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@#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.
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@#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.
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Radiomics is a rapidly developing field,which can transform the black and white gray-scale information of traditional CT,MR1,positron emission tomography(PET),and other images into quantitative radiomics features,obtain rich deep features of lesions,and provide more valuable information for clinical diag-nosis and treatment.Radiomics capture these time-varying lesion characteristics in continuous imaging,and then discover markers and patterns of disease evolution,progression and treatment response,which are used to solve clinical problems.Image data are mineable,and in large enough data sets,they can be used to complete advancements from the individual level to the molecular/digital level.Although the development of radiomics is still in its infancy,there have been many studies on its application in nasopharyngeal carcinoma.This article reviews the application of radiomics in the precise diagnosis,treatment efficacy and prognosis prediction,and differential diagnosis of nasopharyngeal carcinoma,in order to provide a basis for clinical precise diagnosis and individualized treatment of nasopharyngeal carcinoma.
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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)
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Hepatocellular carcinoma(HCC)is the fifth most common malignant tumor in the world and it is characterized by clinically insidious onset and high mortality rate.As a preferred treatment method for patients with moderate and advanced HCC,transcatheter arterial chemoembolization(TACE)has many advantages such as reducing tumor load and relieving patient pain,but the selection of the patients who may get benefits from TACE treatment remains a challenging issue.Therefore,it is essential to predict the efficacy of TACE.At present,various methods including clinical laboratory testing,imaging method,genetic-molecular method,etc.have been used to predict the therapeutic efficacy of TACE.Imaging prediction has the advantages of high visualization and strong interpretability,and MRI functional imaging sequence can better demonstrate the details of the lesion.Radiomics,as an emerging imaging field,can complement or even replace tumor biopsy by quantifying the tumor phenotypic variation.This paper aims to make a review concerning the correlation between the imaging radiomics and the prediction of TACE efficacy in patients with HCC,and to discuss whether MRI imaging radiomics can be used as a valid and reproducible method for predicting TACE efficacy for HCC.(J Intervent Radiol,2024,32:90-94)
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Objective·To analyze the differences and classify hypertrophic cardiomyopathy(HCM)patients and healthy controls(HC)using short-axis cine cardiac magnetic resonance(CMR)images-derived radiomics features.Methods·One hundred HCM subjects were included,and fifty HC were randomly selected at 2∶1 ratio during January 2018 to December 2021 in the Department of Cardiology,Renji Hospital,Shanghai Jiao Tong University School of Medicine.The CMR examinations were performed by experienced radiologists on these subjects.CVI 42 post-processing software was used to obtain left ventricular morphology and function measurements,including left ventricular ejection fraction(LVEF),left ventricular end-diastolic volume(LVEDV)and left ventricular end-diastolic mass(LVEDM).The 3D radiomic features of the end-diastolic myocardial region were extracted from short-axis images CMR cine.The distribution of the radiomic features in the two groups was analysed and machine learning models were constructed to classify the two groups.Results·One hundred and seven 3D radiomic features were selected and extracted.After exclusion of highly correlated features,least absolute shrinkage and selection operator(LASSO)was used,and a 5-fold cross-validation was performed.There were still 11 characteristics with non-zero coefficients.The K-best method was used to decide the top 8 features for subsequent analysis.Among them,four features were significantly different between the two groups(all P<0.05).Support vector machine(SVM)and random forest(RF)models were constructed to discriminate the two groups.The results showed that the maximum area under the curve(AUC)for the single-feature model(first order grayscale:entropy)was 0.833(95%CI 0.685?0.968)and the maximum accuracy for the multi-feature model was 83.3%with an AUC of 0.882(95%CI 0.705?0.980).Conclusion·There are significant differences in both left ventricular function and left ventricular morphology between HCM and HC.The 3D myocardial radiomic features of the two groups are also significantly different.Although single feature is able to distinguish the two groups,the combination of multi-features show better classification performance.
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Objective To utilize sophisticated CT-driven radiomics to prognosticate the mutation situation of KRAS in patients with colorectal cancer(CRC).Methods A total of 393 patients who underwent KRAS mutation testing and preoperative triphasic enhanced CT were analyzed retrospectively.All patients were divided into training group(n=276)and validation group(n=117)with a ratio of 7∶3.The characteristics tightly associated with KRAS mutation were extracted and screened to construct three models,include clinical,radiomics,and clinical-radiomics fusion models for prediction of KRAS mutation.The performance and clinical utility of these three models were assessed via receiver operating characteristic(ROC)curve and decision curve analysis(DCA).Results The study identified significant correlations between KRAS mutation and CEA,CA199,and a set of 13 radiomics features,respective-ly.Based on these clinical indicators and radiomics features,clinical,radiomics,and clinical-radiomics fusion models were constructed to prognosticate KRAS mutation.The radiomics model construc-ted in this study had good performance for the prediction of KRAS mutation status in CRC patients.Most notably,a clinical-radiomics nomogram that amalgamated both clinical risk factors and radiomics parameters emerged as the most effective predictor of KRAS mutation,with an area under the curve(AUC)of 0.782 and 0.744 in the training group and validation group,respectively.Conclusion The refined CT radiomics model serves as a robust,non-invasive,quantitative tool for the assessment of KRAS mutation status in CRC patients.
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Objective To investigate the diagnostic value of diffusion weighted imaging(DWI)-based radiomics model to identify small hepatocellular carcinoma(＜2 cm)(SHCC)and dysplastic nodule(DN)in the background of hepatitis cirrhosis.Methods A total of 93 cases SHCC and 25 cases with DN with complete enhanced MRI images and surgically pathologically confirmed were collected retrospectively.Chi-square test was performed to analyze the signal characteristics of DWI and enhanced triphasic MRI images between the two groups.ITK-SNAP was used to draw the region of interest(ROI)on DWI,and FAE software was applied for extraction,selec-tion,and construction of support vector machine(SVM)models(dividing into training set and test set according to the ratio of 7∶3).The diagnostic performance of model was evaluated by receiver operating characteristic(ROC)curve.Results There were statisti-cally significant differences in enhanced triphasic MRI and DWI between SHCC and DN(P＜0.05).The area under the curve(AUC)of the DWI-SVM model training set was 0.936,and sensitivity,specificity and accuracy was 95.4％,88.2％and 93.9％,respec-tively,and the AUC of the test set was 0.911,and sensitivity,specificity and accuracy was 85.7％,87.5％and 86.1％,respectively,which were all significantly better than the diagnostic efficacy of DWI(AUC=0.720).Conclusion DWI-SVM model with signifi-cantly higher AUC and specificity can effectively differentiate SHCC from DN in the background of hepatitis cirrhosis.
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Objective To construct a radiomics nomogram combining clinical and a radiomics signature for distinguishing type Ⅱpapillary renal cell carcinoma(pRCC)from atypical clear cell renal cell carcinoma(ccRCC).Methods Clinical and CT data of patients with pathologically confirmed type Ⅱ pRCC(62 cases)and atypical ccRCC(56 cases)were analyzed.A random sample was divided into a training set(82 cases)and a test set(36 cases)in a ratio of 7∶3.Clinical factors were screened to construct clinical factor models.A total of 1 595 radiomics features of tumors were extracted from the corticomedullary phase CT images and based on the most effective features to construct a radiomics signature and calculate the radiomics score(Rad-score).A radiomics nomogram was constructed by combining the Rad-score and independent clinical factors.Receiver operating characteristic(ROC)curve was used to assess the clini-cal usefulness of the models.Decision curve analysis(DCA)was used to assess the difference between the models.Results The radiomics signature showed good discrimination in training set area under the curve(AUC)0.894[95％confidence interval(CI)0.834-0.947]and test set AUC 0.879(95％CI 0.774-0.963).The AUC of the clinical factors model in training set and test set were 0.725(95％CI 0.646-0.804)and 0.698(95％CI 0.567-0.819).The AUC of the radiomics nomogram in training set and test set were 0.901(95％CI 0.840-0.953)and 0.901(95％CI 0.809-0.975).DCA demonstrated the radiomics nomogram outmatched the clinical factors model and radiomics signature in the aspects of clinical usefulness.Conclusion Radiomics nomogram based on enhanced CT can provide good prediction of type Ⅱ pRCC and atypical ccRCC preoperatively,improve the diagnostic accuracy and provide guidance for future clinical treatment.
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Objective To investigate the significance of intratumoral and peritumoral radiomics models in predicting occult lymph node metastasis in stage T1 non-small cell lung cancer(NSCLC)and to compare the predictive accuracy in different peritumoral radiomics models.Methods The CT images and clinical data of 211 patients without lymph node metastasis on preoperative CT examination and pathologically confirmed NSCLC after surgery were collected.The radiomics features were derived from the three-dimensional volume of interest(VOI)of the intratumoral and peritumoral at 3-,5-,and 10-mm following lesion segmentation on CT images of each patient.The feature data of all nidus were radomly divide into training set and validation set with a ratio of 7︰3.The Pearson or Spearman correlation test was performed to remove redundancy.Dimensionality was reduced by the least absolute shrinkage and selection operator(LASSO)regression analysis.The linear combination of selected features and corresponding coefficients were used to construct the Radiomics score(Radscore).The clinical model and comprehensive model were constructed by logistic regression analysis.The conprehensive model was visualized with the nomogram,and its performance was evaluated.Results Among the peritumoral radiomics models,the peritumoral 5-mm model showed the best predictive efficacy[validation set,area under the curve(AUC)0.771].The comprehensive model containing Radscore,CT image features and CEA exhibited the best performance(validation set,AUC 0.850).Conclusion Intratumoral and peritumoral radiomics models perform efficiently in predicting occult lymph node metastasis in stage T1 NSCLC,and nomogram can effectively and noninvasively predict occult lymph node metastasis in NSCLC.
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Objective To investigate the value of multimodal MRI radiomics in predicting muscle-invasive bladder cancer.Methods A total of 178 patients with pathology diagnosis of bladder cancer were retrospectively collected,including 31 cases of muscle invasive bladder cancer(MIBC)and 147 cases of non-muscle invasive bladder cancer(NMIBC).Patients were randomly divided into training group and testing group at a ratio of 7︰3.The range of bladder tumors in T2WI,diffusion weighted imaging(DWI)and apparent diffusion coefficient(ADC)images were segmented as volume of interest(VOI)by using ITK-SNAP software.Radiomics features were extracted through A.K software.The optimal radiomics features were obtained through radiomics algorithm and least absolute shrinkage and selection operator(LASSO)method.Finally,the logistic regression analysis method and random forest model method were used to construct prediction models.The performance of prediction models was evaluated by the receiver operating characteristic(ROC)curve.Results This study constructed four groups of models containing T2WI prediction model,DWI prediction model,ADC prediction model,and T2WI+DWI+ADC prediction model.The area under the curve(AUC)of T2WI,DWI,and ADC prediction models for identifying MIBC and NMIBC were separately 0.920,0.914,and 0.954 in the training group while those were respectively 0.881,0.773,and 0.871 in the testing group.There was no statistical significance between T2WI,DWI,and ADC prediction models.In training and testing groups,the AUC of T2WI+DWI+ADC prediction model were respectively 0.959 and 0.909,which were higher than the single sequence prediction model.The sensitivity and specificity of the training group were 0.905 and 0.853 and the sensitivity and specificity of the testing group were 0.778 and 0.795.Conclusion MRI radiomics prediction model can effectively differentiate MIBC and NMIBC.The T2WI+DWI+ADC prediction model shows better prediction efficiency.
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Objective To explore the value of dual-phase enhanced CT radiomics in predicting post-acute pancreatitis diabetes mellitus(PPDM-A).Methods A total of 145 patients with acute pancreatitis(AP)were retrospectively collected,including 62 patients in PPDM-A group and 83 patients in non-PPDM-A group.The patients were randomly divided into training set and test set at a ratio of 7︰3,the pancreatic parenchyma in arterial phase and venous phase was delineated and the radiomics features were extracted.Vari-ance threshold method,univariate selection method and least absolute shrinkage and selection operator(LASSO)were used to screen radiomics features.The prediction performance of the model was evaluated by the area under the curve(AUC).The DeLong test was used to compare the prediction efficiency between the models,and the calibration curve and decision curve were used to evaluate the prediction efficiency of the model.Results The AUC of arterial phase model,venous phase model,combined arterial venous phase model,clinical model and radiomics combined clinical model in the training set were 0.845,0.792,0.829,0.656 and 0.862,respec-tively.The DeLong test results showed that only the difference between the radiomics combined clinical model and the clinical model in the training set and the test set was statistically significant(P<0.05).The decision curve showed that the radiomics combined clinical model had high clinical practicability in a certain range,and the calibration curve showed that the radiomics combined clinical model had the best fitting degree with the actual observation value.Conclusion Based on the dual-phase enhanced CT radiomics combined clinical model,PPDM-A can be predicted more accurately,and it can provide a certain reference value for the clinical development of per-sonalized treatment programs.
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Objective To investigate the correlation between intra-and peri-tumoral radiomics features and the response to con-current chemoradiotherapy(CCRT)in cervical squamous cell carcinoma,and to explore the difference of predictive performance between 2D and 3D radiomics models.Methods The imaging data of 132 patients were analyzed retrospectively and randomly divided into training set(n=92)and validation set(n=40).Radiomics features were extracted based on the dynamic contrast-enhanced magnetic resonance imaging(DCE-MRI),the correlation analysis and least absolute shrinkage and selection operator(LASSO)algorithm were used for dimensionality reduction and screening,then the radiomics score was calculated and the logistic model was constructed.The receiver operating characteristic(ROC)curve,internal validation of Bootstrap and Brier score were used to evaluate the discrimina-tion and calibration of the model,and the improvement in predictive performance of 3D model compared with 2D model was evaluated by the integrated discrimination improvement(IDI).Results In the training set,the ROC curve showed that the area under the curve(AUC)of the models(2D-intratumoral,3D-intratumoral,3D-peritumoral,3D-combined)ranged from 0.774 to 0.893.The internal validation of Bootstrap showed the AUC were 0.772,0.860,0.847 and 0.888,respectively,while in the validation set,the AUC were 0.757,0.849,0.824 and 0.887,respectively.The Brier scores indicated that the models were well calibrated.In the training set and the validation set,the IDI values were 0.155 and 0.179,respectively,and the differences were statistically significant(P<0.05).Conclusion The radiomics analysis based on the tumor volume can fully explore the tumor heterogeneity.The intra-and peri-tumoral radiomics combined model shows the best predictive performance,which is important to assist clinicians in developing individualized therapies.
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Objective To investigate the application value of quantitative computed tomography(QCT)-based imaging histology in the diagnosis of clinical osteoporosis.Methods A total of 182 patients who underwent QCT scans of the chest or abdomen were ana-lyzed retrospectively,and the patients were divided into osteoporosis group(56 cases)and non-osteoporosis group(126 cases)accord-ing to the bone mineral density(BMD)values measured by QCT.The cases were randomly divided into training set(110 cases)and validation set(72 cases)at a ratio of 6︰4.The L1-L2 vertebrae were outlined with the region of interest(ROI)and the image features were extracted using ITK-SNAP 3.6.0 and A.K.software.(1)Radiomics score(Rad-score)model was established using maximum relevance minimum redundancy(mRMR)and least absolute shrinkage and selection operator(LASSO)algorithm.(2)The L1 and L2 vertebrae BMD value and T-value from the dual energy X-ray absorptiometry(DXA)examination of the patient were obtained to build the model separately for analysis and for comparison with the Rad-score model.(3)The discriminative ability,clinical applica-tion performance and calibration ability of Rad-score in the training and validation sets were evaluated using receiver operating charac-teristic(ROC)curves,decision curve analysis(DCA)and calibration curves.(4)Data on sensitivity,specificity,accuracy and area under the curve(AUC)were used to compare the predictive ability of DXA and Rad-score.DeLong test was used for comparison of differences between Rad-score and DXA models.Results Four optimal ima-ging histology features were finally selected to create Rad-score model,which confirmed the significant correlation between Rad-score and QCT for the diagnosis of osteoporosis.The calibration curve showed that Rad-score model had a good fit in both the training set and the validation set.The results of the DeLong test showed that the AUC of Rad-score model were greater than those of DXA model.Conclusion The QCT-based imaging histology model has high sen-sitivity,specificity and accuracy,with outstanding advantages and good performance for osteoporosis diagnosis,and is superior to the DXA model.
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Objective To explore the value of radiomics in differential diagnosis of small cell lung cancer(SCLC)and non-small cell lung cancer(NSCLC).Methods Literature on the differential diagnosis of SCLC and NSCLC using radiomics was searched in Chinese and English databases.After literature screening and data extraction,Meta-DiSc1.4 and State16.0 SE software were used for analysis.Results A total of 910 patients were included in 8 studies.Meta-analysis results showed that the radiomics differential diag-nosis of SCLC and NSCLC had high co-sensitivity(Sen)and specificity(Spe),0.87[95%confidence interval(CI)0.83-0.91]and 0.88(95%CI 0.85-0.90),respectively.Meta-regression analysis showed that heterogeneity was not caused by feature extraction software type,joint machine learning,image pattern,brain metastasis,and sample size.Publication bias results didn't show any sig-nificant publication bias.Conclusion The radiomics method can differentiate and diagnose SCLC from NSCLC more accurately.When Matlab software is used to extract MRI image features combined with machine learning,and the sample size is large enough,the radiomics can differentiate and diagnose SCLC from NSCLC more accurately.
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BACKGROUND:Previous studies on cervical instability failed to explain the dynamic and static interaction relationship and pathological characteristics changes in the development of cervical lesions under the traditional imaging examination.In recent years,the emerging nuclear magnetic resonance imaging(MRI)radiomics can provide a new way for in-depth research on cervical instability. OBJECTIVE:To investigate the application value of MRI radiomics in the study of cervical instability. METHODS:Through recruitment advertisements and the Second Department of Spine of Wangjing Hospital,China Academy of Chinese Medical Sciences,young cervical vertebra unstable subjects and non-unstable subjects aged 18-45 years were included in the cervical vertebra nuclear magnetic image collection.Five specific regions of interest,including the intervertebral disc region,the facet region,the prevertebral muscle region,the deep region of the posterior cervical muscle group,and the superficial region of the posterior cervical muscle group,were manually segmented to extract and screen the image features.Finally,the cervical instability diagnosis classification model was constructed,and the effectiveness of the model was evaluated using the area under the curve. RESULTS AND CONCLUSION:(1)A total of 56 subjects with cervical instability and 55 subjects with non-instability were included,and 1 688 imaging features were extracted for each region of interest.After screening,300 sets of specific image feature combinations were obtained,with 60 sets of regions of interest for each group.(2)Five regions of interest diagnostic classification models for cervical instability were initially established.Among them,the support vector machine model for the articular process region and the support vector machine model for the deep cervical muscle group had certain accuracy for the classification of instability and non-instability,and the average area under the curve of ten-fold cross-validation was 0.719 7 and 0.703 3,respectively.(3)The Logistic model in the intervertebral disc region,the LightGBM model in the prevertebral muscle region,and the Logistic model in the superficial posterior cervical muscle region were generally accurate in the classification of instability and non-instability,and the average area under the curve of ten-fold cross-validation was 0.650 4,0.620 7,and 0.644 2,respectively.(4)This study proved the feasibility of MRI radiomics in the study of cervical instability,further deepened the understanding of the pathogenesis of cervical instability,and also provided an objective basis for the accurate diagnosis of cervical instability.
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Objective To observe the value of radiomics models and nomogram model based on two-dimensional ultrasound and automated breast volume scanning(ABVS)for predicting molecular types of breast cancer.Methods Data of 326 female patients of single breast cancer confirmed by pathology were analyzed retrospectively.The patients were randomly divided into training set(n=260)or validation set(n=66)at the ratio of 8∶2,and further divided into Luminal subgroup and non-Luminal subgroup.Radiomics features were extracted based on two-dimensional ultrasound of breast and ABVS imaging,then model2DUS,modelABVS and modelcombined radiomics were constructed,respectively.Univariate analysis and multivariate logistic regression analysis were used to screen independent factors for predicting molecular types of breast cancer,and nomogram model(modelnomogram)was constructed combined with independent factors and radiomics Radscores.The receiver operating characteristic(ROC)curve was used to evaluate the efficacy of each model for molecular type of breast cancer.Results The maximum diameter of tumor(OR=1.029)and the retraction phenomenon(OR=0.408)were both independent predictive factors for molecular type of breast cancer(both P＜0.05).The area under the curve(AUC)of model2DUS,modelABVS.modelcombined radiomics and modelnomogram for predicting molecular type of breast cancer in validation set was 0.67,0.75,0.84 and 0.83,respectively.No significant difference of AUC of modelcombined radiomics and modelnomogram was found(P＞0.05),which were both higher than AUC of model2DUs and modelABVS(all P＜0.05).Conclusion Combined radiomics model and nomogram model based on two-dimensional ultrasound and ABVS could effectively predict molecular type of breast cancer.
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Objective To observe the value of clinical and CT radiomics features for predicting microsatellite instability-high(MSI-H)status of gastric cancer.Methods Totally 150 gastric cancer patients including 30 cases of MSI-H positive and 120 cases of MSI-H negative were enrolled and divided into training set(n=105)or validation set(n=45)at the ratio of 7∶3.Based on abdominal vein phase enhanced CT images,lesions radiomics features were extracted and screened,and radiomics scores(Radscore)was calculated.Clinical data and Radscores were compared between MSI-H positive and negative patients in training set and validation set.Based on clinical factors and Radscores being significant different between MSI-H positive and negative ones,clinical model,CT radiomics model and clinical-CT radiomics combination model were constructed,and their predictive value for MSI-H status of gastric cancer were observed.Results Significant differences of tumor location and Radscore were found between MSI-H positive and negative patients in both training and validation sets(all P＜0.05).The area under the curve(AUC)of clinical model,CT radiomics model and combination model for evaluating MSI-H status of gastric cancer in training set was 0.760,0.799 and 0.864,respectively,of that in validation set was 0.735,0.812 and 0.849,respectively.AUC of clinical-CT radiomics combination model was greater than that of the other 2 single models(all P＜0.05).Conclusion Clinical-CT radiomics combination model based on tumor location and Radscore could effectively predict MSI-H status of gastric cancer.
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Objective To observe the value of intratumoral and peritumoral radiomics features of apparent diffusion coefficient(ADC)for predicting prognosis of children with medulloblastoma(MB).Methods Data of 74 children with MB were retrospectively analyzed.The children were divided into progression group(n=29)or non-progression group(n=45)according to results of 2-year follow-up,also into training set(n=44)or validation set(n=30)at the ratio of 6∶4.The intratumoral and peritumoral radiomics features were extracted and screened based on ADC images.The intratumoral,peritumoralas well as intratumoral+peritumoral radiomics models were established,and 3 combination models were constructed combining with clinical and conventional imaging features.The predictive efficiency were compared between each 2 combination models.Results In training set,the area under the curve(AUC)of clinical-conventional imaging-peritumoral radiomics model and clinical-conventional imaging-intratumoral+peritumoral radiomics model were both larger than that of peritumoral radiomics models(both P＜0.05).In validation set,AUC of clinical-conventional imaging-intratumoral+peritumoral radiomics model was the largest,but no significant difference of AUC was found among 3 combination models(all P＞0.05).Conclusion The intratumoral and peritumoral ADC radiomics features could be used to predict prognosis of MB children.Combining with clinical and conventional imaging features might improve the efficiency of prediction.
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Purpose To investigate the value of ultrasound radiomics nomogram in predicting lymph node metastasis(LNM)of papillary thyroid carcinoma(PTC).Materials and Methods A retrospective analysis was conducted on 400 cases of PTC in the First Hospital of Shanxi Medical University from March 2021 to January 2022 confirmed by surgery and pathology,all of which underwent preoperative ultrasound examination,and were randomly divided into training cohort(n=280)and testing cohort(n=120)in a ratio of 7∶3.The relationship between ultrasound clinical features and LNM was evaluated via univariate analysis and a clinical model was established via multivariable Logistic regression.A total of 3 348 features were extracted from preoperative ultrasound images.Pearson correlation coefficient was used to screen the features,and Logistic regression was used to establish the radiomics model.Clinical risk factors and rad scores were combined to construct the nomogram,and the receiver operating characteristic curves and decision curve analysis were applied to evaluate the predictive efficacy and clinical benefit of each model for LNM of PTC.Results Age,primary lesion size,C-TIRADS and ultrasound-reported LNM were the independent risk factors for LNM(t/χ2=2.938,55.923,30.081,34.639,all P<0.05).The area under the curve of ultrasound radiomics nomogram to predict LNM of PTC in the training cohort and the testing cohort was 0.860 and 0.847,respectively;the combined model in 43%-85%had the highest clinical benefit.Conclusion Ultrasound radiomics nomogram has a certain value in predicting LNM of PTC.