Characterization of indeterminate breast lesions on B-mode ultrasound using automated machine learning models

Purpose: While mammography has excellent sensitivity for the detection of breast lesions, its specificity is limited. Adjunct screening with ultrasound may partially alleviate this issue, but also increases false positives, resulting in unnecessary biopsies. This study investigated the use of Google AutoML Vision (Mountain View, CA), a commercially available machine learning service, to both identify and characterize indeterminate breast lesions on ultrasound. Methods: B-mode images from 253 independent cases of indeterminate breast lesions scheduled for core biopsy were used for model creation and validation. The performances of two sub-models from AutoML Vision, the image classification model and object detection model were evaluated, while also investigating training strategies to enhance model performances. Pathology from the patient’s biopsy were used as a reference standard. Results: The image classification models trained under different conditions demonstrated areas under the precision recall curve (AUC) ranging from 0.85 to 0.96 during internal validation. Once deployed, the model with highest internal performance demonstrated a sensitivity of 100% (95% confidence interval (CI) of 73.5-100%), specificity of 83.3% (CI=51.6-97.9%), positive predictive value (PPV) of 85.7% (CI=62.9-95.5%), and negative predictive value (NPV) of 100% (CI non-evaluable) in an independent dataset. The object detection model demonstrated lower performance internally during development (AUC=0.67) and during prediction in the independent dataset (sensitivity=75.0% (CI=42.8-94.5), specificity=80.0% (CI=51.9-95.7), PPV=75.0% (CI=50.8-90.0), NPV=80.0% (CI=59.3-91.7%)), but was able to demonstrate the location of the lesion within the image. Conclusions: Two models appear to be useful tools for identifying and classifying suspicious areas on B-mode images of indeterminate breast lesions

Predicting breast cancer response to neoadjuvant treatment using multi-feature MRI: results from the I-SPY 2 TRIAL.

Dynamic contrast-enhanced (DCE) MRI provides both morphological and functional information regarding breast tumor response to neoadjuvant chemotherapy (NAC). The purpose of this retrospective study is to test if prediction models combining multiple MRI features outperform models with single features. Four features were quantitatively calculated in each MRI exam: functional tumor volume, longest diameter, sphericity, and contralateral background parenchymal enhancement. Logistic regression analysis was used to study the relationship between MRI variables and pathologic complete response (pCR). Predictive performance was estimated using the area under the receiver operating characteristic curve (AUC). The full cohort was stratified by hormone receptor (HR) and human epidermal growth factor receptor 2 (HER2) status (positive or negative). A total of 384 patients (median age: 49 y/o) were included. Results showed analysis with combined features achieved higher AUCs than analysis with any feature alone. AUCs estimated for the combined versus highest AUCs among single features were 0.81 (95% confidence interval [CI]: 0.76, 0.86) versus 0.79 (95% CI: 0.73, 0.85) in the full cohort, 0.83 (95% CI: 0.77, 0.92) versus 0.73 (95% CI: 0.61, 0.84) in HR-positive/HER2-negative, 0.88 (95% CI: 0.79, 0.97) versus 0.78 (95% CI: 0.63, 0.89) in HR-positive/HER2-positive, 0.83 (95% CI not available) versus 0.75 (95% CI: 0.46, 0.81) in HR-negative/HER2-positive, and 0.82 (95% CI: 0.74, 0.91) versus 0.75 (95% CI: 0.64, 0.83) in triple negatives. Multi-feature MRI analysis improved pCR prediction over analysis of any individual feature that we examined. Additionally, the improvements in prediction were more notable when analysis was conducted according to cancer subtype

How to improve your breast cancer program: Standardized reporting using the new American College of Radiology Breast Imaging-Reporting and Data System

In the USA, the use of the American College of Radiology Breast Imaging-Reporting and Data System (ACR BI-RADS) has served not only as a quality assurance tool and guide to standardizing breast imaging reports but has also improved communication between referring physicians, researchers, and patients. In fact, in the USA, the Mammography Quality Standards Act of 1997 requires that all mammograms be assigned a BI-RADS category based on the finding of most concern. In this manuscript, we aim to review the recommendations provided in the 4 th edition of the ACR BI-RADS for mammography, USG, and MRI. We also review the major controversies surrounding the use of ACR BI-RADS

MRI for breast cancer: current indications

Mammography is the only imaging study that has been proven in multiple large randomized trials to decrease breast cancer mortality. Mammography, however, has its limitations and, as such, other modalities that can complement it are being studied. One of these is dynamic contrast-enhanced breast MRI, which has emerged as an important adjunctive modality and is at present the most sensitive modality that we have to evaluate the breast. The American College of Radiology, in its 2004 practice guidelines, has outlined the 12 current indications for breast MRI. This manuscript reviews and provides examples of each of these

How to improve your breast cancer program: Standardized reporting using the new American College of Radiology Breast Imaging-Reporting and Data System

In the USA, the use of the American College of Radiology Breast Imaging-Reporting and Data System (ACR BI-RADS) has served not only as a quality assurance tool and guide to standardizing breast imaging reports but has also improved communication between referring physicians, researchers, and patients. In fact, in the USA, the Mammography Quality Standards Act of 1997 requires that all mammograms be assigned a BI-RADS category based on the finding of most concern. In this manuscript, we aim to review the recommendations provided in the 4th edition of the ACR BI-RADS for mammography, USG, and MRI. We also review the major controversies surrounding the use of ACR BI-RADS

Doppler Ultrasound-Visible SignalMark Microspheres are Better Identified than HydroMARK® Clips in a Simulated Intraoperative Setting in Breast and Lung Tissue

BackgroundPreoperative breast and lung markers have significant drawbacks, including migration, patient discomfort, and scheduling difficulties. SignalMark is a novel localizer device with a unique signal on Doppler ultrasound.ObjectiveWe aimed to evaluate intraoperative identification of SignalMark microspheres compared with HydroMARK® clips. We also assessed the safety and efficacy of SignalMark in the lung.MethodsTwelve breasts of lactating pigs were injected with SignalMark or HydroMARK® by a breast radiologist, and subsequently identified using a standard ultrasound machine by three surgeons blinded to marker location. Time to identification of each marker was recorded, with a maximum allotted time of 300 s. To further demonstrate efficacy in lung parenchyma, a second cohort of pigs underwent lung injections.ResultsA total of eight SignalMark markers and four HydroMARK® clips were placed in pig breasts. Overall, the surgeons correctly identified SignalMark 95.8% of the time (n = 23/24) and HydroMARK® clips 41.7% of the time (n = 5/12) within 300 s (p < 0.001). The mean time to identification was significantly faster for SignalMark, at 80.8 ± 20.1 s, than for HydroMARK®, at 209.4 ± 35.2 s (p < 0.002). For the lung injections, all 10 SignalMark markers were visible on Doppler ultrasound at the time of placement, and at the 7- and 21-day time points.ConclusionsSurgeons identified SignalMark in significantly less time than HydroMARK® clips in a simulated intraoperative setting, and SignalMark was easily viewed in the lung. These results suggest that SignalMark is a feasible option for efficient intraoperative localization of non-palpable breast and lung tumors using ultrasound guidance

Breast mass classification in sonography with transfer learning using a deep convolutional neural network and color conversion.

PURPOSE: We propose a deep learning-based approach to breast mass classification in sonography and compare it with the assessment of four experienced radiologists employing breast imaging reporting and data system 4th edition lexicon and assessment protocol. METHODS: Several transfer learning techniques are employed to develop classifiers based on a set of 882 ultrasound images of breast masses. Additionally, we introduce the concept of a matching layer. The aim of this layer is to rescale pixel intensities of the grayscale ultrasound images and convert those images to red, green, blue (RGB) to more efficiently utilize the discriminative power of the convolutional neural network pretrained on the ImageNet dataset. We present how this conversion can be determined during fine-tuning using back-propagation. Next, we compare the performance of the transfer learning techniques with and without the color conversion. To show the usefulness of our approach, we additionally evaluate it using two publicly available datasets. RESULTS: Color conversion increased the areas under the receiver operating curve for each transfer learning method. For the better-performing approach utilizing the fine-tuning and the matching layer, the area under the curve was equal to 0.936 on a test set of 150 cases. The areas under the curves for the radiologists reading the same set of cases ranged from 0.806 to 0.882. In the case of the two separate datasets, utilizing the proposed approach we achieved areas under the curve of around 0.890. CONCLUSIONS: The concept of the matching layer is generalizable and can be used to improve the overall performance of the transfer learning techniques using deep convolutional neural networks. When fully developed as a clinical tool, the methods proposed in this paper have the potential to help radiologists with breast mass classification in ultrasound

Noncontrast MRI with advanced diffusion weighted imaging for breast cancer detection in a lactating woman.

Magnetic resonance imaging (MRI) is used for preoperative evaluation, high-risk screening, and other select indications for breast cancer. However, the interpretation of breast MR images in pregnant and lactating women is complicated by physiologic changes of the breast that may result in marked background enhancement. Breast MRI with contrast administration is contraindicated in pregnancy. Restriction spectrum imaging (RSI) is an advanced diffusion-weighted (DW)-MRI method that theoretically reflects signal from cells with high nuclear-to-cytoplasm ratio without gadolinium-based contrast. This report describes a case in which RSI notably increased tumor conspicuity in a lactating woman, compared to contrast-enhanced (CE)-MRI and conventional DW-MRI

Breast mass segmentation in ultrasound with selective kernel U-Net convolutional neural network

In this work, we propose a deep learning method for breast mass segmentation in ultrasound (US). Variations in breast mass size and image characteristics make the automatic segmentation difficult. To address this issue, we developed a selective kernel (SK) U-Net convolutional neural network. The aim of the SKs was to adjust network's receptive fields via an attention mechanism, and fuse feature maps extracted with dilated and conventional convolutions. The proposed method was developed and evaluated using US images collected from 882 breast masses. Moreover, we used three datasets of US images collected at different medical centers for testing (893 US images). On our test set of 150 US images, the SK-U-Net achieved mean Dice score of 0.826, and outperformed regular U-Net, Dice score of 0.778. When evaluated on three separate datasets, the proposed method yielded mean Dice scores ranging from 0.646 to 0.780. Additional fine-tuning of our better-performing model with data collected at different centers improved mean Dice scores by ~6%. SK-U-Net utilized both dilated and regular convolutions to process US images. We found strong correlation, Spearman's rank coefficient of 0.7, between the utilization of dilated convolutions and breast mass size in the case of network's expansion path. Our study shows the usefulness of deep learning methods for breast mass segmentation. SK-U-Net implementation and pre-trained weights can be found at github.com/mbyr/bus_seg