Deep learning assessment compared to radiologist reporting for metastatic spinal cord compression on CT.

Autor:	Hallinan JTPD; Department of Diagnostic Imaging, National University Hospital, Singapore, Singapore.; Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore., Zhu L; Department of Computer Science, School of Computing, National University of Singapore, Singapore, Singapore., Zhang W; Department of Computer Science, School of Computing, National University of Singapore, Singapore, Singapore., Ge S; Department of Diagnostic Imaging, National University Hospital, Singapore, Singapore., Muhamat Nor FE; Department of Diagnostic Imaging, National University Hospital, Singapore, Singapore.; Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore., Ong HY; Department of Diagnostic Imaging, National University Hospital, Singapore, Singapore.; Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore., Eide SE; Department of Diagnostic Imaging, National University Hospital, Singapore, Singapore.; Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore., Cheng AJL; Department of Diagnostic Imaging, National University Hospital, Singapore, Singapore.; Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore., Kuah T; Department of Diagnostic Imaging, National University Hospital, Singapore, Singapore., Lim DSW; Department of Diagnostic Imaging, National University Hospital, Singapore, Singapore., Low XZ; Department of Diagnostic Imaging, National University Hospital, Singapore, Singapore., Yeong KY; Department of Radiology, Ng Teng Fong General Hospital, Singapore, Singapore., AlMuhaish MI; Department of Diagnostic Imaging, National University Hospital, Singapore, Singapore.; Department of Radiology, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia., Alsooreti AM; Department of Diagnostic Imaging, National University Hospital, Singapore, Singapore.; Department of Diagnostic Imaging, Salmaniya Medical Complex, Manama, Bahrain., Kumarakulasinghe NB; National University Cancer Institute, National University Hospital, Singapore, Singapore., Teo EC; Department of Diagnostic Imaging, National University Hospital, Singapore, Singapore., Yap QV; Biostatistics Unit, Yong Loo Lin School of Medicine, Singapore, Singapore., Chan YH; Biostatistics Unit, Yong Loo Lin School of Medicine, Singapore, Singapore., Lin S; Division of Spine Surgery, Department of Orthopaedic Surgery, Ng Teng Fong General Hospital, Singapore, Singapore., Tan JH; University Spine Centre, Department of Orthopaedic Surgery, National University Health System, Singapore, Singapore., Kumar N; University Spine Centre, Department of Orthopaedic Surgery, National University Health System, Singapore, Singapore., Vellayappan BA; Department of Radiation Oncology, National University Cancer Institute Singapore, National University Hospital, Singapore, Singapore., Ooi BC; Department of Computer Science, School of Computing, National University of Singapore, Singapore, Singapore., Quek ST; Department of Diagnostic Imaging, National University Hospital, Singapore, Singapore.; Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore., Makmur A; Department of Diagnostic Imaging, National University Hospital, Singapore, Singapore.; Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
Jazyk:	angličtina
Zdroj:	Frontiers in oncology [Front Oncol] 2023 May 04; Vol. 13, pp. 1151073. Date of Electronic Publication: 2023 May 04 (Print Publication: 2023).
DOI:	10.3389/fonc.2023.1151073
Abstrakt:	Introduction: Metastatic spinal cord compression (MSCC) is a disastrous complication of advanced malignancy. A deep learning (DL) algorithm for MSCC classification on CT could expedite timely diagnosis. In this study, we externally test a DL algorithm for MSCC classification on CT and compare with radiologist assessment. Methods: Retrospective collection of CT and corresponding MRI from patients with suspected MSCC was conducted from September 2007 to September 2020. Exclusion criteria were scans with instrumentation, no intravenous contrast, motion artefacts and non-thoracic coverage. Internal CT dataset split was 84% for training/validation and 16% for testing. An external test set was also utilised. Internal training/validation sets were labelled by radiologists with spine imaging specialization (6 and 11-years post-board certification) and were used to further develop a DL algorithm for MSCC classification. The spine imaging specialist (11-years expertise) labelled the test sets (reference standard). For evaluation of DL algorithm performance, internal and external test data were independently reviewed by four radiologists: two spine specialists (Rad1 and Rad2, 7 and 5-years post-board certification, respectively) and two oncological imaging specialists (Rad3 and Rad4, 3 and 5-years post-board certification, respectively). DL model performance was also compared against the CT report issued by the radiologist in a real clinical setting. Inter-rater agreement (Gwet's kappa) and sensitivity/specificity/AUCs were calculated. Results: Overall, 420 CT scans were evaluated (225 patients, mean age=60 ± 11.9[SD]); 354(84%) CTs for training/validation and 66(16%) CTs for internal testing. The DL algorithm showed high inter-rater agreement for three-class MSCC grading with kappas of 0.872 (p<0.001) and 0.844 (p<0.001) on internal and external testing, respectively. On internal testing DL algorithm inter-rater agreement (κ=0.872) was superior to Rad 2 (κ=0.795) and Rad 3 (κ=0.724) (both p<0.001). DL algorithm kappa of 0.844 on external testing was superior to Rad 3 (κ=0.721) (p<0.001). CT report classification of high-grade MSCC disease was poor with only slight inter-rater agreement (κ=0.027) and low sensitivity (44.0), relative to the DL algorithm with almost-perfect inter-rater agreement (κ=0.813) and high sensitivity (94.0) (p<0.001). Conclusion: Deep learning algorithm for metastatic spinal cord compression on CT showed superior performance to the CT report issued by experienced radiologists and could aid earlier diagnosis. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2023 Hallinan, Zhu, Zhang, Ge, Muhamat Nor, Ong, Eide, Cheng, Kuah, Lim, Low, Yeong, AlMuhaish, Alsooreti, Kumarakulasinghe, Teo, Yap, Chan, Lin, Tan, Kumar, Vellayappan, Ooi, Quek and Makmur.)
Databáze:	MEDLINE
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