Prognosis assessment in metastatic gastrointestinal stromal tumors treated with tyrosine kinase inhibitors based on CT-texture analysis
Introduction
Gastrointestinal stromal tumors (GIST) are rare mesenchymal non-epithelial tumors arising from the Cajal interstitial cells of the GI-tract [1]. Mutations of the c-KIT are the crucial step for the development of a GIST, but other mutated genes have also been identified (e.g. PDGFR-α) [2]. Based on this knowledge, targeted drugs against these receptors have been developed and by now successfully implemented in the therapeutic armamentarium for GIST [3,4]. Many of the tyrosine kinase inhibitors (TKI) knowingly have also antiangiogenic effects blocking the vascular endothelial growth factor receptors (VEGFR) leading in most responders to a drop in blood supply and consequently to necrosis and cystic transformations of these tumors that are otherwise well vascularized [5]. Based on this knowledge, specific response criteria have been proposed which consider not only size changes induced by therapy but also such reflecting tumor perfusion and attenuation [5]. CT is the mainstay in the diagnostic and response monitoring of GIST to TKI, but other less frequently involved imaging modalities (e.g. MRI, FDG-PET) have also been successfully tested in this clinical setting [[6], [7], [8]]. The main role of imaging is to possibly assess all GIST-manifestations for treatment planning (e.g. surgery vs. systemic treatment) as well as to monitor therapy, predict malignancy risk and prognosis [9,10]. The latter has been already tested using early FDG-PET monitoring, however without success [11].
CT-texture analysis (CTTA) is one part of the radiomics spectrum which delivers quantitative data on tumor heterogeneity by analyzing the distribution and relationship of voxel grey levels in the image [12]. It is based on histogram analysis and comprises different order statistic features that finally all reflect tissue heterogeneity. Contrast enhanced CT (CECT)-data is employed for the diagnostic work-up of GIST and therefore CTTA-results are additionally influenced by the vascular network [13]. For evaluation of well perfused cancerous lesions like the GISTs, focusing on textural changes additionally to visual assessment of drug-related vascular changes (according to CHOI criteria) in the tumor seems plausible.
The aim of this study was to determine the prognostic value of CT-textural features by comparing them with the progressive free survival in our cohort. Moreover, potential associations between textural features, the number of past treatment regimens as well as the tumor mutation status and the tumor vitality (presence of enhancement) were also evaluated.
Section snippets
Subjects
The ethics committee at our institution approved this study. We identified a total of 153 GIST patients at our institution; however, most patients had to be excluded since many provided inadequate (non-standardized) image data (mostly missing thin collimation data). In the end, 25 GIST patients (mean age, 70.58 ± 9.7 years; range, 41.25–84.08 years; 20 males, 5 females) with 123 CT-examinations fulfilled enrollment criteria. Each patient was examined using a standardized CT protocol between
Identification of CTTA-features associated with disease progression
In order to identify relevant textural features associated with disease progression, binary logistic regression analysis was used for all CT-textural features (n = 92).
In a first step ten features could be confirmed to be significantly associated with disease progression (Suppl. Table 2). Interestingly, Glcm (grey-level co-occurrence matrix), Gldm (grey-level dependence matrix) and Glrlm (grey-level run length matrix) group variables represented the most consistent subtypes (80%). Out of these,
Discussion
This study employed CT texture analysis acquired in the portal-venous phase for prediction of response of gastrointestinal stromal tumors to tyrosine kinase inhibitors as well as for their characterization in terms of tissue vitality (presence of enhancement). Further potential associations were drawn with tumor gene mutations, the number of pre-treatments and patient’s age as well as gender.
In our series all patients with progressive disease showed significant higher levels of second order
Conflicts of interest
Kaspar Ekert and Clemens Hinterleitner have no conflicts of interest.
Marius Horger received institutional research support from Siemens Healthineers Germany. He is a scientific advisor of Siemens and has received speaker’s honorarium from Siemens Healthineers Germany.
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Rectal MRI radiomics for predicting pathological complete response: Where we are
2022, Clinical ImagingCitation Excerpt :In gastric cancer, a study that combined radiomic features with clinical parameters showed strong negative association with complete pathologic response, overall survival, and progression-free survival.19 Another one demonstrated that the combination of four texture features showed best discriminatory power in predicting disease progression in patients with gastrointestinal stromal tumors treated with tyrosine kinase inhibitors.20 Radiomics approach also shows potential use in discriminating responding from non-responding liver metastases based on the pre-treatment CT scan, in patients with esophagogastric cancer.21
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2020, Clinical RadiologyCitation Excerpt :The GLN feature of GLRLM is a measure of the similarity of grey-level intensity values in the MRI image, and the higher value in the IDH1-mutant group indicates a lower similarity in intensity values (Fig 2). A previous study of gastrointestinal stromal tumours showed that normalised GLN values were linked to tumour mutations.35 Finally, the GLDM feature referred to as LDHGLE is a measure of the joint distribution of large dependences with higher grey-level values, and a greater value means that higher grey-level voxels were adjacent to each other19 and less inhomogeneous to the tumour.36
CT and MRI of Gastrointestinal Stromal Tumors: New Trends and Perspectives
2024, Canadian Association of Radiologists Journal
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Both authors share equal contribution.