ArticleBMC cancer2024
Development and validation of a
Article in BMC cancer, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 15 citations in OpenAlex.
- Clinical challenges in the adaptation of AI predictive models in radiation oncology for gynaecological cancer: a systematic review by the radiation oncology-AI MITO group.La Radiologia medica · 2026Review
- Pre-treatment MRI-derived fractal dimension as a prognostic marker in cervical cancer treated with definitive radiotherapy with or without concurrent chemotherapy.BMC medical imaging · 2026Article
- Cervical cancer in the modern era: cutting-edge strategies for diagnosis and treatment.Japanese journal of radiology · 2026Review
- Artificial intelligence (AI) and machine learning (ML) in ovarian cancer: transforming detection, treatment, and prevention.Journal of ovarian research · 2026Review
- An exploratory assessment of early and delta PET radiomic features for outcome prediction in locally advanced cervical cancer.European journal of nuclear medicine and molecular imaging · 2025Article
- Habitat-based transformer model in pretreatmentBMC cancer · 2025Article
- Pre-Treatment PET Radiomics for Prediction of Disease-Free Survival in Cervical Cancer.Cancers · 2025Article
- Review
- Multimodal deep learning model for prognostic prediction in cervical cancer receiving definitive radiotherapy: a multi-center study.NPJ digital medicine · 2025Article
- Development and validation of a nomogram for predicting bone marrow involvement in lymphoma patients based onAnnals of nuclear medicine · 2025Article
- Camrelizumab combined with apatinib plus irinotecan as a second-line treatment in advanced or metastatic esophageal squamous cell carcinoma patients.BMC cancer · 2025Article
- Artificial intelligence radiomics in the diagnosis, treatment, and prognosis of gynecological cancer: a literature review.Translational cancer research · 2025Review
- Article
- Machine learning in image-based outcome prediction after radiotherapy: A review.Journal of applied clinical medical physics · 2025Review
- Assessing the Efficacy of 18F FDG PET-CT in Preoperative Staging of Early-Stage Cervical Cancer in Novi Sad, Serbia-A Pilot Study.Journal of clinical medicine · 2024Article
- Comparative Diagnostic Value of 18F-FDG-PET-CT and Intraoperative Examination in Cervical Cancer Staging.Medicina (Kaunas, Lithuania) · 2024Article
Corrections and comments
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Authors and funding
8 authors at 5 institutions in 1 country.
Funding
Abstract
backgroundThe existing staging system cannot meet the needs of accurate survival prediction. Accurate survival prediction for locally advanced cervical cancer (LACC) patients who have undergone concurrent radiochemotherapy (CCRT) can improve their treatment management. Thus, this present study aimed to develop and validate radiomics models based on pretreatment
methodsThe data from 190 consecutive patients with LACC who underwent pretreatment
resultsIn total, one clinical, one PET radiomics, and three CT radiomics features were significantly associated with progression-free survival in the training cohort. Across all three cohorts, the combined model displayed better efficacy and clinical utility than any of these parameters alone in predicting 3-year progression-free survival (area under curve: 0.661, 0.718, and 0.775; C-index: 0.698, 0.724, and 0.705, respectively) and 5-year progression-free survival (area under curve: 0.661, 0.711, and 0.767; C-index, 0.698, 0.722, and 0.676, respectively). On subsequent construction of a nomogram, the calibration curve demonstrated good agreement between actually observed and nomogram-predicted values.
conclusionsIn this study, a clinico-radiomics prediction model was developed and successfully validated using an independent external validation cohort. The nomogram incorporating radiomics and clinical features could be a useful clinical tool for the early and accurate assessment of long-term prognosis in patients with LACC patients who undergo concurrent chemoradiotherapy.
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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.