ReviewMilitary Medical Research2024
Radiomics and radiogenomics: extracting more information from medical images for the diagnosis and prognostic prediction of ovarian cancer.
Review in Military Medical Research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
27 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Radiomics-based machine learning in the prediction of peritoneal metastasis in ovarian cancer: a systematic review and meta-analysis.BMC medical imaging · 2025Pooled it
- AI-Derived Blood Biomarkers for Ovarian Cancer Diagnosis: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Peritumoral ultrasound-driven multimodal AI model for predicting lymphovascular space invasion and prognosis in cervical cancer.Abdominal radiology (New York) · 2026Article
- Deep learning-based classification of gh-secreting pituitary neuroendocrine tumors granulation patterns using H&E-stained histopathology.BMC endocrine disorders · 2026Article
- Temporal deep learning for 28-day mortality prediction in critically ill ovarian cancer patients: a multicenter development and validation study using hourly vital signs.Journal of ovarian research · 2026Article
- Construction of a nomogram model for predicting benignity and malignancy in adnexal masses.Ultrasonography (Seoul, Korea) · 2026Article
- Natural Compounds as Epimodulators in Epithelial Ovarian Cancer.Epigenomes · 2026Review
- Tumor stroma ratio-based radiomics model for predicting platinum resistance and prognosis in epithelial ovarian cancer.Journal of ovarian research · 2026Article
- Artificial intelligence (AI) and machine learning (ML) in ovarian cancer: transforming detection, treatment, and prevention.Journal of ovarian research · 2026Review
- Recent applications of artificial intelligence in cancer radiotherapy and immunotherapy: current status and future directions.Frontiers in immunology · 2026Review
- Radiomics-based MRI model for differentiating ovarian cystadenoma and cystadenocarcinoma.Frontiers in oncology · 2026Article
- A CT-based radiomics model for preoperative risk stratification of gastrointestinal stromal tumors.Frontiers in oncology · 2026Article
- Artificial intelligence in ovarian cancer: advancing in precision diagnosis and clinical management.Frontiers in immunology · 2026Review
- Discrimination of benign and malignant ovarian sex cord-stromal tumors through the analysis of clinical features, MR imaging, and MR-based radiomics.Frontiers in oncology · 2026Article
- 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
- CT radiomics for predicting postoperative disease specific survival in clear cell renal cell carcinoma: a multi-center study.Quantitative imaging in medicine and surgery · 2025Article
- Artificial intelligence in the diagnosis and management of gynecologic cancer.International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics · 2025Review
- MRI-based radiomics for preoperative T-staging of rectal cancer: a retrospective analysis.International journal of colorectal disease · 2025Article
- Artificial intelligence in orthopedics: fundamentals, current applications, and future perspectives.Military Medical Research · 2025Review
- Gut-Vaginal Microbiome Crosstalk in Ovarian Cancer: Implications for Early Diagnosis.Pathogens (Basel, Switzerland) · 2025Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Ovarian cancer (OC) remains one of the most lethal gynecological malignancies globally. Despite the implementation of various medical imaging approaches for OC screening, achieving accurate differential diagnosis of ovarian tumors continues to pose significant challenges due to variability in image performance, resulting in a lack of objectivity that relies heavily on the expertise of medical professionals. This challenge can be addressed through the emergence and advancement of radiomics, which enables high-throughput extraction of valuable information from conventional medical images. Furthermore, radiomics can integrate with genomics, a novel approach termed radiogenomics, which allows for a more comprehensive, precise, and personalized assessment of tumor biological features. In this review, we present an extensive overview of the application of radiomics and radiogenomics in diagnosing and predicting ovarian tumors. The findings indicate that artificial intelligence methods based on imaging can accurately differentiate between benign and malignant ovarian tumors, as well as classify their subtypes. Moreover, these methods are effective in forecasting survival rates, treatment outcomes, metastasis risk, and recurrence for patients with OC. It is anticipated that these advancements will function as decision-support tools for managing OC while contributing to the advancement of precision medicine.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.