ArticleBMC medical imaging2026
Preoperative synthetic magnetic resonance imaging-based biomarkers for predicting tumor budding of rectal cancer.
Article in BMC medical imaging, 2026. 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
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTumor budding (TB) is recognized as an independent prognostic factor in rectal cancer (RC). This study aimed to evaluate the ability of preoperative synthetic MRI (SyMRI) quantitative parameters to predict high-grade TB (≥ 10 buds, Bd 3) in RC.
methodsA total of 105 patients with RC were enrolled, including 42 with high-grade TB and 63 with non-high-grade TB (< 10 buds, Bd 1 and 2) patients. Parameters T1, T2 and PD values of SyMRI were obtained on a GE AW 4.7 workstation. Continuous variables were compared by independent-samples t tests, and categorical variables were analyzed using the chi-square test or Fisher's exact test, as appropriate. Binary logistic regression was performed to identify independent predictors of high-grade TB. ROC analysis was used to assess the predictive performance of T1, T2, and their combined model. Model fit was compared using the Akaike information criterion (AIC), and calibration and overall predictive accuracy were evaluated using the Hosmer-Lemeshow test and the Brier score, respectively.
resultsCompared with non-high-grade TB group, high-grade TB group showed significantly higher T1 value (p < 0.001) and lower T2 value (p < 0.001). Multivariable logistic regression confirmed that both T1 and T2 values were independent predictors of high-grade TB. The combined T1 and T2 values model achieved an AUC of 0.926, outperforming the T1 value alone model (AUC = 0.883) and the T2 value alone model (AUC = 0.838). In addition, the combined model showed improved fit (AIC = 72.22 vs. 92.77 and 108.18) and better overall accuracy (Brier score = 0.107 vs. 0.135 and 0.168).
conclusionsParameters T1 and T2 values of SyMRI are independent predictors of high-grade TB in RC. A simplified model integrating T1 and T2 values provides superior discrimination and improved model fit and accuracy compared with single-parameter models, suggesting that T1 and T2 values may have potential for noninvasive assessment of TB status.
trial registrationNot applicable.
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.