Deep learning-based segmentation of multisite disease in ovarian cancer

• Published in: European radiology experimental 2023-12, Vol.7 (1), p.77-77, Article 77
• Main Authors: Buddenkotte, Thomas, Rundo, Leonardo, Woitek, Ramona, Escudero Sanchez, Lorena, Beer, Lucian, Crispin-Ortuzar, Mireia, Etmann, Christian, Mukherjee, Subhadip, Bura, Vlad, McCague, Cathal, Sahin, Hilal, Pintican, Roxana, Zerunian, Marta, Allajbeu, Iris, Singh, Naveena, Sahdev, Anju, Havrilesky, Laura, Cohn, David E., Bateman, Nicholas W., Conrads, Thomas P., Darcy, Kathleen M., Maxwell, G. Larry, Freymann, John B., Öktem, Ozan, Brenton, James D., Sala, Evis, Schönlieb, Carola-Bibiane
• Format: Article
• Language: English
• Subjects: Automation; Deep Learning; Diagnostic Radiology; Female; Humans; Imaging; Internal Medicine; Interventional Radiology; Medical imaging; Medicine; Medicine & Public Health; Neural Networks, Computer; Neuroradiology; Omentum; Ovarian cancer; Ovarian Cysts; Ovarian Neoplasms; Ovarian Neoplasms - diagnostic imaging; Pelvis; Radiology; Technical Note; Tomography, X-Ray Computed; Ultrasound
• ISSN: 2509-9280; 2509-9280
• DOI: 10.1186/s41747-023-00388-z

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