Trial reportJournal for immunotherapy of cancer2026
Deep learning-based CT model for non-invasive prediction of tertiary lymphoid structures in pancreatic cancer: a multicenter study with prospective validation in an immunochemotherapy cohort.
Trial report in Journal for immunotherapy of cancer, 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
backgroundPancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal malignancies. Tertiary lymphoid structures (TLS) have emerged as key components of the tumor immune microenvironment, associated with improved survival and enhanced immunotherapy response. However, non-invasive detection of TLS remains a significant clinical challenge.
objectiveThis multicenter study aimed to develop and validate a deep learning-based CT model to noninvasively predict TLS status and evaluate its prognostic and predictive value across surgical, non-surgical, and prospective immunochemotherapy cohorts. MATERIALS AND
methodsWe enrolled 223 surgically resected patients with PDAC from two centers for model development and 82 non-surgical patients for clinical validation. A ResNet50-based deep learning network (DLN) was trained to predict TLS status from preoperative contrast-enhanced CT. The locked model was evaluated in 44 patients from a prospective immunochemotherapy trial (ChiCTR2000032293), with pathology-anchored verification, outcome assessment, and spatial immune profiling via cyclic multiplex tyramide signal amplification.
resultsThe DLN achieved area under the curves (AUCs) of 0.987, 0.937, and 0.853 in training, internal, and external validation cohorts. In the non-surgical cohort (n=82), DLN-predicted TLS-positive patients had significantly longer overall survival (OS; median 19 vs 7 months; HR=3.611, p<0.0001). In the prospective cohort, pathology-anchored verification yielded an AUC of 0.929 (specificity=1.000). DLN-high patients demonstrated superior objective response rate (90.0% vs 26.5%, p=0.001), progression-free survival (PFS) (median 13.1 vs 5.3 months, p<0.001), and OS (median 17.6 vs 8.7 months, p<0.001). The DLN score independently predicted OS (HR=0.052, p<0.001) and PFS (HR=0.079, p<0.001) in multivariable analysis. The DLN score was orthogonal to programmed death-ligand 1 (PD-L1) expression (rho=0.011, p=0.946) but correlated with dendritic cell-T helper cell spatial interactions (p=0.037) and T helper-B cell spatial co-localization (rho=0.307, p=0.043)-two hallmarks of functional TLS architecture-and inversely with tumor cell density (rho=-0.399, p=0.007).
conclusionThis DLN-based CT model reliably predicts TLS status in PDAC with prospectively validated prognostic and predictive value in immunochemotherapy, biological grounding in TLS-associated spatial immune architecture, and orthogonal predictive value to PD-L1, supporting a complementary biomarker strategy for immunotherapy patient selection.
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.