Classification Of Breast Lesions Research Articles

Multiregional radiomic model for breast cancer diagnosis: value of ultrasound-based peritumoral and parenchymal radiomics.

Breast cancer consists not only of neoplastic cells but also of significant changes in the surrounding and parenchymal stroma, which can be reflected in radiomics. This study aimed to perform breast lesion classification through an ultrasound-based multiregional (intratumoral, peritumoral, and parenchymal) radiomic model. We retrospectively reviewed ultrasound images of breast lesions from institution #1 (n=485) and institution #2 (n=106). Radiomic features were extracted from different regions (intratumoral, peritumoral, and ipsilateral breast parenchymal) and selected to train the random forest classifier with the training cohort (n=339, a subset of the institution #1 dataset). Then, the intratumoral, peritumoral, and parenchymal, intratumoral & peritumoral (In&Peri), intratumoral & parenchymal (In&P), and intratumoral & peritumoral & parenchymal (In&Peri&P) models were developed and validated on the internal (n=146, another subset of institution 1) and external (n=106, institution #2 dataset) test cohorts. Discrimination was evaluated using the area under the curve (AUC). Calibration curve and Hosmer-Lemeshow test assessed calibration. Integrated discrimination improvement (IDI) was used to assess performance improvement. The performance of the In&Peri (AUC values 0.892 and 0.866), In&P (0.866 and 0.863), and In&Peri&P (0.929 and 0.911) models was significantly better than that of the intratumoral model (0.849 and 0.838) in the internal and external test cohorts (IDI test, all P<0.05). The intratumoral, In&Peri and In&Peri&P models showed good calibration (Hosmer-Lemeshow test, all P>0.05). The multiregional (In&Peri&P) model had the highest discrimination among the 6 radiomic models in the test cohorts, respectively. The multiregional model combining radiomic information of intratumoral, peritumoral, and ipsilateral parenchymal regions yielded better performance than the intratumoral model in distinguishing malignant breast lesions from benign lesions.

Breast lesion classification using features fusion and selection of ensemble ResNet method

AbstractMedical Imaging with Deep Learning has recently become the most prominent topic in the scientific world. Significant results have been obtained in the classification of medical images using deep learning methods, and there has been an increase in studies on malignant types. The main reason for choosing breast cancer is that breast cancer is one of the critical malignant types that increase the death rate in women. In this study, 1236 ultrasound images were collected from Elazig Fethi Sekin City Hospital, and three different ResNet CNN architectures were used for feature extraction. Data were trained with an SVM classifier. In addition, the three ResNet architectures were combined, and novel fused ResNet architecture was used in this study. In addition, these features were used with three different feature selection techniques, MR‐MR, NCA, and Relieff. These results are 89.3% obtained from ALL‐ResNet architecture and the feature selected with NCA in normal and lesion classification. Normal, malignant, and benign classification best accuracy is 84.9% with ALL‐ResNet NCA. Experimental studies show that MR‐MR, NCA, and Relieff feature selection algorithms reduce features and give more results that are successful. This indicates that the proposed method is more successful than classical deep learning methods.

A multi-stage fusion framework to classify breast lesions using deep learning and radiomics features computed from four-view mammograms.

Developing computer aided diagnosis (CAD) schemes of mammograms to classify between malignant and benign breast lesions has attracted a lot of research attention over the last several decades. However, unlike radiologists who make diagnostic decisions based on the fusion of image features extracted from multi-view mammograms, most CAD schemes are single-view-based schemes, which limit CAD performance and clinical utility. This study aims to develop and test a novel CAD framework that optimally fuses information extracted from ipsilateral views of bilateral mammograms using both deep transfer learning (DTL) and radiomics feature extraction methods. An image dataset containing 353 benign and 611 malignant cases is assembled. Each case contains four images: the craniocaudal (CC) and mediolateral oblique (MLO) view of the left and right breast. First, we extract four matching regions of interest (ROIs) from images that surround centers of two suspicious lesion regions seen in CC and MLO views, as well as matching ROIs in the contralateral breasts. Next, the handcrafted radiomics (HCRs) features and VGG16 model-generated automated features are extracted from each ROI resulting in eight feature vectors. Then, after reducing feature dimensionality and quantifying the bilateral and ipsilateral asymmetry of four ROIs to yield four new feature vectors, we test four fusion methods to build three support vector machine (SVM) classifiers by an optimal fusion of asymmetrical image features extracted from four view images. Using a 10-fold cross-validation method, results show that a SVM classifier trained using an optimal fusion of four view images yields the highest classification performance (AUC=0.876±0.031), which significantly outperforms SVM classifiers trained using one projection view alone, AUC=0.817±0.026 and 0.792±0.026 for the CC and MLO view of bilateral mammograms, respectively (p<0.001). The study demonstrates that the shift from single-view CAD to four-view CAD and the inclusion of both DTL and radiomics features significantly increases CAD performance in distinguishing between malignant and benign breast lesions.

Mammography and ultrasound based dual modality classification of breast cancer using a hybrid deep learning approach

Traditional methods of diagnosing breast cancer (BC) suffer from human errors, are less accurate, and consume time. A computer-aided detection (CAD) system can overcome the above-stated limitations and help radiologists with accurate decision-making. However, the existing studies using single imaging modalities have shown limited clinical use due to its low diagnostic accuracy and reliability when compared to multimodal system. Thus, we aim to develop a hybrid deep learning bimodal CAD algorithm for the classification of breast lesions using mammogram and ultrasound imaging modalities combined. A combined convolutional neural network (CNN) and long-short term memory (LSTM) model is implemented using images from both mammogram and ultrasound modalities to improve the early diagnosis of BC. A new real-time dataset consisting of 43 mammogram images and 43 ultrasound images collected from 31 patients is used in this work. Further, each group consists of 25 benign and 18 malignant images. The number of images is increased to 1032 (516 for each modality) using different data augmentation techniques. The proposed bimodal CAD algorithm achieves a classification accuracy of 99.35% and the area under the receiver operating characteristic curve (AUC) of 0.99 over the traditional unimodal CAD systems, which attain the classification accuracy of 97.16% and 98.84% using mammogram and ultrasound, respectively. The proposed bimodal CAD algorithm using combined mammogram and ultrasound outperforms the traditional unimodal CAD systems. The bimodal CAD algorithm can avoid unnecessary biopsies and encourage its clinical application.

Automatic deep learning method for detection and classification of breast lesions in dynamic contrast-enhanced magnetic resonance imaging.

The purpose of this study was to develop a deep learning-based system with a cascade feature pyramid network for the detection and classification of breast lesions in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). This retrospective study enrolled 191 consecutive patients with pathological confirmed breast lesions who underwent preoperative magnetic resonance imaging (MRI) at the Second Affiliated Hospital of Xi'an Jiaotong University. Patients were randomly divided into a training set comprising 153 patients with 126 malignant and 27 benign lesions and a validation set containing 38 patients with 31 malignant and 7 benign lesions under 5-fold cross-validation. Two radiologists annotated the location and classification of all lesions. After augmentation with pseudo-color image fusion, MRI images were fed into the developed cascade feature pyramid network system, feature pyramid network, and faster region-based convolutional neural network (CNN) for breast lesion detection and classification, respectively. The performance on large (>2 cm) and small (≤2 cm) breast lesions was further evaluated. Average precision (AP), mean AP, F1-score, sensitivity, and false positives were used to evaluate different systems. Cohen's kappa scores were calculated to assess agreement between different systems, and Student's t-test and the Holm-Bonferroni method were used to compare multiple groups. The cascade feature pyramid network system outperformed the other systems with a mean AP and highest sensitivity of 0.826±0.051 and 0.970±0.014 (at 0.375 false positives), respectively. The F1-score of the cascade feature pyramid network in real detection was also superior to that of the other systems at both the slice and patient levels. The mean AP values of the cascade feature pyramid network reached 0.779±0.152 and 0.790±0.080 in detecting large and small lesions, respectively. Especially for small lesions, the cascade feature pyramid network achieved the best performance in detecting benign and malignant breast lesions at both the slice and patient levels. The deep learning-based system with the developed cascade feature pyramid network has the potential to detect and classify breast lesions on DCE-MRI, especially small lesions.

A Study on a Parameter Estimator for the Homodyned K Distribution Based on Table Search for Ultrasound Tissue Characterization.

The homodyned K (HK) distribution is considered to be the most suitable distribution in the context of tissue characterization; therefore, the search for a rapid and reliable parameter estimator for HK distribution is important. We propose a novel parameter estimator based on a table search (TS) for HK parameter estimates. The TS estimator can inherit the strength of conventional estimators by integrating various features and taking advantage of the TS method in a rapid and easy operation. Performance of the proposed TS estimator was evaluated and compared with that of XU (the estimation method based on X and U statistics) and artificial neural network (ANN) estimators. The simulation results revealed that the TS estimator is superior to the XU and ANN estimators in terms of normalized standard deviations and relative root mean squared errors of parameter estimation, and is faster. Clinical experiments found that the area under the receiver operating curve for breast lesion classification using the parameters estimated by the TS estimator could reach 0.871. The proposed TS estimator is more accurate, reliable and faster than the state-of-the-art XU and ANN estimators and has great potential for ultrasound tissue characterization based on the HK distribution.

The feasibility of a novel computer-aided classification system for the characterisation and diagnosis of breast masses on ultrasound: a single-centre preliminary test study

To introduce a novel computer-aided classification (CAC) system and investigate the feasibility of characterising and diagnosing breast masses on ultrasound (US). A total of 246 breast masses were included. US features and the final assessment categories of the breast masses were analysed by a radiologist and the CAC system according to the Breast Imaging Reporting and Data System (BI-RADS) lexicon. The CAC system evaluated the BI-RADS assessment from the fusion of multi-view and colour Doppler US images without (SmartBreast) or with combining clinical variables (m-CAC system). The diagnostic performance and agreement of US characteristics between the radiologist and the CAC system were compared. The agreement between the radiologist and the CAC system was substantial for mass shape (κ=0.673), orientation (κ=0.682), margin (κ=0.622), posterior features (κ=0.629), calcifications in a mass (κ=0.709) and vascularity (κ=0.745), fair for echo pattern (κ=0.379), and moderate for BI-RADS assessment (κ=0.575). With BI-RADS 4a as the cut-off value, the specificity (52.5% versus 25%, p<0.0001) and accuracy (73.98% versus 62.6%, p=0.0002) of the m-CAC system were improved without significant loss of sensitivity (94.44% versus 98.41%, p=0.1250) compared with the SmartBreast. The m-CAC system showed similar specificity (52.5% versus 45.83%, p=0.2430) and accuracy (73.98% versus 73.58%, p=1.0000) as the radiologist, but a lower sensitivity (94.44% versus 100%, p=0.0156). The CAC system showed an acceptable agreement with the radiologist for characterisation of breast lesions. It has the potential to mimic the decision-making behaviour of radiologists for the classification of breast lesions.

An efficient hybrid computer-aided breast cancer diagnosis system with wavelet packet transform and synthetically-generated contrast-enhanced spectral mammography images

Contrast-enhanced spectral mammography (CESM) is an emerging modality for breast cancer diagnosis. This work investigates the feasibility of a computationally-efficient computer-aided diagnosis (CAD) system for breast lesion classification. Moreover, to avoid the need for intravenous contrast agents, we investigated the synthesis of contrast-enhanced (SynCESM) images. A total of 504 pairs of low-energy (LE) and CESM images were collected from 160 female subjects. A semi-automatic active-contour method was used for lesion segmentation. Then, 20 morphological and textural features were extracted. To improve the computational efficiency of the proposed system, the wavelet packet transform (WPT) was applied. Then, the same features were extracted from the WPT-approximated segmented lesions. Using LE images, a sigmoid-kernel SVM classifier exhibited a 90.20% accuracy, an 88.39% sensitivity, an 88.26% specificity, and a 0.92 AUC. The per-frame classification time was significantly reduced from 1.1046 s to 0.0734 s with WPT. Using CESM images, we achieved a 93.26% accuracy, a 95.94% sensitivity, a 93.37% specificity, a 0.94 AUC, and a 0.0657 s classification time. Interestingly, with SynCESM images, reasonable performance was still obtained with a 92.14% accuracy, a 93.87% sensitivity, a 91.58% specificity, a 0.93 AUC and a 0.0657 s classification time. Finally, with hybrid pairs of LE and CESM images, we got the best performance with a 96.87% accuracy, a 97.23% sensitivity, a 95.47% specificity, a 0.98 AUC, and a 0.0696 s classification time. The results demonstrate several advantages of the proposed system including its clinical feasibility, lower complexity, and reduced need for contrast agents via synthetic data generation.

Deep learning-enabled fully automated pipeline system for segmentation and classification of single-mass breast lesions using contrast-enhanced mammography: a prospective, multicentre study

Breast cancer is the leading cause of cancer-related deaths in women. However, accurate diagnosis of breast cancer using medical images heavily relies on the experience of radiologists. This study aimed to develop an artificial intelligence model that diagnosed single-mass breast lesions on contrast-enhanced mammography (CEM) for assisting the diagnostic workflow. A total of 1912 women with single-mass breast lesions on CEM images before biopsy or surgery were included from June 2017 to October 2022 at three centres in China. Samples were divided into training and validation sets, internal testing set, pooled external testing set, and prospective testing set. A fully automated pipeline system (FAPS) using RefineNet and the Xception+Pyramid pooling module (PPM) was developed to perform the segmentation and classification of breast lesions. The performances of six radiologists and adjustments in Breast Imaging Reporting and Data System (BI-RADS) category 4 under the FAPS-assisted strategy were explored in pooled external and prospective testing sets. The segmentation performance was assessed using the Dice similarity coefficient (DSC), and the classification was assessed using heatmaps, area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. The radiologists' reading time was recorded for comparison with the FAPS. This trial is registered with China Clinical Trial Registration Centre (ChiCTR2200063444). The FAPS-based segmentation task achieved DSCs of 0.888±0.101, 0.820±0.148 and 0.837±0.132 in the internal, pooled external and prospective testing sets, respectively. For the classification task, the FAPS achieved AUCs of 0.947 (95% confidence interval [CI]: 0.916-0.978), 0.940 (95% [CI]: 0.894-0.987) and 0.891 (95% [CI]: 0.816-0.945). It outperformed radiologists in terms of classification efficiency based on single lesions (6s vs 3min). Moreover, the FAPS-assisted strategy improved the performance of radiologists. BI-RADS category 4 in 12.4% and 13.3% of patients was adjusted in two testing sets with the assistance of FAPS, which may play an important guiding role in the selection of clinical management strategies. The FAPS based on CEM demonstrated the potential for the segmentation and classification of breast lesions, and had good generalisation ability and clinical applicability. This study was supported by the Taishan Scholar Foundation of Shandong Province of China (tsqn202211378), National Natural Science Foundation of China (82001775), Natural Science Foundation of Shandong Province of China (ZR2021MH120), and Special Fund for Breast Disease Research of Shandong Medical Association (YXH2021ZX055).

A novel deep learning model for breast lesion classification using ultrasound Images: A multicenter data evaluation

PurposeBreast cancer is one of the major reasons of death due to cancer in women. Early diagnosis is the most critical key for disease screening, control, and reducing mortality. A robust diagnosis relies on the correct classification of breast lesions. While breast biopsy is referred to as the “gold standard” in assessing both the activity and degree of breast cancer, it is an invasive and time-consuming approach. MethodThe current study’s primary objective was to develop a novel deep-learning architecture based on the InceptionV3 network to classify ultrasound breast lesions. The main promotions of the proposed architecture were converting the InceptionV3 modules to residual inception ones, increasing their number, and altering the hyperparameters. In addition, we used a combination of five datasets (three public datasets and two prepared from different imaging centers) for training and evaluating the model. ResultsThe dataset was split into the train (80%) and test (20%) groups. The model achieved 0.83, 0.77, 0.8, 0.81, 0.81, 0.18, and 0.77 for the precision, recall, F1 score, accuracy, AUC, Root Mean Squared Error, and Cronbach’s α in the test group, respectively. ConclusionsThis study illustrates that the improved InceptionV3 can robustly classify breast tumors, potentially reducing the need for biopsy in many cases.

Abstract P3-04-05: Artificial Intelligence Detects, Classifies, and Describes Lesions in Clinical Breast Ultrasound Images

Abstract Purpose Many low-middle income countries (LMIC) suffer from chronic shortages of resources that inhibit the implementation of effective breast cancer screening programs. Advanced breast cancer rates in the U.S. Affiliated Pacific Islands substantially exceed that of the United States. We propose the use of portable breast ultrasound coupled with artificial intelligence (AI) algorithms to assist non-radiologist field personnel in real-time field lesion detection, classification, and biopsy, as well as determination of breast density for risk assessment. In this study, we examine the ability of an AI algorithm to detect and describe breast cancer lesions in clinically-acquired breast ultrasound images in 40,000 women participating in a Hawaii screening program. Materials and Methods The Hawaii and Pacific Islands Mammography Registry (HIPIMR) collects breast health questionnaires and breast imaging (mammography, ultrasound, and MRI) from participating centers in Hawaii and the Pacific and links this information to the Hawaii Tumor Registry for cancer findings. From the women with either screening or diagnostic B-mode breast ultrasound exams, we selected 3 negative cases (no cancer) for every positive case matched by age, excluding Doppler and elastography images. The blinded images were read by the study radiologist to delineate all lesions and describe in terms of the BI-RADS lexicon. These images were split by woman into training (70%), validation and hyperparameter selection (20%) and testing (20%) subsets. An AI model was fine-tuned for lesion and BI-RADS category classification from a Detectron2 framework [1] pre-trained on the COCO Instance Segmentation Dataset [2]. Model performance was evaluated by computation of precision and sensitivity percentages, as well as Area under the Receiver Operator Curve (AUROC). Detections were considered positive if they overlapped a ground truth lesion delineation by at least 50% (Intersection over Union = 0.5), and a maximum of 4 detections were generated for each image. Timing experiments were run on a GPU-enabled (Nvidia Tesla V100) machine on unbatched images. Results Over the 10-year observation period, we identified 5,214 women with US images meeting our criterion. Of these, 111 were diagnosed with malignant breast cancer and 333 were selected as non-cases for a total of 444 women. These 444 women had a total of 4,623 ultrasound images with 2,028 benign and 1,431 malignant lesions identified by the study radiologist. For cancerous lesions, the AI algorithm had 8% precision at a sensitivity of 90% on the testing set. For benign lesions, a sensitivity of 90% resulted in 5% precision on the testing set. The AUROC for bounding box detections of cancerous lesions was 0.90. The AUROC for bounding box detections of benign lesions was 0.87. The model made predictions at a rate of 25 frames/second time (38.7 milliseconds per image). Conclusion Detection, segmentation, and cancer classification of breast lesions are possible in clinically-acquired ultrasound images using AI. Based on our timing experiments, the model is capable of detecting and classifying lesions in real-time during ultrasound capture. Model performance is expected to improve as more data becomes available for training. Future work would involve further fine-tuning of the model on portable breast ultrasound images and increasing model evaluation speed in order to assess utility in low-resource populations [1] Wu Y, Kirillov A, Massa F, Lo W-Y, Girshick R. Detectron2. https://github.com/facebookresearch/detectron2. [2] Lin T-Y, Maire M, Belongie S, et al. Microsoft COCO: Common Objects in Context. Computer Vision – ECCV 2014. Springer International Publishing; 2014:740-755. Citation Format: Arianna Bunnell, Dustin Valdez, Thomas Wolfgruber, Aleen Altamirano, Brenda Hernandez, Peter Sadowski, John Shepherd. Artificial Intelligence Detects, Classifies, and Describes Lesions in Clinical Breast Ultrasound Images [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P3-04-05.

Fully automatic classification of breast lesions on multi-parameter MRI using a radiomics model with minimal number of stable, interpretable features.

To build an automatic computer-aided diagnosis (CAD) pipeline based on multiparametric magnetic resonance imaging (mpMRI) and explore the role of different imaging features in the classification of breast cancer. A total of 222 histopathology-confirmed breast lesions, together with their BI-RADS scores, were included in the analysis. The cohort was randomly split into training (159) and test (63) cohorts, and another 50lesions were collected as an external cohort. An nnUNet-based lesionsegmentation model was trained to automatically segment lesion ROI, from which radiomics features were extracted for diffusion-weighted imaging (DWI), T2-weighted imaging (T2WI), and contrast-enhanced (DCE) pharmacokinetic parametric maps. Models based on combinations of sequences were built using support vector machine (SVM) and logistic regression (LR). Also, the performance of these sequence combinations and BI-RADS scores were compared. The Dice coefficient and AUC were calculated to evaluate the segmentation and classificationresults. Decision curve analysis (DCA) was used to assess clinical utility. The segmentation model achieved a Dice coefficient of 0.831 in thetest cohort. The radiomics model used only three features from diffusion coefficient (ADC) images, T2WI, and DCE-derived kinetic mapping, andachieved an AUC of 0.946 [0.883-0.990], AUC of 0.842 [0.6856-0.998] in the external cohort, which was higher than the BI-RADS score with an AUC of 0.872 [0.752-0.975]. The joint model using both radiomics score and BI-RADS score achieved the highest test AUC of 0.975 [0.935-1.000], with a sensitivity of 0.920 and a specificity of 0.923. Three radiomics features can be used to construct an automatic radiomics-based pipeline to improve the diagnosis of breast lesions and reduce unnecessary biopsies, especially when using jointly with BI-RADS scores.

MsGoF: Breast lesion classification on ultrasound images by multi-scale gradational-order fusion framework

Background and Objective: Predicting the malignant potential of breast lesions based on breast ultrasound (BUS) images is a crucial component of computer-aided diagnosis system for breast cancers. However, since breast lesions in BUS images generally have various shapes with relatively low contrast and present complex textures, it still remains challenging to accurately identify the malignant potential of breast lesions. Methods: In this paper, we propose a multi-scale gradational-order fusion framework to make full advantages of multi-scale representations incorporating with gradational-order characteristics of BUS images for breast lesions classification. Specifically, we first construct a spatial context aggregation module to generate multi-scale context representations from the original BUS images. Subsequently, multi-scale representations are efficiently fused in feature fusion block that is armed with special fusion strategies to comprehensively capture morphological characteristics of breast lesions. To better characterize complex textures and enhance non-linear modeling capability, we further propose isotropous gradational-order feature module in the feature fusion block to learn and combine multi-order representations. Finally, these multi-scale gradational-order representations are utilized to perform prediction for the malignant potential of breast lesions. Results: The proposed model was evaluated on three open datasets by using 5-fold cross-validation. The experimental results (Accuracy: 85.32%, Sensitivity: 85.24%, Specificity: 88.57%, AUC: 90.63% on dataset A; Accuracy: 76.48%, Sensitivity: 72.45%, Specificity: 80.42%, AUC: 78.98% on dataset B) demonstrate that the proposed method achieves the promising performance when compared with other deep learning-based methods in BUS classification task. Conclusions: The proposed method has demonstrated a promising potential to predict malignant potential of breast lesion using ultrasound image in an end-to-end manner.

Detection and classification of breast lesions using multiple information on contrast-enhanced mammography by a multiprocess deep-learning system: A multicenter study.

Accurate detection and classification of breast lesions in early stage is crucial to timely formulate effective treatments for patients. We aim to develop a fully automatic system to detect and classify breast lesions using multiple contrast-enhanced mammography (CEM) images. In this study, a total of 1,903 females who underwent CEM examination from three hospitals were enrolled as the training set, internal testing set, pooled external testing set and prospective testing set. Here we developed a CEM-based multiprocess detection and classification system (MDCS) to perform the task of detection and classification of breast lesions. In this system, we introduced an innovative auxiliary feature fusion (AFF) algorithm that could intelligently incorporates multiple types of information from CEM images. The average free-response receiver operating characteristic score (AFROC-Score) was presented to validate system's detection performance, and the performance of classification was evaluated by area under the receiver operating characteristic curve (AUC). Furthermore, we assessed the diagnostic value of MDCS through visual analysis of disputed cases, comparing its performance and efficiency with that of radiologists and exploring whether it could augment radiologists' performance. On the pooled external and prospective testing sets, MDCS always maintained a high standalone performance, with AFROC-Scores of 0.953 and 0.963 for detection task, and AUCs for classification were 0.909 [95% confidence interval (95% CI): 0.822-0.996] and 0.912 (95% CI: 0.840-0.985), respectively. It also achieved higher sensitivity than all senior radiologists and higher specificity than all junior radiologists on pooled external and prospective testing sets. Moreover, MDCS performed superior diagnostic efficiency with an average reading time of 5 seconds, compared to the radiologists' average reading time of 3.2 min. The average performance of all radiologists was also improved to varying degrees with MDCS assistance. MDCS demonstrated excellent performance in the detection and classification of breast lesions, and greatly enhanced the overall performance of radiologists.

BI-RADS categories and breast lesions classification of mammographic images using artificial intelligence diagnostic model

According to BI-RADS criteria, radiologists evaluate mammography images, and breast lesions are classified as malignant or benign. In this retrospective study, an evaluation was made on 264 mammogram images of 139 patients. First, data augmentation was applied, and then the total number of images was increased to 565. Two computer-aided models were then designed to classify breast lesions and BI-RADS categories. The first of these models is the support vector machine (SVM) based model, and the second is the convolutional neural network (CNN) based model. The SVM-based model could classify BI-RADS categories and malignant-benign discrimination with an accuracy rate of 86.42% and 92.59%, respectively. On the other hand, the CNN-based model showed 79.01% and 83.95% accuracy for BI-RADS categories and malignant benign discrimination, respectively. These results showed that a well-designed machine learning-based classification model can give better results than a deep learning model. Additionally, it can be used as a secondary system for radiologists to differentiate breast lesions and BI-RADS lesion categories.

Breast cancer classification using a novel hybrid feature selection approach

Many women around the world die due to breast cancer. If breast cancer is treated in the early phase, mortality rates may significantly be reduced. Quite a number of approaches have been proposed to help in the early detection of breast cancer. A novel hybrid feature selection model is suggested in this study. This novel hybrid model aims to build an efficient feature selection method and successfully classify breast lesions. A combination of relief and binary Harris hawk optimization (BHHO) hybrid model is used for feature selection. Then, k-nearest neighbor (k-NN), support vector machine (SVM), logistic regression (LR) and naive Bayes (NB) methods are preferred for the classification task. The suggested hybrid model is tested by three different breast cancer datasets which are Wisconsin diagnostic breast cancer dataset (WDBC), Wisconsin breast cancer dataset (WBCD) and mammographic breast cancer dataset (MBCD). According to the experimental results, the relief and BHHO hybrid model improves the performance of all classification algorithms in all three datasets. For WDBC, relief-BHO-SVM model shows the highest classification rates with an of accuracy of 98.77%, precision of 97.17%, recall of 99.52%, F1-score of 98.33%, specificity of 99.72% and balanced accuracy of 99.62%. For WBCD, relief-BHO-SVM model achieves of accuracy of 99.28%, precision of 98.76%, recall of 99.17%, F1-score of 98.96%, specificity of 99.56% and balanced accuracy of 99.36%. Relief-BHO-SVM model performs the best with an accuracy of 97.44%, precision of 97.41%, recall of 98.26%, F1-score of 97.84%, specificity of 97.47% and balanced accuracy of 97.86% for MBCD. Furthermore, the relief-BHO-SVM model has achieved better results than other known approaches. Compared with recent studies on breast cancer classification, the suggested hybrid method has achieved quite good results.

Microwave Breast Lesion Classification – Results from Clinical Investigation of the SAFE Microwave Breast Cancer System

Microwave breast cancer imaging (MWI) is an emerging non-invasive technology used to clinically assess the internal breast tissue inhomogeneity. MWI utilizes the variance in dielectric properties of healthy and cancerous tissue to identify anomalies inside the breast and make further clinical predictions. In this study, we evaluate our SAFE MWI system in a clinical setting. Capability of SAFE to provide breast pathology is assessed. Patients with BI-RADS category 4 or 5 who were scheduled for biopsy were included in the study. Machine learning approach, more specifically the Adaptive Boosting (AdaBoost) model, was implemented to determine if the level of difference between backscattered signals of breasts with the benign and malignant pathological outcome is significant enough for quantitative breast health classification via SAFE. A dataset of 113 (70 benign and 43 malignant) breast samples was used in the study. The proposed classification model achieved the sensitivity, specificity, and accuracy of 79%, 77%, and 78%, respectively. The non-ionizing and non-invasive nature gives SAFE an opportunity to impact breast cancer screening and early detection positively. Device classified both benign and malignant lesions at a similar rate. Further clinical studies are planned to validate the findings of this study.

The Diagnostic Significance of the BI-RADS Classification Combined With Automated Breast Volume Scanner and Shear Wave Elastography for Breast Lesions.

We herein compared the diagnostic accuracy of the BI-RADS, ABVS, SWE, and combined techniques for the classification of breast lesions. Breast lesions were appraised using the BI-RADS classification system as well as the combinations of BI-RADS plus ABVS (BI-RADS + ABVS) and BI-RADS plus SWE (BI-RADS + SWE), and both methods (BI-RADS + ABVS + SWE) by two specialties Medical Ultrasound physician. The Fisher's exact and χ2 tests were performed to compare the degree of malignancy for the various methods with a pathology ground truth. Receiver operating characteristic curves (ROC) were generated and the corresponding area under the curve (AUC) values were determined to test the diagnostic efficacy of the various methods and identify the optimal SWE cut-off indicative of malignancy. The incidence of the retraction phenomenon on ABVS images of the malignant group was significantly higher (P < .001) than that of the benign group. The specificity, sensitivity, and positive and negative predictive values of the BI-RADS classification were 88.72, 79.38, 83.70, and 85.50%, respectively. BI-RADS plus SWE-Max exhibited enhanced specificity, sensitivity, and positive and negative predictive values of 88.72, 92.78, 85.70, and 94.40%, respectively. Similarly, when BI-RADS + ABVS was utilized, the sensitivity and negative predictive value increased to 95.88 and 96.40%, respectively. BI-RADS + ABVS + SWE possessed the highest overall sensitivity (96.91%), specificity (94.74%), and positive (93.10%) and negative (97.70%) predictive values from all four indices. ABVS and SWE can reduce the subjectivity of BI-RADS. As a result, BI-RADS + ABVS + SWE resulted in the best diagnostic accuracy.

Diagnostic Accuracy of the International Academy of Cytology Yokohama System versus the Modified Masood’s Scoring Index in Categorization and Diagnosis of Palpable Breast Lesions

Introduction: Fine-needle aspiration cytology (FNAC) is a simple, minimally invasive, and effective tool that can be used to accurately diagnose benign and malignant lesions of the breast. The International Academy of Cytologists Yokohama system and Modified Masood’s Scoring Index (MMSI) are two important systems to categorize palpable breast lesions on FNAC. The goal of the present study was to evaluate the IAC Yokohama system and MMSI in the cytological diagnosis and classification of diverse breast lesions and to compare the diagnostic accuracy of both systems through cyto-histological correlation. Methods: A retrospective cross-sectional study was done within the 3-year period from 2019 to 2021. Seven hundred and ninety cases of FNAC breast lesions were included and were categorized following the recommendations by the International Academy of Cytologists Yokohama system and MMSI. The sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), and diagnostic accuracy for both systems were calculated, taking histopathology as the standard. Additionally, the risk of malignancy (ROM) of each category was calculated in the Yokohama system. Results: The IAC Yokohama system’s sensitivity, specificity, PPV, NPV, and diagnostic accuracy were 96.8, 95.8%, 96%, 96.6%, and 96.3%. The MMSI had sensitivity, specificity, PPV, NPV, and diagnostic accuracy of 95.8%, 98.4%, 98.3%, 96.1%, and 97.1%, respectively. The ROM for insufficient, benign, atypical, suspicious for malignancy, and malignant categories were 12.5%, 1.8%, 20%, 90.4%, and 97.5%, respectively. Conclusion: The categorization of breast lumps using the MMSI is more accurate and more specific in diagnosing malignant cases, and thus, the MMSI system is more precise and accurate at diagnosing malignant cases.

Gradient-Boosting Algorithm for Microwave Breast Lesion Classification-SAFE Clinical Investigation.

(1) Background: Microwave breast imaging (MBI) is a promising breast-imaging technology that uses harmless electromagnetic waves to radiate the breast and assess its internal structure. It utilizes the difference in dielectric properties of healthy and cancerous tissue, as well as the dielectric difference between different cancerous tissue types to identify anomalies inside the breast and make further clinical predictions. In this study, we evaluate the capability of our upgraded MBI device to provide breast tissue pathology. (2) Methods: Only patients who were due to undergo biopsy were included in the study. A machine learning (ML) approach, namely Gradient Boosting, was used to understand information from the frequency spectrum, collected via SAFE, and provide breast tissue pathology. (3) Results: A total of 54 patients were involved in the study: 29 of them had benign and 25 had malignant findings. SAFE acquired 20 true-positive, 24 true-negative, 4 false-positive and 4 false-negative findings, achieving the sensitivity, specificity and accuracy of 80%, 83% and 81%, respectively. (4) Conclusions: The use of harmless tissue radiation indicates that SAFE can be used to provide the breast pathology of women of any age without safety restrictions. Results indicate that SAFE is capable of providing breast pathology at a high rate, encouraging further clinical investigations.