Clinical Application of Preliminary Breast Cancer Screening for Dense Breasts Using Real-Time AI-Powered Ultrasound with Deep-Learning Computer Vision

Objective:We propose a solution that is backed by cloud computing,combines a series of AI neural networks of computer vision;is capable of detecting,highlighting,and locating breast lesions from a live ultrasound video feed,provides BI-RADS categorizations;and has reliable sensitivity and specificity.Multiple deep-learning models were trained on more than 300,000 breast ultrasound images to achieve object detection and regions of interest classification.The main objective of this study was to determine whether the performance of our Al-powered solution was comparable to that of ultrasound radiologists.Methods:The noninferiority evaluation was conducted by comparing the examination results of the same screening women between our AI-powered solution and ultrasound radiologists with over 10 years of experience.The study lasted for one and a half years and was carried out in the Duanzhou District Women and Children's Hospital,Zhaoqing,China.1,133 females between 20 and 70 years old were selected through convenience sampling.Results:The accuracy,sensitivity,specificity,positive predictive value,and negative predictive value were 93.03%,94.90%,90.71%,92.68%,and 93.48%,respectively.The area under the curve(AUC)for all positives was 0.91569 and the AUC for all negatives was 0.90461.The comparison indicated that the overall performance of the AI system was comparable to that of ultrasound radiologists.Conclusion:This innovative AI-powered ultrasound solution is cost-effective and user-friendly,and could be applied to massive breast cancer screening.

Role of mammogram and ultrasound imaging in predicting breast cancer subtypes in screening and symptomatic patients

BACKGROUND Breast cancer(BC)radiogenomics,or correlation analysis of imaging features and BC molecular subtypes,can complement genetic analysis with less resourceintensive diagnostic methods to provide an early and accurate triage of BC.This is pertinent because BC is the most prevalent cancer amongst adult women,resulting in rising demands on public health resources.AIM To find combinations of mammogram and ultrasound imaging features that predict BC molecular subtypes in a sample of screening and symptomatic patients.METHODS This retrospective study evaluated 328 consecutive patients in 2017-2018 with histologically confirmed BC,of which 237(72%)presented with symptoms and 91(28%)were detected via a screening program.All the patients underwent mammography and ultrasound imaging prior to biopsy.The images were retrospectively read by two breast-imaging radiologists with 5-10 years of experience with no knowledge of the histology results to ensure statistical independence.To test the hypothesis that imaging features are correlated with tumor subtypes,univariate binomial and multinomial logistic regression models were performed.Our study also used the multivariate logistic regression(with and without interaction terms)to identify combinations of mammogram and ultrasound(US)imaging characteristics predictive of molecular subtypes.RESULTS The presence of circumscribed margins,posterior enhancement,and large size is correlated with triple-negative BC(TNBC),while high-risk microcalcifications and microlobulated margins is predictive of HER2-enriched cancers.Ductal carcinoma in situ is characterized by small size on ultrasound,absence of posterior acoustic features,and architectural distortion on mammogram,while luminal subtypes tend to be small,with spiculated margins and posterior acoustic shadowing(Luminal A type).These results are broadly consistent with findings from prior studies.In addition,we also find that US size signals a higher odds ratio for TNBC if presented during screening.As TNBC tends to display sonographic features such as circumscribed margins and posterior enhancement,resulting in visual similarity with benign common lesions,at the screening stage,size may be a useful factor in deciding whether to recommend a biopsy.CONCLUSION Several imaging features were shown to be independent variables predicting molecular subtypes of BC.Knowledge of such correlations could help clinicians stratify BC patients,possibly enabling earlier treatment or aiding in therapeutic decisions in countries where receptor testing is not readily available.

CACA Guidelines for Holistic Integrative Management of Breast Cancer

Purpose:Breast cancer is now the most common malignant tumor worldwide.About one-fourth of female cancer patients all over the world sufer from breast cancer.And about one in six female cancer deaths worldwide is caused by breast cancer.In terms of absolute numbers of cases and deaths,China ranks frst in the world.The CACA Guidelines for Holistic Integrative Management of Breast Cancer were edited to help improve the diagnosis and comprehensive treatment in China.Methods:The Grading of Recommendations Assessment,Development and Evaluation(GRADE)was used to classify evidence and consensus.Results:The CACA Guidelines for Holistic Integrative Management of Breast Cancer include the epidemiology of breast cancer,breast cancer screening,breast cancer diagnosis,early breast cancer treatment,advanced breast cancer treatment,follow-up,rehabilitation,and traditional Chinese medicine treatment of breast cancer patients.Conclusion:We to standardize the diagnosis and treatment of breast cancer in China through the formulation of the CACA Guidelines.