Texture-based classification of different single liver lesion based on SPAIR T2W MRI images

Abstract

BackgroundTo assess the feasibility of texture analysis (TA) based on spectral attenuated inversion-recovery T2 weighted magnetic resonance imaging (SPAIR T2W-MRI) for the classification of hepatic hemangioma (HH), hepatic metastases (HM) and hepatocellular carcinoma (HCC).MethodsThe SPAIR T2W-MRI data of 162 patients with HH (n=55), HM (n=67) and HCC (n=40) were retrospectively analyzed. We used two independent cohorts for training (n = 112 patients) and validation (n = 50 patients). The TA was performed and textual parameters derived from the gray level co-occurrence matrix (GLCM), gray level gradient co-occurrence matrix (GLGCM), gray-level run-length matrix (GLRLM), Gabor wavelet transform (GWTF), intensity-size-zone matrix (ISZM), and histogram features were calculated. The capacity of each parameter to classify three types of single liver lesions was assessed using the Kruskal-Wallis test. Specificity and sensitivity for each of the studied parameters were derived using ROC curves. Four supervised classification algorithms were trained with the most influential textural features in the classification of tumor types. The test datasets validated the reliability of the models.ResultsThe texture analyses showed that the HH versus HM, HM versus HCC, and HH versus HCC could be differentiated by 9, 16 and 10 feature parameters, respectively. The model’s misclassification rates were 11.7, 9.6 and 9.7% respectively. No texture feature was able to adequately distinguish among the three types of single liver lesions at the same time. The BP-ANN model had better predictive ability.ConclusionTexture features of SPAIR T2W-MRI can classify the three types of single liver lesions (HH, HM and HCC) and may serve as an adjunct tool for accurate diagnosis of these diseases.Electronic supplementary materialThe online version of this article (doi:10.1186/s12880-017-0212-x) contains supplementary material, which is available to authorized users.

Radiomics nomogram based on MRI for predicting white matter hyperintensity progression in elderly adults

“…Furthermore, the current study showed that the RLM features, which reflect roughness and directionality, were also associated with WMH progression. Directionality refers to a specific route or angle of the nerve fasciculus . In normal white matter, the nerve fibers are properly oriented and regulated; nevertheless, when the myelin is injured, the neural structure may be disrupted, and the nerve fasciculus may be thickened or blurred .…”

Improving the diagnosis of common parotid tumors via the combination of CT image biomarkers and clinical parameters

“…Texture features may be acquired from several kinds of image examinations, such as CT, magnetic resonance imaging (MRI), positron emission tomography (PET) and the like, without changing the acquisition protocols and additional costs for patients [27][28][29]. Currently, texture analysis is mainly used to evaluate the treatment effect and prognosis of lung cancer, colorectal cancer, liver cancer and so on [30][31][32]. But it is rarely applied to parotid gland, except for several reports focusing on the alterations of parotid morphology and secretion function induced by radiotherapy for head and neck cancers [33,34].…”

Improving the diagnosis of common parotid tumors via the combination of CT image biomarkers and clinical parameters

“…Imaging features can be derived from CT, magnetic resonance imaging (MRI), and positron emission tomography (PET) without modification of the acquisition protocols and additional costs for patients [27,28]. Currently, texture analysis is mainly used to evaluate the treatment effect and prognosis of lung cancer, colorectal cancer, liver cancer and so on [29][30][31]. But it is rarely applied to parotid gland, except for several reports focusing on the alterations of parotid morphology and secretion function induced by radiotherapy for head and neck cancers [32,33].…”