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Bedrikovetski S, Zhang J, Seow W, Traeger L, Moore JW, Verjans J, Carneiro G, Sammour T. Deep learning to predict lymph node status on pre-operative staging CT in patients with colon cancer. J Med Imaging Radiat Oncol 2024; 68:33-40. [PMID: 37724420 DOI: 10.1111/1754-9485.13584] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Accepted: 09/03/2023] [Indexed: 09/20/2023]
Abstract
INTRODUCTION Lymph node (LN) metastases are an important determinant of survival in patients with colon cancer, but remain difficult to accurately diagnose on preoperative imaging. This study aimed to develop and evaluate a deep learning model to predict LN status on preoperative staging CT. METHODS In this ambispective diagnostic study, a deep learning model using a ResNet-50 framework was developed to predict LN status based on preoperative staging CT. Patients with a preoperative staging abdominopelvic CT who underwent surgical resection for colon cancer were enrolled. Data were retrospectively collected from February 2007 to October 2019 and randomly separated into training, validation, and testing cohort 1. To prospectively test the deep learning model, data for testing cohort 2 was collected from October 2019 to July 2021. Diagnostic performance measures were assessed by the AUROC. RESULTS A total of 1,201 patients (median [range] age, 72 [28-98 years]; 653 [54.4%] male) fulfilled the eligibility criteria and were included in the training (n = 401), validation (n = 100), testing cohort 1 (n = 500) and testing cohort 2 (n = 200). The deep learning model achieved an AUROC of 0.619 (95% CI 0.507-0.731) in the validation cohort. In testing cohort 1 and testing cohort 2, the AUROC was 0.542 (95% CI 0.489-0.595) and 0.486 (95% CI 0.403-0.568), respectively. CONCLUSION A deep learning model based on a ResNet-50 framework does not predict LN status on preoperative staging CT in patients with colon cancer.
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Affiliation(s)
- Sergei Bedrikovetski
- Discipline of Surgery, Faculty of Health and Medical Sciences, School of Medicine, University of Adelaide, Adelaide, South Australia, Australia
- Colorectal Unit, Department of Surgery, Royal Adelaide Hospital, Adelaide, South Australia, Australia
| | - Jianpeng Zhang
- Australian Institute for Machine Learning, School of Computer Science, University of Adelaide, Adelaide, South Australia, Australia
| | - Warren Seow
- Colorectal Unit, Department of Surgery, Royal Adelaide Hospital, Adelaide, South Australia, Australia
| | - Luke Traeger
- Discipline of Surgery, Faculty of Health and Medical Sciences, School of Medicine, University of Adelaide, Adelaide, South Australia, Australia
- Colorectal Unit, Department of Surgery, Royal Adelaide Hospital, Adelaide, South Australia, Australia
| | - James W Moore
- Discipline of Surgery, Faculty of Health and Medical Sciences, School of Medicine, University of Adelaide, Adelaide, South Australia, Australia
- Colorectal Unit, Department of Surgery, Royal Adelaide Hospital, Adelaide, South Australia, Australia
| | - Johan Verjans
- Australian Institute for Machine Learning, School of Computer Science, University of Adelaide, Adelaide, South Australia, Australia
| | - Gustavo Carneiro
- Australian Institute for Machine Learning, School of Computer Science, University of Adelaide, Adelaide, South Australia, Australia
| | - Tarik Sammour
- Discipline of Surgery, Faculty of Health and Medical Sciences, School of Medicine, University of Adelaide, Adelaide, South Australia, Australia
- Colorectal Unit, Department of Surgery, Royal Adelaide Hospital, Adelaide, South Australia, Australia
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