Machine Learning Techniques Using Deep Instinctive Encoder-Based Feature Extraction for Optimized Breast Cancer Detection

Breast cancer(BC)is one of the leading causes of death among women worldwide,as it has emerged as the most commonly diagnosed malignancy in women.Early detection and effective treatment of BC can help save women’s lives.Developing an efficient technology-based detection system can lead to non-destructive and preliminary cancer detection techniques.This paper proposes a comprehensive framework that can effectively diagnose cancerous cells from benign cells using the Curated Breast Imaging Subset of the Digital Database for Screening Mammography(CBIS-DDSM)data set.The novelty of the proposed framework lies in the integration of various techniques,where the fusion of deep learning(DL),traditional machine learning(ML)techniques,and enhanced classification models have been deployed using the curated dataset.The analysis outcome proves that the proposed enhanced RF(ERF),enhanced DT(EDT)and enhanced LR(ELR)models for BC detection outperformed most of the existing models with impressive results.

Assessment and Visualization of Ki67 Heterogeneity in Breast Cancers through Digital Image Analysis

The Ki67 index (KI) is a standard clinical marker for tumor proliferation;however, its application is hindered by intratumoral heterogeneity. In this study, we used digital image analysis to comprehensively analyze Ki67 heterogeneity and distribution patterns in breast carcinoma. Using Smart Pathology software, we digitized and analyzed 42 excised breast carcinoma Ki67 slides. Boxplots, histograms, and heat maps were generated to illustrate the KI distribution. We found that 30% of cases (13/42) exhibited discrepancies between global and hotspot KI when using a 14% KI threshold for classification. Patients with higher global or hotspot KI values displayed greater heterogenicity. Ki67 distribution patterns were categorized as randomly distributed (52%, 22/42), peripheral (43%, 18/42), and centered (5%, 2/42). Our sampling simulator indicated analyzing more than 10 high-power fields was typically required to accurately estimate global KI, with sampling size being correlated with heterogeneity. In conclusion, using digital image analysis in whole-slide images allows for comprehensive Ki67 profile assessment, shedding light on heterogeneity and distribution patterns. This spatial information can facilitate KI surveys of breast cancer and other malignancies.

A Swin Transformer and Residualnetwork Combined Model for Breast Cancer Disease Multi-Classification Using Histopathological Images

Breast cancer has become a killer of women's health nowadays.In order to exploit the potential representational capabilities of the models more comprehensively,we propose a multi-model fusion strategy.Specifically,we combine two differently structured deep learning models,ResNet101 and Swin Transformer(SwinT),with the addition of the Convolutional Block Attention Module(CBAM)attention mechanism,which makes full use of SwinT's global context information modeling ability and ResNet101's local feature extraction ability,and additionally the cross entropy loss function is replaced by the focus loss function to solve the problem of unbalanced allocation of breast cancer data sets.The multi-classification recognition accuracies of the proposed fusion model under 40X,100X,200X and 400X BreakHis datasets are 97.50%,96.60%,96.30 and 96.10%,respectively.Compared with a single SwinT model and ResNet 101 model,the fusion model has higher accuracy and better generalization ability,which provides a more effective method for screening,diagnosis and pathological classification of female breast cancer.

Assessing pathological features of breast cancer via the multimodal information of multiphoton and Raman imaging

For unveiling the pathological evolution of breast cancer, nonlinear multiphoton microscopic(MPM) and confocal Raman microspectral imaging(CRMI) techniques were both utilized to address the structural and constitutional characteristics of healthy(H), ductal carcinoma in situ(DCIS), and invasive ductal carcinoma(IDC) tissues. MPM-based techniques,including two-photon excited fluorescence(TPEF) and second harmonic generation(SHG), visualized label-free and the fine structure of breast tissue. Meanwhile, CRMI not only presented the chemical images of investigated samples with the K-mean cluster analysis method(KCA), but also pictured the distribution of components in the scanned area through univariate imaging. MPM images illustrated that the cancer cells first arranged around the basement membrane of the duct,then proliferated to fill the lumens of the duct, and finally broke through the basement membrane to infiltrate into the stroma.Although the Raman imaging failed to visualize the cell structure with high resolution, it explained spectroscopically the gradual increase of nucleic acid and protein components inside the ducts as cancer cells proliferated, and displayed the distribution pattern of each biological component during the evolution of breast cancer. Thus, the combination of MPM and CRMI provided new insights into the on-site pathological diagnosis of malignant breast cancer, also ensured technical support for the development of multimodal optical imaging techniques for precise histopathological analysis.

A Survey of Convolutional Neural Network in Breast Cancer

Problems:For people all over the world,cancer is one of the most feared diseases.Cancer is one of the major obstacles to improving life expectancy in countries around the world and one of the biggest causes of death before the age of 70 in 112 countries.Among all kinds of cancers,breast cancer is the most common cancer for women.The data showed that female breast cancer had become one of themost common cancers.Aims:A large number of clinical trials have proved that if breast cancer is diagnosed at an early stage,it could give patients more treatment options and improve the treatment effect and survival ability.Based on this situation,there are many diagnostic methods for breast cancer,such as computer-aided diagnosis(CAD).Methods:We complete a comprehensive review of the diagnosis of breast cancer based on the convolutional neural network(CNN)after reviewing a sea of recent papers.Firstly,we introduce several different imaging modalities.The structure of CNN is given in the second part.After that,we introduce some public breast cancer data sets.Then,we divide the diagnosis of breast cancer into three different tasks:1.classification;2.detection;3.segmentation.Conclusion:Although this diagnosis with CNN has achieved great success,there are still some limitations.(i)There are too few good data sets.A good public breast cancer dataset needs to involve many aspects,such as professional medical knowledge,privacy issues,financial issues,dataset size,and so on.(ii)When the data set is too large,the CNN-based model needs a sea of computation and time to complete the diagnosis.(iii)It is easy to cause overfitting when using small data sets.

Preclinical and clinical applications of specific molecular imaging for HER2-positive breast cancer

Precision medicine and personalized therapy are receiving increased attention, and molecular-subtype classification has become crucial in planning therapeutic schedules in clinical practice for patients with breast cancer. Human epidermal growth factor receptor 2(HER2) is associated with high-grade breast tumors, high rates of lymph-node involvement, high risk of recurrence, and high resistance to general chemotherapy. Analysis of HER2 expression is highly important for doctors to identify patients who can benefit from trastuzumab therapy and monitor the response and efficacy of treatment. In recent years, significant efforts have been devoted to achieving specific and noninvasive HER2-positive breast cancer imaging in vivo. In this work, we reviewed existing literature on HER2 imaging in the past decade and summarized the studies from different points of view, such as imaging modalities and HER2-specific probes. We aimed to improve the understanding on the translational process in molecular imaging for HER2 breast cancer.

An Efficient Automated Technique for Classification of Breast Cancer Using Deep Ensemble Model

Breast cancer is one of the leading cancers among women.It has the second-highest mortality rate in women after lung cancer.Timely detection,especially in the early stages,can help increase survival rates.However,manual diagnosis of breast cancer is a tedious and time-consuming process,and the accuracy of detection is reliant on the quality of the images and the radiologist’s experience.However,computer-aided medical diagnosis has recently shown promising results,leading to the need to develop an efficient system that can aid radiologists in diagnosing breast cancer in its early stages.The research presented in this paper is focused on the multi-class classification of breast cancer.The deep transfer learning approach has been utilized to train the deep learning models,and a pre-processing technique has been used to improve the quality of the ultrasound dataset.The proposed technique utilizes two deep learning models,Mobile-NetV2 and DenseNet201,for the composition of the deep ensemble model.Deep learning models are fine-tuned along with hyperparameter tuning to achieve better results.Subsequently,entropy-based feature selection is used.Breast cancer identification using the proposed classification approach was found to attain an accuracy of 97.04%,while the sensitivity and F1 score were 96.87%and 96.76%,respectively.The performance of the proposed model is very effective and outperforms other state-of-the-art techniques presented in the literature.

Diagnostic value of preoperative examination for evaluating margin status in breast cancer

BACKGROUND A positive resection margin is a major risk factor for local breast cancer recurrence after breast-conserving surgery(BCS).Preoperative imaging examinations are frequently employed to assess the surgical margin.AIM To investigate the role and value of preoperative imaging examinations[magnetic resonance imaging(MRI),molybdenum target,and ultrasound]in evaluating margins for BCS.METHODS A retrospective study was conducted on 323 breast cancer patients who met the criteria for BCS and consented to the procedure from January 2014 to July 2021.The study gathered preoperative imaging data(MRI,ultrasound,and molybdenum target examination)and intraoperative and postoperative pathological information.Based on their BCS outcomes,patients were categorized into positive and negative margin groups.Subsequently,the patients were randomly split into a training set(226 patients,approximately 70%)and a validation set(97 patients,approximately 30%).The imaging and pathological information was analyzed and summarized using R software.Non-conditional logistic regression and LASSO regression were conducted in the validation set to identify factors that might influence the failure of BCS.A column chart was generated and applied to the validation set to examine the relationship between pathological margin range and prognosis.This study aims to identify the risk factors associated with failure in BCS.RESULTS The multivariate non-conditional logistic regression analysis demonstrated that various factors raise the risk of positive margins following BCS.These factors comprise non-mass enhancement(NME)on dynamic contrastenhanced MRI,multiple focal vascular signs around the lesion on MRI,tumor size exceeding 2 cm,type III timesignal intensity curve,indistinct margins on molybdenum target examination,unclear margins on ultrasound examination,and estrogen receptor(ER)positivity in immunohistochemistry.LASSO regression was additionally employed in this study to identify four predictive factors for the model:ER,molybdenum target tumor type(MT Xmd Shape),maximum intensity projection imaging feature,and lesion type on MRI.The model constructed with these predictive factors exhibited strong consistency with the real-world scenario in both the training set and validation set.Particularly,the outcomes of the column chart model accurately predicted the likelihood of positive margins in BCS.CONCLUSION The proposed column chart model effectively predicts the success of BCS for breast cancer.The model utilizes preoperative ultrasound,molybdenum target,MRI,and core needle biopsy pathology evaluation results,all of which align with the real-world scenario.Hence,our model can offer dependable guidance for clinical decisionmaking concerning BCS.

Adaptive radiation therapy of breast cancer by repeated imaging during irradiation

Breast cancer is the most frequent cancer among females and also a leading cause of cancer related mortality worldwide.A multimodality treatment approach may be utilized for optimal management of patients with combinations of surgery,radiation therapy(RT)and systemic treatment.RT composes an integral part of breast conserving treatment,and is typically used after breast conserving surgery to improve local control.Recent years have witnessed significant improvements in the discipline of radiation oncology which allow for more focused and precise treatment delivery.Adaptive radiation therapy(ART)is among the most important RT techniques which may be utilized for redesigning of treatment plans to account for dynamic changes in tumor size and anatomy during the course of irradiation.In the context of breast cancer,ART may serve as an excellent tool for patients receiving breast irradiation followed by a sequential boost to the tumor bed.Primary benefits of ART include more precise boost localization and potential for improved normal tissue sparing with adapted boost target volumes particularly in the setting of seroma reduction during the course of irradiation.Herein,we provide a concise review of ART for breast cancer in light of the literature.

THE CLINICAL SIGNIFICANCE OF ^(99m)Tc-MIBI BREAST IMAGING IN THE DIAGNOSIS OF EARLY BREAST CANCER

Objective: To find an effective, sensitive, specific and noninvasive diagnostic method of breast cancer. Methods: 109 masses of 102 patients with breast lesions smaller than 2 cm in diameter were divided into three groups to undergo 99mTc-MIBI imaging and compared with the results of pathology examination. 20 cases without breast lesions were selected as control. Abnormal condensation of 99mTc-MIBI in the breast reaching 10% higher than that in the counterpart of the healthy breast was regarded as positive. Results: Of 32 breast cancers, positive imaging appeared in 25. Negative imaging were found in 31 of 38 benign breast lesions. Of 39 occult breast lesions, positive imaging appeared in 6 and 3 of them were breast cancer, 2 of 3 patients with slightly increased 99mTc-MIBI imaging threshold were breast cancer also. No positive imaging was found in the control group. The diagnostic accuracy, sensitivity, specificity, positive predictive value, negative predictive value of 99mTc-MIBI was 88.4%, 89.2%, 88.0%, 75.0% and 95.3%, respectively. Conclusion: 99mTc-MIBI imaging had higher sensitivity and accuracy in the diagnosis of breast cancer and differentiation between benign and malignant breast lesions. It could provide useful information for the diagnosis of clinically suspected breast cancer.

Progresses of Functional Magnetic Resonance Imaging Diagnosis in Breast Cancer

Breast cancer is the most common malignant tumor that threatens women’s health. Breast magnetic resonance imaging (MRI) is a commonly used method recommended for the diagnosis of breast cancer. Diffusion weighted imaging (DWI) and dynamic enhanced magnetic resonance imaging (DCE-MRI) are now widely used. At present, with the continuous advancement of magnetic resonance technology, Magnetic resonance spectroscopy (MRS), Perfusion weighted imaging (PWI), Positron emission tomography-magnetic resonance imaging (PET-MRI) and so on are gradually being used in clinical practice. Mammography imaging and imaging genomics are hot topics. This article will briefly introduce several functional magnetic resonance techniques and their latest applications.

Magnetic resonance imaging in breast cancer:A literature review and future perspectives

Early detection and diagnosis of breast cancer are essential for successful treatment. Currently mammography and ultrasound are the basic imaging techniques for the detection and localization of breast tumors. The low sensitivity and specificity of these imaging tools resulted in a demand for new imaging modalities and breast magnetic resonance imaging(MRI) has become increasingly important in the detection and delineation of breast cancer in daily practice. However, the clinical benefits of the use of pre-operative MRI in women with newly diagnosed breast cancer is still a matter of debate. The main additional diagnostic value of MRI relies on specific situations such as detecting multifocal, multicentric or contralateral disease unrecognized on conventional assessment(particularly in patients diagnosed with invasive lobular carcinoma), assessing the response to neoadjuvant chemotherapy, detection of cancer in dense breast tissue, recognition of an occult primary breast cancer in patients presenting with cancer metastasis in axillary lymph nodes, among others. Nevertheless, the development of new MRI technolo-gies such as diffusion-weighted imaging, proton spectroscopy and higher field strength 7.0 T imaging offer a new perspective in providing additional information in breast abnormalities. We conducted an expert literature review on the value of breast MRI in diagnosing and staging breast cancer, as well as the future potentials of new MRI technologies.

Ultra-sensitive Nanoprobe Modified with Tumor Cell Membrane for UCL/MRI/PET Multimodality Precise Imaging of Triple-Negative Breast Cancer

Triple-negative breast cancer(TNBC)is a subtype of breast cancer in which the estrogen receptor and progesterone receptor are not expressed,and human epidermal growth factor receptor 2 is not amplified or overexpressed either,which make the clinical diagnosis and treatment very challenging.Molecular imaging can provide an effective way to diagnose TNBC.Upconversion nanoparticles(UCNPs),are a promising new generation of molecular imaging probes.However,UCNPs still need to be improved for tumor-targeting ability and biocompatibility.This study describes a novel probe based on cancer cell membrane-coated upconversion nanoparticles(CCm-UCNPs),owing to the low immunogenicity and homologous-targeting ability of cancer cell membranes,and modified multifunctional UCNPs.This probe exhibits excellent performance in breast cancer molecular classification and TNBC diagnosis through UCL/MRI/PET tri-modality imaging in vivo.By using this probe,MDA-MB-231 was successfully differentiated between MCF-7 tumor models in vivo.Based on the tumor imaging and molecular classification results,the probe is also expected to be modified for drug delivery in the future,contributing to the treatment of TNBC.The combination of nanoparticles with biomimetic cell membranes has the potential for multiple clinical applications.

Optical mammography:Diffuse optical imaging of breast cancer

Existing imaging modalities for breast cancer screening,diagnosis and therapy monitoring,namely X-ray mammography and magnetic resonance imaging,have been proven to have limitations.Diffuse optical imaging is a set of non-invasive imaging modalities that use near-infrared light,which can be an alternative,if not replacement,to those existing modalities.This review covers the background knowledge,recent clinical outcome,and future outlook of this newly emerging medical imaging modality.

An Overview of Active Microwave Imaging for Early Breast Cancer Detection

First,this article reviews the background of microwave imaging for early breast cancer detection,with a focus on active methods.Then active approaches,namely microwave tomography and radar-based microwave imaging,to microwave breast cancer detection are overviewed briefly,where there are recent developments in imaging algorithms as well as antennas,models,phantom and experimental systems.Lastly,we give concluding remarks and future research.In a word,the main objective of this article is to provide an overview of the principles,development,and current research status of these approaches.

Clinical and pathological portraits of axillary presentation breast cancer and effects of preoperative systemic therapy

There is a lack of investigation into the biological characteristics and preoperative systemic therapy （PST） for occult breast cancer （OBC）. For this study, departmental records in Breast Disease Center of Peking University First Hospital from January 2008 to December 2015 were retrospectively reviewed to identify cases of OBC. Eleven cases were included, and all patients were female, with a median age of 56 （range： 29-75） years. The sensitivity of magnetic resonance imaging （MRD was I00%, and the false positive rate was 33.3%. Based on histologic analysis of the axillary node, 9 （81.8%） cases were grade 3, and 2 （18.2%） cases were grade 2; 4 （36.4%） cases were 〉10% estrogen receptor （ER） positive and 6 （54.5%） human epidermal growth receptor 2 （HER2） positive. Nine cases （81.8%） exhibited over 30% Ki67 expression. PST was performed in 5 of the 11 cases. The lymph node response rate was 100% （5/5）, but no complete remission was achieved. In conclusion, aggressive subtypes were predominant among the included cases, and PST should be considered for OBC treatment options.

Malignant cardiac metastasis from breast cancer: Imaging contribution to surgical attitude

Metastasic cardiac disease from the breast is rarely diagnosed in the lifetime. It has a poor prognosis and limited management. Both echocardiography and computerized tomography (CT) should be the first imaging studies in suspicion of this entity. Other diagnostic methods should be based on the possibilities of treatment although a histopathological analysis of the metastasic mass is needed to confirm the diagnosis. Magnetic resonance imaging (MRI) could be useful to complete a morphological and functional evaluation in case of surgical removal.

Diagnostic Value Analysis of Dynamic Contrastenhanced Magnetic Resonance Imaging (DCE-MRI) combined with Diffusion Weighted Imaging (DWI) before Breast Cancer Surgery

Objective:The objective of this study was to investigate the diagnostic efficacy of pre-operative magnetic resonance imaging(MRI)–dynamic contrast imaging(dynamic contrast-enhanced[DCE]-MRI)combined with diffusion-weighted imaging(DWI)in detecting breast cancer.Research Methodology:A retrospective study was performed to compare the results of DCE-MRI combined with DWI in 78 patients with breast cancer who were treated in our hospital between January 20 and December 2018.Results:After diagnosis,the coincidence rate of diagnosis by DCEMRI combined with DWI was significantly higher than ultrasound(91.0%vs.55.1%,respectively,P<0.05).Among the two diagnostic methods,DCE-MRI combined with DWI imaging showed more obvious tumor signals,and the difference was statistically significant(P<0.05).Conclusion:Pre-operative application of DCE-MRI combined with DWI can provide a more accurate and effective reference for surgical planning.

Breast cancer screening and early diagnosis in Chinese women

Breast cancer is the most common malignant tumor in Chinese women,and its incidence is increasing.Regular screening is an effective method for early tumor detection and improving patient prognosis.In this review,we analyze the epidemiological changes and risk factors associated with breast cancer in China and describe the establishment of a screening strategy suitable for Chinese women.Chinese patients with breast cancer tend to be younger than Western patients and to have denser breasts.Therefore,the age of initial screening in Chinese women should be earlier,and the importance of screening with a combination of ultrasound and mammography is stressed.Moreover,Chinese patients with breast cancers have several ancestry-specific genetic features,and aiding in the determination of genetic screening strategies for identifying high-risk populations.On the basis of current studies,we summarize the development of risk-stratified breast cancer screening guidelines for Chinese women and describe the significant improvement in the prognosis of patients with breast cancer in China.