Prognosis assessment in metastatic gastrointestinal stromal tumors treated with tyrosine kinase inhibitors based on CT-texture analysis

Ekert, Kaspar; Hinterleitner, Clemens; Horger, Marius

doi:10.1016/j.ejrad.2019.04.018

Cited by 16 publications

(14 citation statements)

References 25 publications

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“…Early in 2011, Goh et al reported that CT texture could predict the DFS of tyrosine kinase inhibitors (TKIs) treatment in patients with M1 ccRCC (29). The role of radiomics in predicting TKIs efficacy was also found in gastrointestinal stromal tumors and lung cancers (7,8). Taking together, our study as well as the previous ones verified that the prognostic significance of radiomics features could be applied in both early and late stage of ccRCC.…”

Section: Discussionsupporting

confidence: 81%

“…Many studies had explored the association between radiomics analyses and clinical outcomes in patients with various cancers (7)(8)(9). In RCC, radiomics features were proved to be capable of not only differentiating benign and malignant masses but also predicting the stage and Fuhrman nuclear grade of tumor, two critical prognosticators of ccRCC (5,(10)(11)(12).…”

Section: Discussionmentioning

confidence: 99%

“…as well as non-oncologic disease (3)(4)(5)(6). Furthermore, radiomics features were also associated with treatment response and prognosis in several tumors (7)(8)(9).…”

Section: Introductionmentioning

confidence: 98%

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The Prognostic Value of Radiomics Features Extracted From Computed Tomography in Patients With Localized Clear Cell Renal Cell Carcinoma After Nephrectomy

et al. 2021

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Background and purposeRadiomics is an emerging field of quantitative imaging. The prognostic value of radiomics analysis in patients with localized clear cell renal cell carcinoma (ccRCC) after nephrectomy remains unknown.MethodsComputed tomography images of 167 eligible cases were obtained from the Cancer Imaging Archive database. Radiomics features were extracted from the region of interest contoured manually for each patient. Hierarchical clustering was performed to divide patients into distinct groups. Prognostic assessments were performed by Kaplan–Meier curves, COX regression, and least absolute shrinkage and selection operator COX regression. Besides, transcriptome mRNA data were also included in the prognostic analyses. Endpoints were overall survival (OS) and disease-free survival (DFS). Concordance index (C-index), decision curve analysis and calibration curves with 1,000 bootstrapping replications were used for model’s validation.ResultsHierarchical clustering groups from nephrographic features and mRNA can divide patients into different prognostic groups while clustering groups from corticomedullary or unenhanced phase couldn’t distinguish patients’ prognosis. In multivariate analyses, 11 OS-predicting and eight DFS-predicting features were identified in nephrographic phase. Similarly, seven OS-predictors and seven DFS-predictors were confirmed in mRNA data. In contrast, limited prognostic features were found in corticomedullary (two OS-predictor and two DFS-predictors) and unenhanced phase (one OS-predictors and two DFS-predictors). Prognostic models combining both nephrographic features and mRNA showed improved C-index than any model alone (C-index: 0.927 and 0.879 for OS- and DFS-predicting, respectively). In addition, decision curves and calibration curves also revealed the great performance of the novel models.ConclusionWe firstly investigated the prognostic significance of preoperative radiomics signatures in ccRCC patients. Radiomics features obtained from nephrographic phase had stronger predictive ability than features from corticomedullary or unenhanced phase. Multi-omics models combining radiomics and transcriptome data could further increase the predictive accuracy.

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Section: Discussionsupporting

confidence: 81%

Section: Discussionmentioning

confidence: 99%

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The Prognostic Value of Radiomics Features Extracted From Computed Tomography in Patients With Localized Clear Cell Renal Cell Carcinoma After Nephrectomy

et al. 2021

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“…Two studies combined radiomic features with clinical parameters [ 33 , 34 ]. A three-point risk classification score was obtained to predict complete response in patients with gastroesophageal cancer after chemoradiotherapy.…”

Section: Resultsmentioning

confidence: 99%

Advanced analytics and artificial intelligence in gastrointestinal cancer: a systematic review of radiomics predicting response to treatment

Wesdorp

Hellingman

Jansma

et al. 2020

Eur J Nucl Med Mol Imaging

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Purpose Advanced medical image analytics is increasingly used to predict clinical outcome in patients diagnosed with gastrointestinal tumors. This review provides an overview on the value of radiomics in predicting response to treatment in patients with gastrointestinal tumors. Methods A systematic review was conducted, according to PRISMA guidelines. The protocol was prospectively registered (PROSPERO: CRD42019128408). PubMed, Embase, and Cochrane databases were searched. Original studies reporting on the value of radiomics in predicting response to treatment in patients with a gastrointestinal tumor were included. A narrative synthesis of results was conducted. Results were stratified by tumor type. Quality assessment of included studies was performed, according to the radiomics quality score. Results The comprehensive literature search identified 1360 unique studies, of which 60 articles were included for analysis. In 37 studies, radiomics models and individual radiomic features showed good predictive performance for response to treatment (area under the curve or accuracy > 0.75). Various strategies to construct predictive models were used. Internal validation of predictive models was often performed, while the majority of studies lacked external validation. None of the studies reported predictive models implemented in clinical practice. Conclusion Radiomics is increasingly used to predict response to treatment in patients suffering from gastrointestinal cancer. This review demonstrates its great potential to help predict response to treatment and improve patient selection and early adjustment of treatment strategy in a non-invasive manner.

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“…Survival analysis for disease progression according to texture features was carried out also by Ekert et al[ 34 ] on contrast-enhanced CT, while only one study[ 36 ] has performed radiomics analysis on MRI. Fu et al[ 36 ] extracted texture features from T2-weighted, DWI and ADC map images to determine prognosis of metastatic GISTs, reporting that texture features on DWI and ADC map well-correlated with overall survival.…”

Section: Radiomics Applications In Gistsmentioning

confidence: 99%

New advances in radiomics of gastrointestinal stromal tumors

Cannella¹,

Grutta²,

Midiri³

et al. 2020

WJG

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Gastrointestinal stromal tumors (GISTs) are uncommon neoplasms of the gastrointestinal tract with peculiar clinical, genetic, and imaging characteristics. Preoperative knowledge of risk stratification and mutational status is crucial to guide the appropriate patients’ treatment. Predicting the clinical behavior and biological aggressiveness of GISTs based on conventional computed tomography (CT) and magnetic resonance imaging (MRI) evaluation is challenging, unless the lesions have already metastasized at the time of diagnosis. Radiomics is emerging as a promising tool for the quantification of lesion heterogeneity on radiological images, extracting additional data that cannot be assessed by visual analysis. Radiomics applications have been explored for the differential diagnosis of GISTs from other gastrointestinal neoplasms, risk stratification and prediction of prognosis after surgical resection, and evaluation of mutational status in GISTs. The published researches on GISTs radiomics have obtained excellent performance of derived radiomics models on CT and MRI. However, lack of standardization and differences in study methodology challenge the application of radiomics in clinical practice. The purpose of this review is to describe the new advances of radiomics applied to CT and MRI for the evaluation of gastrointestinal stromal tumors, discuss the potential clinical applications that may impact patients’ management, report limitations of current radiomics studies, and future directions.

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Prognosis assessment in metastatic gastrointestinal stromal tumors treated with tyrosine kinase inhibitors based on CT-texture analysis

Cited by 16 publications

References 25 publications

The Prognostic Value of Radiomics Features Extracted From Computed Tomography in Patients With Localized Clear Cell Renal Cell Carcinoma After Nephrectomy

The Prognostic Value of Radiomics Features Extracted From Computed Tomography in Patients With Localized Clear Cell Renal Cell Carcinoma After Nephrectomy

Advanced analytics and artificial intelligence in gastrointestinal cancer: a systematic review of radiomics predicting response to treatment

New advances in radiomics of gastrointestinal stromal tumors

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