Deep Learning Prediction of Ovarian Malignancy at US Compared with O-RADS and Expert Assessment

A survey on deep learning in medical image analysis

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Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

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The practical implementation of artificial intelligence technologies in medicine

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Epithelial ovarian cancer: Evolution of management in the era of precision medicine

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Staging classification for cancer of the ovary, fallopian tube, and peritoneum

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Terms, definitions and measurements to describe the sonographic features of adnexal tumors: a consensus opinion from the International Ovarian Tumor Analysis (IOTA) group

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Epidemiology of epithelial ovarian cancer

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Deep Learning and Its Applications in Biomedicine

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Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on MRI

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O-RADS US Risk Stratification and Management System: A Consensus Guideline from the ACR Ovarian-Adnexal Reporting and Data System Committee

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Evaluating the risk of ovarian cancer before surgery using the ADNEX model to differentiate between benign, borderline, early and advanced stage invasive, and secondary metastatic tumours: prospective multicentre diagnostic study

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Diagnosis of thyroid cancer using deep convolutional neural network models applied to sonographic images: a retrospective, multicohort, diagnostic study

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Lymph Node Metastasis Prediction from Primary Breast Cancer US Images Using Deep Learning

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A Survey of Decision Fusion and Feature Fusion Strategies for Pattern Classification

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Subjective assessment of adnexal masses with the use of ultrasonography: an analysis of interobserver variability and experience

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The new WHO classification of ovarian, fallopian tube, and primary peritoneal cancer and its clinical implications

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Benign and malignant ovarian masses: selection of the most discriminating gray-scale and Doppler sonographic features.

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Ultrasound image analysis using deep neural networks for discriminating between benign and malignant ovarian tumors: Comparison with expert subjective assessment

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Ovarian cancer screening: Current status and future directions

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Adnexal Masses: US Characterization and Reporting

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A Technical Review of Convolutional Neural Network-Based Mammographic Breast Cancer Diagnosis

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First International Consensus Report on Adnexal Masses: Management Recommendations

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Estimating risk of malignancy in adnexal masses: external validation of the ADNEX model and comparison with other frequently used ultrasound methods

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Diagnostic accuracy and cost‐effectiveness of different strategies to triage women with adnexal masses: a prospective study

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Diagnostic accuracy and inter-observer reliability of the O-RADS scoring system among staff radiologists in a North American academic clinical setting

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Triage for surgical management of ovarian tumors in asymptomatic women: assessment of an ultrasound‐based scoring system

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The role of transvaginal ultrasound in screening for ovarian cancer

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An Evaluation of the Effectiveness of Image-based Texture Features Extracted from Static B-mode Ultrasound Images in Distinguishing between Benign and Malignant Ovarian Masses

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Enhanced Image-Based Endoscopic Pathological Site Classification Using an Ensemble of Deep Learning Models

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Performance of Selected Models for Predicting Malignancy in Ovarian Tumors in Relation to the Degree of Diagnostic Uncertainty by Subjective Assessment With Ultrasound

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Artificial Intelligence Performance in Image-Based Ovarian Cancer Identification: A Systematic Review and Meta-Analysis

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Development and validation of an interpretable model integrating multimodal information for improving ovarian cancer diagnosis

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Ovarian tumor diagnosis using deep convolutional neural networks and a denoising convolutional autoencoder

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Systematic Review and Meta-Analysis of O-RADS Ultrasound and O-RADS MRI for Risk Assessment of Ovarian and Adnexal Lesions

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Artificial intelligence-based risk stratification, accurate diagnosis and treatment prediction in gynecologic oncology

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Deep learning in ovarian cancer diagnosis: a comprehensive review of various imaging modalities

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External Validation of the Ovarian-Adnexal Reporting and Data System ( O-RADS) Lexicon and the International Ovarian Tumor Analysis 2-Step Strategy to Stratify Ovarian Tumors Into O-RADS Risk Groups

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Advances in artificial intelligence for the diagnosis and treatment of ovarian cancer (Review)

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Evolving the Era of 5D Ultrasound? A Systematic Literature Review on the Applications for Artificial Intelligence Ultrasound Imaging in Obstetrics and Gynecology

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Artificial Intelligence in Ultrasound Diagnoses of Ovarian Cancer: A Systematic Review and Meta-Analysis

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Role of artificial intelligence applied to ultrasound in gynecology oncology: A systematic review

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Attention-based deep learning for breast lesions classification on contrast enhanced spectral mammography: a multicentre study

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Artificial intelligence-aided method to detect uterine fibroids in ultrasound images: A retrospective study

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Multi-modality deep learning model reaches high prediction accuracy in the diagnosis of ovarian cancer

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Deep convolutional neural networks for multiple histologic types of ovarian tumors classification in ultrasound images

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Development and validation of an ultrasound-based deep learning radiomics nomogram for predicting the malignant risk of ovarian tumours

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Ultrasound-based deep learning radiomics model for differentiating benign, borderline, and malignant ovarian tumours: a multi-class classification exploratory study

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Diagnostic Performance of Ultrasonography-Based Risk Models in Differentiating Between Benign and Malignant Ovarian Tumors in a US Cohort

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New Trends in Ovarian Cancer Diagnosis Using Deep Learning: A Systematic Review

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Associating Peritoneal Metastasis With T2‐Weighted MRI Images in Epithelial Ovarian Cancer Using Deep Learning and Radiomics: A Multicenter Study

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Prediction of benign and malignant ovarian tumors using Resnet34 on ultrasound images

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A Deep Learning Model System for Diagnosis and Management of Adnexal Masses

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A Transvaginal Ultrasound-Based Deep Learning Model for the Noninvasive Diagnosis of Myometrial Invasion in Patients with Endometrial Cancer: Comparison with Radiologists

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Development and validation of a deep learning pipeline to diagnose ovarian masses using ultrasound screening: a retrospective multicenter study

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Developing a deep learning model for predicting ovarian cancer in Ovarian-Adnexal Reporting and Data System Ultrasound (O-RADS US) Category 4 lesions: A multicenter study

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Discriminative diagnosis of ovarian endometriosis cysts and benign mucinous cystadenomas based on the ConvNeXt algorithm

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Enhancing non-small cell lung cancer tumor segmentation with a novel two-step deep learning approach

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Multimodal ultrasound deep learning to detect fibrosis in early chronic kidney disease

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Machine learning models in evaluating the malignancy risk of ovarian tumors: a comparative study

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Predicting Neoadjuvant Chemotherapy Response and High-Grade Serous Ovarian Cancer From CT Images in Ovarian Cancer with Multitask Deep Learning: A Multicenter Study

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AI-powered diagnosis of ovarian conditions: insights from a newly introduced ultrasound dataset

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Diagnostic performance of IOTA SR and O-RADS combined with CA125, HE4, and risk of malignancy algorithm to distinguish benign and malignant adnexal masses

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High‐level feature‐guided attention optimized neural network for neonatal lateral ventricular dilatation prediction

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Multimodal ultrasound-based radiomics and deep learning for differential diagnosis of O-RADS 4–5 adnexal masses

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DeepOvaNet: A Comprehensive Deep Learning Framework for Predicting and Diagnosing Ovarian Cancer in Women Across Menopausal Transitions

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An explainable machine learning model to solid adnexal masses diagnosis based on clinical data and qualitative ultrasound indicators

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Decision-Level Fusion Classification of Ovarian CT Benign and Malignant Tumors Based on Radiomics and Deep Learning of Dual Views

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Ovarian cancer data analysis using deep learning: A systematic review

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Artificial intelligence for ovarian cancer diagnosis via ultrasound: a systematic review and quantitative assessment of model performance

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Immunodiagnosis — the promise of personalized immunotherapy

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Ultrasonographic diagnosis of ovarian tumors through the deep convolutional neural network

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Deep Learning Analysis With Gray Scale and Doppler Ultrasonography Images to Differentiate Graves’ Disease

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Nomogram based on the O-RADS for predicting the malignancy risk of adnexal masses with complex ultrasound morphology

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Advancements in artificial intelligence for ultrasound diagnosis of ovarian cancer: a comprehensive review

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Saliency-aware multi-resolution graph fusion via self-supervised contrastive learning for robust ultrasound endometrial cancer diagnosis

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Ultrasound-based deep learning model as an assistant improves the diagnosis of ovarian tumors: a multicenter study

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Accuracy of O-RADS System in Differentiating Between Benign and Malignant Adnexal Masses Assessed via External Validation by Inexperienced Gynecologists

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Evaluating the Margins of Breast Cancer Tumors by Using Digital Breast Tomosynthesis with Deep Learning: A Preliminary Assessment

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Additional file 4 of Development and validation of an ultrasound-based deep learning radiomics nomogram for predicting the malignant risk of ovarian tumours

Du, Y., Xiao, Y., Guo, W., Yao, J., Lan, T., Li, S., Wen, H., Zhu, W., He, G., Zheng, H., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26704227

Additional file 1 of Development and validation of an ultrasound-based deep learning radiomics nomogram for predicting the malignant risk of ovarian tumours

Du, Y., Xiao, Y., Guo, W., Yao, J., Lan, T., Li, S., Wen, H., Zhu, W., He, G., Zheng, H., et al. (2024) · Figshare · doi:10.6084/m9.figshare.25572533

Additional file 2 of Development and validation of an ultrasound-based deep learning radiomics nomogram for predicting the malignant risk of ovarian tumours

Du, Y., Xiao, Y., Guo, W., Yao, J., Lan, T., Li, S., Wen, H., Zhu, W., He, G., Zheng, H., et al. (2024) · Figshare · doi:10.6084/m9.figshare.25572654

Additional file 6 of Development and validation of an ultrasound-based deep learning radiomics nomogram for predicting the malignant risk of ovarian tumours

Du, Y., Xiao, Y., Guo, W., Yao, J., Lan, T., Li, S., Wen, H., Zhu, W., He, G., Zheng, H., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26704233

Additional file 3 of Development and validation of an ultrasound-based deep learning radiomics nomogram for predicting the malignant risk of ovarian tumours

Du, Y., Xiao, Y., Guo, W., Yao, J., Lan, T., Li, S., Wen, H., Zhu, W., He, G., Zheng, H., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26704224

Additional file 5 of Development and validation of an ultrasound-based deep learning radiomics nomogram for predicting the malignant risk of ovarian tumours

Du, Y., Xiao, Y., Guo, W., Yao, J., Lan, T., Li, S., Wen, H., Zhu, W., He, G., Zheng, H., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26704230

Additional file 1 of Ultrasound-based deep learning radiomics model for differentiating benign, borderline, and malignant ovarian tumours: a multi-class classification exploratory study

Du, Y., Guo, W., Xiao, Y., Chen, H., Yao, J., & Wu, J. (2024) · Figshare · doi:10.6084/m9.figshare.25608756

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Investigation of multimodal deep learning models for predicting ovarian tumor malignancy based on ultrasound images and clinical information – a comprehensive comparative study against readers and O-RADS

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Investigation of multimodal deep learning models for predicting ovarian tumor malignancy based on ultrasound images and clinical information – a comprehensive comparative study against readers and O-RADS

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EP23.07: Deep learning prediction of ovarian tumours based on ultrasound images and clinical information compared with radiologist assessment

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