학술논문
Deep learning-based segmentation of multisite disease in ovarian cancer
Document Type
article
Author
Thomas Buddenkotte; Leonardo Rundo; Ramona Woitek; Lorena Escudero Sanchez; Lucian Beer; Mireia Crispin-Ortuzar; Christian Etmann; Subhadip Mukherjee; Vlad Bura; Cathal McCague; Hilal Sahin; Roxana Pintican; Marta Zerunian; Iris Allajbeu; Naveena Singh; Anju Sahdev; Laura Havrilesky; David E. Cohn; Nicholas W. Bateman; Thomas P. Conrads; Kathleen M. Darcy; G. Larry Maxwell; John B. Freymann; Ozan Öktem; James D. Brenton; Evis Sala; Carola-Bibiane Schönlieb
Source
European Radiology Experimental, Vol 7, Iss 1, Pp 1-10 (2023)
Subject
Language
English
ISSN
2509-9280
Abstract
Abstract Purpose To determine if pelvic/ovarian and omental lesions of ovarian cancer can be reliably segmented on computed tomography (CT) using fully automated deep learning-based methods. Methods A deep learning model for the two most common disease sites of high-grade serous ovarian cancer lesions (pelvis/ovaries and omentum) was developed and compared against the well-established “no-new-Net” framework and unrevised trainee radiologist segmentations. A total of 451 CT scans collected from four different institutions were used for training (n = 276), evaluation (n = 104) and testing (n = 71) of the methods. The performance was evaluated using the Dice similarity coefficient (DSC) and compared using a Wilcoxon test. Results Our model outperformed no-new-Net for the pelvic/ovarian lesions in cross-validation, on the evaluation and test set by a significant margin (p values being 4 × 10–7, 3 × 10–4, 4 × 10–2, respectively), and for the omental lesions on the evaluation set (p = 1 × 10–3). Our model did not perform significantly differently in segmenting pelvic/ovarian lesions (p = 0.371) compared to a trainee radiologist. On an independent test set, the model achieved a DSC performance of 71 ± 20 (mean ± standard deviation) for pelvic/ovarian and 61 ± 24 for omental lesions. Conclusion Automated ovarian cancer segmentation on CT scans using deep neural networks is feasible and achieves performance close to a trainee-level radiologist for pelvic/ovarian lesions. Relevance statement Automated segmentation of ovarian cancer may be used by clinicians for CT-based volumetric assessments and researchers for building complex analysis pipelines. Key points • The first automated approach for pelvic/ovarian and omental ovarian cancer lesion segmentation on CT images has been presented. • Automated segmentation of ovarian cancer lesions can be comparable with manual segmentation of trainee radiologists. • Careful hyperparameter tuning can provide models significantly outperforming strong state-of-the-art baselines. Graphical Abstract