Evidence mapPaperPMID 40778975Full record

ArticleInternational journal of colorectal disease2025

MRI-based radiomics for preoperative T-staging of rectal cancer: a retrospective analysis.

Vittorio Patanè, Umberto Atripaldi, Mario Sansone, Luca Marinelli, Sara Del Tufo, Gianluca Arrichiello, Davide Ciardiello, Francesco Selvaggi, Erika Martinelli, Alfonso Reginelli

Abstract read
In one paragraph

Article in International journal of colorectal disease, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
field-weighted citation impact
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

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.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Vittorio PatanèDepartment of Precision Medicine, University of Campania "Luigi Vanvitelli", Piazza Luigi Miraglia 2, 80138, Naples, Italy. Vittorio.patane@unicampania.it.
Umberto AtripaldiDepartment of Precision Medicine, University of Campania "Luigi Vanvitelli", Piazza Luigi Miraglia 2, 80138, Naples, Italy.
Mario SansoneDepartment of Electrical Engineering and Information Technology, University of Naples "Federico II", 80125, Naples, Italy.
Luca MarinelliDepartment of Precision Medicine, University of Campania "Luigi Vanvitelli", Piazza Luigi Miraglia 2, 80138, Naples, Italy.
Sara Del TufoDepartment of Precision Medicine, University of Campania "Luigi Vanvitelli", Piazza Luigi Miraglia 2, 80138, Naples, Italy.
Gianluca ArrichielloOncology Unit, AORN "S. Anna e S. Sebastiano", Caserta, Italy.
Davide CiardielloDivision of Gastrointestinal Medical Oncology and Neuroendocrine Tumors, European Institute of Oncology (IEO), IRCCS, Milan, Italy.
Francesco SelvaggiDepartment of Advanced Medical and Surgical Sciences, Università Degli Studi Della Campania "Luigi Vanvitelli", Naples, Italy.
Erika MartinelliDepartment of Precision Medicine, University of Campania "Luigi Vanvitelli", Piazza Luigi Miraglia 2, 80138, Naples, Italy.
Alfonso ReginelliDepartment of Precision Medicine, University of Campania "Luigi Vanvitelli", Piazza Luigi Miraglia 2, 80138, Naples, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

puroposePreoperative T-staging in rectal cancer is essential for treatment planning, yet conventional MRI shows limited accuracy (~ 60-78). Our study investigates whether radiomic analysis of high-resolution T2-weighted MRI can non-invasively improve staging accuracy through a retrospective evaluation in a real-world surgical cohort.

methodsThis single-center retrospective study included 200 patients (January 2024-April 2025) with pathologically confirmed rectal cancer, all undergoing preoperative high-resolution T2-weighted MRI within one week prior to curative surgery and no neoadjuvant therapy. Manual segmentation was performed using ITK‑SNAP, followed by extraction of 107 radiomic features via PyRadiomics. Feature selection employed mRMR and LASSO logistic regression, culminating in a Rad-score predictive model. Statistical performance was evaluated using ROC curves (AUC), accuracy, sensitivity, specificity, and Delong's test.

resultsAmong 200 patients, 95 were pathologically staged as T2 and 105 as T3-T4 (55 T3, 50 T4). After preprocessing, 26 radiomic features were retained; key features including ngtdm_contrast and ngtdm_coarseness showed AUC values > 0.70. The LASSO-based model achieved an AUC of 0.82 (95% CI: 0.75-0.89), with overall accuracy of 81%, sensitivity of 78%, and specificity of 84%.

conclusionRadiomic analysis of standard preoperative T2-weighted MRI provides a reliable, non-invasive method to predict rectal cancer T-stage. This approach has the potential to enhance staging accuracy and inform personalized surgical planning. Prospective multicenter validation is required for broader clinical implementation.

Indexed as

Neoplasm StagingRadiomicsRectal NeoplasmsAdultAgedAged, 80 and overFemaleHumansMagnetic Resonance ImagingMaleMiddle AgedModels, BiologicalRetrospective StudiesArtificial IntelligenceMRIOncologic ImagingRadiomicsRectal cancer

Identifiers

PMID40778975
PMCPMC12334464

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.