed radiomics models with a higher AUROC (0.912 vs 0.895), but both radiomics and deep-learning models outperformed the radiologist's interpretation in isolation (0.774). Pooled results for radiomics nomograms amongst tumour subtypes demonstrated the highest AUC 0.895 (95 %CI, 0.810-0.980) for urological malignancy, and the lowest AUC 0.798 (95 %CI, 0.744-0.852) for colorectal malignancy. CONCLUSION: Radiomics models improve the diagnostic accuracy of lymph node staging for abdominopelvic malignancies in comparison with radiologist's assessment. Deep learning models may further improve on this, but data remain limited.
Authors: T C Sluckin; Y F L Rooker; S Q Kol; S J A Hazen; J B Tuynman; J Stoker; P J Tanis; K Horsthuis; M Kusters Journal: Eur Radiol Date: 2022-05-18 Impact factor: 7.034
Authors: Sergei Bedrikovetski; Nagendra N Dudi-Venkata; Hidde M Kroon; Warren Seow; Ryash Vather; Gustavo Carneiro; James W Moore; Tarik Sammour Journal: BMC Cancer Date: 2021-09-26 Impact factor: 4.430