Diagnostic Accuracy of CT Texture Analysis in Adrenal Masses: A Systematic Review

Adrenal incidentalomas (AIs) are incidentally discovered adrenal neoplasms. Overt endocrine secretion (glucocorticoids, mineralocorticoids, and catecholamines) and malignancy (primary or metastatic disease) are assessed at baseline evaluation. Size, lipid content, and washout characterise benign AIs (respectively, <4 cm, <10 Hounsfield unit, and rapid release); nonetheless, 30% of adrenal lesions are not correctly indicated. Recently, image-based texture analysis from computed tomography (CT) may be useful to assess the behaviour of indeterminate adrenal lesions. We performed a systematic review to provide the state-of-the-art of texture analysis in patients with AI. We considered 9 papers (from 70 selected), with a median of 125 patients (range 20–356). Histological confirmation was the most used criteria to differentiate benign from the malignant adrenal mass. Unenhanced or contrast-enhanced data were available in all papers; TexRAD and PyRadiomics were the most used software. Four papers analysed the whole volume, and five considered a region of interest. Different texture features were reported, considering first- and second-order statistics. The pooled median area under the ROC curve in all studies was 0.85, depicting a high diagnostic accuracy, up to 93% in differentiating adrenal adenoma from adrenocortical carcinomas. Despite heterogeneous methodology, texture analysis is a promising diagnostic tool in the first assessment of patients with adrenal lesions.

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Image classification of vaginal microecology detection based on gabor texture and LSTM model

Technology and Health Care ◽

10.3233/thc-213509 ◽

2021 ◽

pp. 1-18

Author(s):

Gaoteng Yuan ◽

Yinping Dong ◽

Xiaofeng Zhou

Keyword(s):

Image Classification ◽

Texture Analysis ◽

Short Term Memory ◽

Imbalanced Data ◽

Region Of Interest ◽

Texture Features ◽

Image Features ◽

Imbalanced Data Sets ◽

Area Of Interest ◽

Gynecological Diseases

BACKGROUND: Gynecological diseases threaten women’s health, and vaginal microecological testing is a common method for detecting gynecological diseases. Efficient and accurate microecological testing methods have always been the goal pursued by gynecologists. OBJECTIVE: In order to automatically identify different types of microbial images in vaginal micromorphology detection, this paper proposes a vaginal microecological image recognition method based on Gabor texture analysis combined with long and short-term memory network (LSTM) model. METHOD: Firstly, we denoise the microecological morphological im-ages, which selects the area of interest and sets the label of the microorganism according to the doctors label. Secondly, texture analysis is carried out for the region of interest, which uses Gabor filters with 8 directions and 5 scales to filter the region of interest to extract the texture features on the image. Comparing the differences between different microbial image features, and screening suitable features to reduce the number of features. Then, we design an LSTM model to analyze the relationship of image features in different categories of microorganisms. Finally, we use the full connection layer and Softmax function to realize the automatic recognition of different microbial images. RESULTS: The experimental results show that the image classification accuracy of 8 common microorganisms is 81.26%. CONCLUSION: Texture analysis combined with LSTM network strategy can identify different kinds of vaginal micro ecological images. Gabor-LSTM model has better classification effect on imbalanced data sets.

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Diagnostic accuracy of virtual non-contrast enhanced dual-energy CT for diagnosis of adrenal adenoma: A systematic review and meta-analysis

European Radiology ◽

10.1007/s00330-017-4785-0 ◽

2017 ◽

Vol 27 (10) ◽

pp. 4324-4335 ◽

Cited By ~ 23

Author(s):

Michael J. Connolly ◽

Matthew D. F. McInnes ◽

Mohamed El-Khodary ◽

Trevor A. McGrath ◽

Nicola Schieda

Keyword(s):

Systematic Review ◽

Diagnostic Accuracy ◽

Adrenal Adenoma ◽

Meta Analysis ◽

Dual Energy ◽

Dual Energy Ct ◽

Contrast Enhanced

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CT texture analysis of mediastinal lymphadenopathy: Combining with US-based elastographic parameter and discrimination between sarcoidosis and lymph node metastasis from small cell lung cancer

PLoS ONE ◽

10.1371/journal.pone.0243181 ◽

2020 ◽

Vol 15 (12) ◽

pp. e0243181

Author(s):

Eriko Koda ◽

Tsuneo Yamashiro ◽

Rintaro Onoe ◽

Hiroshi Handa ◽

Shinya Azagami ◽

...

Keyword(s):

Lung Cancer ◽

Logistic Regression ◽

Regression Analysis ◽

Lymph Node ◽

Diagnostic Accuracy ◽

Texture Analysis ◽

Texture Features ◽

Small Cell ◽

Mediastinal Lymphadenopathy ◽

Small Cell Lung

Objectives To investigate the potential of computed tomography (CT)-based texture analysis and elastographic data provided by endobronchial ultrasonography (EBUS) for differentiating the mediastinal lymphadenopathy by sarcoidosis and small cell lung cancer (SCLC) metastasis. Methods Sixteen patients with sarcoidosis and 14 with SCLC were enrolled. On CT images showing the largest mediastinal lymph node, a fixed region of interest was drawn on the node, and texture features were automatically measured. Among the 30 patients, 19 (12 sarcoidosis and 7 SCLC) underwent endobronchial ultrasound transbronchial needle aspiration, and the fat-to-lesion strain ratio (FLR) was recorded. Texture features and FLRs were compared between the 2 patient groups. Logistic regression analysis was performed to evaluate the diagnostic accuracy of these measurements. Results Of the 31 texture features, the differences between 11 texture features of CT ROIs in the patients with sarcoidosis versus patients with SCLC were significant. Among them, the grey-level run length matrix with high gray-level run emphasis (GLRLM-HGRE) showed the greatest difference (P<0.01). Differences between FLRs were significant (P<0.05). Logistic regression analysis together with receiver operating characteristic curve analysis demonstrated that the FLR combined with the GLRLM-HGRE showed a high diagnostic accuracy (100% sensitivity, 92% specificity, 0.988 area under the curve) for discriminating between sarcoidosis and SCLC. Conclusion Texture analysis, particularly combined with the FLR, is useful for discriminating between mediastinal lymphadenopathy caused by sarcoidosis from that caused by metastasis from SCLC.

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Applying arterial enhancement fraction (AEF) texture features to predict the tumor response in hepatocellular carcinoma (HCC) treated with Transarterial chemoembolization (TACE)

Cancer Imaging ◽

10.1186/s40644-021-00418-2 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Xiaonan Mao ◽

Yan Guo ◽

Feng Wen ◽

Hongyuan Liang ◽

Wei Sun ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Logistic Regression ◽

Texture Analysis ◽

Transarterial Chemoembolization ◽

Tumor Response ◽

Region Of Interest ◽

Texture Features ◽

Discriminative Performance ◽

Arterial Enhancement ◽

Enhanced Computed Tomography

Abstract Background To evaluate the application of Arterial Enhancement Fraction (AEF) texture features in predicting the tumor response in Hepatocellular Carcinoma (HCC) treated with Transarterial Chemoembolization (TACE) by means of texture analysis. Methods HCC patients treated with TACE in Shengjing Hospital of China Medical University from June 2018 to December 2019 were retrospectively enrolled in this study. Pre-TACE Contrast Enhanced Computed Tomography (CECT) and imaging follow-up within 6 months were both acquired. The tumor responses were categorized according to the modified RECIST (mRECIST) criteria. Based on the CECT images, Region of Interest (ROI) of HCC lesion was drawn, the AEF calculation and texture analysis upon AEF values in the ROI were performed using CT-Kinetics (C.K., GE Healthcare, China). A total of 32 AEF texture features were extracted and compared between different tumor response groups. Multi-variate logistic regression was performed using certain AEF features to build the differential models to predict the tumor response. The Receiver Operator Characteristic (ROC) analysis was implemented to assess the discriminative performance of these models. Results Forty-five patients were finally enrolled in the study. Eight AEF texture features showed significant distinction between Improved and Un-improved patients (p < 0.05). In multi-variate logistic regression, 9 AEF texture features were applied into modeling to predict “Improved” outcome, and 4 AEF texture features were applied into modeling to predict “Un-worsened” outcome. The Area Under Curve (AUC), diagnostic accuracy, sensitivity, and specificity of the two models were 0.941, 0.911, 1.000, 0.826, and 0.824, 0.711, 0.581, 1.000, respectively. Conclusions Certain AEF heterogeneous features of HCC could possibly be utilized to predict the tumor response to TACE treatment.

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Accuracy of Computed Tomography Attenuation Value of Lumbar Vertebra to Assess Bone Mineral Density

Malaysian Journal of Medical Sciences ◽

10.21315/mjms2021.28.1.6 ◽

2021 ◽

Vol 28 (1) ◽

pp. 41-50

Author(s):

Kok King Chia ◽

Juhara Haron ◽

Nik Fatimah Salwati Nik Malek

Keyword(s):

Computed Tomography ◽

Diagnostic Accuracy ◽

Lumbar Vertebra ◽

Region Of Interest ◽

Hounsfield Unit ◽

Mineral Density ◽

Roc Curve Analysis ◽

Cross Sectional ◽

T Score ◽

Contrast Enhanced Ct

Background: Computed tomography (CT) attenuation (Hounsfield unit [HU]) value of lumbar vertebra may provide an alternative method in the detection of osteoporosis during CT scans. Methods: A cross-sectional study on 50 patients of age 50 and above with contrast-enhanced CT (CECT) and dual-energy X-ray absorptiometry (DXA) was conducted from November 2018 to November 2019. Single region of interest (ROI) was placed at the anterior trabecular part of L1 vertebra on CECT to obtain HU value. Correlation of CT HU value of L1 vertebra and DXA T-score, interrater reliability agreement between HU value of L1 vertebra and T-score in determining groups of with and without osteoporosis, ROC curve analysis for diagnostic accuracy and cut-off value of CT for detection of osteoporosis were identified. Results: Significant correlation between HU value of L1 vertebra and L1 T-score (r = 0.683)/lowest skeletal T-score (r = 0.703) (P < 0.001). Substantial agreement between HU value of L1 vertebra and DXA in determining the groups with and without osteoporosis (k = 0.8; P < 0.001). The area under the receiver operating characteristic (AUROC) curve was 0.93 (95% CI: 0.86, 1.00) using HU value (P < 0.001). Cut-off value for osteoporosis was 149 HU. Conclusion: HU value of lumbar vertebra is an effective alternative for the detection of osteoporosis with high diagnostic accuracy in hospitals without DXA facility.

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