ArticleBMC medical imaging2022
Ultrasound-based radiomics for predicting different pathological subtypes of epithelial ovarian cancer before surgery.
Article in BMC medical imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence for ovarian cancer diagnosis via ultrasound: a systematic review and quantitative assessment of model performance.Frontiers in artificial intelligence · 2025Pooled it
- Differentiating high- and low-grade serous ovarian carcinoma using radiomics: a pilot study.Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology · 2026Observational
- Radiomics: Current Applications and Future Directions.MedComm · 2026Review
- Multitask deep learning models for ultrasound image analysis: identification of high-grade serous ovarian cancer and segmentation of tumor regions and intratumoral solid components.Journal of ovarian research · 2026Article
- A review of ovarian-adnexal reporting and data system and radiomics in ovarian cancer: focus on ultrasound applications.Frontiers in oncology · 2026Review
- Radiomics and radiogenomics in ovarian cancer: a review with a focus on ultrasound applications.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Review
- A multimodal uncertainty-aware AI system optimizes ovarian cancer risk assessment workflow.NPJ digital medicine · 2025Article
- A nomogram combining clinical features, O-RADS US, and radiomics based on ultrasound imaging for diagnosing ovarian cancer.Scientific reports · 2025Article
- Ultrasound-based radiomics for predicting the five major histological subtypes of epithelial ovarian cancer.BMC medical imaging · 2025Article
- Prognostic and Predictive Value of Systemic Inflammatory Markers in Epithelial Ovarian Cancer.Medicina (Kaunas, Lithuania) · 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
- Advancements in artificial intelligence for ultrasound diagnosis of ovarian cancer: a comprehensive review.Frontiers in oncology · 2025Review
- Radiomics and radiogenomics: extracting more information from medical images for the diagnosis and prognostic prediction of ovarian cancer.Military Medical Research · 2024Review
- Enhancing Ovarian Tumor Diagnosis: Performance of Convolutional Neural Networks in Classifying Ovarian Masses Using Ultrasound Images.Journal of clinical medicine · 2024Article
- AI-based automated segmentation for ovarian/adnexal masses and their internal components on ultrasound imaging.Journal of medical imaging (Bellingham, Wash.) · 2024Article
- Advances in artificial intelligence for the diagnosis and treatment of ovarian cancer (Review).Oncology reports · 2024Review
- Integration of ultrasound radiomics features and clinical factors: A nomogram model for identifying the Ki-67 status in patients with breast carcinoma.Frontiers in oncology · 2022Article
- Noninvasive Assessment of Tumor Histological Grade in Invasive Breast Carcinoma Based on Ultrasound Radiomics and Clinical Characteristics: A Multicenter Study.Technology in cancer research & treatmentArticle
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12 authors.
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Abstract
objectiveTo evaluate the value of ultrasound-based radiomics in the preoperative prediction of type I and type II epithelial ovarian cancer.
methodsA total of 154 patients with epithelial ovarian cancer were enrolled retrospectively. There were 102 unilateral lesions and 52 bilateral lesions among a total of 206 lesions. The data for the 206 lesions were randomly divided into a training set (53 type I + 71 type II) and a test set (36 type I + 46 type II) by random sampling. ITK-SNAP software was used to manually outline the boundary of the tumor, that is, the region of interest, and 4976 features were extracted. The quantitative expression values of the radiomics features were normalized by the Z-score method, and the 7 features with the most differences were screened by using the Lasso regression tenfold cross-validation method. The radiomics model was established by logistic regression. The training set was used to construct the model, and the test set was used to evaluate the predictive efficiency of the model. On the basis of multifactor logistic regression analysis, combined with the radiomics score of each patient, a comprehensive prediction model was established, the nomogram was drawn, and the prediction effect was evaluated by analyzing the area under the receiver operating characteristic curve (AUC), calibration curve and decision curve.
resultsThe AUCs of the training set and test set in the radiomics model and comprehensive model were 0.817 and 0.731 and 0.982 and 0.886, respectively. The calibration curve showed that the two models were in good agreement. The clinical decision curve showed that both methods had good clinical practicability.
conclusionThe radiomics model based on ultrasound images has a good predictive effect for the preoperative differential diagnosis of type I and type II epithelial ovarian cancer. The comprehensive model has higher prediction efficiency.
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