ArticleFrontiers in oncology2025
Machine learning for prognostic impact in elderly unresectable hepatocellular carcinoma undergoing radiotherapy.
Article in Frontiers in oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Explainable Lightweight Model Using Low-Rank and Convolutional Block Attention for Pancreatic Cancer Diagnosis.The international journal of medical robotics + computer assisted surgery : MRCAS · 2026Article
- A robust ammonia metabolism gene signature identified by machine learning predicts prognosis and immunotherapy response in clear cell renal cell carcinoma.Frontiers in oncology · 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
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background/Aim: This study develops a machine learning-based predictive model to evaluate the survival outcomes of elderly patients with unresectable hepatocellular carcinoma (HCC) undergoing radiotherapy. Methods: The 2377 patients from SEER database were divided into training and internal validation cohorts. Additionally, 99 patients from our hospital were used for an external validation cohort. In the training cohort, 101 machine learning-based radiomics models were developed, and the optimal model's performance was subsequently evaluated in both the internal and external validation cohorts. Results: The StepCox + GBM model demonstrated the highest C-index of 0.7 in the training cohort. The model was further evaluated using area under the receiver operating characteristic (AUC-ROC) curves, with AUC values ranging from 0.736 to 0.783, indicating strong predictive performance. Furthermore, the calibration curve and decision curves confirmed that the model had good predictive performance. Conclusions: The StepCox + GBM model could help optimize the use of radiotherapy for elderly HCC patients, improving survival outcomes and guiding personalized treatment strategies.
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.