ArticleBMC cancer2024
Integrating
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 8 papers.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- Prognostic significance of liver-to-muscle FDG uptake ratio and ınflammatory biomarkers in small cell lung cancer.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Article
- Updated evidence on the accuracy ofQuantitative imaging in medicine and surgery · 2026Review
- Machine learning survival prediction in esophageal cancer using radiomics and body composition from pretreatment and follow-up T12-level computed tomography.World journal of gastrointestinal oncology · 2025Article
- Bibliometric examination of neoadjuvant immunotherapy in esophageal cancer: insights, trends, collaborative networks, and prospective directions.Journal of thoracic disease · 2025Article
- Radiomics meets sarcopenia: Machine learning-based multimodal modeling for esophageal cancer outcomes.World journal of gastrointestinal oncology · 2025Review
- 18F-FDG PET/CT-based deep radiomic models for enhancing chemotherapy response prediction in breast cancer.Medical oncology (Northwood, London, England) · 2025Article
- Machine Learning and Deep Learning Hybrid Approach Based on Muscle Imaging Features for Diagnosis of Esophageal Cancer.Diagnostics (Basel, Switzerland) · 2025Article
- Synergic value of 3D CT-derived body composition and triglyceride glucose body mass for survival prognostic modeling in unresectable pancreatic cancer.Frontiers in nutrition · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
backgroundThis study aimed to develop a predictive model utilizing radiomics and body composition features derived from
methodsWe analyzed data from 91 patients who underwent baseline
resultsMultivariate analysis identified Rad-score
conclusionsThis study underscored the potential of combining Rad-score with clinical and body composition data to refine prognostic assessment in ESCC patients.
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.