A mutation-based radiomics signature predicts response to imatinib in Gastrointestinal Stromal Tumors (GIST)

• Published in: European journal of radiology Open 2023-12, Vol.11, p.100505-100505, Article 100505
• Main Authors: Cappello, Giovanni, Giannini, Valentina, Cannella, Roberto, Tabone, Emanuele, Ambrosini, Ilaria, Molea, Francesca, Damiani, Nicolò, Landolfi, Ilenia, Serra, Giovanni, Porrello, Giorgia, Gozzo, Cecilia, Incorvaia, Lorena, Badalamenti, Giuseppe, Grignani, Giovanni, Merlini, Alessandra, D’Ambrosio, Lorenzo, Bartolotta, Tommaso Vincenzo, Regge, Daniele
• Format: Article
• Language: English
• Subjects: Artificial intelligence; GIST; Mutational status; Radiomics; Response to therapy
• ISSN: 2352-0477; 2352-0477
• DOI: 10.1016/j.ejro.2023.100505

To develop a mutation-based radiomics signature to predict response to imatinib in Gastrointestinal Stromal Tumors (GISTs). Eighty-two patients with GIST were enrolled in this retrospective study, including 52 patients from one center that were used to develop the model, and 30 patients from a second center to validate it. Reference standard was the mutational status of tyrosine-protein kinase (KIT) and platelet-derived growth factor α (PDGFRA). Patients were dichotomized in imatinib sensitive (group 0 - mutation in KIT or PDGFRA, different from exon 18-D842V), and imatinib non-responsive (group 1 - PDGFRA exon 18-D842V mutation or absence of mutation in KIT/PDGFRA). Initially, 107 texture features were extracted from the tumor masks of baseline computed tomography scans. Different machine learning methods were then implemented to select the best combination of features for the development of the radiomics signature. The best performance was obtained with the 5 features selected by the ANOVA model and the Bayes classifier, using a threshold of 0.36. With this setting the radiomics signature had an accuracy and precision for sensitive patients of 82 % (95 % CI:60–95) and 90 % (95 % CI:73–97), respectively. Conversely, a precision of 80 % (95 % CI:34–97) was obtained in non-responsive patients using a threshold of 0.9. Indeed, with the latter setting 4 patients out of 5 were correctly predicted as non-responders. The results are a first step towards using radiomics to improve the management of patients with GIST, especially when tumor tissue is unavailable for molecular analysis or when molecular profiling is inconclusive. •Only GIST with specific genotypes are sensitive to imatinib.•Radiomics helps to correlate pre-treatment CT features to GIST mutational status, predicting response to imatinib treatment.•The developed radiomics signature improve treatment allocation, accuracy and appropriateness.