Development and validation of a nomogram based on CT images and 3D texture analysis for preoperative prediction of the malignant potential in gastrointestinal stromal tumors

PurposeThis study established and verified a radiomics model for the preoperative prediction of the Ki67 index of gastrointestinal stromal tumors (GISTs).Materials and MethodsA total of 344 patients with GISTs from three hospitals were divided into a training set and an external validation set. The tumor region of interest was delineated based on enhanced computed-tomography (CT) images to extract radiomic features. The Boruta algorithm was used for dimensionality reduction of the features, and the random forest algorithm was used to construct the model for radiomics prediction of the Ki67 index. The receiver operating characteristic (ROC) curve was used to evaluate the model’s performance and generalization ability.ResultsAfter dimensionality reduction, a feature subset having 21 radiomics features was generated. The generated radiomics model had an the area under curve (AUC) value of 0.835 (95% confidence interval(CI): 0.761–0.908) in the training set and 0.784 (95% CI: 0.691–0.874) in the external validation cohort.ConclusionThe radiomics model of this study had the potential to predict the Ki67 index of GISTs preoperatively.

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Gastrointestinal Stromal Tumors: A “Benign” Tumor with Hepatic Metastasis after 11 Years

Tumori Journal ◽

10.1177/030089169808400117 ◽

1998 ◽

Vol 84 (1) ◽

pp. 78-81 ◽

Cited By ~ 29

Author(s):

Carlo Ballarini ◽

Mattia Intra ◽

Andrea Pisani Ceretti ◽

Francesco Prestipino ◽

Filippo Maria Bianchi ◽

...

Keyword(s):

Surgical Treatment ◽

Hepatic Metastasis ◽

Tumor Size ◽

Gastrointestinal Stromal Tumors ◽

Cellular Proliferation ◽

Malignant Potential ◽

Mitotic Count ◽

Proliferation Index ◽

Ki 67 ◽

Stromal Tumors

Gastrointestinal stromal tumors (GIST) constitue the largest category of primary non-epithelial neoplasms of the stomach and small bowel. They are characterized by a remarkable cellular variability and their malignant potential is sometimes difficult to predict. Very recent studies, using mitotic count and tumor size as the best determinants of biological behavior, divide GISTs into three groups: benign, borderline and malignant tumors. We report on a male patient who underwent a right hepatectomy for a large metastasis 11 years after the surgical treatment of an antral-pyloric gastric neoplasm, histologically defined as leiomyoblastoma and with clinical, morphological and immunohistochemical features of benignity (low mitotic count, tumor size < 5 cm, low cellular proliferation index). Histological and immunohistochemical analysis of the hepatic metastasis showed the cellular proliferation index (Ki-67) to be positive in 25% of neoplastic cells, as opposed to the primary gastric tumor in which Ki-67 was positive in only 5% of neoplastic cells. In conclusion, although modern immunohistochemical techniques are now available to obtain useful prognostic information, the malignant potential of GISTs is sometimes difficult to predict: neoplasms clinically and histologically defined as benign could metastasize a long time after oncologically correct surgical treatment. Therefore, benign GISTs also require consistent, long-term follow-up.

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Reliability of 3D texture analysis: A multicenter MRI study of the brain

Journal of Magnetic Resonance Imaging ◽

10.1002/jmri.26904 ◽

2019 ◽

Vol 51 (4) ◽

pp. 1200-1209 ◽

Cited By ~ 4

Author(s):

Daniel Ta ◽

Muhammad Khan ◽

Abdullah Ishaque ◽

Peter Seres ◽

Dean Eurich ◽

...

Keyword(s):

Texture Analysis ◽

3D Texture ◽

3D Texture Analysis ◽

Mri Study ◽

The Brain

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3D Texture Analysis of Corpus Caliosum Based on MR Images Inpatients with Alzheimer’s Disease and Mild Cognitive Impairment

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.533.415 ◽

2014 ◽

Vol 533 ◽

pp. 415-420 ◽

Cited By ~ 4

Author(s):

Wei Fang Liu ◽

Xu Wang ◽

Hong Xia

Keyword(s):

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Corpus Callosum ◽

Texture Analysis ◽

Three Dimensional ◽

Texture Features ◽

Grey Level ◽

Run Length ◽

3D Texture ◽

3D Texture Analysis

This study investigated three-dimensional (3D) texture as a possible diagnostic marker of Alzheimers disease (AD). Methods: T1-weighted MRI of 18 AD patients, 18 Mild Cognitive Impairment (MCI) patients and 18 normal controls (NC) were selected.3D Texture parameters of the corpus callosum,including contrast, inverse difference moment , entropy, short run emphasis, long run emphasis, grey level nonuniformity, run length nonuniformity and fraction were extracted from the gray level co-occurrence matrix and run length matrix. Finally statistic significance was tested among three groups, and the correlations between parameters and Mini-Mental State Examination (MMSE) scores were calculated. Results: The results showed that the 3D texture features had significant differences (p<0.05) among three groups except grey level nonuniformity and run length nonuniformity that the difference was not significant (p>0.05) between MCI and NC or AD and MCI , and they were correlated with MMSE scores.Conclusions: 3D texture analysis can reflect the pathological changes of corpus callosum in patients with AD and MCI, and it may be helpful to AD early diagnosis.

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