Radiomics model based on CT images for distinguishing invasive lung adenocarcinoma with micropapillary or solid structure / 中国胸心血管外科临床杂志

@#Objective    To investigate the radiomics features to distinguish invasive lung adenocarcinoma with micropapillary or solid structure. Methods    A retrospective analysis was conducted on patients who received surgeries and pathologically confirmed invasive lung adenocarcinoma in our hospital from April 2016 to August 2019. The dataset was randomly divided into a training set [including a micropapillary/solid structure positive group (positive group) and a micropapillary/solid structure negative group (negative group)] and a testing set (including a positive group and a negative group) with a ratio of 7∶3. Two radiologists drew regions of interest on preoperative high-resolution CT images to extract radiomics features. Before analysis, the intraclass correlation coefficient was used to determine the stable features, and the training set data were balanced using synthetic minority oversampling technique. After mean normalization processing, further radiomics features selection was conducted using the least absolute shrinkage and selection operator algorithm, and a 5-fold cross validation was performed. Receiver operating characteristic (ROC) curves were depicted on the training and testing sets to evaluate the diagnostic performance of the radiomics model. Results    A total of 340 patients were enrolled, including 178 males and 162 females with an average age of 60.31±6.69 years. There were 238 patients in the training set, including 120 patients in the positive group and 118 patients in the negative group. There were 102 patients in the testing set, including 52 patients in the positive group and 50 patients in the negative group. The radiomics model contained 107 features, with the final 2 features selected for the radiomics model, that is, Original_ glszm_ SizeZoneNonUniformityNormalized and Original_ shape_ SurfaceVolumeRatio. The areas under the ROC curve of the training and the testing sets of the radiomics model were 0.863 (95%CI 0.815-0.912) and 0.857 (95%CI 0.783-0.932), respectively. The sensitivity was 91.7% and 73.7%, the specificity was 78.8% and 84.0%, and the accuracy was 85.3% and 78.4%, respectively. Conclusion    There are differences in radiomics features between invasive pulmonary adenocarcinoma with or without micropapillary and solid structures, and the radiomics model is demonstrated to be with good diagnostic value.

Construction of a machine learning model for identifying clinical high-risk carotid plaques based on radiomics / 中国胸心血管外科临床杂志

@#Objective     To construct a radiomics model for identifying clinical high-risk carotid plaques. Methods     A retrospective analysis was conducted on patients with carotid artery stenosis in China-Japan Friendship Hospital from December 2016 to June 2022. The patients were classified as a clinical high-risk carotid plaque group and a clinical low-risk carotid plaque group according to the occurrence of stroke, transient ischemic attack and other cerebrovascular clinical symptoms within six months. Six machine learning models including eXtreme Gradient Boosting, support vector machine, Gaussian Naive Bayesian, logical regression, K-nearest neighbors and artificial neural network were established. We also constructed a joint predictive model combined with logistic regression analysis of clinical risk factors. Results    Finally 652 patients were collected, including 427 males and 225 females, with an average age of 68.2 years. The results showed that the prediction ability of eXtreme Gradient Boosting was the best among the six machine learning models, and the area under the curve (AUC) in validation dataset was 0.751. At the same time, the AUC of eXtreme Gradient Boosting joint prediction model established by clinical data and carotid artery imaging data validation dataset was 0.823. Conclusion     Radiomics features combined with clinical feature model can effectively identify clinical high-risk carotid plaques.

Predictive value of multimodal magnetic resonance imaging based radiomics model for micro-satellite instability of rectal cancer / 中华消化外科杂志

Objective:To investigate the predictive value of multimodal magnetic resonance imaging (MRI) based radiomics model for microsatellite instability (MSI) of rectal cancer.Methods:The retrospective cohort study was conducted. The clinicopathological data of 117 patients with rectal cancer who were admitted to 2 medical centers, including 74 in Ningbo Urology & Nephrology Hospital and 43 in the First Affiliated Hospital of Zhejiang University School of Medicine, from January 2020 to December 2022 were collected. There were 73 males and 44 females, aged (63±5)years. Based on random number table, all 117 patients were divided into the training dataset of 70 cases and the test dataset of 47 cases with a ratio of 7:3. All patients underwent pelvic MRI exami-nation. Observation indicators: (1) construction of radiomics prediction model and analysis of charac-teristics; (2) analysis of factors influencing MSI of rectal cancer in the training dataset; (3) construc-tion and evaluation of the prediction model for MSI of rectal cancer. Measurement data with normal distribution were represented as Mean± SD, and comparison between groups was conducted using the t test. Measurement data with skewed distribution were represented as M( Q1, Q3), and compari-son between groups was conducted using the Mann-Whitney U test. Count data were described as absolute numbers, and comparison between groups was conducted using the chi-square test. Univariate analysis was conducted using the one way ANOVA and multivariate analysis was conducted using the Logistic regression model with forward method. The receiver operating characteristic curve was drawn, and the area under the curve (AUC), decision curve, calibration curve and Delong test were used to evaluate the predictive ability of prediction model. Results:(1) Construction of radiomics prediction model and analysis of characteristics. Five thousand five hundred and eighty radiomics features were finally extracted from the 117 patients. Based on the feature selection using the maximum correlation minimum redundancy method, and the least absolute shrinkage and selection operator fitting algorithm, 9 radiomics features were finally selected. The radiomics prediction model was constructed based on calculation of the radiomics score. (2) Analysis of factors influencing MSI of rectal cancer in the training dataset. Results of multivariate analysis showed that platelet count was an independent influencing factor for MSI of rectal cancer [ odds ratio=1.13, 95% confidence interval ( CI) as 1.06-1.21, P<0.05]. (3) Construction and evaluation of the prediction model for MSI of rectal cancer. The clinical prediction model and clinical-radiomics combined prediction model were constructed based on the results of multivariate analysis. The AUC of clinical prediction model, radiomics prediction model, clinical-radiomics combined prediction model in the training dataset was 0.94 (95% CI as 0.86-0.98), 0.96 (95% CI as 0.88-0.99), 0.99 (95% CI as 0.93-1.00), respectively, with the sensitivity and specificity as 90.7%, 91.2%, 96.9% and 85.0%, 88.9%, 94.3%. Results of Delong test showed that there was a significant difference in the predictive performance between the clinical-radiomics combined prediction model and the clinical prediction model ( Z=2.20, P<0.05), and there was no significant difference between the radiomics prediction model and the clinical-radiomics combined prediction model or the clinical prediction model ( Z=1.94, 0.60, P>0.05). The AUC of clinical prediction model, radiomics prediction model, clinical-radiomics combined prediction model in the test dataset was 0.97 (95% CI as 0.88-1.00), 0.86 (95% CI as 0.73-0.95), 0.97(95% CI as 0.87-1.00), respectively, with the sensitivity and specificity as 99.3%, 95.8%, 99.3% and 85.7%, 73.9%, 90.5%. Results of Delong test showed that there was a significant difference in the predictive performance between the clinical-radiomics combined prediction model and the radiomics prediction model ( Z=2.21, P<0.05), and there was no significant difference between the clinical prediction model and the clinical-radiomics combined prediction model or the radiomics prediction model ( Z=0.17, 1.82, P>0.05). Results of calibration curve showed that clinical prediction model, radiomics prediction model, clinical-radiomics combined prediction model had good ability in predicting the MSI status of rectal cancer. Results of decision curve showed that compared to clinical prediction model and radiomics prediction model, clinical-radiomics combined prediction model had greatest net gain in predicting the MSI of rectal cancer. Conclusion:The prediction model based on 9 radiomics features after selecting can effectively predict the MSI status of rectal cancer, and the clinical-radiomics combined prediction model has a better prediction efficiency.

Construction and application value of CT based radiomics model in predicting the prognosis of patients with gastric neuroendocrine neoplasm / 中华消化外科杂志

Objective:To construct of a computed tomography (CT) based radiomics model for predicting the prognosis of patients with gastric neuroendocrine neoplasm (GNEN) and inves-tigate its application value.Methods:The retrospective cohort study was conducted. The clinico-pathological data of 182 patients with GNEN who were admitted to 2 medical centers, including the First Affiliated Hospital of Zhengzhou University of 124 cases and the Affiliated Cancer Hospital of Zhengzhou University of 58 cases, from August 2011 to December 2020 were collected. There were 130 males and 52 females, aged 64(range, 56-70)years. Based on random number table, all 182 patients were divided into the training dataset of 128 cases and the validation dataset of 54 cases with a ratio of 7:3. All patients underwent enhanced CT examination. Observation indicators: (1) construction and validation of the radiomics prediction model; (2) analysis of prognostic factors for patients with GNEN in the training dataset; (3) construction and evaluation of the prediction model for prognosis of patients with GNEN. Measurement data with skewed distribution were represented as M(range), and comparison between groups was conducted using the Mann-Whitney U test. Count data were described as absolute numbers, and the chi-square test, corrected chi-square test or Fisher exact probability were used for comparison between groups. The Kaplan-Meier method was used to calculate survival rate and draw survival curve, and the Log-rank test was used for survival analysis. The COX regression model was used for univariate and multivariate analyses. The R software (version 4.0.3) glmnet software package was used for least absolute shrinkage and selection operator (LASSO)-COX regression analysis. The rms software (version 4.0.3) was used to generate nomogram and calibration curve. The Hmisc software (version 4.0.3) was used to calculate C-index values. The dca.R software (version 4.0.3) was used for decision curve analysis. Results:(1) Construction and valida-tion of the radiomics prediction model. One thousand seven hundred and eighty-one radiomics features were finally extracted from the 182 patients. Based on the feature selection using intra-group correlation coefficient >0.75, and the reduce dimensionality using LASSO-COX regression analysis, 14 non zero coefficient radiomics features were finally selected from the 1 781 radiomics features. The radiomics prediction model was constructed based on the radiomics score (R-score) of these non zero coefficient radiomics features. According to the best cutoff value of the R-score as -0.494, 128 patients in the training dataset were divided into 64 cases with high risk and 64 cases with low risk, 54 patients in the validation dataset were divided into 35 cases with high risk and 19 cases with low risk. The area under curve (AUC) of radiomics prediction model in predicting 18-, 24-, 30-month overall survival rate of patients in the training dataset was 0.83[95% confidence interval ( CI ) as 0.76-0.87, P<0.05], 0.84(95% CI as 0.73-0.91, P<0.05), 0.91(95% CI as 0.78-0.95, P<0.05), respectively. The AUC of radiomics prediction model in predicting 18-, 24-, 30-month overall survival rate of patients in the validation dataset was 0.84(95% CI as 0.75-0.92, P<0.05), 0.84 (95% CI as 0.73-0.91, P<0.05), 0.86(95% CI as 0.82-0.94, P<0.05), respectively. (2) Analysis of prognostic factors for patients with GNEN in the training dataset. Results of multivariate analysis showed gender, age, treatment method, tumor boundary, tumor T staging, tumor N staging, tumor M staging, Ki-67 index, CD56 expression were independent factors influencing prognosis of patients with GNEN in the training dataset ( P<0.05). (3) Construction and evaluation of the prediction model for prognosis of patients with GNEN. The clinical prediction model was constructed based on the independent factors influen-cing prognosis of patients with GNEN including gender, age, treatment method, tumor boundary, tumor T staging, tumor N staging, tumor M staging, Ki-67 index, CD56 expression. The C-index value of clinical prediction model in the training dataset and the validation dataset was 0.86 (95% CI as 0.82-0.90) and 0.80(95% CI as 0.72-0.87), respectively. The C-index value of radiomics prediction model in the training dataset and the validation dataset was 0.80 (95% CI as 0.74-0.86, P<0.05) and 0.75(95% CI as 0.66-0.84, P<0.05), respectively. The C-index value of clinical-radiomics combined prediction model in the training dataset and the validation dataset was 0.88(95% CI as 0.85-0.92) and 0.83 (95% CI as 0.77-0.89), respectively. Results of calibration curve show that clinical prediction model, radiomics prediction model and clinical-radiomics combined prediction model had good predictive ability. Results of decision curve show that the clinical-radiomics combined prediction model is superior to the clinical prediction model, radiomics prediction model in evaluating the prognosis of patients with GNEN. Conclusions:The predection model for predicting the prognosis of patients with GNEN is constructed based on 14 radiomics features after selecting. The prediction model can predict the prognosis of patients with GNEN well, and the clinical-radiomics combined prediction model has a better prediction efficiency.

Application of CT-based radiomics analysis in predicting and identifying of treatment-associated pneumonitis / 国际肿瘤学杂志

As a non-invasive image analysis method, radiomics can deeply explore the clinical information hidden behind medical images, and has been widely used in medicine in recent years. Consolidation immunotherapy after concurrent chemoradiotherapy has become the standard treatment for locally advanced non-small cell lung cancer. The prediction and identification of treatment-associated adverse events radiation pneumonitis (RP) and immune checkpoint inhibitor-related pneumonitis (CIP) are of vital importance for the formulation of treatment plan and the selection of subsequent treatment. CT-based radiomics analysis shows great potential in predicting and identifying RP and CIP.

Progress in TN staging of rectal cancer based on multimodal magnetic resonance imaging / 国际生物医学工程杂志

Rectal cancer is one of the most common gastrointestinal malignancies in China. Accurate and reasonable assessment of the preoperative staging of rectal cancer can significantly enhance treatment outcomes and improve patient prognosis. Magnetic resonance imaging is the technique of choice for local staging of rectal cancer and has significant advantages in the diagnosis of rectal primary tumors (T) and peri-intestinal lymph nodes (N). In this review paper, the research ideas and progress of traditional radiomics and deep learning methods for preoperative TN staging prediction of rectal cancer were reviewed around multimodal magnetic resonance images, with the aim of providing new ideas for realizing fully automated TN staging algorithms for rectal cancer.

Prediction of 1p/19q codeletion status in diffuse lower-grade glioma using multimodal MRI radiomics / 南方医科大学学报

OBJECTIVE@#To develop a noninvasive method for prediction of 1p/19q codeletion in diffuse lower-grade glioma (DLGG) based on multimodal magnetic resonance imaging (MRI) radiomics.@*METHODS@#We collected MRI data from 104 patients with pathologically confirmed DLGG between October, 2015 and September, 2022. A total of 535 radiomics features were extracted from T2WI, T1WI, FLAIR, CE-T1WI and DWI, including 70 morphological features, 90 first order features, and 375 texture features. We constructed logistic regression (LR), logistic regression least absolute shrinkage and selection operator (LRlasso), support vector machine (SVM) and Linear Discriminant Analysis (LDA) radiomics models and compared their predictive performance after 10-fold cross validation. The MRI images were reviewed by two radiologists independently for predicting the 1p/19q status. Receiver operating characteristic curves were used to evaluate classification performance of the radiomics models and the radiologists.@*RESULTS@#The 4 radiomics models (LR, LRlasso, SVM and LDA) achieved similar area under the curve (AUC) in the validation dataset (0.833, 0.819, 0.824 and 0.819, respectively; P>0.1), and their predictive performance was all superior to that of resident physicians of radiology (AUC=0.645, P=0.011, 0.022, 0.016, 0.030, respectively) and similar to that of attending physicians of radiology (AUC=0.838, P>0.05).@*CONCLUSION@#Multiparametric MRI radiomics models show good performance for noninvasive prediction of 1p/19q codeletion status in patients with in diffuse lower-grade glioma.

Research Progress in Preoperative Evaluation of Lymph Node Metastasis of Bladder Cancer / 中国医学科学院学报

Bladder cancer is a common malignant tumor of the urinary system.The prognosis of patients with positive lymph nodes is worse than that of patients with negative lymph nodes.An accurate assessment of preoperative lymph node statushelps to make treatmentdecisions,such as the extent of pelvic lymphadenectomy and the use of neoadjuvant chemotherapy.Imaging examination and pathological examination are the primary methods used to assess the lymph node status of bladder cancer patients before surgery.However,these methods have low sensitivity and may lead to inaccuate staging of patients.We reviewed the research progress and made an outlook on the application of clinical diagnosis,imaging techniques,radiomics,and genomics in the preoperative evaluation of lymph node metastasis in bladder cancer patients at different stages.

Development and Application of Medical Imaging Analysis Platform Based on Radiomics and Machine Learning Technologies / 中国医疗器械杂志

OBJECTIVE@#In order to solve the technical problems, clinical researchers face the process of medical imaging analysis such as data labeling, feature extraction and algorithm selection, a medical imaging oriented multi-disease research platform based on radiomics and machine learning technology was designed and constructed.@*METHODS@#Five aspects including data acquisition, data management, data analysis, modeling and data management were considered. This platform provides comprehensive functions such as data retrieve and data annotation, image feature extraction and dimension reduction, machine learning model running, results validation, visual analysis and automatic generation of analysis reports, thus an integrated solution for the whole process of radiomics analysis has been generated.@*RESULTS@#Clinical researchers can use this platform for the whole process of radiomics and machine learning analysis for medical images, and quickly produce research results.@*CONCLUSIONS@#This platform greatly shortens the time for medical image analysis research, decreasing the work difficulty of clinical researchers, as well as significantly promoting their working efficiency.

Visual and quantitative assessment of the effectiveness of non-vascularized bone grafting in osteonecrosis of the femoral head via CT-based radiomics and clinical data / 中国修复重建外科杂志

OBJECTIVE@#To investigate the value of CT-based radiomics and clinical data in predicting the efficacy of non-vascularized bone grafting (NVBG) in hip preservation, and to construct a visual, quantifiable, and effective method for decision-making of hip preservation.@*METHODS@#Between June 2009 and June 2019, 153 patients (182 hips) with osteonecrosis of the femoral head (ONFH) who underwent NVBG for hip preservation were included, and the training and testing sets were divided in a 7∶3 ratio to define hip preservation success or failure according to the 3-year postoperative follow-up. The radiomic features of the region of interest in the CT images were extracted, and the radiomics-scores were calculated by the linear weighting and coefficients of the radiomic features after dimensionality reduction. The clinical predictors were screened using univariate and multivariate Cox regression analysis. The radiomics model, clinical model, and clinical-radiomics (C-R) model were constructed respectively. Their predictive performance for the efficacy of hip preservation was compared in the training and testing sets, with evaluation indexes including area under the curve, C-Index, sensitivity, specificity, and calibration curve, etc. The best model was visualised using nomogram, and its clinical utility was assessed by decision curves.@*RESULTS@#At the 3-year postoperative follow-up, the cumulative survival rate of hip preservation was 70.33%. Continued exposure to risk factors postoperative and Japanese Investigation Committee (JIC) staging were clinical predictors of the efficacy of hip preservation, and 13 radiomic features derived from least absolute shrinkage and selection operator downscaling were used to calculate Rad-scores. The C-R model outperformed both the clinical and radiomics models in predicting the efficacy of hip preservation 1, 2, 3 years postoperative in both the training and testing sets ( P<0.05), with good agreement between the predicted and observed values. A nomogram constructed based on the C-R model showed that patients with lower Rad-scores, no further postoperative exposure to risk factors, and B or C1 types of JIC staging had a higher probability of femoral survival at 1, 2, 3 years postoperatively. The decision curve analysis showed that the C-R model had a higher total net benefit than both the clinical and radiomics models with a single predictor, and it could bring more net benefit to patients within a larger probability threshold.@*CONCLUSION@#The prediction model and nomogram constructed by CT-based radiomics combined with clinical data is a visual, quantifiable, and effective method for decision-making of hip preservation, which can predict the efficacy of NVBG before surgery and has a high value of clinical application.

Research Progress in Imaging-based Diagnosis of Benign and Malignant Enlarged Lymph Nodes in Non-small Cell Lung Cancer / 中国肺癌杂志

Non-small cell lung cancer (NSCLC) can be detected with enlarged lymph nodes on imaging, but their benignity and malignancy are difficult to determine directly, making it difficult to stage the tumor and design radiotherapy target volumes. The clinical diagnosis of malignant lymph nodes is often based on the short diameter of lymph nodes ≥1 cm or the maximum standard uptake value ≥2.5, but the sensitivity and specificity of these criteria are too low to meet the clinical needs. In recent years, many advances have been made in diagnosing benign and malignant lymph nodes using other imaging parameters, and with the development of radiomics, deep learning and other technologies, models of mining the image information of enlarged lymph node regions further improve the diagnostic accuracy. The purpose of this paper is to review recent advances in imaging-based diagnosis of benign and malignant enlarged lymph nodes in NSCLC for more accurate and noninvasive assessment of lymph node status in clinical practice.
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Radiomics based on biparametric MRI for the detection of significant residual prostate cancer after androgen deprivation therapy: using whole-mount histopathology as reference standard / 亚洲男科学杂志(英文版)

We aimed to study radiomics approach based on biparametric magnetic resonance imaging (MRI) for determining significant residual cancer after androgen deprivation therapy (ADT). Ninety-two post-ADT prostate cancer patients underwent MRI before prostatectomy (62 with significant residual disease and 30 with complete response or minimum residual disease [CR/MRD]). Totally, 100 significant residual, 52 CR/MRD lesions, and 70 benign tissues were selected according to pathology. First, 381 radiomics features were extracted from T2-weighted imaging, diffusion-weighted imaging, and apparent diffusion coefficient (ADC) maps. Optimal features were selected using a support vector machine with a recursive feature elimination algorithm (SVM-RFE). Then, ADC values of significant residual, CR/MRD lesions, and benign tissues were compared by one-way analysis of variance. Logistic regression was used to construct models with SVM features to differentiate between each pair of tissues. Third, the efficiencies of ADC value and radiomics models for differentiating the three tissues were assessed by area under receiver operating characteristic curve (AUC). The ADC value (mean ± standard deviation [s.d.]) of significant residual lesions ([1.10 ± 0.02] × 10-3 mm2 s-1) was significantly lower than that of CR/MRD ([1.17 ± 0.02] × 10-3 mm2 s-1), which was significantly lower than that of benign tissues ([1.30 ± 0.02] × 10-3 mm2 s-1; both P < 0.05). The SVM feature models were comparable to ADC value in distinguishing CR/MRD from benign tissue (AUC: 0.766 vs 0.792) and distinguishing residual from benign tissue (AUC: 0.825 vs 0.835) (both P > 0.05), but superior to ADC value in differentiating significant residual from CR/MRD (AUC: 0.748 vs 0.558; P = 0.041). Radiomics approach with biparametric MRI could promote the detection of significant residual prostate cancer after ADT.

Based on CT radiomics model for predicting the response to first-line chemotherapy of diffuse large B-cell lymphoma / 中华肿瘤杂志

Objective: To investigate the potential value of CT Radiomics model in predicting the response to first-line chemotherapy in diffuse large B-cell lymphoma (DLBCL). Methods: Pre-treatment CT images and clinical data of DLBCL patients treated at Shanxi Cancer Hospital from January 2013 to May 2018 were retrospectively analyzed and divided into refractory patients (73 cases) and non-refractory patients (57 cases) according to the Lugano 2014 efficacy evaluation criteria. The least absolute shrinkage and selection operator (LASSO) regression algorithm, univariate and multivariate logistic regression analyses were used to screen out clinical factors and CT radiomics features associated with efficacy response, followed by radiomics model and nomogram model. Receiver operating characteristic (ROC) curve, calibration curve and clinical decision curve were used to evaluate the models in terms of the diagnostic efficacy, calibration and clinical value in predicting chemotherapy response. Results: Based on pre-chemotherapy CT images, 850 CT texture features were extracted from each patient, and 6 features highly correlated with the first-line chemotherapy effect of DLBCL were selected, including 1 first order feature, 1 gray level co-occurence matrix, 3 grey level dependence matrix, 1 neighboring grey tone difference matrix. Then, the corresponding radiomics model was established, whose ROC curves showed AUC values of 0.82 (95% CI: 0.76-0.89) and 0.73 (95% CI: 0.60-0.86) in the training and validation groups, respectively. The nomogram model, built by combining validated clinical factors (Ann Arbor stage, serum LDH level) and CT radiomics features, showed an AUC of 0.95 (95% CI: 0.90-0.99) and 0.91 (95% CI: 0.82-1.00) in the training group and the validation group, respectively, with significantly better diagnostic efficacy than that of the radiomics model. In addition, the calibration curve and clinical decision curve showed that the nomogram model had good consistency and high clinical value in the assessment of DLBCL efficacy. Conclusion: The nomogram model based on clinical factors and radiomics features shows potential clinical value in predicting the response to first-line chemotherapy of DLBCL patients.

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)

CT radiomics for prediction of microvascular invasion in hepatocellular carcinoma: A systematic review and meta-analysis

Abstract The power of computed tomography (CT) radiomics for preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) demonstrated in current research is variable. This systematic review and meta-analysis aim to evaluate the value of CT radiomics for MVI prediction in HCC, and to investigate the methodologic quality in the workflow of radiomics research. Databases of PubMed, Embase, Web of Science, and Cochrane Library were systematically searched. The methodologic quality of included studies was assessed. Validation data from studies with Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) statement type 2a or above were extracted for meta-analysis. Eleven studies were included, among which nine were eligible for meta-analysis. Radiomics quality scores of the enrolled eleven studies varied from 6 to 17, accounting for 16.7%-47.2% of the total points, with an average score of 14. Pooled sensitivity, specificity, and Area Under the summary receiver operator Characteristic Curve (AUC) were 0.82 (95% CI 0.77-0.86), 0.79 (95% CI 0.75-0.83), and 0.87 (95% CI 0.84-0.91) for the predictive performance of CT radiomics, respectively. Meta-regression and subgroup analyses showed radiomics model based on 3D tumor segmentation, and deep learning model achieved superior performances compared to 2D segmentation and non-deep learning model, respectively (AUC: 0.93 vs. 0.83, and 0.97 vs. 0.83, respectively). This study proves that CT radiomics could predict MVI in HCC. The heterogeneity of the included studies precludes a definition of the role of CT radiomics in predicting MVI, but methodology warrants uniformization in the radiology community regarding radiomics in HCC.

Prediction of IDH1 gene mutation by a nomogram based on multiparametric and multiregional MR images

Abstract Objective To investigate the value of a nomogram based on multiparametric and multiregional MR images to predict Isocitrate Dehydrogenase-1 (IDH1) gene mutations in glioma. Data and methods The authors performed a retrospective analysis of 110 MR images of surgically confirmed pathological gliomas; 33 patients with IDH1 gene Mutation (IDH1-M) and 77 patients with Wild-type IDH1 (IDH1-W) were divided into training and validation sets in a 7:3 ratio. The clinical features were statistically analyzed using SPSS and R software. Three glioma regions (rCET, rE, rNEC) were outlined using ITK-SNAP software and projected to four conventional sequences (T1, T2, Flair, T1C) for feature extraction using AI-Kit software. The extracted features were screened using R software. A logistic regression model was established, and a nomogram was generated using the selected clinical features. Eight models were developed based on different sequences and ROIs, and Receiver Operating Characteristic (ROC) curves were used to evaluate the predictive efficacy. Decision curve analysis was performed to assess the clinical usefulness. Results Age was selected with Radscore to construct the nomogram. The Model 1 AUC values based on four sequences and three ROIs were the highest in these models, at 0.93 and 0.89, respectively. Decision curve analysis indicated that the net benefit of model 1 was higher than that of the other models for most Pt-values. Conclusion A nomogram based on multiparametric and multiregional MR images can predict the mutation status of the IDH1 gene accurately.

Research progress on liquid biopsy and its combination of radiology in diagnosis of pulmonary nodules / 中国胸心血管外科临床杂志

@#Lung cancer is a malignant tumor with the highest mortality worldwide, and its early diagnosis and evaluation have a crucial impact on the comprehensive treatment of patients. Early preoperative diagnosis of lung cancer depends on a variety of imaging and tumor marker indicators, but it cannot be accurately assessed due to its high false positive rate. Liquid biopsy biomarkers can detect circulating tumor cells and DNA in peripheral blood by non-invasive methods and are gradually becoming a powerful diagnostic tool in the field of precision medicine for tumors. This article reviews the research progress of liquid biopsy biomarkers and their combination with clinical imaging features in the early diagnosis of lung cancer.

Construction of a clinical-radiomic nomogram model for the differential diagnosis of adrenal adenoma and small diameter pheochromocytoma / 现代泌尿外科杂志

【Objective】 To effectively differentiate adrenal adenoma (AA) and small diameter pheochromocytoma (PCC) by establishing a clinical-radiomic nomogram model before surgery. 【Methods】 A total of 132 pathologically confirmed patients (45 PCC cases, 87 AA cases) were enrolled. After the features of enhanced CT were assessed, the radiomic features and related clinical indicators were extracted. Based on multiple Logistic regression, a clinical-radiomic nomogram with radiomic features and independent clinical predictors was developed. The area under the receiver operating characteristic (ROC) curve (AUC) was used for internal evaluation to compare the diagnostic effectiveness of the three models. The clinical effectiveness of the nomogram was verified with decision curve analysis (DCA). 【Results】 One of the 108 candidate features was used to construct the radiological feature score. Individualized clinical-radiomic nomogram included independent clinical factors (24 h urinary vanmandelic acid/renin/angiotensin I) and original first-order median radiological feature scores. Internal evaluation of the prediction model showed that the AUC was 0.945 (95%CI:0.906-0.984), superior to the single clinical model or radiological model in precise differentiation. DCA showed that the nomogram had the best clinical use. 【Conclusion】 The clinical-radiomic nomogram model can effectively differentiate adrenal adenoma from small diameter pheochromocytoma, which can improve the preoperative differential diagnosis and preparation.

Research Progress in Artificial Intelligence for Diagnosis， Prediction of Recurrence and Prognosis in Hepatocellular Carcinoma / 中山大学学报(医学科学版)

With the rapid development of artificial intelligence （AI） technology in the field of medicine， AI models show great potential in the diagnosis， prognosis and efficacy prediction of hepatocellular carcinoma （HCC）. AI techniques include computational search algorithms， machine learning （ML） and deep learning （DL） models. Based on histopathology， radiomics and related molecular markers， the ML or DL algorithm is used to extract key information and then establish the diagnosis or prediction model， which may serve as a tool to aid in clinical decision-making. Further technical support， large-scale clinical validation and regulatory approvals are still needed due to the limitations on the application of AI models. This review summarizes the advances of AI in HC diagnosis， prediction of recurrence and prognosis， and highlights the radiomics， histopathology and molecular marker data.

Application value of radiomics-based machine learning model in identifying the degree of pulmonary ground-glass nodule infiltration / 中国胸心血管外科临床杂志

@#Objective    To establish a machine learning model based on computed tomography (CT) radiomics for preoperatively predicting invasive degree of lung ground-glass nodules (GGNs). Methods    We retrospectively analyzed the clinical data of GGNs patients whose solid component less than 3 cm in the Department of Thoracic Surgery of Shanghai Pulmonary Hospital from March 2021 to July 2021 and the First Hospital of Lanzhou University from January 2019 to May 2022. The lesions were divided into pre-invasiveness and invasiveness according to postoperative pathological results, and the patients were randomly divided into a training set and a test set in a ratio of 7∶3. Radiomic features (1 317) were extracted from CT images of each patient, the max-relevance and min-redundancy (mRMR) was used to screen the top 100 features with the most relevant categories, least absolute shrinkage and selection operator (LASSO) was used to select radiomic features, and the support vector machine (SVM) classifier was used to establish the prediction model. We calculated the area under the curve (AUC), sensitivity, specificity, accuracy, negative predictive value, positive predictive value to evaluate the performance of the model, drawing calibration and decision curves of the prediction model to evaluate the accuracy and clinical benefit of the model, analyzed the performance in the training set and subgroups with different nodule diameters, and compared the prediction performance of this model with Mayo and Brock models. Two primary thoracic surgeons were required to evaluate the invasiveness of GGNs to investigate the clinical utility of the mode. Results    A total of 400 patients were divided into the training set (n=280) and the test set (n=120) according to the admission criteria. There were 267 females and 133 males with an ……