ArticleQuantitative imaging in medicine and surgery2023
CT-based deep learning segmentation of ovarian cancer and the stability of the extracted radiomics features.
Article in Quantitative imaging in medicine and surgery, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Two-staged CT-based radiomics model in characterising early-stage ovarian carcinoma and benign ovarian masses.Journal of ovarian research · 2026Article
- Deep learning accurately and reliably segments pelvic vascular structure in CT scans of gynecologic cancer patients.Frontiers in oncology · 2026Article
- Predicting the early response to neoadjuvant chemotherapy in high-grade serous ovarian cancer by intratumoral habitat heterogeneity based onEuropean journal of nuclear medicine and molecular imaging · 2026Article
- CT-based radiomics model to predict platinum sensitivity in epithelial ovarian carcinoma: a multicentre study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Automatic segmentation model and machine learning model grounded in ultrasound radiomics for distinguishing between low malignant risk and intermediate-high malignant risk of adnexal masses.Insights into imaging · 2025Article
- Research progress of artificial intelligence in the early screening, diagnosis, precise treatment and prognosis prediction of three central gynecological malignancies.Frontiers in oncology · 2025Review
- AI-enhanced PET/CT image synthesis using CycleGAN for improved ovarian cancer imaging.Polish journal of radiology · 2025Article
- PET/CT-based 3D multi-class semantic segmentation of ovarian cancer and the stability of the extracted radiomics features.Physical and engineering sciences in medicine · 2024Article
- Integrating a deep neural network and Transformer architecture for the automatic segmentation and survival prediction in cervical cancer.Quantitative imaging in medicine and surgery · 2024Article
- AI-based automated segmentation for ovarian/adnexal masses and their internal components on ultrasound imaging.Journal of medical imaging (Bellingham, Wash.) · 2024Article
- Interobserver Agreement in Automatic Segmentation Annotation of Prostate Magnetic Resonance Imaging.Bioengineering (Basel, Switzerland) · 2023Article
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8 authors.
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Abstract
Background: Radiomics analysis could provide complementary tissue characterization in ovarian cancer (OC). However, OC segmentation required in radiomics analysis is time-consuming and labour-intensive. In this study, we aim to evaluate the performance of deep learning-based segmentation of OC on contrast-enhanced CT images and the stability of radiomics features extracted from the automated segmentation. Methods: Staging abdominopelvic CT images of 367 patients with OC were retrospectively recruited. The training and cross-validation sets came from center A (n=283), and testing set (n=84) came from centers B and C. The tumours were manually delineated by a board-certified radiologist. Four model architectures provided by no-new-Net (nnU-Net) method were tested in this task. The segmentation performance evaluated by Dice score, Jaccard score, sensitivity and precision were compared among 4 architectures. The Pearson correlation coefficient ( Results: The 3D U-Net cascade architecture achieved highest median Dice score, Jaccard score, sensitivity and precision for OC segmentation in the testing set, 0.941, 0.890, 0.973 and 0.925, respectively. Tumour volumes of manual and automated segmentations were highly correlated ( Conclusions: The presented deep-learning segmentation could provide highly accurate automated segmentation of OC on CT images with high stability of the extracted radiomics features, showing the potential as a batch-processing segmentation tool.
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