Application of MRI-based radiomics model in predicting the tumor response in patients with advanced hepatocellular carcinoma after transcatheter arterial chemoembolization / 介入放射学杂志

Objective To develop a nomogram model based on the clinical features and the radiomics texture analysis of multimodal magnetic resonance imaging(MRI),so as to predict the tumor response in patients with advanced hepatocellular carcinoma(HCC)3 months after receiving transcatheter arterial chemoembolization(TACE).Methods A total of 105 patients with advanced HCC,whose diagnosis was pathologically-confirmed at the Suzhou Municipal Ninth People's Hospital between January 2017 and December 2021,were enrolled in this study.The patients were randomly divided into training group(n=63)and verification group(n=42).Before chemotherapy,T1WI,T2WI,dynamic contrast-enhanced(DCE)scan,and diffusion-weighted imaging(DWI)were performed by using a 3.0T MRI scanner.A.K.software was used to extract the texture.Three months after chemotherapy,according to the modified Response Evaluation Criteria in Solid Tumors(mRECIST)the patients were divided into response group(n=63)and non-response group(n=42).Results Compared with the response group,in the non-response group the percentage of Child-Pugh grade B and BCLC stage C was obviously higher and the serum alpha fetoprotein(AFP)level was remarkably elevated(P＜0.05).A.K.software extracted 396 MRI texture features,and LASSO regression analysis screened out 6 optimal predictors.The radiation score(Rad-score)was calculated by ROC.The AUC of Rad-score for predicting tumor non-response after TACE by ROC in the training group and verification group were 0.842 and 0.803 respectively.Multivariate logistic regression model analysis showed that AFP≥50 ng/mL(OR=1.568,95%CI=1.234-1.902,P=0.003),Child-Pugh grade B(OR=1.754,95%CI=1.326-2.021,P=0.001),BCLC stage C(OR=1.847,95%CI=1.412-2.232,P=0.001)and Rad-score(OR=2.023,95%CI=1.569-2.457,P＜0.001)were the independent risk factors for tumor non-response.Clinico-radiomics combination had the highest AUC value for predicting tumor non-response.The correction curve showed that the nomogram model had a good agreement.Conclusion The quantitative score of radiomics texture analysis of multimodal MRI has a certain value in predicting tumor non-response in advanced HCC patients 3 months after TACE,and the nomogram model,which is constructed if combined with clinical factors,carries good practical potential.(J Intervent Radiol,2024,32:63-68)

CT images texture analysis versus clinical scores in predicting hemorrhagic transformation of acute ischemic stroke / 介入放射学杂志

Objective To assess the value of CT image texture features in predicting the occurrence of hemorrhagic transformation(HT)in ischemic stroke,and to compare it with the traditional clinical prediction scores.Methods A total of 73 patients with acute anterior circulation ischemic stroke were enrolled in this study.All patients received reperfusion treatment.The region of interesting(ROI)of the infarction area was outlined according to the diffusion restricted area displayed on the follow-up ADC images,which were matched to the corresponding ischemic region on computed tomographic angiography(CTA)and on plain CT scan(non-contrast CT,NCCT).Five patients with HT and 5 patients with non-HT were randomly selected and used as the test set,and the remaining patients were assigned to the train set.The 6 texture features that had the most predictive value were separately selected from the CTA sets and NCCT train set,then the training of classifiers was earried out by using the 5-fold cross-validation method.Finally,the test set was evaluated according to the trained classifier.Besides,the determination of four clinical scores(HAT,SEDAN,HIAT2,THRIVE-c)was performed for all patients in the train set.Results The trained classifiers model performed well in not only CTA but also NCCT.In the CTA prediction model,linear SVM was chosen as the final classifier with 0.816 validation accuracy and 0.890 AUC value;and with 0.800 test accuracy,0.600 sensitivity,and 1.000 specificity in external test set Logistic regression(LR)was the best-performing classifier in NCCT.The predicted performance of HT was slightly worse than that of CTA,which had 0.697 validation accuracy and 0.763 AUC value.The test set of NCCT achieved 0.700 accuracy with 0.600 sensitivity and 0.800 specificity.Compared to the texture analysis models,all the four clinical scores showed a modest prediction efficiency in HT and AUC values,which were no more than 0.700.Conclusion Texture analysis of cerebral ischemic area based on CT images(CTA and NCCT)has the ability to predict HT after reperfusion treatment in AIS patients,and it is superior to traditional clinical scoring methods.(J Intervent Radiol,2024,33:230-235)

Application of CT imaging texture analysis in predicting simplified pathological types of thymic epithelial tumors / 实用放射学杂志

Objective To investigate the value of CT imaging texture analysis in predicting simplified pathological types of thymic epithelial tumors(TETs).Methods The CT data from 114 patients with TETs confirmed by surgical or pathology were analyzed retrospectivel,and the types of TETs were divided into three groups,including low-risk thymoma(LRT)group,high-risk thymoma(HRT)group,and thymic carcinoma(TC)group.First,the texture parameters of CT images were extracted,and then the weighted Rad-score values were obtained,and the predictive performance of the texture features was evaluated by using the receiver operating characteristic(ROC)curve.Results There were 114 TETs patients,including 45 patients with LRT,44 patients with HRT,and 25 patients with TC.Based on CT imaging texture analysis,the area under the curve(AUC)in differentiating LRT and HRT or TC via CT plain scan,arterial phase,and venous phase were 0.776,0.885,and 0.761,respectively.In differentiating HRT from TC,the AUC of CT plain scan,arterial phase,and venous phase were 0.828,0.808,and 0.804,respectively.In differentiating thymoma from TC,the AUC of CT plain scan,arterial phase,and venous phase were 0.808,0.769,and 0.774,respectively.Conclusion CT imaging texture analysis can serve as an effective auxiliary tool for predicting the simplified pathological types of TETs,helping to develop personal-ized treatment plans for TETs patients.CT enhanced scanning of arterial phase texture parameters has the highest differential diag-nostic efficiency.

Application value of CT texture analysis for evaluating Ki-67 expression in patient with esophageal squamous cell carcinoma / 实用放射学杂志

Objective To explore the application value of CT texture analysis for evaluating Ki-67 expression in patient with esophageal squamous cell carcinoma.Methods Sixty-one cases of pathologically confirmed esophageal squamous cell carcinoma patients were selected to obtain the Ki-67 protein expression status of the patients'pathological tissues,and the patients were divided into a high-expression group and a low-expression group.All patients underwent plain and enhanced chest CT within two weeks before surgery.Lesions delineation and texture feature extraction of esophageal cancer were obtained via Omni-Kinetics software.The texture parameters included Min Intensity,Max Intensity,Median Intensity,Mean Intensity,Deviation,Skewness,Kurtosis,Entropy,Energy,Correlation,Haralick,short run high grey level emphasis(SRHGLE),short run low grey level emphasis(SRLGLE),long run high grey level emphasis(LRHGLE),long run low grey level emphasis(LRLGLE),Grey Level Nonuniformity,Run Length Nonuniformity.The differences of texture features among different Ki-67 expression groups were compared,and the receiver operating characteristic(ROC)curve was used to analyze the predictive value of Ki-67 expression in patient with esophageal cancer.Results In plain CT images,the SRHGLE and Grey Level Nonuniformity of the high expression group were significantly higher than those of the low expression group(P=0.010,0.002,respectively).In enhanced CT images,the Mean Intensity,Entropy and Grey Level Nonuniformity of the high expression group were significantly higher than those of the low expression group(P=0.026,0.037,0.001,respectively),and SRHGLE and LRHGLE of the high expression group were significantly lower than those of the low expression group(P=0.016,0.010,respectively).The area under the curve(AUC)of texture features in plain CT were 0.676-0.740,and the AUC of combined diagnosis reached 0.770[95%confidence interval(CI):0.645,0.868],and sensitivity and specificity was 0.921,0.565,respectively.In enhanced CT,the AUC of texture features were 0.629-0.750,the AUC of combined diagnosis increased to 0.903(95%CI:0.799,0.964),and sensitivity and specificity was 0.816,0.826,respectively.Conclusion CT texture analysis can early and non-invasively predict Ki-67 expression in patient with esophageal squamous cell carcinoma,it can be used as an imaging marker to evaluate the proliferative activity of esophageal cancer cells,and may provide diagnosis and treatment information for clinical decision-making of esophageal squamous cell carcinoma.

Mechanism of Ki-67 in lung cancer and related progress in research on radiomics / 中国医科大学学报

Radiomics is a new diagnostic and treatment technology,which can be employed to extract high-throughput radiomics features from CT,MR,and PET images and screen features closely related to diagnostic and treatment purposes,so as to accurately predict tumor or disease classification,prognosis,or genomic changes.Ki-67 is a type of nuclear protein,which is present only in nuclei of proliferative and dividing cells but not in those of quiescent phase cells;hence,it can be used as a predictor of cell proliferation and has been proven to be closely related to prognosis of lung cancer.This article reviews the mechanism and progress in radiomics research related to Ki-67 in lung cancer.

Diagnostic Value of Texture Analysis Based on Quantitative Susceptibility Mapping in Parkinson's Disease / 中国医学影像学杂志

Purpose To explore the value of texture analysis in the diagnosis and course evaluation of Parkinson's disease(PD)by analyzing the texture features of gray matter nuclei and white matter on quantitative susceptibility mapping(QSM)sequences.Materials and Methods A total of 30 PD patients and 22 normal controls from July 2019 to November 2020 in Jiangyin People's Hospital were prospectively enrolled to perform enhanced gradient echo T2* weighted angiography(ESWAN)sequence scanning.All QSM images were obtained through post-processing.Region of interest was manually obtained,including bilateral caudate heads,globus pallidus,putamen,substantia nigra,red nucleus,cerebellar dentate nucleus and white matter at the center of the semicircle.The texture features of the region of interest were extracted.After dimension reduction and screening,a set of optimal texture parameters were obtained,and a random forest prediction model was constructed.The diagnostic efficiency of the model was analyzed and evaluated and the reliability of the model was evaluated.The correlation between the selected texture features and the clinical scale of PD patients was analyzed.Results A group(n=5)of the best texture feature parameters were obtained from QSM map.The area under curve range of independent prediction PD was 0.697-0.823,the area under curve of random forest model was 0.910,and the accuracy of cross validation was 0.888.Texture feature(r4_wavelet_LLL_firstorder_Energy)of PD group was negatively correlated with the scores of the mini mental state examination(r=-0.470,P=0.011).Conclusion The texture analysis based on QSM has a high diagnostic value for PD,and the texture features of the left putamen have a certain correlation with the cognitive function of PD patients.

Value of CT texture analysis parameters in differential diagnosis of benign and malignant thyroid nodules in Hashimoto’s thyroiditis / 中华内分泌外科杂志

Objective:To study the value of CT texture analysis (CTTA) parameters in differential diagnosis of benign and malignant thyroid nodules in Hashimoto’s thyroiditis.Methods:From May. 2020 to Oct. 2021, 110 patients with thyroid nodules in the background of Hashimoto’s thyroiditis in the Radiology Department of Nanjing Integrated Hospital of Traditional Chinese and Western Medicine were selected, and CTTA was performed. CTTA parameters (entropy value, peak state and skewness) were counted. The pathological diagnosis results were taken as the "gold standard". Statistical pathological examination results were used to compare the general clinical characteristics and CTTA parameters of benign and malignant thyroid nodules. The receiver operating characteristic (ROC) was used to analyze the diagnostic value of CTTA parameters for thyroid nodules.Results:According to the clinicopathological examination, 43 of 110 patients with Hashimoto’s thyroiditis were malignant, accounting for 39.09%. Among them, 22 were papillary carcinoma, 13 were follicular carcinoma, 6 were medullary carcinoma, and 2 were malignant lymphoma; 67 cases were benign, accounting for 60.91%, including 32 nodular goiters, 20 Hashimoto’s nodules, 8 thyroid adenomas, and 7 focal inflammation. The levels of TSH, irregular shape, blurry border and calcification in patients with malignant thyroid nodules were higher than those in patients with benign thyroid nodules ( t/ χ2=13.167, 18.364, 20.180,17.621, P<0.001). In the background of Hashimoto’s thyroiditis, there was no significant difference in the peak and skewness of CTTA parameters between benign and malignant thyroid nodules ( t=1.633, 1.382, P=0.105, 0.170). The entropy value of patients with malignant thyroid nodules was higher than that of patients with benign thyroid nodules, and the difference was statistically significant ( t=9.862, P<0.001). ROC analysis showed that the cut-off value of entropy value for diagnosing benign and malignant thyroid nodules was 6.28, AUC value was 0.909, 95% CI was 0.839-0.955, sensitivity was 86.05% (37/43), and specificity was 88.06% (69/67) . Conclusion:CTTA parameters in Hashimoto’s thyroiditis patients with benign and malignant thyroid nodules are different, and CTTA parameters have certain diagnostic value for benign and malignant thyroid nodules.

Prediction of myometrial invasion of bladder cancer based on texture analysis of the bladder wall at tumor base / 中华超声影像学杂志

Objective:To identify the value of ultrasound radiomic features extracted from the bladder wall at tumor base in predicting myometrial invasion of bladder cancer.Methods:A total of 175 cases with bladder cancer confirmed by pathology from January 2017 to February 2022 in the First Affiliated Hospital of Guangxi Medical University were retrospectively analyzed. They were divided into training set and testing set in a ratio of 7∶3. The MaZda texture analysis software was used to draw the region of interest (ROI) of the bladder wall and the tumor region for extracting texture features. The minimum absolute reduction and variable selection operator (LASSO) regression and 10-fold cross-validation were used to screen the features of training set for establishing the models. And the ROC curve was used to evaluate the efficiency of the models.Results:A total of 279 texture features were extracted from the ROI of the bladder wall and the tumor region, and 5 texture features were screened out for constructing omics scoring models by LASSO regression and 10-fold cross-test. The area under ROC curve (AUC)s used in training set and testing set of the bladder wall were 0.921 and 0.856, while the AUCs applied in training set and testing set of the tumor region were 0.849 and 0.704. Both in the training set and test set, the AUCs of the model of the bladder wall were higher than those of the model of the tumor region (all P<0.05). Conclusions:The omics scoring model based on the texture features of the bladder wall at tumor base can effectively identify muscle-invasive bladder cancer(MIBC) and non-muscle-invasive bladder cancer(NMIBC), and has better performance than the model based on the texture feature of the tumor region.

Nomogram based on CT texture analysis and morphological characteristics for differentiating Borrmann Ⅳ type gastric cancer from gastric diffuse large B-cell lymphoma / 中华放射学杂志

Objective:To explore the value in differentiating Borrmann Ⅳ type gastric cancer (BT4-GC) from gastric diffuse large B-cell lymphoma (DLBCL) using a nomogram based on CT texture analysis (CTTA) and morphological characteristics.Methods:From June 2011 to December 2020, a total of 60 patients with BT4-GC and 24 patients with DLBCL were retrospectively collected in Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University. Morphological characteristics were evaluated, including major location, long axis range, circumferential range, mucosal line status, and perigastric enlarged lymph nodes. CTTA parameters were calculated using venous CT images with a manual region of interest. The morphological characteristics and CTTA parameters between BT4-GC and DLBCL were compared by χ 2 test, Fisher exact test or Mann-Whitney U test. The multivariate binary logistic regression analysis was used to filter factors into the diagnostic model and construct a nomogram. The receiver operating characteristic (ROC) curve was used to evaluate the diagnostic performance of CTTA parameters and the diagnostic model in differentiating BT4-GC from DLBCL. Results:For morphological characteristics, mucosal line status showed a significant difference between BT4-GC and DLBCL (χ 2=12.99, P<0.001). For CTTA parameters, 16 parameters showed significant differences between BT4-GC and DLBCL (all P<0.05). The area under the ROC curve (AUC) of 16 CTTA parameters in differentiating BT4-GC from DLBCL was 0.662-0.833. Percentile 90 showed the highest AUC of 0.833 (95%CI 0.736-0.906). The mucosal line status (OR 4.82, 95%CI 1.21-19.25, P=0.026) and percentile 90 (OR 1.09, 95%CI 1.04-1.15, P=0.001) were brought into the diagnostic model and constructed a nomogram. The AUC of the model in differentiating BT4-GC from DLBCL was 0.898 (95%CI 0.813-0.953), sensitivity was 0.833, and specificity was 0.817. Conclusions:The nomogram based on CTTA percentile 90 and morphological characteristics mucosal line status can effectively distinguish BT4-GC from DLBCL and shows high diagnostic efficacy.

Study on the effect of virtual grid on chest X-ray image quality / 中华放射学杂志

Objective:To explore the image quality and its evaluation method using virtual grid under different tube voltages in the clinical chest X-ray exam.Methods:According to the conditions of chest X-ray photography commonly used in clinical practice, the corresponding thickness of plexiglass (20 cm, including CDRAD phantom) was determined as the experimental object. With a fixed tube loading of 4 mAs and the tube voltage from 60 to 125 kV, the experimental object was imaged in three ways: physical grid, none grid and virtual grid. The common physical parameters (CNR, σ, C, SNR), texture analysis (Angular second moment, texture Contrast, Correlation, Inverse difference moment, Entropy) and CDRAD phantom score (IQF inv) were evaluated. Two-way ANOVA test was used for each group of common physical parameters, and further pairwise comparisons were made. At the same time, applying virtual grids on the obtained images with chest anthropomorphic model and texture indexing the images with and without virtual grids, then rank sum test of paired sample can be conducted. Results:There were differences in image quality among the three groups of grid mode( P<0.05), and the physical grid delivered the best image quality. The tube voltage had an impact on all image quality evaluation indexes ( P<0.05). The tube voltage was positively correlated with CNR, SNR, angular second moment, inverse difference moment and IQF inv ( P<0.05), and negatively correlated with σ, C, texture contrast and entropy ( P<0.05). There was no significant correlation between the tube voltage and Correlation ( P>0.05). The chest anthropomorphic model images were used to evaluate the virtual grids, and the texture indexes (Angle second moment, Contrast, Correlation, Inverse difference moment, Entropy) were statistically significant (P<0.05). Conclusions:The virtual grid can improve the image quality of chest X-ray photography, and the image texture analysis method can be a useful supplement to the image quality evaluation parameters.

CT texture analysis for predicting pseudoprogression in metastatic clear cell renal cell carcinoma during PD-1 inhibitor therapy / 中华内科杂志

Objective:To evaluate the effectiveness of enhanced CT texture feature analysis in predicting pseudoprogression in patients with metastatic clear cell renal cell carcinoma (mccRCC) undergoing programmed cell death protein 1 (PD-1) inhibitor therapy.Methods:A cross-sectional study. Data from 32 patients with mccRCC were retrospectively collected who received monotherapy with PD-1 inhibitors after standard treatment failure at Henan Cancer Hospital, from June 2015 to January 2021. Clinical information and enhanced CT images were analyzed to assess target lesion response. The lesions were divided into pseudoprogression and non-pseudoprogression groups. Manual segmentation of target lesions was performed using ITK-Snap software on baseline enhanced CT, and texture analysis was conducted using A.K. software to extract feature parameters. Differences in texture features between the pseudoprogression and non-pseudoprogression groups were analyzed using univariate and multivariate logistic regression. A predictive model for pseudoprogression was constructed, and its performance was evaluated using ROC curve analysis.Results:A total of 32 patients with 89 lesions were included in the study. Statistical analysis revealed significant differences in seven texture features between the pseudoprogression and non-pseudoprogression groups. These features included“original_ngtdm_Strength”(0.49 vs. -0.61, P=0.006), “wavelet-HLH_glszm_ZonePercentage”(0.67 vs. -0.22, P=0.024),“wavelet-LHL_ngtdm_Strength”(1.20 vs. -0.51, P=0.002), “wavelet-HLL_gldm_LargeDependenceEmphasis”(-0.84 vs. 0.19, P=0.002), “wavelet-HLH_glcm_Id” (-0.30 vs. 0.43, P=0.037),“wavelet- HLH_glrlm_RunPercentage”(0.45 vs. -0.01, P=0.032),“wavelet-LHH_firstorder_Skewness”(0.25 vs. -0.27, P=0.011). Based on these features, a pseudoprogression prediction model was developed with a P-value of 0.000 2 and an odds ratio of 0.045 (95% CI 0.009-0.227). The model exhibited a high predictive performance with an AUC of 0.907 (95% CI 0.817-0.997) according to ROC curve analysis. Conclusions:Enhanced CT texture feature analysis shows promise in predicting lesion pseudoprogression in patients with metastatic ccRCC undergoing PD-1 inhibitor therapy. The developed predictive model based on texture features demonstrates good performance and may assist in evaluating treatment response in these patients.

Application value of magnetic resonance imaging intravoxel incoherent motion diffusion-weighted imaging and texture analysis in differential diagnosis and staging of nasopharyngeal carcinoma / 肿瘤研究与临床

Objective:To investigate the application value of magnetic resonance imaging (MRI) intravoxel incoherent motion (IVIM)-diffusion-weighted imaging (DWI) metrics and texture analysis in the differential diagnosis and staging of nasopharyngeal carcinoma.Methods:The clinical data of 125 nasopharyngeal carcinoma patients (the research group) in Tangshan People's Hospital from October 2019 to October 2021 and 76 patients with nasopharyngeal hyperplasia during the same period (the control group) were retrospectively analyzed. All patients underwent MRI T2WI and IVIM-DWI sequence scanning, and then the plain T2WI images, DWI, and IVIM-DWI quantitative parameter pseudo-color maps including pure diffusion coefficient (D), pseudo-diffusion coefficient (D*), and perfusion fraction (f) were obtained. The texture analysis metrics like apparent diffusion coefficient (ADC), D, D* and f were recorded. IVIM-DWI and texture analysis metrics were compared among patients in both groups and patients in different clinical stages; and the receiver operating characteristic (ROC) curve was plotted to evaluate the efficacy of IVIM sequence parameters and texture analysis metrics in the differential diagnosis and staging of nasopharyngeal carcinoma.Results:Compared with the control group, a marked reduction in D value [(0.80±0.13)×10 -3 mm 2/s vs. (1.19±0.27)×10 -3 mm 2/s], f value [(11.3±2.2)% vs. (15.6±3.3)%], mean ADC value [(0.92±0.17)×10 -3 mm 2/s vs. (1.16±0.19)×10 -3 mm 2/s] and variance (2 189±862 vs. 3 563±925) (all P < 0.05); a notable increase in skewness (0.50±0.17 vs. 0.31±0.12), kurtosis (0.56±0.13 vs. -0.21±0.06) and entropy (10.5±2.3 vs. 7.1±2.1) (all P < 0.05). The area under the curve (AUC) of IVIM sequence parameters and texture analysis metrics in the differential diagnosis of nasopharyngeal carcinoma was 0.763 and 0.803, respectively; the AUC, sensitivity and specificity of the combination of IVIM sequence parameters and texture analysis metrics for the differential diagnosis of nasopharyngeal carcinoma was 0.868, 89.6% and 86.8%, respectively. Compared with patients in stage Ⅰ-Ⅱ nasopharyngeal carcinoma, patients in stage Ⅲ-Ⅳ reported the lower D value [(0.75±0.13)×10 -3 mm 2/s vs. (0.89±0.16)×10 -3 mm 2/s], f value [(10.8±2.8)% vs. (12.1±3.0)%] (all P < 0.05), and the lower mean ADC value [(0.90±0.14)×10 -3 mm 2/s vs. (0.96±0.16)×10 -3 mm 2/s], and variance (2 063±831 vs. 2 431±846) (all P < 0.05), skewness (0.56±0.15 vs. 0.39±0.16), kurtosis (0.62±0.15 vs. 0.44±0.13) and entropy (11.0±2.1 vs. 9.1±2.4) (all P < 0.05). The AUC of IVIM sequence parameters and texture analysis metrics in differentiating nasopharyngeal carcinoma with different stages was 0.863 and 0.796, respectively; the AUC, sensitivity and specificity of the combination of IVIM sequence parameters and texture analysis metrics in differentiating nasopharyngeal carcinoma with different stages was 0.894, 85.4% and 90.7%, respectively. Conclusions:MRI texture analysis and IVIM quantitative analysis are of high value in the differential diagnosis and staging of nasopharyngeal carcinoma; and the texture analysis achieves higher sensitivity and specificity in the differential diagnosis and staging of nasopharyngeal carcinoma compared with IVIM quantitative analysis; the combined application of both has the highest overall efficacy.

Magnetic resonance imaging texture analysis of the temporomandibular joint for changes in the articular disc in individuals with migraine headache / Análise de textura por ressonância magnética da articulação temporomanbibular com alterações no disco articular em individuos com cefaleia migranea

Objective: the aim of this study was to analyse the performance of the technique of texture analysis (TA) with magnetic resonance imaging (MRI) scans of temporomandibular joints (TMJs) as a tool for identification of possible changes in individuals with migraine headache (MH) by relating the findings to the presence of internal derangements. Material and Methods: thirty MRI scans of the TMJ were selected for study, of which 15 were from individuals without MH or any other type of headache (control group) and 15 from those diagnosed with migraine. T2-weighted MRI scans of the articular joints taken in closed-mouth position were used for TA. The co-occurrence matrix was used to calculate the texture parameters. Fisher's exact test was used to compare the groups for gender, disc function and disc position, whereas Mann-Whitney's test was used for other parameters. The relationship of TA with disc position and function was assessed by using logistic regression adjusted for side and group. Results: the results indicated that the MRI texture analysis of articular discs in individuals with migraine headache has the potential to determine the behaviour of disc derangements, in which high values of contrast, low values of entropy and their correlation can correspond to displacements and tendency for non-reduction of the disc in these individuals. Conclusion: the TA of articular discs in individuals with MH has the potential to determine the behaviour of disc derangements based on high values of contrast and low values of entropy (AU)

Objetivo: o objetivo deste estudo foi analisar o desempenho da técnica de análise de textura (AT) em exames de ressonância magnética (RM) das articulações temporomandibulares (ATM) como ferramenta para identificação de possíveis alterações em indivíduos com cefaléia migrânea (CM) relacionando os achados com a presença de desarranjos internos. Material e Métodos: trinta exames de RM das ATM foram selecionados para estudo, sendo 15 de indivíduos sem cefaleia migrânea ou qualquer outro tipo de cefaléia (grupo controle) e 15 diagnosticados com CM. As imagens de RM ponderadas em T2 das articulações realizadas na posição de boca fechada foram usadas para AT. A matriz de co-ocorrência foi usada para calcular os parâmetros de textura. O teste exato de Fisher foi usado para comparar os grupos quanto ao sexo, função do disco e posição do disco, enquanto o teste de Mann-Whitney foi usado para os demais parâmetros. A relação da AT com a posição e função do disco foi avaliada por meio de regressão logística ajustada para lado e grupo. Resultados: a AT por RM dos discos articulares em indivíduos com cefaleia migrânea tem o potencial de determinar o comportamento dos desarranjos discais, em que altos valores de contraste, baixos valores de entropia e sua correlação podem corresponder a deslocamentos e tendência a não redução do disco nesses indivíduos. Conclusão: a análise de textura dos discos articulares em indivíduos com CM tem potencial para determinar o comportamento dos desarranjos do disco com base em altos valores de contraste e baixos valores de entropia. (AU)

The value of intravoxel incoherent motion magnetic resonance imaging and texture analysis for predicting BRAF gene mutation in rectal cancer / 中华消化外科杂志

Objective:To investigate the value of intravoxel incoherent motion (IVIM) magnetic resonance imaging (MRI) and texture analysis for predicting BRAF gene mutation in rectal cancer.Methods:The clinical diagnositic trial was conducted. The clinicopathological data of 36 rectal cancer patients who were admitted to the First People's Hospital of Shangqiu from January 2016 to June 2021 were collected. There were 28 males and 8 females, aged (50±4)years. All the 36 patients were confirmed by pathological examination. After genetic testing, 12 patients with BRAF mutant type of BRAF V600E mutation were allocated into the mutation group, and 24 patients with BRAF wild type were allocated into the non-mutation group. All patients underwent MRI scan before surgery, and IVIM related post-processing images were received by Function Tool post-processing software. Observation indicators: (1) consistency test between observers of IVIM para-meters and texture parameters; (2) comparison of IVIM parameters on MRI between the two groups; (3) comparison of texture parameters on MRI between the two groups; (4) diagnostic efficacy of IVIM and texture parameters. The intraclass correlation coefficient (ICC) was used to evaluate the consistency between observers, with ICC >0.80 as good consistency. The average values of para-meters with ICC >0.80 were included for further analysis. Measurement data with normal distribu-tion were represented as Mean± SD, and comparison between groups was analyzed by the indepen-dent sample t test. Measurement data with skewed distribution were represented as M( Q1, Q3), and comparison between groups was analyzed using the Mann-Whitney U test. Count data were described as absolute numbers, and comparison between groups was analyzed by the chi-square test. Comparison of ordinal data was analyzed by the non-parameter rank sum test. The texture parameters were combined using the Logistic regression model. Receiver operating charac-teristic curve was used to analyze the predictive performance and calculate the sensitivity and specificity. Results:(1) Consistency test between observers of IVIM parameters and texture parameters: the ICCs between two observers of IVIM parameters including apparent diffusion coefficient, diffusion related coefficient, perfusion-related diffusion coefficient and perfusion-related parameter were 0.91, 0.90, 0.91, 0.89, respectively. The ICCs of texture parameters including the minimum value, the maximum value, the 10th percentile and the 25th percentile between two observers were <0.80 while the ICCs of texture parameters including mean value, the 50th percentile, the 75th percentile, the 90th percentile, energy, entropy, skewness and kurtosis between two observers were >0.80. (2) Comparison of IVIM parameters on MRI between the two groups: IVIM parameters of diffusion related coefficient and perfusion-related parameter on MRI were (0.70±0.13)×10 -3 mm 2/s and 0.39±0.30 for the mutation group, versus (0.79±0.12)×10 -3 mm 2/s and 0.17±0.10 for the non-mutation group, showing significant differences between the two groups ( t=-2.17, 2.46, P<0.05). (3) Comparison of texture parameters on MRI between the two groups: the texture parameters of mean value and energy on diffusion related coefficient image were 0.54±0.23 and 0.00(0.00,0.01) for the mutation group, versus 0.77±0.34 and 0.01(0.00,0.01) for the non-mutation group, showing significant differences between the two groups ( t=-2.12, Z=-1.35, P<0.05). (4) Diagnostic efficacy of IVIM and texture parameters: the areas under the curve (AUCs) of diffusion related coefficient, perfusion-related parameter, IVIM parameters combination, mean value of diffu-sion related coefficient image, energy value of diffusion related coefficient image, texture parameters combination were 0.69[95% confidence interval ( CI) as 0.52-0.84], 0.76(95% CI as 0.59-0.88), 0.79(95% CI as 0.62-0.91), 0.71(95% CI as 0.52-0.85), 0.79(95% CI as 0.62-0.91), 0.84(95% CI as 0.68-0.94), which were all lower than the AUC of IVIM and texture parameters combination as 0.92(95% CI as 0.79-0.99). Conclusions:IVIM parameters and texture parameters of MRI can non-invasively predict the mutation status of BRAF gene in rectal cancer. The combination of IVIM and texture parameters has a better predictive efficacy.

The value of intravoxel incoherent motion diffusion weighted imaging parameters combined with texture analysis of primary lesion of rectal adenocarcinoma to predict preoperation non-enlarged lymph node metastasis / 中华放射学杂志

Objective:To investigate the value of intravoxel incoherent motion diffusion weighted imaging (IVIM-DWI) parameters combined with T 2WI texture analysis of primary lesions of rectal adenocarcinoma in preoperative prediction of lymph node metastasis with short diameter ≤9 mm. Methods:Retrospective analysis was performed on 115 cases of rectal adenocarcinoma confirmed by surgical pathology in Affiliated Provincial Hospital of Anhui Medical University from June 2015 to October 2020. All patients underwent total mesorectal resection and received conventional rectal MRI and IVIM-DWI scan before surgery. According to the pathological results of lymph node, the patients were divided into lymph node metastatic group ( n=44) and non-metastatic group ( n=71). IVIM-DWI parameters of primary rectal adenocarcinoma were measured including apparent diffusion coefficient (ADC), diffusion coefficient (D), pseudo diffusion coefficient (D *) and perfusion fraction (f). The region of interest (ROI) of the whole lesion of rectal adenocarcinoma was delineated on axial T 2WI; then the ROIs were imported into GE Analysis Kit software to extract 3D texture feature. The differences of IVIM-DWI parameters and texture feature parameters were compared between two groups using independent sample t test or Mann-Whitney U test. The optimal texture feature parameters with independent predictive function were screened by multivariate logistic regression. Then the texture feature model and combined model based IVIM-DWI and texture feature parameters were established. The receiver operating characteristic (ROC) curves were used to evaluate the performances of IVIM-DWI, texture feature parameters, texture feature model and combined model in predicting lymph node metastasis in patients with rectal adenocarcinoma. The area under the ROC curve (AUC) were compared with DeLong test. Results:Among all the IVIM-DWI parameters, the D * and f values of primary rectal adenocarcinoma were significantly different between the lymph node metastasis group and the non-lymph node metastasis group ( Z=3.39, P=0.001, Z=-3.06, P=0.002); no statistical significance was found in the ADC and D values between two groups (both P>0.05). A total of 828 texture feature parameters were obtained based on T 2WI of primary lesion of rectal adenocarcinoma, among which 3 optimal texture feature parameters were selected, including firstorder_Skewness, shape_Sphericity and glcm_Idn. The ROC curve results showed that the AUC of D * and f were 0.689 and 0.670, respectively. The AUC of 3 texture feature parameters were 0.651, 0.628, 0.631, respectively. The AUC of texture feature model and the combined model were 0.775 and 0.803. The AUC of combined model was larger than D *, f and the three texture feature parameters (all P<0.05). Conclusion:IVIM-DWI parameters combined with T 2WI texture feature parameters in primary lesion of rectal adenocarcinoma show good diagnostic efficacy in preoperative prediction of lymph node metastasis with short diameter≤9 mm.

Progress in the diagnosis and differential diagnosis of small renal cell carcinoma by CT texture analysis / 中华全科医师杂志

Small renal cell carcinoma refers to a renal malignant tumor with a maximum diameter of 4 cm.Due to the small size, its diagnosis and differential diagnosis have been difficult points in clinical work. CT texture analysis is an emerging technique, it determines the tumor heterogeneity by analyzing the distribution and relationship of pixel or voxel gray-scale levels in the CT images, it acts to more accurately predict the benign and malignant tumors and the classification of tumors.This paper reviews CT texture analysis on the diagnosis and differential diagnosis of small renal cell carcinoma, in order to guide the correct diagnosis of doctors and effectively clinical treatment.

Análisis de texturas homogéneas para la estimación volumétrica de la materia cerebral por tomografía computarizada / Analysis of Homogeneous Textures for the VolumetricEstimation of Brain Matter by Computed Tomography

Las aplicaciones de análisis de texturas y su extracción de características son consideradas tendencias de investigación en las neurociencias. La textura como método de análisis de imágenes ha mostrado resultados prometedores en la detección de lesiones visibles y no visibles, y en estudios de tomografía computarizada (TC) son escasos. La presente investigación tiene como objetivo determinar la aplicabilidad del procesamiento automático de índices de texturas homogéneas en la estimación volumétrica de la sustancia gris cerebral en imágenes de TC craneal. Para ello se utilizaron imágenes artificiales con regiones predefinidas y la selección de imágenes de TC en los pacientes con indicaciones previas de TC de cráneo. Dos pasos fundamentales son conducidos para la implementación de este enfoque. Como resultado se obtuvo un método automático de reconocimiento de patrones sin ventanas por medio de la extracción de características de textura homogéneas a través de la matriz de co-ocurrencia(AU)

Texture analysis applications and their extraction of features are considered research trends in neuroscience. Texture as a method of image analysis has shown promising results in the detection of visible and non-visible lesions, and in computed tomography (CT) studies they are scarce. The present research aims to determine the applicability of the automatic processing of homogeneous texture indices in the volumetric estimation of brain gray matter in cranial CT images. For this, artificial images with predefined regions and the selection of CT images were used in patients with previous indications for CT of the skull. Two fundamental steps are taken for the implementation of this approach. As a result, an automatic windowless pattern recognition method was obtained by means of the extraction of homogeneous texture characteristics through the co-occurrence matrix(AU)

Evaluation of brain injury after cardiopulmonary resuscitation onby T2W images texture analysis: A preliminary study / 中华急诊医学杂志

Objective:To evaluate the feasibility of brain injury after cardiopulmonary resuscitation (CPR) in rats based on T2WI image texture analysis.Methods:Eighteen SD rats were randomly divided into the sham group ( n=8) and model group ( n=10). The rats in the model group underwent MRI scanning at 6 h after return of spontaneous circulation (ROSC), and the rats in the sham group received MRI scanning at 6 h after the operation. The differences in the texture features of T2WI images and the expressions of AQP4 and NSE between the two groups were analyzed. The receiver operating characteristic curve (ROC) was used to evaluate the diagnostic efficacy of statistically different texture features between the two groups for brain injury. The associations between texture features and AQP4 and NSE expressions in the sham group and model group were analyzed using Spearman correlation coefficients. Results:The minimum intensity, standard deviation, and inverse difference moment of the whole brain T2WI texture features of the model group were significantly lower than those of the sham group ( P<0.05), while the difference entropy and characteristics of high gray in homogeneity were significantly higher than those of the sham group ( P<0.05). The difference entropy was the best with an area under curve (AUC) of 0.922, a sensitivity of 100% and a specificity of 75%. The AQP4 and NSE expressions in the model group were significantly higher than those in the sham group ( P<0.05). The minimum intensity value was positively correlated with AQP4 and NSE expressions ( r=0.501, 0.568, P=0.048, 0.022). The standard deviation was positively correlated with AQP4 and NSE expressions ( r=0.620, 0.530, P=0.010, 0.035). The difference entropy was negatively correlated with AQP4 expression ( r=-0.535, P=0.033). Conclusions:Texture analysis on T2WI images can evaluate the degree of brain edema and neuronal damage. The minimum intensity, standard deviation, and difference entropy are sensitive indicators to evaluate brain injury after CPR, and difference entropy has the highest sensitivity and specificity.

CT texture analysis for differential diagnosis of renal chromophobe cell carcinoma and renal oncocytoma / 中国医学影像技术

Objective: To investigate the value of texture analysis based on enhanced renal CT for identification of chromophobe cell renal carcinoma (CCRC) and renal oncocytoma (RO). Methods: CT images of 64 patients with CCRC and 31 with RO were retrospectively analyzed. ITK-SNAP version 4.11.0 software was used to delineate the region of interest, and A.K.Version v3.0.0.R software was used to extract texture features. Random forest model was established using texture features included in random forest algorithm. Logistic regression was used to evaluate the discriminative the efficacy of the established models for differential diagnosis of CCRC and RO. Results: The first 20 texture parameters selected with random forest algorithm from corticomedullary phase, nephrographic phase and both of them, with weight values from high to low, were evaluated with Logistic regression, and the AUC values were 0.876, 0.861 and 0.945, respectively. Conclusion: Texture analysis based on enhanced renal CT images has clinical value in differential diagnosis of CCRC and RO.

Identification of invasive lung adenocarcinoma and non-calcified lung tuberculoma on plain CT images based on texture analysis / 中国医学影像技术

Objective: To investigate the feasibility of differential diagnosis of invasive lung adenocarcinoma and non-calcified lung tuberculoma on CT plain images based on texture analysis. Methods: Data of plain CT images of 52 patients with single pulmonary nodules confirmed pathologically were retrospectively analyzed, including 31 cases of invasive lung adenocarcinoma and 21 cases of non-calcified lung tuberculosis. Totally 300 texture features of each kind of lesions were extracted with MaZda software, then 10 optimized texture parameters were selected for texture analysis with fisher coefficient (Fisher), minimization of both probability of classification error and average correction coefficient (POE+ACC), mutual information coefficients (MI) methods, respectively, and the optimal texture features combination combined with three methods (MPF) was obtained. The four groups of optimal texture characteristics were classified using linear discriminant analysis (LDA) and nonlinear discriminant analysis (NDA), while classification of LDA and NDA were performed using K-nearest neighbor classifier (K-NN) and artificial neural network (ANN), respectively. The minimum error probability of 4 groups of texture features in differential diagnosing of 2 kinds of lesions was analyzed, the differences of 30 optimal texture features were compared between 2 kinds of lesions, their ROC curves for identifying 2 kinds of lesions were drawn, and then AUC of the curves were calculated to evaluate their diagnostic performance. Results: For single group of optimal texture features, NDA/ANN-Fisher method had the lowest error rate (7.69% [4/52]), while for MPF, the error rate of NDA/ANN-MPF was the lowest (5.77% [3/52]). There was no statistical difference of error rate between NDA/ ANN-Fisher and NDA/ ANN-MPF method (χ2=0.15, P>0.05). Statistical differences of 10 optimal texture features were noticed between 2 kinds of lesions, among which difference entropy S(1,1), difference variance S(1,1) and gradient variance had good diagnostic efficacy (AUC=0.71, 0.71, 0.70), and their AUC were not statistically different (all P>0.05). Conclusion: Based on texture analysis of plain CT images, invasive lung adenocarcinoma and non-calcified lung tuberculosis can be well distinguished, providing objective and reliable basis for differential diagnosis of these two lesions.