ArticleDiscover oncology2025
Nomogram for predicting postoperative recurrence in elderly patients with hepatocellular carcinoma based on multimodal ultrasound.
Article in Discover oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
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
objectiveTo develop and validate a nomogram prediction model for postoperative recurrence in elderly patients with hepatocellular carcinoma (HCC) based on multimodal ultrasound parameters.
methodsClinical data of 299 elderly HCC patients who underwent laparoscopic hepatectomy in our hospital from January 2021 to June 2024 were retrospectively collected. Patients were randomly divided into a training cohort (n = 209) and a validation cohort (n = 90) at a ratio of 7:3. According to tumor recurrence within 1 year after surgery, patients were classified into recurrence and non-recurrence groups. Preoperative multimodal ultrasound parameters and other clinical characteristics were recorded. Multivariate logistic regression analysis was performed to identify independent risk factors for postoperative recurrence. A nomogram prediction model was constructed based on multimodal ultrasound parameters using R software. Model performance was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA).
resultsNo significant differences were observed in baseline characteristics between the training and validation cohorts (P > 0.05). In the training cohort, the recurrence group had a lower proportion of intact tumor capsules, lower hepatic artery pulsatility index (HA-PI) and resistive index (HA-RI), higher tumor stiffness and strain ratio (SR), shorter washout time (WT), and a higher prevalence of chronic viral hepatitis compared with the non-recurrence group (all P < 0.05). Multivariate logistic regression revealed that non-intact tumor capsule, lower HA-RI, higher SR, shorter WT, and concomitant chronic viral hepatitis were independent risk factors for postoperative recurrence (P < 0.05). Based on these predictors, a nomogram prediction model was developed. ROC analysis showed areas under the curve (AUC) of 0.906 (95% CI: 0.866-0.947) in the training cohort and 0.926 (95% CI: 0.868-0.983) in the validation cohort, indicating excellent discrimination. Calibration curves demonstrated good agreement between predicted and observed outcomes in both cohorts (P > 0.05). DCA demonstrated substantial clinical net benefit across a wide range of threshold probabilities (0.01-0.92 in the training cohort; 0.01-0.96 in the validation cohort).
conclusionThe nomogram prediction model based on multimodal ultrasound parameters demonstrates favorable predictive performance for postoperative recurrence in elderly HCC patients.
Indexed as
Identifiers
What Socratic 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 Socratic graph.