Hereditary breast cancer(HBC)is one of the important subtypes of breast cancer(BC),primarily characterized by mutations in the BRCA1/2 genes.Accurate identification of BRCA1/2 mutation carriers remains a significant clinical challenge.Traditional genetic testing methods for BRCA1/2 mutations are often expensive and complex.In recent years,advancements in radiomics and artificial intelligence(AI)have opened new avenues for non-invasive prediction of BRCA1/2 mutations.This review summarized the current research progresses in utilizing radiomics and AI for screening and predicting of BRCA1/2-related HBC.

Objective:To measure and estimate the radiation dose to breast cone-beam CT (CBCT) -scanned examinees, which can provide a dose reference for the selection of mammography equipment in the clinic.Methods:In this study, using a 400 cm 3 Alderson radiation therapy (ART) breast phantom and thermoluminescent detectors (TLDs), the distribution of absorbed dose, and the average glandular dose (AGD), to the examined breasts caused by a breast CBCT scanner was measured and calculated scanner at 50 and 100 mA tube currents. Results:At 50 and 100 mA tube currents, the range of breast absorbed dose inside the examined breast measured based on the phantom was 2.25-7.97 mGy and 3.88-15.68 mGy, respectively, with breast absorbed dose decreasing from the periphery to the centre of the phantom, and the related AGDs were 4.87 and 9.81 mGy, respectively.Conclusions:The AGDs to the breast of CBCT-scanned examinees measured in this study was higher than in the case of commonly used digital mammography. This will be meaningful to provide the guidance on the rational choice of imaging equipment in future clinical practice.

Objective:To measure and estimate the radiation dose to breast cone-beam CT (CBCT) -scanned examinees, which can provide a dose reference for the selection of mammography equipment in the clinic.Methods:In this study, using a 400 cm 3 Alderson radiation therapy (ART) breast phantom and thermoluminescent detectors (TLDs), the distribution of absorbed dose, and the average glandular dose (AGD), to the examined breasts caused by a breast CBCT scanner was measured and calculated scanner at 50 and 100 mA tube currents. Results:At 50 and 100 mA tube currents, the range of breast absorbed dose inside the examined breast measured based on the phantom was 2.25-7.97 mGy and 3.88-15.68 mGy, respectively, with breast absorbed dose decreasing from the periphery to the centre of the phantom, and the related AGDs were 4.87 and 9.81 mGy, respectively.Conclusions:The AGDs to the breast of CBCT-scanned examinees measured in this study was higher than in the case of commonly used digital mammography. This will be meaningful to provide the guidance on the rational choice of imaging equipment in future clinical practice.

Hereditary breast cancer(HBC)is one of the important subtypes of breast cancer(BC),primarily characterized by mutations in the BRCA1/2 genes.Accurate identification of BRCA1/2 mutation carriers remains a significant clinical challenge.Traditional genetic testing methods for BRCA1/2 mutations are often expensive and complex.In recent years,advancements in radiomics and artificial intelligence(AI)have opened new avenues for non-invasive prediction of BRCA1/2 mutations.This review summarized the current research progresses in utilizing radiomics and AI for screening and predicting of BRCA1/2-related HBC.

Although immune checkpoint inhibitors(ICIs)enhance the immune system's capacity to destroy tumor cells,they can also induce a range of toxic responses that lead to cardiovascular damage.Among these adverse effects,ICI-associated myocarditis(ICI-M)has the highest morbidity and mortality rates,presenting a significant challenge in clinical practice.Cardiovascular magnetic resonance(CMR)is re-garded as the"gold standard"for noninvasive assessment of cardiac structure,function,and tissue characteristics.While this imaging tech-nique has advanced rapidly in recent years,with both domestic and international guidelines recognizing it as a crucial tool for evaluating ICI-M.This article provides a comprehensive overview of the pathological mechanisms underlying ICI-M,its diagnostic criteria,and the latest re-search developments in CMR and its clinical applications;it also discusses the most recent domestic and international guidelines and re-search findings.

Objective To observe the value of lumbar spine bone marrow MR T1 mapping radiomics for predicting clinical risk of acute lymphoblastic leukemia(ALL)in children.Methods Lumbar bone marrow T1 mappings were prospectively acquired from 77 newly diagnosed ALL children.The volume of interest(VOI)of L3 vertebral body was segmented using 3D Slicer software and 2 060 radiomics features were extracted,and the best features were screened.The children were divided into training and testing sets at the ratio of 8:2.Logistic regression(LR),support vector machine(SVM)and random forest(RF)were used to established radiomics models based on the best features,respectively,which were trained in training set and verified in testing set.The clinical risk was evaluated according to newly diagnosed risk and the response to chemotherapy after MR examination.Receiver operating characteristic(ROC)curve was drawn,and the area under the curve(AUC)was calculated to evaluate the efficacy of each model for predicting clinical risk of ALL in children.Results There were 52 cases in low-medium risk group and 25 in high risk group.The training set consisted of 44 cases of low-medium risk and 17 of high risk,while the testing set consisted of 8 cases of low-medium risk and 8 of high risk.Twelve best features were selected to establish radiomics models.The sensitivity and accuracy of RF model in training set were both 100％,but its sensitivity(50.00％)and accuracy(75.00％)in testing set were both low,which indicating overfitting.The AUC(0.95)of LR model was slightly higher than that of SVM model(0.92)in testing set,but no significantly difference was found(P＞0.05),and the accuracy of these two models was consistent.Conclusion Both lumbar bone marrow T1 mapping LR and SVM radiomics models could be used to predict clinical risk of ALL in children,and LR model had better predictive efficacy.

Purpose To use CT-arterial phase images of pre-treatment ovarian cancer patients,combined with deep learning algorithms and machine learning to build a model to predict the efficacy of neoadjuvant chemotherapy in ovarian cancer.Materials and Methods A total of 302 consecutive patients who underwent surgery and were pathologically diagnosed with ovarian cancer from March 2013 to August 2019 in Tianjin Medical University were retrospectively collected.All patients were partitioned into training and test sets according to the ratio of 7∶3.In the python environment,VGG13 model was integrated via combining deep learning network and machine learning,and features were filtered via least absolute shrinkage and selection operator algorithm to build a prediction model for classification and prediction of CT images.The area under the curve(AUC),accuracy,sensitivity,specificity,and Fl-Score were calculated,respectively.Results The AUC,accuracy,sensitivity,specificity,and Fl-Score of the model in the training set were 0.87,0.81,0.80,0.82 and 0.79,and 0.90,0.84,0.93,0.77 and 0.83 in the test set,respectively.The AUC of five-fold cross-validation were 0.86,0.88,0.88,0.90 and 0.87,respectively.Conclusion Predictive model based on CT images combined with deep learning and machine learning methods can provide a new clinical perspective for developing chemotherapy regimens for ovarian cancer.

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.

Although immune checkpoint inhibitors(ICIs)enhance the immune system's capacity to destroy tumor cells,they can also induce a range of toxic responses that lead to cardiovascular damage.Among these adverse effects,ICI-associated myocarditis(ICI-M)has the highest morbidity and mortality rates,presenting a significant challenge in clinical practice.Cardiovascular magnetic resonance(CMR)is re-garded as the"gold standard"for noninvasive assessment of cardiac structure,function,and tissue characteristics.While this imaging tech-nique has advanced rapidly in recent years,with both domestic and international guidelines recognizing it as a crucial tool for evaluating ICI-M.This article provides a comprehensive overview of the pathological mechanisms underlying ICI-M,its diagnostic criteria,and the latest re-search developments in CMR and its clinical applications;it also discusses the most recent domestic and international guidelines and re-search findings.

Mammography has played an essential role in the screening and treatment of breast cancer. However, the application of X-rays will also increase the risks of breast cancer while improving its detection rate. Moreover, the risks will increase with an increase in the radiation dose. Since the glandular tissue in breasts is sensitive to radiation, the evaluation of the average glandular dose (AGD) in mammography has attracted considerable international attention. Compared to relatively mature dosimetric studies on traditional two-dimension mammography and digital breast tomosynthesis, the method for the dose evaluation of the new cone beam CT for breasts are still subjected to research. This paper reviews and explores the current status of studies on the assessment method and relevant influencing factors of AGD under different types of mammography equipment.