Assessment of Functional Differences in Malignant and Benign Breast Lesions and Improvement of Diagnostic Accuracy by Using US-guided Diffuse Optical Tomography in Conjunction with Conventional US

Quantitative Analysis of Shear Wave Elastography and US-Guided Diffuse Optical Tomography for Evaluating Biological Characteristics of Breast Cancer

RATIONALE AND OBJECTIVES

Shear Wave Elastography (SWE) and Ultrasound-guided Diffuse Optical Tomography (US-guided DOT) demonstrate promise in distinguishing between benign and malignant breast lesions. This study aims to assess the feasibility and correlation of SWE and US-guided DOT in evaluating the biological characteristics of breast cancer.

MATERIALS AND METHODS

A cohort of 235 breast cancer patients with 238 lesions, scheduled for surgery within one to three days, underwent B-mode ultrasound (US), US-guided DOT, and SWE. Parameters such as Total Hemoglobin Concentration (THC), Maximal Elasticity (Emax), Mean Elasticity (Emean), Standard Deviation of Elasticity (Esd), and Area Ratio were measured. Correlation with post-surgical pathology reports was examined to explore associations between THC, SWE Parameters, and pathology characteristics.

RESULTS

Lesions in patient groups with ER-, PR-, HER2 + , high Ki67, LVI+ , and ALN+ exhibited higher THC, Emax, and Esd compared to groups with ER+ , PR+ , HER2-, low Ki67, LVI-, and ALN-. The increase was seen in all grades of IDC-I to -III. THC significantly correlated with Smax (r = 0.340, P < 0.001), Emax (r = 0.339, P < 0.001), Emean (r = 0.201, P = 0.003), and Esd (r = 0.313, P < 0.001).

CONCLUSION

US-guided DOT and SWE prove valuable for the quantitative assessment of breast cancer's biological characteristics, with THC positively correlated with Emax, Emean, and Esd.

Improving diffuse optical tomography imaging quality using APU-Net: an attention-based physical U-Net model

Significance

Traditional diffuse optical tomography (DOT) reconstructions are hampered by image artifacts arising from factors such as DOT sources being closer to shallow lesions, poor optode-tissue coupling, tissue heterogeneity, and large high-contrast lesions lacking information in deeper regions (known as shadowing effect). Addressing these challenges is crucial for improving the quality of DOT images and obtaining robust lesion diagnosis.

Aim

We address the limitations of current DOT imaging reconstruction by introducing an attention-based U-Net (APU-Net) model to enhance the image quality of DOT reconstruction, ultimately improving lesion diagnostic accuracy.

Approach

We designed an APU-Net model incorporating a contextual transformer attention module to enhance DOT reconstruction. The model was trained on simulation and phantom data, focusing on challenges such as artifact-induced distortions and lesion-shadowing effects. The model was then evaluated by the clinical data.

Results

Transitioning from simulation and phantom data to clinical patients' data, our APU-Net model effectively reduced artifacts with an average artifact contrast decrease of 26.83% and improved image quality. In addition, statistical analyses revealed significant contrast improvements in depth profile with an average contrast increase of 20.28% and 45.31% for the second and third target layers, respectively. These results highlighted the efficacy of our approach in breast cancer diagnosis.

Conclusions

The APU-Net model improves the image quality of DOT reconstruction by reducing DOT image artifacts and improving the target depth profile.

Optimizing breast cancer diagnosis with photoacoustic imaging: An analysis of intratumoral and peritumoral radiomics

Background

The differentiation between benign and malignant breast tumors extends beyond morphological structures to encompass functional alterations within the nodules. The combination of photoacoustic (PA) imaging and radiomics unveils functional insights and intricate details that are imperceptible to the naked eye.

Purpose

This study aims to assess the efficacy of PA imaging in breast cancer radiomics, focusing on the impact of peritumoral region size on radiomic model accuracy.

Materials and methods

From January 2022 to November 2023, data were collected from 358 patients with breast nodules, diagnosed via PA/US examination and classified as BI-RADS 3-5. The study used the largest lesion dimension in PA images to define the region of interest, expanded by 2 mm, 5 mm, and 8 mm, for extracting radiomic features. Techniques from statistics and machine learning were applied for feature selection, and logistic regression classifiers were used to build radiomic models. These models integrated both intratumoral and peritumoral data, with logistic regressions identifying key predictive features.

Results

The developed nomogram, combining 5 mm peritumoral data with intratumoral and clinical features, showed superior diagnostic performance, achieving an AUC of 0.950 in the training cohort and 0.899 in validation. This model outperformed those based solely on clinical features or other radiomic methods, with the 5 mm peritumoral region proving most effective in identifying malignant nodules.

Conclusion

This research demonstrates the significant potential of PA imaging in breast cancer radiomics, especially the advantage of integrating 5 mm peritumoral with intratumoral features. This approach not only surpasses models based on clinical data but also underscores the importance of comprehensive radiomic analysis in accurately characterizing breast nodules.

Assessment of Oxygen Saturation in Breast Lesions Using Photoacoustic Imaging: Correlation With Benign and Malignant Disease

BACKGROUND

Hypoxia is a hallmark of breast cancer (BC). Photoacoustic (PA) imaging, based on the use of laser-generated ultrasound (US), can detect oxygen saturation (So2) in the tissues of breast lesion patients.

PURPOSE

To measure the oxygenation status of tissue in and on both sides of the lesion in breast lesion participants using a multimodal Photoacoustic/ultrasound (PA/US) imaging system and to determine the correlation between So2 measured by PA imaging and benign or malignant disease.

MATERIALS AND METHODS

Multimodal PA/US imaging and gray-scale US (GSUS) of breast lesion was performed in consecutive breast lesion participants imaged in the US Outpatient Clinic between 2022 and 2023. Dual-wavelength PA imaging was used to measure the So2 value inside the lesion and on both sides of the tissue, and to distinguish benign from malignant lesions based on the So2 value. The ability of So2 to distinguish benign from malignant breast lesions was evaluated by the receiver operating characteristic curve (ROC) and the De-Long test.

RESULTS

A total of 120 breast lesion participants (median age, 42.5 years) were included in the study. The malignant lesions exhibited lower So2 levels compared to benign lesions (malignant: 71.30%; benign: 83.81%; P < .01). Moreover, PA/US imaging demonstrates superior diagnostic results compared to GSUS, with an area under the curve (AUC) of 0.89 versus 0.70, sensitivity of 89.58% versus 85.42%, and specificity of 86.11% versus 55.56% at the So2 cut-off value of 78.85 (P < .001). The false positive rate in GSUS reduced by 30.75%, and the false negative rate diminished by 4.16% with PA /US diagnosis. Finally, the So2 on both sides tissues of malignant lesions are lower than that of benign lesions (P < .01).

CONCLUSION

PA imaging allows for the assessment of So2 within the lesions of breast lesion patients, thereby facilitating a superior distinction between benign and malignant lesions.

Semiconducting polymer dots for multifunctional integrated nanomedicine carriers

The expansion applications of semiconducting polymer dots (Pdots) among optical nanomaterial field have long posed a challenge for researchers, promoting their intelligent application in multifunctional nano-imaging systems and integrated nanomedicine carriers for diagnosis and treatment. Despite notable progress, several inadequacies still persist in the field of Pdots, including the development of simplified near-infrared (NIR) optical nanoprobes, elucidation of their inherent biological behavior, and integration of information processing and nanotechnology into biomedical applications. This review aims to comprehensively elucidate the current status of Pdots as a classical nanophotonic material by discussing its advantages and limitations in terms of biocompatibility, adaptability to microenvironments in vivo, etc. Multifunctional integration and surface chemistry play crucial roles in realizing the intelligent application of Pdots. Information visualization based on their optical and physicochemical properties is pivotal for achieving detection, sensing, and labeling probes. Therefore, we have refined the underlying mechanisms and constructed multiple comprehensive original mechanism summaries to establish a benchmark. Additionally, we have explored the cross-linking interactions between Pdots and nanomedicine, potential yet complete biological metabolic pathways, future research directions, and innovative solutions for integrating diagnosis and treatment strategies. This review presents the possible expectations and valuable insights for advancing Pdots, specifically from chemical, medical, and photophysical practitioners' standpoints.

Real-time breast lesion classification combining diffuse optical tomography frequency domain data and BI-RADS assessment

Ultrasound (US)-guided diffuse optical tomography (DOT) has demonstrated potential for breast cancer diagnosis, in which real-time or near real-time diagnosis with high accuracy is desired. However, DOT's relatively slow data processing and image reconstruction speeds have hindered real-time diagnosis. Here, we propose a real-time classification scheme that combines US breast imaging reporting and data system (BI-RADS) readings and DOT frequency domain measurements. A convolutional neural network is trained to generate malignancy probability scores from DOT measurements. Subsequently, these scores are integrated with BI-RADS assessments using a support vector machine classifier, which then provides the final diagnostic output. An area under the receiver operating characteristic curve of 0.978 is achieved in distinguishing between benign and malignant breast lesions in patient data without image reconstruction.

Automated pipeline for breast cancer diagnosis using US assisted diffuse optical tomography

Ultrasound (US)-guided diffuse optical tomography (DOT) is a portable and non-invasive imaging modality for breast cancer diagnosis and treatment response monitoring. However, DOT data pre-processing and imaging reconstruction often require labor intensive manual processing which hampers real-time diagnosis. In this study, we aim at providing an automated US-assisted DOT pre-processing, imaging and diagnosis pipeline to achieve near real-time diagnosis. We have developed an automated DOT pre-processing method including motion detection, mismatch classification using deep-learning approach, and outlier removal. US-lesion information needed for DOT reconstruction was extracted by a semi-automated lesion segmentation approach combined with a US reading algorithm. A deep learning model was used to evaluate the quality of the reconstructed DOT images and a two-step deep-learning model developed earlier is implemented to provide final diagnosis based on US imaging features and DOT measurements and imaging results. The presented US-assisted DOT pipeline accurately processed the DOT measurements and reconstruction and reduced the procedure time to 2 to 3 minutes while maintained a comparable classification result with manually processed dataset.

Optical Breast Imaging: A Review of Physical Principles, Technologies, and Clinical Applications

Optical imaging involves the propagation of light through tissue. Current optical breast imaging technologies, including diffuse optical spectroscopy, diffuse optical tomography, and photoacoustic imaging, capitalize on the selective absorption of light in the near-infrared spectrum by deoxygenated and oxygenated hemoglobin. They provide information on the morphological and functional characteristics of different tissues based on their varied interactions with light, including physiologic information on lesion vascular content and anatomic information on tissue vascularity. Fluorescent contrast agents, such as indocyanine green, are used to visualize specific tissues, molecules, or proteins depending on how and where the agent accumulates. In this review, we describe the physical principles, spectrum of technologies, and clinical applications of the most common optical systems currently being used or developed for breast imaging. Most notably, US co-registered photoacoustic imaging and US-guided diffuse optical tomography have demonstrated efficacy in differentiating benign from malignant breast masses, thereby improving the specificity of diagnostic imaging. Diffuse optical tomography and diffuse optical spectroscopy have shown promise in assessing treatment response to preoperative systemic therapy, and photoacoustic imaging and diffuse optical tomography may help predict tumor phenotype. Lastly, fluorescent imaging using indocyanine green dye performs comparably to radioisotope mapping of sentinel lymph nodes and appears to improve the outcomes of autologous tissue flap breast reconstruction.

FDU-Net: Deep Learning-Based Three-Dimensional Diffuse Optical Image Reconstruction

Near-infrared diffuse optical tomography (DOT) is a promising functional modality for breast cancer imaging; however, the clinical translation of DOT is hampered by technical limitations. Specifically, conventional finite element method (FEM)-based optical image reconstruction approaches are time-consuming and ineffective in recovering full lesion contrast. To address this, we developed a deep learning-based reconstruction model (FDU-Net) comprised of a Fully connected subnet, followed by a convolutional encoder-Decoder subnet, and a U-Net for fast, end-to-end 3D DOT image reconstruction. The FDU-Net was trained on digital phantoms that include randomly located singular spherical inclusions of various sizes and contrasts. Reconstruction performance was evaluated in 400 simulated cases with realistic noise profiles for the FDU-Net and conventional FEM approaches. Our results show that the overall quality of images reconstructed by FDU-Net is significantly improved compared to FEM-based methods and a previously proposed deep-learning network. Importantly, once trained, FDU-Net demonstrates substantially better capability to recover true inclusion contrast and location without using any inclusion information during reconstruction. The model was also generalizable to multi-focal and irregularly shaped inclusions unseen during training. Finally, FDU-Net, trained on simulated data, could successfully reconstruct a breast tumor from a real patient measurement. Overall, our deep learning-based approach demonstrates marked superiority over the conventional DOT image reconstruction methods while also offering over four orders of magnitude acceleration in computational time. Once adapted to the clinical breast imaging workflow, FDU-Net has the potential to provide real-time accurate lesion characterization by DOT to assist the clinical diagnosis and management of breast cancer.

Two-stage classification strategy for breast cancer diagnosis using ultrasound-guided diffuse optical tomography and deep learning

Significance

Ultrasound (US)-guided diffuse optical tomography (DOT) has demonstrated great potential for breast cancer diagnosis in which real-time or near real-time diagnosis with high accuracy is desired.

Aim

We aim to use US-guided DOT to achieve an automated, fast, and accurate classification of breast lesions.

Approach

We propose a two-stage classification strategy with deep learning. In the first stage, US images and histograms created from DOT perturbation measurements are combined to predict benign lesions. Then the non-benign suspicious lesions are passed through to the second stage, which combine US image features, DOT histogram features, and 3D DOT reconstructed images for final diagnosis.

Results

The first stage alone identified 73.0% of benign cases without image reconstruction. In distinguishing between benign and malignant breast lesions in patient data, the two-stage classification approach achieved an area under the receiver operating characteristic curve of 0.946, outperforming the diagnoses of all single-modality models and of a single-stage classification model that combines all US images, DOT histogram, and imaging features.

Conclusions

The proposed two-stage classification strategy achieves better classification accuracy than single-modality-only models and a single-stage classification model that combines all features. It can potentially distinguish breast cancers from benign lesions in near real-time.

Fusion deep learning approach combining diffuse optical tomography and ultrasound for improving breast cancer classification

Diffuse optical tomography (DOT) is a promising technique that provides functional information related to tumor angiogenesis. However, reconstructing the DOT function map of a breast lesion is an ill-posed and underdetermined inverse process. A co-registered ultrasound (US) system that provides structural information about the breast lesion can improve the localization and accuracy of DOT reconstruction. Additionally, the well-known US characteristics of benign and malignant breast lesions can further improve cancer diagnosis based on DOT alone. Inspired by a fusion model deep learning approach, we combined US features extracted by a modified VGG-11 network with images reconstructed from a DOT deep learning auto-encoder-based model to form a new neural network for breast cancer diagnosis. The combined neural network model was trained with simulation data and fine-tuned with clinical data: it achieved an AUC of 0.931 (95% CI: 0.919-0.943), superior to those achieved using US images alone (0.860) or DOT images alone (0.842).

Optoacoustic Imaging With Decision Support for Differentiation of Benign and Malignant Breast Masses: A 15-Reader Retrospective Study

BACKGROUND. Overlap in ultrasound features of benign and malignant breast masses yields high rates of false-positive interpretations and benign biopsy results. Optoacoustic imaging is an ultrasound-based functional imaging technique that can increase specificity. OBJECTIVE. The purpose of this study was to compare specificity at fixed sensitivity of ultrasound images alone and of fused ultrasound and optoacoustic images evaluated with machine learning-based decision support tool (DST) assistance. METHODS. This retrospective Reader-02 study included 480 patients (mean age, 49.9 years) with 480 breast masses (180 malignant, 300 benign) that had been classified as BI-RADS category 3-5 on the basis of conventional gray-scale ultrasound findings. The patients were selected by stratified random sampling from the earlier prospective 16-site Pioneer-01 study. For that study, masses were further evaluated by ultrasound alone followed by fused ultrasound and optoacoustic imaging between December 2012 and September 2015. For the current study, 15 readers independently reviewed the previously acquired images after training in optoacoustic imaging interpretation. Readers first assigned probability of malignancy (POM) on the basis of clinical history, mammographic findings, and conventional ultrasound findings. Readers then evaluated fused ultrasound and optoacoustic images, assigned scores for ultrasound and optoacoustic imaging features, and viewed a POM prediction score derived by a machine learning-based DST before issuing final POM. Individual and mean specificities at fixed sensitivity of 98% and partial AUC (pAUC) (95-100% sensitivity) were calculated. RESULTS. Averaged across all readers, specificity at fixed sensitivity of 98% was significantly higher for fused ultrasound and optoacoustic imaging with DST assistance than for ultrasound alone (47.2% vs 38.2%; p = .03). Across all readers, pAUC was higher (p < .001) for fused ultrasound and optoacoustic imaging with DST assistance (0.024 [95% CI, 0.023-0.026]) than for ultrasound alone (0.021 [95% CI, 0.019-0.022]). Better performance using fused ultrasound and optoacoustic imaging with DST assistance than using ultrasound alone was observed for 14 of 15 readers for specificity at fixed sensitivity and for 15 of 15 readers for pAUC. CONCLUSION. Fused ultrasound and optoacoustic imaging with DST assistance had significantly higher specificity at fixed sensitivity than did conventional ultrasound alone. CLINICAL IMPACT. Optoacoustic imaging, integrated with reader training and DST assistance, may help reduce the frequency of biopsy of benign breast masses.

Method to improve the localization accuracy and contrast recovery of lesions in separately acquired X-ray and diffuse optical tomographic breast imaging

Near-infrared diffuse optical tomography (DOT) has the potential to improve the accuracy of breast cancer diagnosis and aid in monitoring the response of breast tumors to chemotherapy by providing hemoglobin-based functional imaging. The use of structural lesion priors derived from clinical breast imaging methods, such as mammography, can improve recovery of tumor optical contrast; however, accurate lesion prior placement is essential to take full advantage of prior-guided DOT image reconstruction. Simultaneous optical and anatomical imaging may not always be possible or desired, which can make the accurate registration of the lesion prior challenging. In this paper, we present a three-step lesion prior scanning approach to facilitate improved accuracy in lesion localization based on the optical contrast quantified by the total hemoglobin concentration (HbT) for non-simultaneous multimodal DOT and digital breast tomosynthesis (DBT) imaging. In three challenging breast cancer patient cases, where no clear optical contrast was present initially, we have demonstrated consistent improvement in the recovered HbT lesion contrast by utilizing this method.

Difference imaging from single measurements in diffuse optical tomography: a deep learning approach

SIGNIFICANCE

"Difference imaging," which reconstructs target optical properties using measurements with and without target information, is often used in diffuse optical tomography (DOT) in vivo imaging. However, taking additional reference measurements is time consuming, and mismatches between the target medium and the reference medium can cause inaccurate reconstruction.

AIM

We aim to streamline the data acquisition and mitigate the mismatch problems in DOT difference imaging using a deep learning-based approach to generate data from target measurements only.

APPROACH

We train an artificial neural network to output data for difference imaging from target measurements only. The model is trained and validated on simulation data and tested with simulations, phantom experiments, and clinical data from 56 patients with breast lesions.

RESULTS

The proposed method has comparable performance to the traditional approach using measurements without mismatch between the target side and the reference side, and it outperforms the traditional approach using measurements when there is a mismatch. It also improves the target-to-artifact ratio and lesion localization in patient data.

CONCLUSIONS

The proposed method can simplify the data acquisition procedure, mitigate mismatch problems, and improve reconstructed image quality in DOT difference imaging.

Image processing improvements afford second-generation handheld optoacoustic imaging of breast cancer patients

BACKGROUND

Since the initial breast transillumination almost a century ago, breast cancer imaging using light has been considered in different implementations aiming to improve diagnostics, minimize the number of available biopsies, or monitor treatment. However, due to strong photon scattering, conventional optical imaging yields low resolution images, challenging quantification and interpretation. Optoacoustic imaging addresses the scattering limitation and yields high-resolution visualization of optical contrast, offering great potential value for breast cancer imaging. Nevertheless, the image quality of experimental systems remains limited due to a number of factors, including signal attenuation with depth and partial view angle and motion effects, particularly in multi-wavelength measurements.

METHODS

We developed data analytics methods to improve the accuracy of handheld optoacoustic breast cancer imaging, yielding second-generation optoacoustic imaging performance operating in tandem with ultrasonography.

RESULTS

We produced the most advanced images yet with handheld optoacoustic examinations of the human breast and breast cancer, in terms of resolution and contrast. Using these advances, we examined optoacoustic markers of malignancy, including vasculature abnormalities, hypoxia, and inflammation, on images obtained from breast cancer patients.

CONCLUSIONS

We achieved a new level of quality for optoacoustic images from a handheld examination of the human breast, advancing the diagnostic and theranostic potential of the hybrid optoacoustic-ultrasound (OPUS) examination over routine ultrasonography.

Prospective assessment of adjunctive ultrasound-guided diffuse optical tomography in women undergoing breast biopsy: Impact on BI-RADS assessments

PURPOSE

To assess the impact of adjunctive ultrasound guided diffuse optical tomography (US-guided DOT) on BI-RADS assessment in women undergoing US-guided breast biopsy.

METHOD

This prospective study enrolled women referred for US-guided breast biopsy between 3/5/2019 and 3/19/2020. Participants underwent US-guided DOT immediately before biopsy. The US-guided DOT acquisition generated average maximum total hemoglobin (HbT) spatial maps and quantitative HbT values. Four radiologists blinded to histopathology assessed conventional imaging (CI) to assign a CI BI-RADS assessment and then integrated DOT information in assigning a CI&DOT BI-RADS assessment. HbT was compared between benign and malignant lesions using an ANOVA test and Tukey's test. Benign biopsies were tabulated, deeming BI-RADS ≥ 4A as positive. Reader agreement was assessed.

RESULTS

Among 61 included women (mean age 48 years), biopsy demonstrated 15 (24.6%) malignant and 46 (75.4%) benign lesions. Mean HbT was 55.3 ± 22.6 µM in benign lesions versus 85.4 ± 15.6 µM in cancers (p < .001). HbT threshold of 78.5 µM achieved sensitivity 80% (12/15) and specificity 89% (41/46) for malignancy. Across readers and patients, 197 pairs of CI BI-RADS and CI&DOT BI-RADS assessments were assigned. Adjunctive US-guided DOT achieved a net decrease in 23.5% (31/132) of suspicious (CI BI-RADS ≥ 4A) assessments of benign lesions (34 correct downgrades and 3 incorrect upgrades). 38.3% (31/81) of 4A assessments were appropriately downgraded. No cancer was downgraded to a non-actionable assessment. Interreader agreement analysis demonstrated kappa = 0.48-0.53 for CI BI-RADS and kappa = 0.28-0.44 for CI&DOT BI-RADS.

CONCLUSIONS

Integration of US-guided DOT information achieved a 23.5% reduction in suspicious BI-RADS assessments for benign lesions. Larger studies are warranted, with attention to improved reader agreement.

Deep learning-based method to accurately estimate breast tissue optical properties in the presence of the chest wall

SIGNIFICANCE

In general, image reconstruction methods used in diffuse optical tomography (DOT) are based on diffusion approximation, and they consider the breast tissue as a homogenous, semi-infinite medium. However, the semi-infinite medium assumption used in DOT reconstruction is not valid when the chest wall is underneath the breast tissue.

AIM

We aim to reduce the chest wall's effect on the estimated average optical properties of breast tissue and obtain accurate forward model for DOT reconstruction.

APPROACH

We propose a deep learning-based neural network approach where a convolution neural network (CNN) is trained to simultaneously obtain accurate optical property values for both the breast tissue and the chest wall.

RESULTS

The CNN model shows great promise in reducing errors in estimating the optical properties of the breast tissue in the presence of a shallow chest wall. For patient data, the CNN model predicted the breast tissue optical absorption coefficient, which was independent of chest wall depth.

CONCLUSIONS

Our proposed method can be readily used in DOT and diffuse spectroscopy measurements to improve the accuracy of estimated tissue optical properties.

Machine learning model with physical constraints for diffuse optical tomography

A machine learning model with physical constraints (ML-PC) is introduced to perform diffuse optical tomography (DOT) reconstruction. DOT reconstruction is an ill-posed and under-determined problem, and its quality suffers by model mismatches, complex boundary conditions, tissue-probe contact, noise etc. Here, for the first time, we combine ultrasound-guided DOT with ML to facilitate DOT reconstruction. Our method has two key components: (i) a neural network based on auto-encoder is adopted for DOT reconstruction, and (ii) physical constraints are implemented to achieve accurate reconstruction. Both qualitative and quantitative results demonstrate that the accuracy of the proposed method surpasses that of existing models. In a phantom study, compared with the Born conjugate gradient descent (Born-CGD) reconstruction method, the ML-PC method decreases the mean percentage error of the reconstructed maximum absorption coefficient from 16.41% to 13.4% for high contrast phantoms and from 23.42% to 9.06% for low contrast phantoms, with improved depth distribution of the target absorption maps. In a clinical study, better contrast was obtained between malignant and benign breast lesions, with the ratio of the medians of the maximum absorption coefficient improved from 1.63 to 2.22.

Ultrasound segmentation-guided edge artifact reduction in diffuse optical tomography using connected component analysis

Ultrasound (US)-guided diffuse optical tomography (DOT) has demonstrated potential value for breast cancer diagnosis and treatment response assessment. However, in clinical use, the chest wall, poor probe-tissue contact, and tissue heterogeneity can all cause image artifacts. These image artifacts, appearing commonly as hot spots in the non-lesion regions (edge artifacts), can decrease the reconstruction accuracy and cause misinterpretation of lesion images. Here we introduce an iterative, connected component analysis-based image artifact reduction algorithm. A convolutional neural network (CNN) is used to segment co-registered US images to extract the lesion location and size to guide the artifact reduction. We demonstrate its performance using Monte Carlo simulations on VICTRE digital breast phantoms and breast patient images. In simulated tissue mismatch models, this algorithm successfully reduces edge artifacts without significantly changing the reconstructed target absorption coefficients. With clinical data it improves the optical contrast between malignant and benign groups, from 1.55 without artifact reduction to 1.91 with artifact reduction. The proposed algorithm has a broad range of applications in other modality-guided DOT imaging.

Quo Vadis, Nanoparticle-Enabled In Vivo Fluorescence Imaging?

The exciting advancements that we are currently witnessing in terms of novel materials and synthesis approaches are leading to the development of colloidal nanoparticles (NPs) with increasingly greater tunable properties. We have now reached a point where it is possible to synthesize colloidal NPs with functionalities tailored to specific societal demands. The impact of this new wave of colloidal NPs has been especially important in the field of biomedicine. In that vein, luminescent NPs with improved brightness and near-infrared working capabilities have turned out to be optimal optical probes that are capable of fast and high-resolution in vivo imaging. However, luminescent NPs have thus far only reached a limited portion of their potential. Although we believe that the best is yet to come, the future might not be as bright as some of us think (and have hoped!). In particular, translation of NP-based fluorescence imaging from preclinical studies to clinics is not straightforward. In this Perspective, we provide a critical assessment and highlight promising research avenues based on the latest advances in the fields of luminescent NPs and imaging technologies. The disillusioned outlook we proffer herein might sound pessimistic at first, but we consider it necessary to avoid pursuing "pipe dreams" and redirect the efforts toward achievable-yet ambitious-goals.

Effect and correction of optode coupling errors in breast imaging using diffuse optical tomography

In diffuse optical tomography (DOT) and spectroscopy (DOS) using handheld probes, tissue curvature can cause bad fiber-to-tissue contact. Understanding and minimizing image artifacts caused by these coupling errors would significantly improve DOT and DOS image quality. In this work, we utilized Monte Carlo simulations and experiments with gelatin-Intralipid phantoms to systematically study the influence of source or detector (optode) coupling errors. Optode coupling errors can increase the amplitude and decrease the phase of the measured diffuse reflectance, creating artifacts in the reconstructed absorption maps, such as hot spots on the edges. We propose an outlier removal algorithm that can correct these image artifacts, and we demonstrate its performance using simulations, phantom experiments, and breast patient data acquired with bad probe contact due to a dense or small breast. Further, we designed and implemented a new resistance-type thin-film force sensor array that provides real-time optode coupling feedback and guides the outlier removal to minimize optode coupling errors. Our approaches and study results have significant implications for reducing image artifacts arising from handheld probes, which are commonly used with mobile and wearable DOT and DOS devices.

A review of optical breast imaging: Multi-modality systems for breast cancer diagnosis

This review of optical breast imaging describes basic physical and system principles and summarizes technological evolution with a focus on multi-modality platforms and recent clinical trial results. Ultrasound-guided diffuse optical tomography and co-registered ultrasound and photoacoustic imaging systems are emphasized as models of state of the art optical technology that are most conducive to clinical translation.

Target depth-regularized reconstruction in diffuse optical tomography using ultrasound segmentation as prior information

Ultrasound (US)-guided diffuse optical tomography (DOT) is a promising non-invasive functional imaging technique for diagnosing breast cancer and monitoring breast cancer treatment response. However, because larger lesions are highly absorbing, reconstructions of these lesions using reflection geometry may exhibit light shadowing, which leads to inaccurate quantification of their deeper portions. Here we propose a depth-regularized reconstruction algorithm combined with a semi-automated interactive neural network (CNN) for depth-dependent reconstruction of absorption distribution. CNN segments co-registered US to extract both spatial and depth priors, and the depth-regularized algorithm incorporates these parameters into the reconstruction. Through simulation and phantom data, the proposed algorithm is shown to significantly improve the depth distribution of reconstructed absorption maps of large targets. Evaluated with 26 patients with larger breast lesions, the algorithm shows 2.4 to 3 times improvement in the top-to-bottom reconstructed homogeneity of the absorption maps for these lesions.

Optimal breast cancer diagnostic strategy using combined ultrasound and diffuse optical tomography

Ultrasound (US)-guided near-infrared diffuse optical tomography (DOT) has demonstrated great potential as an adjunct breast cancer diagnosis tool to US imaging alone, especially in reducing unnecessary benign biopsies. However, DOT data processing and image reconstruction speeds remain slow compared to the real-time speed of US. Real-time or near real-time diagnosis with DOT is an important step toward the clinical translation of US-guided DOT. Here, to address this important need, we present a two-stage diagnostic strategy that is both computationally efficient and accurate. In the first stage, benign lesions are identified in near real-time by use of a random forest classifier acting on the DOT measurements and the radiologists' US diagnostic scores. Any lesions that cannot be reliably classified by the random forest classifier will be passed on to the second stage which begins with image reconstruction. Functional information from the reconstructed hemoglobin concentrations is employed by a Support Vector Machine (SVM) classifier for diagnosis at the end of the second stage. This two-step classification approach which combines both perturbation data and functional features, results in improved classification, as denoted by the receiver operating characteristic (ROC) curve. Using this two-step approach, the area under the ROC curve (AUC) is 0.937 ± 0.009, with a sensitivity of 91.4% and specificity of 85.7%. In comparison, using functional features and US score yields an AUC of 0.892 ± 0.027, with a sensitivity of 90.2% and specificity of 74.5%. Most notably, the specificity is increased by more than 10% due to the implementation of the random forest classifier.

Improving DOT reconstruction with a Born iterative method and US-guided sparse regularization

Ultrasound (US)-guided diffuse optical tomography (DOT) is a promising low-cost imaging technique for diagnosis and assessment of breast cancer. US-guided DOT is best implemented in reflection geometry, which can be co-registered with US pulse-echo imaging and also minimizes the tissue depth for adequate light penetration. However, due to intense light scattering, the DOT reconstruction problem is ill-posed. In this communication, we describe a new non-linear Born iterative reconstruction method with US-guided depth-dependent ℓ 1 sparse regularization for improving DOT reconstruction by incorporating a priori lesion depth and shape information from the co-registered US image. Our method iteratively solves the inverse problem by updating the photon-density wave using the finite difference method, computing the weight matrix based on Born approximation, and reconstructing the absorption map using the fast iterative shrinkage-thresholding optimization algorithm (FISTA). We validate our method using both phantom and patient data and compare the results with those using the first order linear Born method. Phantom experiments demonstrate that the non-linear Born method provides more accurate target absorption reconstruction and better resolution than the linear Born method. Clinical studies on 20 patients show that non-linear Born reconstructs more realistic tumor shapes than linear Born, and improves the malignant-to-benign lesion contrast ratio from 2.73 to 3.07 , which is a 12.5 % improvement. For lesions approximately more than 2.0 cm in diameter, the average malignant-to-benign lesion contrast ratio is increased from 2.68 to 3.31 , which is a 23.5 % improvement.

An Automated Preprocessing Method for Diffuse Optical Tomography to Improve Breast Cancer Diagnosis

The ultrasound-guided diffuse optical tomography is a noninvasive imaging technique for breast cancer diagnosis and treatment monitoring. The technique uses a handheld probe capable of providing measurements of multiple wavelengths in a few seconds. These measurements are used to estimate optical absorptions of lesions and calculate the total hemoglobin concentration. Any measurement errors caused by low signal to noise ratio data and/or movements during data acquisition would reduce the accuracy of reconstructed total hemoglobin concentration. In this article, we introduce an automated preprocessing method that combines data collected from multiple sets of lesion measurements of 4 optical wavelengths to detect and correct outliers in the perturbation. Two new measures of correlation between each pair of wavelength measurements and a wavelength consistency index of all reconstructed absorption maps are introduced. For phantom and patients' data without evidence of measurement errors, the correlation coefficient between each pair of wavelength measurements was above 0.6. However, for patients with measurement errors, the correlation coefficient was much lower. After applying the correction method to 18 patients' data with measurement errors, the correlation has improved and the wavelength consistency index is in the same range as the cases without wavelength-dependent measurement errors. The results show an improvement in classification of malignant and benign lesions.

Back-propagation neural network-based reconstruction algorithm for diffuse optical tomography

Diffuse optical tomography (DOT) is a promising noninvasive imaging modality and is capable of providing functional characteristics of biological tissue by quantifying optical parameters. The DOT image reconstruction is ill-posed and ill-conditioned, due to the highly diffusive nature of light propagation in biological tissues and limited boundary measurements. The widely used regularization technique for DOT image reconstruction is Tikhonov regularization, which tends to yield oversmoothed and low-quality images containing severe artifacts. It is necessary to accurately choose a regularization parameter for Tikhonov regularization. To overcome these limitations, we develop a noniterative reconstruction method, whereby optical properties are recovered based on a back-propagation neural network (BPNN). We train the parameters of BPNN before DOT image reconstruction based on a set of training data. DOT image reconstruction is achieved by implementing a single evaluation of the trained network. To demonstrate the performance of the proposed algorithm, we compare with the conventional Tikhonov regularization-based reconstruction method. The experimental results demonstrate that image quality and quantitative accuracy of reconstructed optical properties are significantly improved with the proposed algorithm.

Compact ultrasound-guided diffuse optical tomography system for breast cancer imaging

Near-infrared diffuse optical tomography (DOT) has demonstrated a great potential as an adjunct modality for differentiation of malignant and benign breast lesions and for monitoring treatment response in patients with locally advanced breast cancers. The path toward commercialization of DOT techniques depends upon the improvement of robustness and user-friendliness of this technique in hardware and software. In this study, we introduce our recently developed ultrasound-guided DOT system, which has been improved in system compactness, robustness, and user-friendliness by custom-designed electronics, automated data preprocessing, and implementation of a new two-step reconstruction algorithm. The system performance has been tested with several sets of solid and blood phantoms and the results show accuracy in reconstructed absorption coefficients as well as blood oxygen saturation. A clinical example of a breast cancer patient, who was undergoing neoadjuvant chemotherapy, is given to demonstrate the system performance.

Optoacoustic Breast Imaging: Imaging-Pathology Correlation of Optoacoustic Features in Benign and Malignant Breast Masses

OBJECTIVE

Optoacoustic ultrasound breast imaging is a fused anatomic and functional modality that shows morphologic features, as well as hemoglobin amount and relative oxygenation within and around breast masses. The purpose of this study is to investigate the positive predictive value (PPV) of optoacoustic ultrasound features in benign and malignant masses.

SUBJECTS AND METHODS

In this study, 92 masses assessed as BI-RADS category 3, 4, or 5 in 94 subjects were imaged with optoacoustic ultrasound. Each mass was scored by seven blinded independent readers according to three internal features in the tumor interior and two external features in its boundary zone and periphery. Mean and median optoacoustic ultrasound scores were compared with histologic findings for biopsied masses and nonbiopsied BI-RADS category 3 masses, which were considered benign if they were stable at 12-month follow-up. Statistical significance was analyzed using a two-sided Wilcoxon rank sum test with a 0.05 significance level.

RESULTS

Mean and median optoacoustic ultrasound scores for all individual internal and external features, as well as summed scores, were higher for malignant masses than for benign masses (p < 0.0001). High external scores, indicating increased hemoglobin and deoxygenation and abnormal vessel morphologic features in the tumor boundary zone and periphery, better distinguished benign from malignant masses than did high internal scores reflecting increased hemoglobin and deoxygenation within the tumor interior.

CONCLUSION

High optoacoustic ultrasound scores, particularly those based on external features in the boundary zone and periphery of breast masses, have high PPVs for malignancy and, conversely, low optoacoustic ultrasound scores have low PPV for malignancy. The functional component of optoacoustic ultrasound may help to overcome some of the limitations of morphologic overlap in the distinction of benign and malignant masses.

Weighting function effects in a direct regularization method for image-guided near-infrared spectral tomography of breast cancer

Structural image-guided near-infrared spectral tomography (NIRST) has been developed as a way to use diffuse NIR spectroscopy within the context of image-guided quantification of tissue spectral features. A direct regularization imaging (DRI) method for NIRST has the value of not requiring any image segmentation. Here, we present a comprehensive investigational study to analyze the impact of the weighting function implied when weighting the recovery of optical coefficients in DRI based NIRST. This was done using simulations, phantom and clinical patient exam data. Simulations where the true object is known indicate that changes to this weighting function can vary the contrast by 10%, the contrast to noise ratio by 20% and the full width half maximum (FWHM) by 30%. The results from phantoms and human images show that a linear inverse distance weighting function appears optimal, and that incorporation of this function can generally improve the recovered total hemoglobin contrast of the tumor to the normal surrounding tissue by more than 15% in human cases.

Neoplasm of uncertain behaviour of the breast-a retrospective study in a breast unit

Introduction

Breast lesions include a heterogeneous group of entities with variable clinical behaviour and morphological presentation, mostly classified as benign or malignant, with predictable behaviour. However, there are lesions with clinical, breast imaging and/or biopsy characteristics that do not clarify their nature. These lesions have an uncertain behaviour regarding their malignant potential at diagnosis.We intend to relate the preoperative diagnosis of neoplasm of uncertain behaviour of the breast (NUnBB) regarding the core needle biopsy and the histological result after excisional biopsy.

Methods

This is a retrospective study of patients submitted to local excision of breast lesions with a perioperative diagnosis of NUnBB, classified as 2383 at 'International Statistical Classification of Diseases and Related Health Problems' (ICD 9), between January 2007 and October 2016 in a breast unit.

Results

Ninety-two cases with the diagnosis of NUnBB were analysed: 91 females with a mean age of 59 ± 14 years. All were submitted to local excision of breast lesion as ambulatory surgery with the following histology: 64% benign, 3% malignant potential and 33% malignant. Of those who presented malignant results, 69% underwent a surgical re-intervention for local control of the disease.

Discussion

Regarding the considerable number of malignant lesions at final histology and the high percentage of which are re-operated, NUnBB should be treated with the same priority as a confirmed malignant neoplasm and whenever possible, using the most appropriate surgical technique.

Identifying an early treatment window for predicting breast cancer response to neoadjuvant chemotherapy using immunohistopathology and hemoglobin parameters

BACKGROUND

Breast cancer pathologic complete response (pCR) to neoadjuvant chemotherapy (NAC) varies with tumor subtype. The purpose of this study was to identify an early treatment window for predicting pCR based on tumor subtype, pretreatment total hemoglobin (tHb) level, and early changes in tHb following NAC.

METHODS

Twenty-two patients (mean age 56 years, range 34-74 years) were assessed using a near-infrared imager coupled with an Ultrasound system prior to treatment, 7 days after the first treatment, at the end of each of the first three cycles, and before their definitive surgery. Pathologic responses were dichotomized by the Miller-Payne system. Tumor vascularity was assessed from tHb; vascularity changes during NAC were assessed from a percentage tHb normalized to the pretreatment level (%tHb). After training the logistic prediction models using the previous study data, we assessed the early treatment window for predicting pathological response according to their tumor subtype (human epidermal growth factor receptor 2 (HER2), estrogen receptor (ER), triple-negative (TN)) based on tHb, and %tHb measured at different cycles and evaluated by the area under the receiver operating characteristic (ROC) curve (AUC).

RESULTS

In the new study cohort, maximum pretreatment tHb and %tHb changes after cycles 1, 2, and 3 were significantly higher in responder Miller-Payne 4-5 tumors (n = 13) than non-or partial responder Miller-Payne 1-3 tumors (n = 9). However, no significance was found at day 7. The AUC of the predictive power of pretreatment tHb in the cohort was 0.75, which was similar to the performance of the HER2 subtype as a single predictor (AUC of 0.78). A greater predictive power of pretreatment tHb was found within each subtype, with AUCs of 0.88, 0.69, and 0.72, in the HER2, ER, and TN subtypes, respectively. Using pretreatment tHb and cycle 1 %tHb, AUC reached 0.96, 0.91, and 0.90 in HER2, ER, and TN subtypes, respectively, and 0.95 regardless of subtype. Additional cycle 2 %tHb measurements moderately improved prediction for the HER2 subtype but did not improve prediction for the ER and TN subtypes.

CONCLUSIONS

By combining tumor subtypes with tHb, we predicted the pCR of breast cancer to NAC before treatment. Prediction accuracy can be significantly improved by incorporating cycle 1 and 2 %tHb for the HER2 subtype and cycle 1 %tHb for the ER and TN subtypes.

TRIAL REGISTRATION

ClinicalTrials.gov, NCT02092636 . Registered in March 2014.

US-guided Diffuse Optical Tomography: Clinicopathological Features Affect Total Hemoglobin Concentration in Breast Cancer

PURPOSE: To investigate breast cancers total hemoglobin concentration (THC) characteristics and its association with clinical pathologic findings. MATERIALS AND METHODS: The study was approved by the institutional review board and all patients provided written informed consent. 447 breast cancer patients, totally 455 lesions were included in our study. The size and THC of breast lesions were measured by conventional ultrasound (US) and US-guided Diffuse Optical Tomography (DOT) 1–2 days before surgery. Clinical and pathology information of patients was collected. RESULT: The average THC values of ER- or PR- lesions were significantly higher than the positive ones (P = .005 and P = .01,respectively); The average THC values of axillar LN+ or LVI+ were higher than the negative ones (P = .042 and P = .043, respectively). No significant THC difference was found in groups of infiltrating vs. non-infiltrating, HER2+ vs. HER2-, Ki67 high vs. Ki67 low, and different menstrual phases (P = .457, P = .917, P = .417, P = .213, respectively).The incidence ages and the lesion-nipple distances of T3 patients were lower than that of T1 and T2 (P < .001 and P < .001 respectively). The THC values and Ki67 indexes of T2 and T3 lesions were similar, but were higher than that of the T1 group (P < =0.001 and P = .006, respectively). CONCLUSION: Clinicopathological features of breast cancer, such as ER and PR status, axillary lymph node metastasis, lymphovascular invasion, correlate with THC values. Furthermore, the Ki67 indexes can be predicted using tumor size and THC, useful for pre-surgical evaluation of cancer biology and real-time, non-invasive monitoring of NAC efficacy.

Diffuse optical tomography using semiautomated coregistered ultrasound measurements

Diffuse optical tomography (DOT) has demonstrated huge potential in breast cancer diagnosis and treatment monitoring. DOT image reconstruction guided by ultrasound (US) improves the diffused light localization and lesion reconstruction accuracy. However, DOT reconstruction depends on tumor geometry provided by coregistered US. Experienced operators can manually measure these lesion parameters; however, training and measurement time are needed. The wide clinical use of this technique depends on its robustness and faster imaging reconstruction capability. This article introduces a semiautomated procedure that automatically extracts lesion information from US images and incorporates it into the optical reconstruction. An adaptive threshold-based image segmentation is used to obtain tumor boundaries. For some US images, posterior shadow can extend to the chest wall and make the detection of deeper lesion boundary difficult. This problem can be solved using a Hough transform. The proposed procedure was validated from data of 20 patients. Optical reconstruction results using the proposed procedure were compared with those reconstructed using extracted tumor information from an experienced user. Mean optical absorption obtained from manual measurement was 0.21±0.06  cm-1 for malignant and 0.12±0.06  cm-1 for benign cases, whereas for the proposed method it was 0.24±0.08  cm-1 and 0.12±0.05  cm-1, respectively.

Two step imaging reconstruction using truncated pseudoinverse as a preliminary estimate in ultrasound guided diffuse optical tomography

Due to the correlated nature of diffused light, the problem of reconstructing optical properties using diffuse optical tomography (DOT) is ill-posed. US-, MRI- or x-ray-guided DOT approaches can reduce the total number of parameters to be estimated and improve optical reconstruction accuracy. However, when the target volume is large, the number of parameters to estimate can exceed the number of measurements, resulting in an underdetermined imaging model. In such cases, accurate image reconstruction is difficult and regularization methods should be employed to obtain a useful solution. In this manuscript, a simple two-step reconstruction method that can produce useful image estimates in DOT is proposed and investigated. In the first step, a truncated Moore-Penrose Pseudoinverse solution is computed to obtain a preliminary estimate of the image that can be reliably determined from the measured data; subsequently, this preliminary estimate is incorporated into the design of a penalized least squares estimator that is employed to compute the final image estimate. By use of phantom data, the proposed method was demonstrated to yield more accurate images than those produced by conventional reconstruction methods. The method was also evaluated with clinical data that included 10 benign and 10 malignant cases. The capability of reconstructing high contrast malignant lesions was demonstrated to be improved by use of the proposed method.

A Pivotal Study of Optoacoustic Imaging to Diagnose Benign and Malignant Breast Masses: A New Evaluation Tool for Radiologists

Purpose To compare the diagnostic utility of an investigational optoacoustic imaging device that fuses laser optical imaging (OA) with grayscale ultrasonography (US) to grayscale US alone in differentiating benign and malignant breast masses. Materials and Methods This prospective, 16-site study of 2105 women (study period: 12/21/2012 to 9/9/2015) compared Breast Imaging Reporting and Data System (BI-RADS) categories assigned by seven blinded independent readers to benign and malignant breast masses using OA/US versus US alone. BI-RADS 3, 4, or 5 masses assessed at diagnostic US with biopsy-proven histologic findings and BI-RADS 3 masses stable at 12 months were eligible. Independent readers reviewed US images obtained with the OA/US device, assigned a probability of malignancy (POM) and BI-RADS category, and locked results. The same independent readers then reviewed OA/US images, scored OA features, and assigned OA/US POM and a BI-RADS category. Specificity and sensitivity were calculated for US and OA/US. Benign and malignant mass upgrade and downgrade rates, positive and negative predictive values, and positive and negative likelihood ratios were compared. Results Of 2105 consented subjects with 2191 masses, 100 subjects (103 masses) were analyzed separately as a training population and excluded. An additional 202 subjects (210 masses) were excluded due to technical failures or incomplete imaging, 72 subjects (78 masses) due to protocol deviations, and 41 subjects (43 masses) due to high-risk histologic results. Of 1690 subjects with 1757 masses (1079 [61.4%] benign and 678 [38.6%] malignant masses), OA/US downgraded 40.8% (3078/7535) of benign mass reads, with a specificity of 43.0% (3242/7538, 99% confidence interval [CI]: 40.4%, 45.7%) for OA/US versus 28.1% (2120/7543, 99% CI: 25.8%, 30.5%) for the internal US of the OA/US device. OA/US exceeded US in specificity by 14.9% (P < .0001; 99% CI: 12.9, 16.9%). Sensitivity for biopsied malignant masses was 96.0% (4553/4745, 99% CI: 94.5%, 97.0%) for OA/US and 98.6% (4680/4746, 99% CI: 97.8%, 99.1%) for US (P < .0001). The negative likelihood ratio of 0.094 for OA/US indicates a negative examination can reduce a maximum US-assigned pretest probability of 17.8% (low BI-RADS 4B) to a posttest probability of 2% (BI-RADS 3). Conclusion OA/US increases the specificity of breast mass assessment compared with the device internal grayscale US alone. Online supplemental material is available for this article. ^© RSNA, 2017.

Addition of T2-guided optical tomography improves noncontrast breast magnetic resonance imaging diagnosis

Background

While dynamic contrast-enhanced magnetic resonance imaging (DCE MRI) is recognized as the most sensitive examination for breast cancer detection, it has a substantial false positive rate and gadolinium (Gd) contrast agents are not universally well tolerated. As a result, alternatives to diagnosing breast cancer based on endogenous contrast are of growing interest. In this study, endogenous near-infrared spectral tomography (NIRST) guided by T2 MRI was evaluated to explore whether the combined imaging modality, which does not require contrast injection or involve ionizing radiation, can achieve acceptable diagnostic performance.

Methods

Twenty-four subjects—16 with pathologically confirmed malignancy and 8 with benign abnormalities—were simultaneously imaged with MRI and NIRST prior to definitive pathological diagnosis. MRIs were evaluated independently by three breast radiologists blinded to the pathological results. Optical image reconstructions were constrained by grayscale values in the T2 MRI. MRI and NIRST images were used, alone and in combination, to estimate the diagnostic performance of the data. Outcomes were compared to DCE results.

Results

Sensitivity, specificity, accuracy, and area under the curve (AUC) of noncontrast MRI when combined with T2-guided NIRST were 94%, 100%, 96%, and 0.95, respectively, whereas these values were 94%, 63%, 88%, and 0.81 for DCE MRI alone, and 88%, 88%, 88%, and 0.94 when DCE-guided NIRST was added.

Conclusion

In this study, the overall accuracy of imaging diagnosis improved to 96% when T2-guided NIRST was added to noncontrast MRI alone, relative to 88% for DCE MRI, suggesting that similar or better diagnostic accuracy can be achieved without requiring a contrast agent.

Electronic supplementary material

The online version of this article (doi:10.1186/s13058-017-0902-x) contains supplementary material, which is available to authorized users.

Multimodal breast cancer imaging using coregistered dynamic diffuse optical tomography and digital breast tomosynthesis

Diffuse optical tomography (DOT) is emerging as a noninvasive functional imaging method for breast cancer diagnosis and neoadjuvant chemotherapy monitoring. In particular, the multimodal approach of combining DOT with x-ray digital breast tomosynthesis (DBT) is especially synergistic as DBT prior information can be used to enhance the DOT reconstruction. DOT, in turn, provides a functional information overlay onto the mammographic images, increasing sensitivity and specificity to cancer pathology. We describe a dynamic DOT apparatus designed for tight integration with commercial DBT scanners and providing a fast (up to 1 Hz) image acquisition rate to enable tracking hemodynamic changes induced by the mammographic breast compression. The system integrates 96 continuous-wave and 24 frequency-domain source locations as well as 32 continuous wave and 20 frequency-domain detection locations into low-profile plastic plates that can easily mate to the DBT compression paddle and x-ray detector cover, respectively. We demonstrate system performance using static and dynamic tissue-like phantoms as well as in vivo images acquired from the pool of patients recalled for breast biopsies at the Massachusetts General Hospital Breast Imaging Division.

Improving breast cancer diagnosis by reducing chest wall effect in diffuse optical tomography

We have developed the ultrasound (US)-guided diffuse optical tomography technique to assist US diagnosis of breast cancer and to predict neoadjuvant chemotherapy response of patients with breast cancer. The technique was implemented using a hand-held hybrid probe consisting of a coregistered US transducer and optical source and detector fibers which couple the light illumination from laser diodes and photon detection to the photomultiplier tube detectors. With the US guidance, diffused light measurements were made at the breast lesion site and the normal contralateral reference site which was used to estimate the background tissue optical properties for imaging reconstruction. However, background optical properties were affected by the chest wall underneath the breast tissue. We have analyzed data from 297 female patients, and results have shown statistically significant correlation between the fitted optical properties ( ? a and ? s ? ) and the chest wall depth. After subtracting the background ? a at each wavelength, the difference of computed total hemoglobin (tHb) between malignant and benign lesion groups has improved. For early stage malignant lesions, the area-under-the-receiver operator characteristic curve (AUC) has improved from 88.5% to 91.5%. For all malignant lesions, the AUC has improved from 85.3% to 88.1%. Statistical test has revealed the significant difference of the AUC improvements after subtracting background tHb values.

Diffuse optical tomography changes correlate with residual cancer burden after neoadjuvant chemotherapy in breast cancer patients

PURPOSE

Breast cancer (BC) patients who achieve a favorable residual cancer burden (RCB) after neoadjuvant chemotherapy (NACT) have an improved recurrence-free survival. Those who have an unfavorable RCB will have gone through months of ineffective chemotherapy. No ideal method exists to predict a favorable RCB early during NACT. Diffuse optical tomography (DOT) is a novel imaging modality that uses near-infrared light to assess hemoglobin concentrations within breast tumors. We hypothesized that the 2-week percent change in DOT-measured hemoglobin concentrations would associate with RCB.

METHODS

We conducted an observational study of 40 women with stage II-IIIC BC who received standard NACT. DOT imaging was performed at baseline and 2 weeks after treatment initiation. We evaluated the associations between the RCB index (continuous measure), class (categorical 0, I, II, III), and response (RCB class 0/I = favorable, RCB class II/III = unfavorable) with changes in DOT-measured hemoglobin concentrations.

RESULTS

The RCB index correlated significantly with the 2-week percent change in oxyhemoglobin [HbO₂] (r = 0.5, p = 0.003), deoxyhemoglobin [Hb] (r = 0.37, p = 0.03), and total hemoglobin concentrations [HbT] (r = 0.5, p = 0.003). The RCB class and response significantly associated with the 2-week percent change in [HbO₂] (p ≤ 0.01) and [HbT] (p ≤ 0.02). [HbT] 2-week percent change had sensitivity, specificity, positive, and negative predictive values for a favorable RCB response of 86.7, 68.4, 68.4, and 86.7%, respectively.

CONCLUSION

The 2-week percent change in DOT-measured hemoglobin concentrations was associated with the RCB index, class, and response. DOT may help guide NACT for women with BC.

Diffuse optical tomography reconstruction method using ultrasound images as prior for regularization matrix

Ultrasound-guided diffuse optical tomography (DOT) is a promising imaging technique that maps hemoglobin concentrations of breast lesions to assist ultrasound (US) for cancer diagnosis and treatment monitoring. The accurate recovery of breast lesion optical properties requires an effective image reconstruction method. We introduce a reconstruction approach in which US images are encoded as prior information for regularization of the inversion matrix. The framework of this approach is based on image reconstruction package “NIRFAST.” We compare this approach to the US-guided dual-zone mesh reconstruction method, which is based on Born approximation and conjugate gradient optimization developed in our laboratory. Results were evaluated using phantoms and clinical data. This method improves classification of malignant and benign lesions by increasing malignant to benign lesion absorption contrast. The results also show improvements in reconstructed lesion shapes and the spatial distribution of absorption maps.

Automated data selection method to improve robustness of diffuse optical tomography for breast cancer imaging

Imaging-guided near infrared diffuse optical tomography (DOT) has demonstrated a great potential as an adjunct modality for differentiation of malignant and benign breast lesions and for monitoring treatment response of breast cancers. However, diffused light measurements are sensitive to artifacts caused by outliers and errors in measurements due to probe-tissue coupling, patient and probe motions, and tissue heterogeneity. In general, pre-processing of the measurements is needed by experienced users to manually remove these outliers and therefore reduce imaging artifacts. An automated method of outlier removal, data selection, and filtering for diffuse optical tomography is introduced in this manuscript. This method consists of multiple steps to first combine several data sets collected from the same patient at contralateral normal breast and form a single robust reference data set using statistical tests and linear fitting of the measurements. The second step improves the perturbation measurements by filtering out outliers from the lesion site measurements using model based analysis. The results of 20 malignant and benign cases show similar performance between manual data processing and automated processing and improvement in tissue characterization of malignant to benign ratio by about 27%.