Research progress of radiomics in nasopharyngeal carcinoma / 重庆医学

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.

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)

Research progress in MRI-based radiomics for predicting the efficacy of transcatheter arterial chemoembolization for hepatocellular carcinoma / 介入放射学杂志

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)

Short-axis cine cardiac magnetic resonance images-derived radiomics for hypertrophic cardiomyopathy and healthy control classification / 上海交通大学学报（医学版）

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.

Development of a predictive model for KRAS mutation in patient with colorectal cancer based on CT radiomics / 实用放射学杂志

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.

Differential diagnosis of small hepatocellular carcinoma and dysplastic nodule based on diffusion weighted imaging radiomics model / 实用放射学杂志

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.

Differential value of CT radiomics in papillary renal cell carcinoma and clear cell renal cell carcinoma / 实用放射学杂志

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.

The significance of intratumoral and peritumoral radiomics models in predicting occult lymph node metastasis in stage T1 non-small cell lung cancer / 实用放射学杂志

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.

The value of multimodal MRI radiomics in predicting muscle-invasive bladder cancer / 实用放射学杂志

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.

Contrast-enhanced CT-based radiomics in predicting post-acute pancreatitis diabetes mellitus incidence / 实用放射学杂志

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.

Intra-and peri-tumoral radiomics model for predicting the response to concurrent chemoradiotherapy in cervical squamous cell carcinoma based on dynamic contrast-enhanced MRI / 实用放射学杂志

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.

Application of quantitative CT-based imaging histology in the diagnosis of osteoporosis / 实用放射学杂志

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.

A Meta-analysis of radiomics in differential diagnosis of small cell lung cancer and non-small cell lung cancer / 实用放射学杂志

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.

Feasibility of constructing a diagnostic classification model for cervical instability by magnetic resonance imaging radiomics / 中国组织工程研究

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.

Application and progress of radiomics in bone and soft tissue tumors / 中国医学装备

Bone and soft tissue tumors are diverse and with complicated histologic components and significantly divergent biological behaviors.Conventional imaging examinations,such as CT,magnetic resonance imaging(MRI)and positron emission tomography(PET),are limited to the identification of anatomical structures and abnormal signals,which are difficult to meet the qualitative requirements of imaging.With the improvement of digitalization in hospitals and medical institutions,the introduction of electronic medical records and the improvement of computational power,modern intelligent medical treatment gradually evolves to the combination of human brain,big data and artificial intelligence.Researchers are committed to mining deeper image data information,and radiomics came into being.Radiomics is a method of extracting and analyzing subvisual quantitative features from medical images and quantifying tumor heterogeneity through modeling,which is of great significance in the accurate diagnosis and treatment of bone and soft tissue tumors.

Research progress in radiomics and deep learning for early prediction and efficacy evaluation in colorectal cancer liver metastases / 中国肿瘤临床

Radiomics-based early prediction and treatment efficacy evaluation is critical for personalized treatment strategies in patients with colorectal cancer liver metastases(CCLM).Owing to the high artificial intelligence(AI)participation,repeatability,and reliable perform-ance,deep learning(DL)based on convolutional neural networks enhances the predictive efficacy of the models,enabling its potential clinic-al application more promising.Subsequent to the gradual construction of a multimodal fusion model and multicenter large sample database,radiomics and DL will become increasingly essential in the management of CCLM.This review focuses on the main steps of radiomics and DL,and summarizes the value of its application in early state prediction and treatment efficacy evaluation of different treatment modalities in CCLM,we also look forward to the potential of its in-depth application in the clinical management of CCLM.

Prediction of pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer using contrast-enhanced ultrasound radiomics / 中华超声影像学杂志

Objective:To evaluate the diagnostic performance of radiomics model based on contrast-enhanced ultrasound(CEUS) in predicting pathological complete response(pCR) after neoadjuvant chemoradiotherapy(nCRT) in patients with locally advanced rectal cancer(LARC).Methods:One hundred and six patients with LARC who underwent total mesorectal excision after nCRT between April 2018 and April 2023 in the First Affiliated Hospital of Guangxi Medical University were retrospectively included, the patients were randomly divided into a training set of 63(14 pCR patients) and a validation set of 43(12 pCR patients) in a 6∶4 ratios. Radiomics features were extracted from the tumors′ region of interest of CEUS images based on PyRadiomics. Intra-class correlation coefficient(ICC), Mann-Whitney U test, and least absolute shrinkage and selection operator(LASSO) algorithms were used to reduce features dimension. Finally, 7 radiomics features relevanted to pCR were selected to construct an ultrasomics model using elastic network regression, based on the R language. A combined model was constructed by jointing clinical feature. The performance of the models was assessed with the area under the ROC curve(AUC). Results:The AUC of the ultrasomics model and the combined model was 0.695(95% CI=0.532-0.859) and 0.726(95% CI=0.584-0.868) respectively in the training set. The AUC of the ultrasomics model and the combined model was 0.763(95% CI=0.625-0.902) and 0.790(95% CI=0.653-0.928) respectively in the validation set. Both univariate and multivariate Logistic regression analyses showed that CA199( P<0.05) and ultrasomics score( P<0.001) could be an independent predictor of pCR after nCRT in patients with LARC. Conclusions:The CEUS-based radiomics scores has certain predictive value for whether LARC patients achieve pCR after nCRT, and may provide a non-invasive imaging biomarker for predicting LARC patients achieve pCR after nCRT.

Prediction of Ki-67 expression status in breast cancer based on ultrasound radiomics combined with clinicopathologic features / 中华超声影像学杂志

Objective:To investigate the prediction of the tumor proliferation antigen(Ki-67) expression status in breast cancer patients based on ultrasound radiomics combined with clinicopathologic features.Methods:Breast cancer patients who underwent 2D ultrasound and Ki-67 examination from January 2018 to February 2022 in Changzhou Second People′s Hospital, Nanjing Medical University were retrospectively analyzed. Among them, 427 patients from Chengzhong campus were randomly divided into training and validation sets in the ratio of 8∶2, and 229 patients from Yanghu campus were used as an independent external test set. Radiomics features were extracted from the region of interest of 2D ultrasound images, and the Mann-Whitney U test, recursive feature elimination, and minimum absolute shrinkage and selection operators were used to perform feature dimensionality reduction and to establish a radiomics score(Rad-score). Subsequently, single/multifactor logistic regression regression analyses were used to construct a joint prediction model based on Rad-score and clinicopathological features. Model performance and utility were assessed using the subject operating characteristic area under the curve (AUC), calibration curve, and decision curve analyses. Results:The AUCs of the joint model for predicting Ki-67 expression status in breast cancer in the training, validation, and test sets were 0.858, 0.797, and 0.802, respectively, which were superior to those of the radiomics (0.772, 0.731, and 0.713) and clinical models (0.738, 0.750, and 0.707). Calibration curve and decision curve analyses indicated that the joint model had good calibration and clinical value.Conclusions:A joint model based on ultrasound radiomics and clinicopathological features can effectively predict the Ki-67 expression status of breast cancer, which is expected to become a non-invasive tool for Ki-67 detection and provide clinicians with an important auxiliary diagnostic and therapeutic decision-making basis.

The value of CT radiomics of the primary gastric cancer and the adipose tissue outside the gastric wall beside cancer in evaluating T staging of gastric cancer / 中华放射学杂志

Objective:To investigate the value of CT radiomic model based on analysis of primary gastric cancer and the adipose tissue outside the gastric wall beside cancer in differentiating stage T1-2 from stage T3-4 gastric cancer.Methods:This study was a case-control study. Totally 465 patients with gastric cancer treated in Affiliated People′s Hospital of Jiangsu University from December 2011 to December 2019 were retrospectively collected. According to postoperative pathology, they were divided into 2 groups, one with 150 cases of T1-2 tumors and another with 315 cases of T3-4 tumors. The cases were divided into a training set (326 cases) and a test set (139 cases) by stratified sampling method at 7∶3. There were 104 cases of T1-2 stage and 222 cases of T3-4 stage in the training set, 46 cases of T1-2 stage and 93 cases of T3-4 stage in the test set. The axial CT images in the venous phase during one week before surgery were selected to delineate the region of interest (ROI) at the primary lesion and the extramural gastric adipose tissue adjacent to the cancer areas. The radiomic features of the ROIs were extracted by Pyradiomics software. The least absolute shrinkage and selection operator was used to screen features related to T stage to establish the radiomic models of primary gastric cancer and the adipose tissue outside the gastric wall beside cancer. Independent sample t test or χ2 test were used to compare the differences in clinical features between T1-2 and T3-4 patients in the training set, and the features with statistical significance were combined to establish a clinical model. Two radiomic signatures and clinical features were combined to construct a clinical-radiomics model and generate a nomogram. The area under the receiver operating characteristic curve (AUC) was used to evaluate the efficacy of each model in differentiating stage T1-2 from stage T3-4 gastric cancer. The calibration curve was used to evaluate the consistency between the T stage predicted by the nomogram and the actual T stage of gastric cancer. And the decision curve analysis was used to evaluate the clinical net benefit of treatment guided by the nomogram and by the clinical model. Results:There were significant differences in CT-T stage and CT-N stage between T1-2 and T3-4 patients in the training set ( χ2=10.59, 15.92, P=0.014, 0.001) and the clinical model was established. After screening and dimensionality reduction, the 5 features from primary gastric cancer and the 6 features from the adipose tissue outside the gastric wall beside cancer established the radiomic models respectively. In the training set and the test set, the AUC values of the primary gastric cancer radiomic model were 0.864 (95% CI 0.820-0.908) and 0.836 (95% CI 0.762-0.910), and the adipose tissue outside the gastric wall beside cancer radiomic model were 0.782 (95% CI 0.731-0.833) and 0.784 (95% CI 0.702-0.866). The AUC values of the clinical model were 0.761 (95% CI 0.705-0.817) and 0.758 (95% CI 0.671-0.845), and the nomogram were 0.876 (95% CI 0.835-0.917) and 0.851 (95% CI 0.781-0.921). The calibration curve reflected that there was a high consistency between the T stage predicted by the nomogram and the actual T stage in the training set ( χ2=1.70, P=0.989). And the decision curve showed that at the risk threshold 0.01-0.74, a higher clinical net benefit could be obtained by using a nomogram to guide treatment. Conclusions:The CT radiomics features of primary gastric cancer lesions and the adipose tissue outside the gastric wall beside cancer can effectively distinguish T1-2 from T3-4 gastric cancer, and the combination of CT radiomic features and clinical features can further improve the prediction accuracy.

The value of radiomics combined with deep learning based on preoperative CT images in predicting the curative effect of transarterial chemoembolization for hepatocellular carcinoma / 中华放射学杂志

Objective:To explore the value of radiomics and deep learning in predicting the efficacy of initial transarterial chemoembolization (TACE) for hepatocellular carcinoma (HCC).Methods:This was a cohort study. The imaging and clinical information of HCC patients treated with TACE in the Second Affiliated Hospital of Harbin Medical University from January 2015 to January 2021 were collected retrospectively. A total of 265 patients were divided into response group (175 cases) and non-response group (90 cases) according to the modified solid tumor efficacy evaluation criteria (mRECIST) 1 to 2 months after initial TACE. According to the proportion of 8∶2, the patients were randomly divided into training group (212 cases, 140 responders and 72 non-responders) and test set (53 cases, 35 responders and 18 non-responders). Univariate and multivariate logistic regression was used to screen clinical variables and construct a clinical model. The radiomics features were extracted from the preoperative CT images, and radiomics model was constructed after feature dimensionality reduction. Using the deep learning method, three residual network (ResNet) models (ResNet18, ResNet50 and ResNet101) were established, and their effectiveness was compared and integrated to build a deep learning model with best performance. Univariate and multivariate logistic regression was used to combine pairwise three models to establish the combined model. The receiver operating characteristic curve was used to evaluate the performance of the model to distinguish between TACE response and non-response groups.Results:In the test set, the area under the curve (AUC) of the clinical model and the radiomics model in the differentiation between response and non-response after TACE were 0.730 (95% CI 0.569-0.891) and 0.775 (95% CI 0.642-0.907). The AUC of ResNet18, ResNet50 and ResNet101 were 0.719, 0.748 and 0.533, respectively. The AUC for deep learning model obtained by integrating ResNet18 and ResNet50 was 0.806 (95% CI 0.665-0.946). After pairwise fusion, the combined deep learning-radiomics model showed the highest performance, with an AUC of 0.843 (95% CI 0.730-0.956), which was better than those of the deep learning-clinical model (AUC of 0.838, 95% CI 0.719-0.957) and the radiomics-clinical model (AUC of 0.786, 95% CI 0.648-0.898). Conclusions:The combined model of radiomics and deep learning has high performance in predicting the curative effect of TACE in patients with HCC before operation.