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Correlation of CT radiomic features for GISTs with pathological classification and molecular subtypes: preliminary and monocentric experience

  • Abdominal Radiology
  • Published:
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Abstract

Purpose

Our primary purpose was to search for computed tomography (CT) radiomic features of gastrointestinal stromal tumors (GISTs) that could potentially correlate with the risk class according to the Miettinen classification. Subsequently, assess the existence of features with possible predictive value in differentiating responder from non-responder patients to first-line therapy with Imatinib.

Methods

A retrospective study design was carried out using data from June 2009 to December 2020. We analyzed all the preoperative CTs of patients undergoing surgery for GISTs. We segmented non-contrast-enhanced CT (NCECT) and contrast-enhanced venous CT (CECT) images obtained either on three different CT scans (heterogeneous cohort) or on a single CT scan (homogeneous cohort). We then divided the patients into two groups according to Miettinen classification criteria and based on the predictive value of response to first-line therapy with Imatinib.

Results

We examined 54 patients with pathological confirmation of GISTs. For the heterogeneous cohort, we found a statistically significant relationship between 57 radiomic features for NCECT and 56 radiomic features for CECT using the Miettinen risk classification. In the homogeneous cohort, we found the same relationship between 8 features for the NCECT and 5 features for CECT, all included in the heterogeneous cohort. The various radiomic features are distributed with different values in the two risk stratification groups according to the Miettinen classification. We also found some features for groups predictive of response to first-line therapy with Imatinib.

Conclusions

We found radiomic features that correlate with statistical significance for both the Miettinen risk classification and the molecular subtypes of response. All features found in the homogeneous study cohort were also found in the heterogeneous cohort. CT radiomic features may be useful in assessing the risk class and prognosis of GISTs.

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Authors and Affiliations

  1. Department of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy

    Daniele Palatresi, Filippo Fedeli, Ginevra Danti, Silvia Pradella & Vittorio Miele

  2. Pathology Unit, Department of Health Sciences, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy

    Elisa Pasqualini & Daniela Massi

  3. Histopathology and Molecular Diagnostics Unit, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy

    Francesca Castiglione

  4. Department of Experimental and Clinical Medicine, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy

    Luca Messerini

  5. Medical Physics Department, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy

    Silvia Bettarini, Paolo Tortoli & Simone Busoni

Authors
  1. Daniele Palatresi
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  2. Filippo Fedeli
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Correspondence to Ginevra Danti.

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Palatresi, D., Fedeli, F., Danti, G. et al. Correlation of CT radiomic features for GISTs with pathological classification and molecular subtypes: preliminary and monocentric experience. Radiol med 127, 117–128 (2022). https://doi.org/10.1007/s11547-021-01446-5

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  • DOI: https://doi.org/10.1007/s11547-021-01446-5

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