ArticleTranslational oncology2026
CT texture analysis and delta radiomics in predicting R0 resection after preoperative chemotherapy in resectable pancreatic ductal adenocarcinoma.
Article in Translational oncology, 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
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo evaluate the predictive performance of CT-based radiomic delta-features for predicting R0 resection in resectable pancreatic ductal adenocarcinoma (PDAC) following NALIRIFOX neoadjuvant chemotherapy. MATERIALS AND
methodsThis prospective study included 107 consecutive patients with resectable PDAC treated with NALIRIFOX. After applying strict selection criteria for imaging quality and timing, 55 patients underwent comprehensive radiomic analysis. Radiomic features were systematically extracted from pre-treatment and post-treatment CT scans, with delta-features calculated as percentage variations between timepoints. Features demonstrating inter-observer intraclass correlation coefficient greater than 0.90 in both arterial and venous phases were selected. Following correlation analysis to remove redundant features (absolute correlation greater than 0.80), three predictive models were developed based on pre-treatment, post-treatment, and delta-features. Model validation was performed using 1000-replication bootstrap methodology.
resultsSurgical resectability was achieved in 80.0% of cases, with R0 resection margins in 56.8% of operated patients. Seven radiomic features met reproducibility criteria: VolumeNum, Sphericity, Entropy, Contrast, Energy, SmallAreaEmphasis, and RunLengthNonUniformity. Energy was excluded due to high correlation with Contrast, resulting in six features. The delta-features model demonstrated superior performance (AUC=0.85, 95% CI: 0.78-0.91) compared to pre-treatment (AUC=0.76, p = 0.008) and post-treatment models (AUC=0.82, p = 0.042) for predicting R0 margins. Δ-VolumeNum emerged as the strongest independent predictor (OR=3.14, 95% CI: 1.89-5.21, p < 0.001), followed by Δ-Entropy (OR=2.45, 95% CI: 1.56-3.85, p < 0.001) and Δ-Contrast (OR=1.92, 95% CI: 1.24-2.97, p = 0.003).
conclusionRadiomic delta-features analysis provides a robust and non-invasive tool for predicting R0 resection following NALIRIFOX therapy, with dynamic measurements significantly outperforming static assessments in resectable PDAC.
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.