ArticleNature communications2019
A mathematical-descriptor of tumor-mesoscopic-structure from computed-tomography images annotates prognostic- and molecular-phenotypes of epithelial ovarian cancer.
Article in Nature communications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 106 papers, 3 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.
Nodule IMmunophenotyping Biomarker for Lung Cancer Early Diagnosis Study
Prospective Validation of CT Based Radiomic Models to Predict Surgical and Clinical Outcomes in Advanced Epithelial Ovarian Cancer
Who cites it
106 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Ovarian cancer staging and follow-up: updated guidelines from the European Society of Urogenital Radiology female pelvic imaging working group.European radiology · 2025Guideline
- What Genetics Can Do for Oncological Imaging: A Systematic Review of the Genetic Validation Data Used in Radiomics Studies.International journal of molecular sciences · 2022Pooled it
- A meta-analysis of Watson for Oncology in clinical application.Scientific reports · 2021Pooled it
- Development and validation of a radiomics model of magnetic resonance for predicting liver metastasis in resectable pancreatic ductal adenocarcinoma patients.Radiation oncology (London, England) · 2023Trial
- A Novel Approach to Ovarian Cancer Diagnosis via CT Imaging: GPT-4o-Driven Automated Feature Recognition and Validation in Clinical Settings.Annals of surgical oncology · 2026Article
- Lobar-level radiomic clustering reveals background lung changes associated with lung cancer risk: a new perspective for early screening.Insights into imaging · 2026Article
- Integrating ultrasound-CT-MR for preoperative multi-task prediction in ovarian cancer: achieving diagnostic parity with multidisciplinary team consensus.NPJ digital medicine · 2026Article
- Dynamic ecosystems of tumor drug resistance mechanisms: from molecular heterogeneity to systemic interventions.Apoptosis : an international journal on programmed cell death · 2026Review
- Radiogenomic landscape of the hallmarks of cancer.Biomarker research · 2026Review
- An explainable imaging-clinical biomarker for non-small cell lung cancer prognostication based on normalised hotspot to centroid distance and [European journal of nuclear medicine and molecular imaging · 2026Article
- Evolving landscape of imaging-based evaluation in systemic autoimmune rheumatic disease-associated interstitial lung disease: from visual assessment to quantitative artificial intelligence-assisted evaluation.Journal of rheumatic diseases · 2026Review
- XGBoost-Cox modeling with SHAP analysis for survival prediction in ovarian cancer patients: a retrospective cohort study.BMC cancer · 2026Article
- A radio-genomics biomarker for precision epidermal growth factor receptor mutation targeting therapy in non-small cell lung cancer.Scientific reports · 2026Article
- Radiomics of T2-weighted MRI for pretreatment prediction of prognosis and temozolomide chemosensitivity in glioma.BMC cancer · 2026Article
- Radiomics in spinal research: a narrative review.Frontiers in bioengineering and biotechnology · 2026Review
- Clinical value of habitat radiomics within the 2-cm edema zone surrounding the postoperative residual cavity in predicting glioma recurrence.Frontiers in oncology · 2026Article
- Article
- CT radiomic stratification signature to optimize clinical decisions for ovarian cancer patients receiving neoadjuvant chemotherapy and the underlying biological basis: a multicenter retrospective study.Journal of translational medicine · 2025Article
- CT-based radiomics models decode fibrosis content and molecular differences in pancreatic ductal adenocarcinoma: a multi-institutional study.Insights into imaging · 2025Article
- Enhancing ovarian cancer prognosis with an artificial intelligence-derived model: Multi-omics integration and therapeutic implications.Translational oncology · 2025Article
46 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors.
Funding
Abstract
The five-year survival rate of epithelial ovarian cancer (EOC) is approximately 35-40% despite maximal treatment efforts, highlighting a need for stratification biomarkers for personalized treatment. Here we extract 657 quantitative mathematical descriptors from the preoperative CT images of 364 EOC patients at their initial presentation. Using machine learning, we derive a non-invasive summary-statistic of the primary ovarian tumor based on 4 descriptors, which we name "Radiomic Prognostic Vector" (RPV). RPV reliably identifies the 5% of patients with median overall survival less than 2 years, significantly improves established prognostic methods, and is validated in two independent, multi-center cohorts. Furthermore, genetic, transcriptomic and proteomic analysis from two independent datasets elucidate that stromal phenotype and DNA damage response pathways are activated in RPV-stratified tumors. RPV and its associated analysis platform could be exploited to guide personalized therapy of EOC and is potentially transferrable to other cancer types.
Indexed as
Identifiers
What OpenQuestion holds
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.