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MR image-based radiomics to differentiate type Ι and type ΙΙ epithelial ovarian cancers

https://doi.org/10.1007/s00330-020-07091-2
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/jmri.25722openalex-snapshot
Comparison between types I and II epithelial ovarian cancer using histogram analysis of monoexponential, biexponential, and stretched‐exponential diffusion models
inferredcites →https://doi.org/10.1002/jmri.27084openalex-snapshot
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inferredcites →https://doi.org/10.1007/s00330-019-06124-9openalex-snapshot
Magnetic resonance imaging radiomics in categorizing ovarian masses and predicting clinical outcome: a preliminary study
inferredcites →https://doi.org/10.1007/s00428-016-2040-8openalex-snapshot
Ovarian borderline tumors in the 2014 WHO classification: evolving concepts and diagnostic criteria
inferredcites →https://doi.org/10.1016/j.cell.2018.02.010openalex-snapshot
Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning
inferredcites →https://doi.org/10.1016/j.ejca.2011.11.036openalex-snapshot
Radiomics: Extracting more information from medical images using advanced feature analysis
inferredcites →https://doi.org/10.1016/j.ejrad.2015.01.005openalex-snapshot
Diffusion-weighted MRI of epithelial ovarian cancers: Correlation of apparent diffusion coefficient values with histologic grade and surgical stage
inferredcites →https://doi.org/10.1016/j.mri.2012.06.010openalex-snapshot
Radiomics: the process and the challenges
inferredcites →https://doi.org/10.1016/j.oret.2016.12.009openalex-snapshot
Deep Learning Is Effective for Classifying Normal versus Age-Related Macular Degeneration OCT Images
inferredcites →https://doi.org/10.1016/j.ultrasmedbio.2016.12.016openalex-snapshot
Sonoelastomics for Breast Tumor Classification: A Radiomics Approach with Clustering-Based Feature Selection on Sonoelastography
inferredcites →https://doi.org/10.1016/s0140-6736(18)32552-2openalex-snapshot
Epithelial ovarian cancer
inferredcites →https://doi.org/10.1097/dcr.0000000000000794openalex-snapshot
MRI for Local Staging of Colon Cancer: Can MRI Become the Optimal Staging Modality for Patients With Colon Cancer?
inferredcites →https://doi.org/10.1097/igc.0000000000000089openalex-snapshot
Epithelial Ovarian Cancer
inferredcites →https://doi.org/10.1097/pas.0b013e3181cf3d79openalex-snapshot
The Origin and Pathogenesis of Epithelial Ovarian Cancer: A Proposed Unifying Theory
inferredcites →https://doi.org/10.1097/pgp.0b013e318161e4f5openalex-snapshot
Pathogenesis of Ovarian Cancer
inferredcites →https://doi.org/10.1111/j.1365-2591.1974.tb01123.xopenalex-snapshot
A Comparison of the Flexibility of Giromatic and Hand Operated Instruments in Endodontics
inferredcites →https://doi.org/10.1111/j.1467-9868.2007.00607.xopenalex-snapshot
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MR Imaging Radiomics Signatures for Predicting the Risk of Breast Cancer Recurrence as Given by Research Versions of MammaPrint, Oncotype DX, and PAM50 Gene Assays
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Radiomics Signature: A Potential Biomarker for the Prediction of Disease-Free Survival in Early-Stage (I or II) Non—Small Cell Lung Cancer
inferredcites →https://doi.org/10.1148/radiol.2413051276openalex-snapshot
Malignant Astrocytic Tumors: Clinical Importance of Apparent Diffusion Coefficient in Prediction of Grade and Prognosis
inferredcites →https://doi.org/10.1148/rg.313105066openalex-snapshot
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inferredcites →https://doi.org/10.1186/1756-9966-31-14openalex-snapshot
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inferredcites →https://doi.org/10.1186/s13048-019-0498-0openalex-snapshot
Tomm34 is commonly expressed in epithelial ovarian cancer and associates with tumour type and high FIGO stage
inferredcites →https://doi.org/10.1200/jco.2015.65.9128openalex-snapshot
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International Agency for Research on Cancer
inferredcites →https://doi.org/10.3389/fonc.2020.00319openalex-snapshot
Novel Therapeutic Strategies for Ovarian Cancer Stem Cells
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What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4124049openalex-extracted
Artificial Intelligence Performance in Image-Based Ovarian Cancer Identification: A Systematic Review and Meta-Analysis
inferred← citeshttps://doi.org/10.1001/jamanetworkopen.2022.45141openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/jmri.27592openalex-snapshot
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inferred← citeshttps://doi.org/10.1002/jmri.28982openalex-snapshot
MRI ‐Based Radiomics and Deep Learning in Biological Characteristics and Prognosis of Hepatocellular Carcinoma: Opportunities and Challenges
inferred← citeshttps://doi.org/10.1007/978-3-030-98950-7_24openalex-snapshot
MR-Derived Biomarkers for Cancer Characterization
inferred← citeshttps://doi.org/10.1007/s00261-020-02820-zopenalex-snapshot
Radiomics and radiogenomics in ovarian cancer: a literature review
inferred← citeshttps://doi.org/10.1007/s00261-023-03802-7openalex-snapshot
Preoperative prediction of miliary changes in the small bowel mesentery in advanced high-grade serous ovarian cancer using MRI radiomics nomogram
inferred← citeshttps://doi.org/10.1007/s00261-024-04343-3openalex-snapshot
Radiomics combined with clinical and MRI features may provide preoperative evaluation of suboptimal debulking surgery for serous ovarian carcinoma
inferred← citeshttps://doi.org/10.1007/s00261-024-04437-yopenalex-snapshot
CT-based radiomic analysis for categorization of ovarian sex cord-stromal tumors and epithelial ovarian cancers
inferred← citeshttps://doi.org/10.1007/s00261-024-04752-4openalex-snapshot
Feasibility of iodine concentration parameter and extracellular volume fraction derived from dual-energy CT for distinguishing type I and type II epithelial ovarian carcinoma
inferred← citeshttps://doi.org/10.1007/s00270-022-03221-zopenalex-snapshot
Preoperative CECT-based Radiomic Signature for Predicting the Response of Transarterial Chemoembolization (TACE) Therapy in Hepatocellular Carcinoma
inferred← citeshttps://doi.org/10.1007/s00330-022-09318-wopenalex-snapshot
Computed tomography–based radiomics machine learning classifiers to differentiate type I and type II epithelial ovarian cancers
inferred← citeshttps://doi.org/10.1007/s11547-023-01666-xopenalex-snapshot
Seeing beyond the tumor: computed tomography image-based radiomic analysis helps identify ovarian clear cell carcinoma subtype in epithelial ovarian cancer
inferred← citeshttps://doi.org/10.1007/s11547-024-01861-4openalex-snapshot
Recent trends in AI applications for pelvic MRI: a comprehensive review
inferred← citeshttps://doi.org/10.1007/s11547-025-02006-xopenalex-snapshot
Integrative deep learning and radiomics analysis for ovarian tumor classification and diagnosis: a multicenter large-sample comparative study
inferred← citeshttps://doi.org/10.1007/s11604-024-01545-zopenalex-snapshot
Magnetic resonance imaging-based radiomics analysis of the differential diagnosis of ovarian clear cell carcinoma and endometrioid carcinoma: a retrospective study
inferred← citeshttps://doi.org/10.1007/s11831-026-10590-yopenalex-snapshot
Deep Learning and Explainable AI for Ovarian Cancer Detection: A Comprehensive Literature Review
inferred← citeshttps://doi.org/10.1007/s13246-021-01022-1openalex-snapshot
Bi-parametric magnetic resonance imaging based radiomics for the identification of benign and malignant prostate lesions: cross-vendor validation
inferred← citeshttps://doi.org/10.1007/s42058-023-00131-yopenalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.acra.2022.06.007openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.acra.2023.08.002openalex-snapshot
Deep Learning Radiomics Nomogram Based on Magnetic Resonance Imaging for Differentiating Type I/II Epithelial Ovarian Cancer
inferred← citeshttps://doi.org/10.1016/j.acra.2023.11.032openalex-snapshot
CT-Based Radiomics for the Preoperative Prediction of Occult Peritoneal Metastasis in Epithelial Ovarian Cancers
inferred← citeshttps://doi.org/10.1016/j.acra.2024.05.032openalex-snapshot
Multiparametric MRI-based Radiomics Analysis for Prediction of Lymph Node Metastasis and Survival Outcome in Gastric Cancer: A Dual-center Study
inferred← citeshttps://doi.org/10.1016/j.acra.2026.04.011openalex-snapshot
Habitat Radiomics Based on Diffusion-weighted MRI for Predicting PARP Inhibitor Resistance in Advanced High-grade Serous Ovarian Cancer
inferred← citeshttps://doi.org/10.1016/j.bspc.2021.102446openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.crad.2023.05.004openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.crad.2024.01.018openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.crad.2025.106833openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.eclinm.2022.101662openalex-snapshot
Artificial intelligence performance in image-based ovarian cancer identification: A systematic review and meta-analysis
inferred← citeshttps://doi.org/10.1016/j.knosys.2026.115749openalex-snapshot
ACDA: Anatomically constrained distribution alignment for robust medical image segmentation
inferred← citeshttps://doi.org/10.1016/j.media.2025.103907openalex-snapshot
CIA-net: Cross-modality interaction and aggregation network for ovarian tumor segmentation from multi-modal MRI
inferred← citeshttps://doi.org/10.1016/j.semcancer.2023.09.005openalex-snapshot
Artificial intelligence-based risk stratification, accurate diagnosis and treatment prediction in gynecologic oncology
inferred← citeshttps://doi.org/10.1038/s41598-023-29814-3openalex-snapshot
Deep learning for the ovarian lesion localization and discrimination between borderline and malignant ovarian tumors based on routine MR imaging
inferred← citeshttps://doi.org/10.1093/bjr/tqae144openalex-snapshot
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inferred← citeshttps://doi.org/10.1093/oncolo/oyac036openalex-snapshot
Artificial Intelligence-based Radiomics in the Era of Immuno-oncology
inferred← citeshttps://doi.org/10.1097/js9.0000000000004924openalex-snapshot
CT deep learning radiomics and genomics for predicting staging of epithelial ovarian cancer
inferred← citeshttps://doi.org/10.1097/rct.0000000000001279openalex-snapshot
Radiogenomics: A Valuable Tool for the Clinical Assessment and Research of Ovarian Cancer
inferred← citeshttps://doi.org/10.1097/rct.0000000000001497openalex-snapshot
Differentiation of Sinonasal NKT From Diffuse Large B-Cell Lymphoma Using Machine Learning and MRI-Based Radiomics
inferred← citeshttps://doi.org/10.1109/bibm55620.2022.9995556openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/ojemb.2023.3271455openalex-snapshot
Normalization Strategies in Multi-Center Radiomics Abdominal MRI: Systematic Review and Meta-Analyses
inferred← citeshttps://doi.org/10.1155/2022/1716268openalex-snapshot
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inferred← citeshttps://doi.org/10.1186/s12880-025-01624-1openalex-snapshot
Ultrasound-based radiomics for predicting the five major histological subtypes of epithelial ovarian cancer
inferred← citeshttps://doi.org/10.1186/s12880-026-02231-4openalex-snapshot
A dual-center study: multimodal fusion-based deep learning approach for pathological subtype prediction of type I and type II ovarian cancer
inferred← citeshttps://doi.org/10.1186/s12887-023-04057-3openalex-snapshot
Identification and validation of radiomic features from computed tomography for preoperative classification of neuroblastic tumors in children
inferred← citeshttps://doi.org/10.1186/s12967-025-07409-yopenalex-snapshot
Radiology-based artificial intelligence for predicting targeted therapy response in pan-cancer: a comprehensive review
inferred← citeshttps://doi.org/10.1186/s13048-021-00941-7openalex-snapshot
MR-based radiomics-clinical nomogram in epithelial ovarian tumor prognosis prediction: tumor body texture analysis across various acquisition protocols
inferred← citeshttps://doi.org/10.1186/s13048-024-01456-7openalex-snapshot
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inferred← citeshttps://doi.org/10.1186/s13048-025-01906-wopenalex-snapshot
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inferred← citeshttps://doi.org/10.1186/s13048-026-02097-8openalex-snapshot
Spatial habitats radiomics from multiparametric magnetic resonance imaging predict platinum resistance in epithelial ovarian cancer
inferred← citeshttps://doi.org/10.1186/s13244-022-01264-xopenalex-snapshot
T2-weighted MRI-based radiomics for discriminating between benign and borderline epithelial ovarian tumors: a multicenter study
inferred← citeshttps://doi.org/10.1186/s13244-023-01412-xopenalex-snapshot
Machine learning combined with radiomics and deep learning features extracted from CT images: a novel AI model to distinguish benign from malignant ovarian tumors
inferred← citeshttps://doi.org/10.1186/s13244-023-01464-zopenalex-snapshot
A systematic review and meta-analysis of CT and MRI radiomics in ovarian cancer: methodological issues and clinical utility
inferred← citeshttps://doi.org/10.1186/s13244-023-01500-yopenalex-snapshot
Radiomics in the evaluation of ovarian masses — a systematic review
inferred← citeshttps://doi.org/10.1186/s13244-025-02047-wopenalex-snapshot
The value of a radiomics model in predicting ovarian malignancy: a retrospective multi-center comparison with O-RADS and radiologists
inferred← citeshttps://doi.org/10.1186/s40779-024-00580-1openalex-snapshot
Radiomics and radiogenomics: extracting more information from medical images for the diagnosis and prognostic prediction of ovarian cancer
inferred← citeshttps://doi.org/10.1259/bjr.20211332openalex-snapshot
Nomogram based on ultrasound radiomics score and clinical variables for predicting histologic subtypes of epithelial ovarian cancer
inferred← citeshttps://doi.org/10.1259/bjro.20220055openalex-snapshot
Virtual biopsy in abdominal pathology: where do we stand?
inferred← citeshttps://doi.org/10.21037/qims-22-494openalex-snapshot
Deep learning-based segmentation of epithelial ovarian cancer on T2-weighted magnetic resonance images
inferred← citeshttps://doi.org/10.21203/rs.3.rs-4476893/v1openalex-snapshot
Feasibility of iodine concentration parameter and extracellular volume fraction derived from dual-energy CT for distinguishing type Ⅰ and type Ⅱ epithelial ovarian carcinoma
inferred← citeshttps://doi.org/10.21203/rs.3.rs-4560599/v1openalex-snapshot
Value of an 18F-FDG-based peritoneal cancer index in predicting tumor grade, tumor burden, and completeness of cytoreductive surgery in epithelial ovarian cancer
inferred← citeshttps://doi.org/10.21569/2222-7415-2023-13-3openalex-snapshot
inferred← citeshttps://doi.org/10.21569/2222-7415-2023-13-3-110-130openalex-snapshot
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inferred← citeshttps://doi.org/10.2174/1573405618666220516122145openalex-snapshot
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inferred← citeshttps://doi.org/10.3233/xst-221244openalex-snapshot
Application of enhanced computed tomography-based radiomics nomogram analysis to differentiate metastatic ovarian tumors from epithelial ovarian tumors
inferred← citeshttps://doi.org/10.3389/fgene.2021.753948openalex-snapshot
Diagnosis of Ovarian Neoplasms Using Nomogram in Combination With Ultrasound Image-Based Radiomics Signature and Clinical Factors
inferred← citeshttps://doi.org/10.3389/fimmu.2026.1795144openalex-snapshot
Artificial intelligence in ovarian cancer: advancing in precision diagnosis and clinical management
inferred← citeshttps://doi.org/10.3389/fmed.2026.1889837openalex-snapshot
The use of MRI in the diagnosis of ovarian cancer
inferred← citeshttps://doi.org/10.3389/fonc.2021.610742openalex-snapshot
Preoperative Prediction of Metastasis for Ovarian Cancer Based on Computed Tomography Radiomics Features and Clinical Factors
inferred← citeshttps://doi.org/10.3389/fonc.2021.698373openalex-snapshot
Progress of MRI Radiomics in Hepatocellular Carcinoma
inferred← citeshttps://doi.org/10.3389/fonc.2022.1073983openalex-snapshot
Magnetic resonance imaging radiomics to differentiate ovarian sex cord-stromal tumors and primary epithelial ovarian cancers
inferred← citeshttps://doi.org/10.3389/fonc.2022.978123openalex-snapshot
Diffusion-weighted imaging-based radiomics in epithelial ovarian tumors: Assessment of histologic subtype
inferred← citeshttps://doi.org/10.3389/fonc.2024.1456932openalex-snapshot
Advances in ovarian cancer radiomics: a bibliometric analysis from 2010 to 2024
inferred← citeshttps://doi.org/10.3390/app11177833openalex-snapshot
Radiomics Analysis in Ovarian Cancer: A Narrative Review
inferred← citeshttps://doi.org/10.3390/app132111839openalex-snapshot
Radiomics in Gynaecological Imaging: A State-of-the-Art Review
inferred← citeshttps://doi.org/10.3390/cancers14040987openalex-snapshot
Diagnosing Ovarian Cancer on MRI: A Preliminary Study Comparing Deep Learning and Radiologist Assessments
inferred← citeshttps://doi.org/10.3390/cancers14102464openalex-snapshot
The Value of Magnetic Resonance Diffusion-Weighted Imaging and Dynamic Contrast Enhancement in the Diagnosis and Prognosis of Treatment Response in Patients with Epithelial Serous Ovarian Cancer
inferred← citeshttps://doi.org/10.3390/cancers16213569openalex-snapshot
Evaluation of Selected MRI Parameters in the Differentiation of Mucinous Ovarian Carcinomas and Metastatic Ovarian Tumors
inferred← citeshttps://doi.org/10.3802/jgo.2024.35.e75openalex-snapshot
Lymphadenectomy in clinically early epithelial ovarian cancer and survival analysis (LILAC): a Gynecologic Oncology Research Investigators Collaboration (GORILLA-3002) retrospective study
inferred← citeshttps://doi.org/10.3892/or.2024.8705openalex-snapshot
Advances in artificial intelligence for the diagnosis and treatment of ovarian cancer (Review)
inferred← citeshttps://doi.org/10.6084/m9.figshare.18318235openalex-snapshot
Additional file 1 of MR-based radiomics-clinical nomogram in epithelial ovarian tumor prognosis prediction: tumor body texture analysis across various acquisition protocols
inferred← citeshttps://doi.org/10.6084/m9.figshare.26085161openalex-snapshot
Additional file 1 of Predicting CD27 expression and clinical prognosis in serous ovarian cancer using CT-based radiomics
inferred← citeshttps://doi.org/10.61186/rabms.11.1.19openalex-snapshot
Advancements in MRI radiomics for hepatocellular carcinoma: a narrative review
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