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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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@#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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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.
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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.
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Humanos , Imagen por Resonancia Magnética , Aberraciones Cromosómicas , Área Bajo la Curva , Glioma/genética , Curva ROCRESUMEN
@#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 ……
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Hepatocellular carcinoma is one of the common malignant tumors, and most patients with hepatocellular carcinoma are already in the middle stage at the time of clinical detection, transarterial chemoembolization is the treatment of choice for mid-stage hepatocellular carcinoma.Due to the high degree of tumor heterogeneity, accurately predicting the outcome of patients with hepatocellular carcinoma after transarterial chemoembolization remains one of the difficulties in clinical practice.As an emerging technology, radiomics can not only reflect tumor heterogeneity non-invasively, but also monitor, evaluate and predict tumor progression by analyzing changes in the tumor microenvironment to guide patients′ personalized treatment and prolong their survival time.This article reviews the progress of the application of radiomics in predicting the efficacy of transarterial chemoembolization for hepatocellular carcinoma.
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Objective:To construct a diabetic foot classification prediction model based on radiomics features of fundus photographs.Methods:A total of 2 035 fundus photographs of patients with type 2 diabetes diagnosed at Nanfang Hospital between December 2011 and December 2018 were retrospectively collected [282 photographs from patients with diabetic foot(DF), and 1 753 from patients with diabetes mellitus(DM)]. All fundus photographs were randomly divided into a training set(1 424 photos) and a test set(611 photos) using a computer generated random number at 7∶3. After pre-processing the fundus photographs, a total of 4 128 texture features based on the gray matrix were extracted by the Radiomic toolkit, and 11 339 other features were extracted using the ToolboxDESC toolkit. The LASSO algorithm was used to select the 30 features most relevant to DF, and then the Bootstrap + 0.632 self-sampling method was used to further select the 7 best combinations. Logistic regression analysis was used to obtain the regression coefficients and establish the final diabetic foot classification prediction model. ROC curve was drawn, and AUC, sensitivity, specificity, and accuracy of the training and test sets were calculated to verify its prediction performance. Results:We screened 7 fundus radiomics markers for diabetic foot patients, and based on this established a DF/DM classification prediction model. The AUC, sensitivity, specificity, and accuracy of the model were 0.958 6, 0.984 0, 0.920 0, and 0.928 0 in the training set, and 0.927 1, 0.988 9, 0.881 0, and 0.896 9 in the test set, respectively.Conclusion:In this study, seven DF fundus markers were screened using radiomics technology. Based on this, a highly accurate and easy-to-use DF/DM classification model was constructed. This technology has the potential to increase the efficiency of DF screening programs.
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Nasopharyngeal carcinoma (NPC) is one of the most common malignant tumors of the head and neck. In clinical practice, imaging examination plays an important role in the diagnosis, staging and risk assessment of NPC. However, it is difficult to distinguish the heterogeneity within the tumor, so the ability to classify and predict NPC is limited. Radiomics can extract a large amount of data from medical images for quantitative analysis, which further improves the ability of imaging features to diagnose and predict tumors. The purpose of this review is to introduce the application value of radiomics of different imaging modality such as CT, MRI and PET in differential diagnosis, predictions of treatment response, prognosis and radiotherapy complications of NPC.
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Objective:To explore the prognostic value of 18F-FDG PET-based radiomics features by machine learning in older patients(≥60 years) with diffuse large B-cell lymphoma (DLBCL). Methods:A total of 166 older patients (88 males, 78 females, age: 60-93 years) with DLBCL who underwent pre-therapy 18F-FDG PET/CT from March 2011 to November 2019 were enrolled in the retrospective study. There were 115 patients in training cohort and 51 patients in validation cohort. The lesions in PET images were manually drawn and the obtained radiomics features from patients in training cohort were selected by the least absolute shrinkage and selection operator (LASSO), random forest (RF), and extreme gradient boosting (Xgboost), and then classified by support vector machine (SVM) to build radiomics signatures (RS) for predicting overall survival (OS). A multi-parameter model was constructed by using Cox proportional hazard model and assessed by concordance index (C-index). Results:A total of 1 421 PET radiomics features were extracted and 10 features were selected to build RS. The univariate Cox regression analysis showed that RS was a predictor of OS (hazard ratio ( HR)=5.685, 95% CI: 2.955-10.939; P<0.001). The multi-parameter model that incorporated RS, metabolic metrics, and clinical risk factors, exhibited significant prognostic superiority over the clinical model, PET-based model, and the National Comprehensive Cancer Network International Prognostic Index (NCCN-IPI) in terms of OS (training cohort: C-index: 0.752 vs 0.737 vs 0.739 vs 0.688; validation cohort: C-index: 0.845 vs 0.798 vs 0.844 vs 0.775). Conclusions:RS can be used as a survival predictor for older patients(≥60 years) with DLBCL. Furthermore, the multi-parameter model incorporating RS is able to successfully predict prognosis.
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Objective:To construct machine learning models based on CT imaging and clinical parameters for predicting progression-free survival (PFS) of locally advanced cervical cancer (LACC) patients after concurrent chemoradiotherapy (CCRT).Methods:Clinical data of 167 LACC patients treated with CCRT at Shandong Cancer Hospital from September 2015 to October 2021 were retrospectively analyzed. All patients were randomly divided into the training and validation cohorts according to the ratio of 7 vs. 3. Clinical features were selected by univariate and multivariate Cox proportional hazards model ( P<0.1). Radiomics models and nomograms were constructed by radiomics features which were selected by least absolute shrinkage and selection operator (LASSO) Cox regression model to predict the 1-, 3- and 5-year PFS. Combined models and nomogram models were developed by selected clinical and radiomics features. The Kaplan Meier-curve, receiver operating characteristic (ROC) curve, C-index and calibration curve were used to evaluate the model performance. Results:A total of 1 409 radiomics features were extracted based on the region of interest (ROI) in CT images. CT radiomics models showed better performance for predicting 1-, 3-and 5-year PFS than the clinical model in the training and validation cohorts. The combined model displayed the optimal performance in predicting 1-, 3-and 5-year PFS in the training cohort [area under the curve (AUC): 0.760, 0.648, 0.661, C-index: 0.740, 0.667, 0.709] and verification cohort (AUC: 0.763, 0.677, 0.648, C-index: 0.748, 0.668, 0.678).Conclusions:Combined model constructed based on CT radiomics and clinical features yield better prediction performance than that based on radiomics or clinical features alone. As an objective image analysis approach, it possesses high prediction efficiency for PFS of LACC patients after CCRT, which can provide reference for clinical decision-making.
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Esophageal cancer is a tumor with high morbidity and mortality in China, which is generally diagnosed at late stage and yields poor prognosis. Early diagnosis and correct staging are the basis, and reasonable treatment is the most important. Radiomics can make use of existing imaging resources for deeper mining, and make secondary use of its potential high-throughput data through deep learning or machine learning, thereby establishing a radiomics prediction model. This may become an essential marker of tumor prognosis to predict overall survival or tumor progression, thus stratifying patients at different risk for individualized treatment. In this article, the basic concepts of radiomics, its application in prognostic prediction of esophageal cancer and its combination with clinical and genetic studies were reviewed.
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Predicting and evaluating the efficacy of neoadjuvant therapy for rectal cancer are of clinical significance and health economic value. At present, exploring the methods of predicting and evaluating the efficacy of neoadjuvant therapy have become research hotspot, focus and difficulty at home and abroad. Radiomics and artificial intelligence (AI) are two rapidly developing technologies. It is worthy of integrating radiomics with AI to build a model for predicting and evaluating the efficacy of neoadjuvant therapy and support individualized clinical decision-making and treatment options. In this article, literature review related to neoadjuvant chemoradiotherapy for rectal cancer based on radiomics and AI was conducted, aiming to explore the prospect and advantages of radiomics and AI in the prediction and evaluation of neoadjuvant therapy.
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Objective:To investigate the value of nomograms based on clinical parameters, apparent diffusion coefficient (ADC) and MRI-derived radiomics in predicting survival of patients with locally advanced cervical cancer (LACC) after concurrent chemoradiotherapy (CCRT).Methods:Clinical data of 423 patients with IB-IVA cervical cancer treated with CCRT at Anhui Provincial Hospital Affiliated to Anhui Medical University from March 2014 to March 2020 were retrospectively analyzed and randomly divided into the training and validation groups at a ratio of 2∶1 using the simple randomization method. The values of ADC min, ADC mean, ADC max and 3D texture parameters of diffusion weighted imaging (DWI), T 2WI, T 2WI-fat suppression of pre-treatment primary lesions in all patients were measured. The least absolute shrinkage and selection operator (LASSO) algorithm and logistic regression analysis were used to screen the texture features and calculate radiomics score (Rad-score). Cox regression analysis was employed to construct nomogram models for predicting overall survival (OS) and cancer-specific survival (CS) of patients with LACC after CCRT, which were subject to internal and external validation. Results:Squamous cell carcinoma antigen (SCC-Ag), external beam radiotherapy dose, ADCmin and Rad-score were the independent prognostic factors for OS and CS of LACC patients after CCRT and constituted predictive models for OS and CS. The area under the receiver operating characteristic (ROC) curve (AUC) of two models in predicting 1-year, 3-year, 5-year OS and CS was 0.906, 0.917, 0.916 and 0.911, 0.918, 0.920, with internally validated consistency indexes (C-indexes) of 0.897 and 0.900. Then, models were brought into the validation group for external validation with AUC of 0.986, 0.942, 0.932 and 0.986, 0.933, 0.926 in predicting 1-year, 3-year, 5-year OS and CS.Conclusion:The nomograms based on clinical parameters, ADC values and MRI-derived radiomics are of high clinical value in predicting OS and CS of patients with LACC after CCRT, which can be used as prognostic markers for patients with cervical cancer to certain extent.
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Objective:To investigate the predictive value of enhanced CT-based radiomics for brain metastasis (BM) and selective use of prophylactic cranial irradiation (PCI) in limited-stage small cell lung cancer (LS-SCLC).Methods:Clinical data of 97 patients diagnosed with LS-SCLC confirmed by pathological and imaging examination in Shanxi Provincial Cancer Hospital from January 2012 to December 2018 were retrospectively analyzed. The least absolute shrinkage and selection operator (LASSO) Cox and Spearman correlation tests were used to select the radiomics features significantly associated with the incidence of BM and calculate the radiomics score. The calibration curve, the area under the receiver operating characteristic (ROC) curve (AUC), 5-fold cross-validation, decision curve analysis (DCA), and integrated Brier score (IBS) were employed to evaluate the predictive power and clinical benefits of the radiomics score. Kaplan-Meier method and log-rank test were adopted to draw survival curves and assess differences between two groups.Results:A total of 1272 radiomics features were extracted from enhanced CT. After the LASSO Cox regression and Spearman correlation tests, 8 radiomics features associated with the incidence of BM were used to calculate the radiomics score. The AUCs of radiomics scores to predict 1-year and 2-year BM were 0.845 (95% CI=0.746-0.943) and 0.878 (95% CI=0.774-0.983), respectively. The 5-fold cross validation, calibration curve, DCA and IBS also demonstrated that the radiomics model yielded good predictive performance and net clinical benefit. Patients were divided into the high-risk and low-risk cohorts based on the radiomics score. For patients at high risk, the 1-year and 2-year cumulative incidence rates of BM were 0% and 18.2% in the PCI group, and 61.8% and 75.4% in the non-PCI group, respectively ( P<0.001). In the PCI group, the 1-year and 2-year overall survival rates were 92.9% and 78.6%, and 85.3% and 36.8% in the non-PCI group, respectively ( P=0.023). For patients at low risk, the 1-year and 2-year cumulative incidence rates of BM were 0% and 0% in the PCI group, and 10.0% and 20.2% in the non-PCI group, respectively ( P=0.062). In the PCI group, the 1-year and 2-year overall survival rates were 100% and 77.0%, and 96.7% and 79.3% in the non-PCI group, respectively ( P=0.670). Conclusion:The radiomics model based on enhanced CT images yields excellent performance for predicting BM and individualized PCI.
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Objective:To explore the feasibility of a classification prediction model for gamma pass rates (GPRs) under different intensity-modulated radiation therapy techniques for pelvic tumors using a radiomics-based machine learning approach, and compare the classification performance of four integrated tree models.Methods:With a retrospective collection of 409 plans using different IMRT techniques, the three-dimensional dose validation results were adopted based on modality measurements, with a GPR criterion of 3%/2 mm and 10% dose threshold. Then prediction were built models by extracting radiomics features based on dose documentation. Four machine learning algorithms were used, namely random forest (RF), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM). Their classification performance was evaluated by calculating sensitivity, specificity, F1 score, and AUC value. Results:The RF, AdaBoost, XGBoost, and LightGBM models had sensitivities of 0.96, 0.82, 0.93, and 0.89, specificities of 0.38, 0.54, 0.62, and 0.62, F1 scores of 0.86, 0.81, 0.88, and 0.86, and AUC values of 0.81, 0.77, 0.85, and 0.83, respectively. XGBoost model showed the highest sensitivity, specificity, F1 score, and AUC value, outperforming the other three models. Conclusions:To build a GPR classification prediction model using a radiomics-based machine learning approach is feasible for plans using different intensity-modulated radiotherapy techniques for pelvic tumors, providing a basis for future multi-institutional collaborative research on GPR prediction.
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Objective:To evaluate the feasibility and clinical value of pre-treatment non-enhanced chest CT radiomics features and machine learning algorithm to predict the mutation status and subtype (19Del/21L858R) of epidermal growth factor receptor (EGFR) for patients with non-small cell lung cancer (NSCLC).Methods:This retrospective study enrolled 280 NSCLC patients from first and second affiliated hospital of University of South China who were confirmed by biopsy pathology, gene examination, and have pre-treatment non-enhanced CT scans. There are 136 patients were confirmed EGFR mutation. Primary lung gross tumor volume was contoured by two experienced radiologists and oncologists, and 851 radiomics features were subsequently extracted. Then, spearman correlation analysis and RELIEFF algorithm were used to screen predictive features. The two hospitals were training and validation cohort, respectively. Clinical-radiomics model was constructed using selected radiomics and clinical features, and compared with models built by radiomics features or clinical features respectively. In this study, machine learning models were established using support vector machine (SVM) and a sequential modeling procedure to predict the mutation status and subtype of EGFR. The area under receiver operating curve (AUC-ROC) was employed to evaluate the performances of established models.Results:After feature selection, 21 radiomics features were found to be efffective in predicting EGFR mutation status and subtype and were used to establish radiomics models. Three types models were established, including clinical model, radiomics model, and clinical-radiomics model. The clinical-radiomics model showed the best predictive efficacy, AUCs of predicting EGFR mutation status for training dataset and validation dataset were 0.956 (95% CI: 0.952-1.000) and 0.961 (95% CI: 0.924-0.998), respectively. The AUCs of predicting 19Del/L858R mutation subtype for training dataset and validation dataset were 0.926 (95% CI: 0.893-0.959), 0.938 (95% CI: 0.876-1.000), respectively. Conclusions:The constructed sequential models based on integration of CT radiomics, clinical features and machine learning can accurately predict the mutation status and subtype of EGFR.
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Objective:Based on radiomics characteristics, different machine learning classification models are constructed to predict the gamma pass rate of dose verification in intensity-modulated radiotherapy for pelvic tumors, and to explore the best prediction model.Methods:The results of three-dimensional dose verification based on phantom measurement were retrospectively analyzed in 196 patients with pelvic tumor intensity-modulated radiotherapy plans. The gamma pass rate standard was 3%/2 mm and 10% dose threshold. Prediction models were constructed by extracting radiomic features based on dose documentation. Four machine learning algorithms, random forest, support vector machine, adaptive boosting, and gradient boosting decision tree were used to calculate the AUC value, sensitivity, and specificity respectively. The classification performance of the four prediction models was evaluated.Results:The sensitivity and specificity of the random forest, support vector machine, adaptive boosting, and gradient boosting decision tree models were 0.93, 0.85, 0.93, 0.96, 0.38, 0.69, 0.46, and 0.46, respectively. The AUC values were 0.81 and 0.82 for the random forest and adaptive boosting models, respectively, and 0.87 for the support vector machine and gradient boosting decision tree models.Conclusions:Machine learning method based on radiomics can be used to construct a prediction model of gamma pass rate for specific dosimetric verification of pelvic intensity-modulated radiotherapy. The classification performance of the support vector machine model and gradient boosting decision tree model is better than that of the random forest model and adaptive boosting model.
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Objective:To evaluate the value of preoperative prediction of vessel invasion (VI) of locally advanced gastric cancer by machine learning model based on the venous phase enhanced CT radiomics features.Methods:A retrospective analysis of 296 patients with locally advanced gastric cancer confirmed by pathology in the First Affiliated Hospital of Zhengzhou University from July 2011 to December 2020 was performed. The patients were divided into VI positive group ( n=213) and VI negative group ( n=83) based on pathological results. The data were divided into training set ( n=207) and test set ( n=89) according to the ratio of 7∶3 with stratification sampling. The clinical characteristics of patients were recorded, and the independent risk factors of gastric cancer VI were screened by multivariate logistic regression. Pyradiomics software was used to extract radiomic features from the venous phase enhanced CT images, and the minimum absolute shrinkage and selection algorithm (LASSO) was used to screen the features, obtain the optimal feature subset, and establish the radiomics signature. Four machine learning algorithms, including extreme gradient boosting (XGBoost), logistic, naive Bayes (GNB), and support vector machine (SVM) models, were used to build prediction models for the radiomics signature and the screened clinical independent risk factors. The efficacy of the model in predicting gastric cancer VI was evaluated by the receiver operating characteristic curve. Results:The degree of differentiation (OR=13.651, 95%CI 7.265-25.650, P=0.003), Lauren′s classification (OR=1.349, 95%CI 1.011-1.799, P=0.042) and CA199 (OR=1.796, 95%CI 1.406-2.186, P=0.044) were independent risk factors for predicting the VI of locally advanced gastric cancer. Based on the venous phase enhanced CT images, 864 quantitative features were extracted, and 18 best constructed radiomics signature were selected by LASSO. In the training set, the area under the curve (AUC) of XGBoost, logistic, GNB and SVM models for predicting gastric cancer VI were 0.914 (95%CI 0.875-0.953), 0.897 (95%CI 0.853-0.940), 0.880 (95%CI 0.832-0.928) and 0.814 (95%CI 0.755-0.873), respectively, and in the test set were 0.870 (95%CI 0.769-0.971), 0.877 (95%CI 0.788-0.964), 0.859 (95%CI 0.755-0.961) and 0.773 (95%CI 0.647-0.898). The logistic model had the largest AUC in the test set. Conclusions:The machine learning model based on the venous phase enhanced CT radiomics features has high efficacy in predicting the VI of locally advanced gastric cancer before the operation, and the logistic model demonstrates the best diagnostic efficacy.
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Objective:To evaluate the value of radiomics based on contrast-enhanced spectral mammography (CESM) of internal and peripheral regions combined with clinical factors in predicting benign and malignant breast lesions of breast imaging reporting and data system category 4 (BI-RADS 4).Methods:A retrospective analysis was performed on the clinical and imaging data of patients with breast lesions who were treated in Yantai Yuhuangding Hospital (Center 1) Affiliated to Qingdao University from July 2017 to July 2020 and in Fudan University Cancer Hospital (Center 2) from June 2019 to July 2020. Center 1 included 835 patients, all female, aged 17-80 (49±12) years, divided into training set (667 cases) and test set (168 cases) according to the "train-test-split" function in Python software at a ratio of 8∶2; and 49 patients were included from Center 2 as external validation set, all female, aged 34-70 (51±8) years. The radiomics features were extracted from the intralesional region (ITR), the perilesional regions of 5, 10 mm (PTR 5 mm, PTR10 mm) and the intra-and perilesional regions of 5, 10 mm (IPTR 5 mm, IPTR 10 mm) and were selected by variance filtering, SelectKBest algorithm, and least absolute shrinkage and selection operator. Then five radiomics signatures were constructed including ITR signature, PTR 5 mm signature, PTR 10 mm signature, IPTR 5 mm signature, IPTR 10 mm signature. In the training set, univariable and multivariable logistic regressions were used to construct nomograms by selecting radiomics signatures and clinical factors with significant difference between benign and malignant BI-RADS type 4 breast lesions. The efficacy of nomogram in predicting benign and malignant BI-RADS 4 breast lesions was evaluated by the receiver operating characteristic curve and area under the curve (AUC). Decision curve and calibration curve were used to evaluate the net benefit and calibration capability of the nomogram.Results:The nomogram included ITR signature, PTR 5 mm signature, PTR 10 mm signature, IPTR 5 mm signature, age, and BI-RADS category 4 subclassification for differentiating malignant and benign BI-RADS category 4 breast lesions and obtained AUCs of 0.94, 0.92, and 0.95 in the training set, test set, and external validation set, respectively. The calibration curve showed good agreement between the predicted probabilities and actual results and the decision curve indicated a good net benefit of the nomogram for predicting malignant BI-RADS 4 lesions in the training set, test set, and external validation set.Conclusion:The nomogram constructed from the radiomics features of the internal and surrounding regions of CESM breast lesions combined with clinical factors is attributed to differentiate benign from malignant BI-RADS category 4 breast lesions.
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Objective:To investigate the value of radiomics based on three-dimensional high resolution MR vessel wall imaging (3D HRMR-VWI) for identifying culprit plaques in symptomatic patients with middle cerebral atherosclerosis.Methods:The clinical and imaging features of 117 patients (139 middle cerebral artery plaques) with cerebrovascular diseases in Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology from October 2018 to October 2020 were respectively reviewed. Stratified random sampling was used to divide 139 plaques into training set (97 plaques) and validation set (42 plaque) at the ratio of 7∶3. The plaques were divided into 69 culprit plaques and 70 non-culprit plaques based on plaque MR features and clinical symptoms. The clinical and imaging characteristics of culprit plaques and non-culprit plaques were compared by independent sample t-test, Mann-Whitney U test and χ 2 test, and factors with significant difference between two groups in univariate analysis were further analyzed by multivariate logistic regression to find out the independent predictors of culprit plaques. Radiomics features were extracted, screened and radiomics model was constructed using pre-and post-contrast 3D HRMR-VWI based on the training set. The combined model was constructed by combining the independent predictors and radiomics model. Receiver operating characteristic curve and area under curve (AUC) were used to evaluate the efficacy of each model, and DeLong test was used to compare the efficacy of different models. Results:Significant difference was found in intraplaque hemorrhage, lumen area of stenosis, stenosis diameter, stenosis rate, plaque burden and enhancement rate between culprit and non-culprit plaques (all P<0.05). Multivariate logistic regression analysis confirmed that only intraplaque hemorrhage was the independent predictor for culprit plaques (OR=7.045,95%CI 1.402-35.397, P=0.018). In the validation set, the AUC of the pre-contrast 3D HRMR-VWI model was lower than that of the post-contrast 3D HRMR-VWI model ( Z=-2.01, P=0.044). The AUC of pre+post-contrast 3D HRMR-VWI model was not significantly different from that of post-contrast 3D HRMR-VWI model ( Z=0.79, P=0.427). The AUC showed no significant difference between combined model and pre+post-contrast 3D HRMR-VWI model ( Z=-0.59, P>0.05). The combined model showed the best performance in predicting culprit plaques of middle cerebral artery (AUC=0.939), with the sensitivity, specificity and accuracy of 95.24%, 76.19% and 85.71%. Conclusion:Radiomics based on 3D HRMR-VWI has potential values in identifying culprit plaques in symptomatic patients with middle cerebral atherosclerosis.