Evidence map›Paper›PMID 33881567›Full record

ArticleEuropean radiology2021

Assessment of morphological CT imaging features for the prediction of risk stratification, mutations, and prognosis of gastrointestinal stromal tumors.

Roberto Cannella, Emanuele Tabone, Giorgia Porrello, Giovanni Cappello, Cecilia Gozzo, Lorena Incorvaia, Giovanni Grignani, Alessandra Merlini, Lorenzo D'Ambrosio, Giuseppe Badalamenti and 2 more

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Article in European radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed
4.6field-weighted citation impact, top 5% of its field
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

23 citing papers in PubMed, 31 citations in OpenAlex.

  1. Trial
  2. Machine learning based on preoperative CT for noninvasive prediction of recurrence-free survival in gastrointestinal stromal tumors.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026
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  3. An interpretable machine learning model integrating [European journal of nuclear medicine and molecular imaging · 2026
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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

12 authors at 5 institutions in 2 countries.

Roberto CannellaDepartment of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Via del Vespro 129, 90127, Palermo, Italy. rob.cannella89@gmail.com.ORCID http://orcid.org/0000-0002-3808-0785
Emanuele TaboneUnit of Radiology, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Turin, Italy.
Giorgia PorrelloDepartment of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Via del Vespro 129, 90127, Palermo, Italy.
Giovanni CappelloUnit of Radiology, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Turin, Italy.
Cecilia GozzoDepartment of Medical Surgical Sciences and Advanced Technologies "G.F. Ingrassia"-Radiology I Unit, University Hospital "Policlinico Vittorio Emanuele", Catania, Italy.
Lorena IncorvaiaDepartment of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Via del Vespro 129, 90127, Palermo, Italy.
Giovanni GrignaniDivision of Medical Oncology, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Turin, Italy.
Alessandra MerliniDivision of Medical Oncology, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Turin, Italy.
Lorenzo D'AmbrosioDivision of Medical Oncology, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Turin, Italy.
Giuseppe BadalamentiDepartment of Surgical, Oncological, and Oral Sciences, Section of Medical Oncology, University of Palermo, Palermo, Italy.
Daniele ReggeUnit of Radiology, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Turin, Italy.
Tommaso Vincenzo BartolottaDepartment of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Via del Vespro 129, 90127, Palermo, Italy.
Candiolo Cancer Institute · ITUniversity of Palermo · ITAzienda Ospedaliero-Universitaria Policlinico - Vittorio Emanuele · ITDepartment of Health · ESFondazione Istituto G. Giglio di Cefalù · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo investigate the correlation between CT imaging features and risk stratification of gastrointestinal stromal tumors (GISTs), prediction of mutation status, and prognosis.

methodsThis retrospective dual-institution study included patients with pathologically proven GISTs meeting the following criteria: (i) preoperative contrast-enhanced CT performed between 2008 and 2019; (ii) no treatments before imaging; (iii) available pathological analysis. Tumor risk stratification was determined according to the National Institutes of Health (NIH) 2008 criteria. Two readers evaluated the CT features, including enhancement patterns and tumor characteristics in a blinded fashion. The differences in distribution of CT features were assessed using univariate and multivariate analyses. Survival analyses were performed by using the Cox proportional hazard model, Kaplan-Meier method, and log-rank test.

resultsThe final population included 88 patients (59 men and 29 women, mean age 60.5 ± 11.1 years) with 45 high-risk and 43 low-to-intermediate-risk GISTs (median size 6.3 cm). At multivariate analysis, lesion size ≥ 5 cm (OR: 10.52, p = 0.009) and enlarged feeding vessels (OR: 12.08, p = 0.040) were independently associated with the high-risk GISTs. Hyperenhancement was significantly more frequent in PDGFRα-mutated/wild-type GISTs compared to GISTs with KIT mutations (59.3% vs 23.0%, p = 0.004). Ill-defined margins were associated with shorter progression-free survival (HR 9.66) at multivariate analysis, while ill-defined margins and hemorrhage remained independently associated with shorter overall survival (HR 44.41 and HR 30.22). Inter-reader agreement ranged from fair to almost perfect (k: 0.32-0.93).

conclusionsMorphologic contrast-enhanced CT features are significantly different depending on the risk status or mutations and may help to predict prognosis. KEY POINTS: • Lesions size ≥ 5 cm (OR: 10.52, p = 0.009) and enlarged feeding vessels (OR: 12.08, p = 0.040) are independent predictors of high-risk GISTs. • PDGFRα-mutated/wild-type GISTs demonstrate more frequently hyperenhancement compared to GISTs with KIT mutations (59.3% vs 23.0%, p = 0.004). • Ill-defined margins (hazard ratio 9.66) were associated with shorter progression-free survival at multivariate analysis, while ill-defined margins (hazard ratio 44.41) and intralesional hemorrhage (hazard ratio 30.22) were independently associated with shorter overall survival.

Indexed as

Gastrointestinal Stromal TumorsAgedFemaleHumansMaleMiddle AgedMutationPrognosisRetrospective StudiesRisk AssessmentTomography, X-Ray ComputedGastrointestinal stromal tumorsMutationProgression-free survivalSurvival analysisTomography, X-ray computed

Identifiers

PMID33881567
OpenAlexW3154270942

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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 OpenQuestion graph.