Evidence map›Paper›PMID 42340656›Full record

ArticleLa Radiologia medica2026

Radiomic MRI model for predicting the development of worrisome features in branch-duct intraductal papillary mucinous neoplasms (BD-IPMNs).

Sofia Boccioli, Diletta Cozzi, Domenico Fortuna, Tommaso Innocenti, Gabriele Dragoni, Beatrice Orlandini, Andrea Galli, Sebastiano Paolucci, Ginevra Danti, Vittorio Miele

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Article in La Radiologia medica, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

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.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors.

Sofia BoccioliDepartment of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy.
Diletta CozziDepartment of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy.
Domenico FortunaDepartment of Experimental and Clinical Biomedical Sciences "Mario Serio", University of Florence, Viale G.B. Morgagni 50, 50134, Florence, Italy.
Tommaso InnocentiDepartment of Experimental and Clinical Biomedical Sciences "Mario Serio", University of Florence, Viale G.B. Morgagni 50, 50134, Florence, Italy.
Gabriele DragoniDepartment of Experimental and Clinical Biomedical Sciences "Mario Serio", University of Florence, Viale G.B. Morgagni 50, 50134, Florence, Italy.
Beatrice OrlandiniClinical Gastroenterology Unit, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy.
Andrea GalliDepartment of Experimental and Clinical Biomedical Sciences "Mario Serio", University of Florence, Viale G.B. Morgagni 50, 50134, Florence, Italy.
Sebastiano PaolucciDepartment of Health Physics, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy.
Ginevra DantiDepartment of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy. ginevra.danti@gmail.com.ORCID http://orcid.org/0000-0002-7109-8119
Vittorio MieleDepartment of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Branch-duct intraductal papillary mucinous neoplasms (BD-IPMNs) are pancreatic cystic lesions originating from the pancreatic ducts, characterized by mucin production and progressive ductal dilation. They exhibit a wide spectrum of biological behavior, ranging from indolent lesions to entities with significant malignant potential. Although the 2024 Kyoto guidelines define worrisome features (WF) and high-risk stigmata (HRS) to support risk stratification and clinical management, predicting disease progression remains challenging. In this retrospective study, we investigated whether MRI-based radiomic analysis could identify, at the time of initial imaging, patients with BD-IPMNs who subsequently develop WF or HRS according to 2024 Kyoto guidelines. A total of 194 adult patients who underwent at least two MRI examinations between January 2011 and March 2025 were included, with a median follow-up of 53 months; progression was observed in 28.3% of patients, involving only some WF/HRS. Radiomic analysis included manual lesion segmentation, extraction of 107 features (shape, first- and second-order), and selection via LASSO within a weighted logistic regression framework to address class imbalance, using fivefold cross-validation; model performance was assessed with AUC and precision-recall metrics to account for skewed class distribution. After statistical analysis, nine shape-related features were found to be significant and a LASSO-based radiomic model, incorporating five features, was constructed. The model achieved an area under the curve (AUC) of 0.70 (95% CI 0.62-0.79). These results suggest that MRI-based radiomics may represent a valuable noninvasive tool for early risk stratification, predicting progression according to clinical-radiological criteria and potentially supporting personalized management of patients with BD-IPMNs. However, this study presents some limitations, including the retrospective design, the relatively small sample size and possible variability due to the use of multiple MRI scanners from different vendors. Prospective and multicentric studies with standardized imaging protocols are necessary to validate these findings and assess the added value of integrating radiomic data with clinical, histopathological and molecular information.

Indexed as

Carcinoma, Pancreatic DuctalMagnetic Resonance ImagingPancreatic Intraductal NeoplasmsPancreatic NeoplasmsRadiomicsAdenocarcinoma, MucinousAgedDisease ProgressionFemaleHumansMaleMiddle AgedPredictive Value of TestsRetrospective StudiesBD-IPMNsHigh-risk stigmataMRIRadiomicWorrisome features

Identifiers

What Socratic holds

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Registered trials

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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.