Breastfeeding plays a crucial role in public health, but relatively few imaging and sensing technologies are employed to study human lactation physiology. As a consequence, many breastfeeding problems are not well understood. We hypothesize that diffuse optical spectroscopic
imaging (DOSI) can potentially reveal important physiological parameters that help to define milk synthesis and secretion: glandular tissue content, hemodynamics and milk ejection. The aim of this study is to investigate the sensitivity of DOSI to these physiological parameters in (i) a case study (1 subject) on mammary involution of the lactating breast to its pre-pregnant state and (ii) a pilot study during milk extraction with a breast pump (4 lactating subjects, 5 non-lactating subjects). For the case study, the measured changes in the DOSI parameters (water, lipid, hemoglobin concentration) were consistent with the gradual replacement of fibro-glandular tissue by adipose tissue and vascular regression during mammary involution. For the pilot study, the measured changes in the DOSI parameters correlated with the extracted milk volume and occurrence of the milk ejection reflex. In conclusion, DOSI is sensitive to human lactation physiology, which can potentially aid to obtaining an in-depth understanding on the origin and treatment of breastfeeding problems.
Significance: Current imaging paradigms for differential diagnosis of suspicious breast lesions suffer from high false positive rates that force patients to undergo unnecessary biopsies. Diffuse optical spectroscopic imaging (DOSI) noninvasively probes functional hemodynamic and compositional parameters in deep tissue and has been shown to be sensitive to contrast between normal and malignant tissues.
Aim: DOSI methods are under investigation as an adjunct to mammography and ultrasound that could reduce false positive rates and unnecessary biopsies, particularly in radiographically dense breasts.
Methods: We performed a retrospective analysis of 212 subjects with suspicious breast lesions who underwent DOSI imaging. Physiological tissue parameters were z-score normalized to the patient’s contralateral breast tissue and input to univariate logistic regression models to discriminate between malignant tumors and the surrounding normal tissue. The models were then used to differentiate malignant lesions from benign lesions.
Results: Models incorporating several individual hemodynamic parameters were able to accurately distinguish malignant tumors from both the surrounding background tissue and benign lesions with area under the curve (AUC) ≥0.85. Z-score normalization improved the discriminatory ability and calibration of these predictive models relative to unnormalized or ratio-normalized data.
Conclusions: Findings from a large subject population study show how DOSI data normalization that accounts for normal tissue heterogeneity and quantitative statistical regression approaches can be combined to improve the ability of DOSI to diagnose malignant lesions. This improved diagnostic accuracy, combined with the modality’s inherent logistical advantages of portability, low cost, and nonionizing radiation, could position DOSI as an effective adjunct modality that could be used to reduce the number of unnecessary invasive biopsies.
Radiotherapy is a common and effective treatment for certain breast cancers. One potential drawback from this therapy is the development of varying degrees of erythema that typically occur after the treatment has been completed. Currently there are no tools to quantify radiation-induced skin changes during and after radiation and the standardized scoring systems remain subjective. Developing these tools would not only allow clinicians to objectively assess patients, but could potentially inform them as to which patients are likely to develop more severe side effects. Spatial Frequency Domain Imaging (SFDI) is a non-invasive, non-contact imaging technique capable of quantitatively mapping tissue absorption and scattering properties that can be converted into tissue oxygen saturation, total hemoglobin concentration and reduced scattering coefficients. Here we present a study of 13 breast cancer patients that have been prescribed radiation therapy and imaged using SFDI before, during, and after radiation treatment over the course of several weeks. A preliminary analysis of the data shows increases in total hemoglobin concentration as high as 75% in the treated breast tissue compared to highs of 10% in control regions at the end of the radiation treatments. Additionally, changes in the reduced scattering coefficient as high as 25% in the treated breast tissue can be seen a week before the treatment is complete and hyperpigmentation is visible. The aim of this study is to characterize radiation induced changes in skin using SFDI in order to provide clinicians with a technology that can inform radiation protocols (such as dose, frequency and duration) thereby minimizing unnecessary skin toxicity while maximizing treatment efficacy.
Relatively few imaging and sensing technologies are employed to study human lactation physiology. In particular, human mammary development during pregnancy as well as mammary involution after lactation have been poorly described, despite their importance for breast cancer diagnosis and treatment during these phases. Our case study shows the potential of diffuse optical spectroscopic imaging (DOSI) to uniquely study the spatiotemporal changes in mammary tissue composition during the involution of the lactating breast toward its pre-pregnant state. At nine time intervals over a period of eight months after the cessation of breastfeeding, we reconstructed 2-D maps of mammary water content, lipid content, total hemoglobin (THb) concentration, oxygen saturation (StO2), and tissue optical scattering. Mammary lipid content in the nonareolar region showed a significant relative increase of 59%, whereas water content and THb concentration showed a significant relative decrease of 50% and 48%, respectively. Significant changes were also found in StO2 and tissue optical scattering. Our findings are consistent with the gradual replacement of fibroglandular tissue by adipose tissue and vascular regression during mammary involution. Moreover, our data provide unique insight into the dynamics of breast tissue composition and demonstrate the effectiveness of DOSI as a technique to study human lactation physiology.
Several studies have demonstrated that hormone-blocking therapies are more effective at reducing breast cancer risk in women who exhibit >10% reduction in breast density compared to women who had little or no density change, suggesting that breast density is a predictor of tamoxifen effectiveness. The goal of this prospective study was to assess whether diffuse optical spectroscopic imaging (DOSI) can measure the changes in breast composition under adjuvant tamoxifen treatment for breast cancer.
The primary aim was to determine whether the change in the DOSI measurement of water correlates with the change in the MRI-derived quantitative measurement of breast density after 18 months of treatment in the contralateral normal breast of subjects receiving tamoxifen. Pre-menopausal subjects receiving tamoxifen (N=11 total, N=9 analyzable) and controls (N=18 total, N=15 analyzable) were enrolled and measured with co-registered DOSI and non-contrast MRI before, and 6, 12 and 18 months after beginning tamoxifen. Across all subjects, baseline MRI fibroglandular density correlated strongly with DOSI water (r=0.86, p<0.001), moderately with lipid (r=-0.63, p=0.001), and weakly with oxyhemoglobin (r=0.55, p=0.005) and deoxyhemoglobin (r=0.42, p=0.040) concentrations. Generalized estimating equation analysis revealed significant longitudinal differences between treated subjects and controls in the percentage change of MRI fibroglandular density (at 6 and 12 mo. timepoints), DOSI water (12 and 18 mo.), DOSI lipid (6, 12 and 18 mo.) Overall the data suggest that DOSI is sensitive to tamoxifen-induced changes in the human breast, and should be investigated further as a low-cost and low-risk means to predict response to tamoxifen treatment.
Ideally, neoadjuvant chemotherapy (NAC) assessment should predict pathologic complete response (pCR), a surrogate clinical endpoint for 5-year survival, as early as possible during typical 3- to 6-month breast cancer treatments. We introduce and demonstrate an approach for predicting pCR within 10 days of initiating NAC. The method uses a bedside diffuse optical spectroscopic imaging (DOSI) technology and logistic regression modeling. Tumor and normal tissue physiological properties were measured longitudinally throughout the course of NAC in 33 patients enrolled in the American College of Radiology Imaging Network multicenter breast cancer DOSI trial (ACRIN-6691). An image analysis scheme, employing z-score normalization to healthy tissue, produced models with robust predictions. Notably, logistic regression based on z-score normalization using only tissue oxygen saturation (StO2) measured within 10 days of the initial therapy dose was found to be a significant predictor of pCR (AUC = 0.92; 95% CI: 0.82 to 1). This observation suggests that patients who show rapid convergence of tumor tissue StO2 to surrounding tissue StO2 are more likely to achieve pCR. This early predictor of pCR occurs prior to reductions in tumor size and could enable dynamic feedback for optimization of chemotherapy strategies in breast cancer.
We present a framework for characterizing the performance of an experimental imaging technology, diffuse optical spectroscopic imaging (DOSI), in a 2-year multicenter American College of Radiology Imaging Network (ACRIN) breast cancer study (ACRIN-6691). DOSI instruments combine broadband frequency-domain photon migration with time-independent near-infrared (650 to 1000 nm) spectroscopy to measure tissue absorption and reduced scattering spectra and tissue hemoglobin, water, and lipid composition. The goal of ACRIN-6691 was to test the effectiveness of optically derived imaging endpoints in predicting the final pathologic response of neoadjuvant chemotherapy (NAC). Sixty patients were enrolled over a 2-year period at participating sites and received multiple DOSI scans prior to and during 3- to 6-month NAC. The impact of three sources of error on accuracy and precision, including different operators, instruments, and calibration standards, was evaluated using a broadband reflectance standard and two different solid tissue-simulating optical phantoms. Instruments showed <0.0010 mm−1 (10.3%) and 0.06 mm−1 (4.7%) deviation in broadband absorption and reduced scattering, respectively, over the 2-year duration of ACRIN-6691. These variations establish a useful performance criterion for assessing instrument stability. The proposed procedures and tests are not limited to DOSI; rather, they are intended to provide methods to characterize performance of any instrument used in translational optical imaging.
Diffuse optical spectroscopic imaging (DOSI) and diffuse correlation spectroscopy (DCS) are model-based near-infrared (NIR) methods that measure tissue optical properties (broadband absorption, μa, and reduced scattering, μs′) and blood flow (blood flow index, BFI), respectively. DOSI-derived μa values are used to determine composition by calculating the tissue concentration of oxy- and deoxyhemoglobin (HbO2, HbR), water, and lipid. We developed and evaluated a combined, coregistered DOSI/DCS handheld probe for mapping and imaging these parameters. We show that uncertainties of 0.3 mm−1 (37%) in μs′ and 0.003 mm−1 (33%) in μa lead to ∼53% and 9% errors in BFI, respectively. DOSI/DCS imaging of a solid tissue-simulating flow phantom and a breast cancer patient reveals well-defined spatial distributions of BFI and composition that clearly delineates both the flow channel and the tumor. BFI reconstructed with DOSI-corrected μa and μs′ values had a tumor/normal contrast of 2.7, 50% higher than the contrast using commonly assumed fixed optical properties. In conclusion, spatially coregistered imaging of DOSI and DCS enhances intrinsic tumor contrast and information content. This is particularly important for imaging diseased tissues where there are significant spatial variations in μa and μs′ as well as potential uncoupling between flow and metabolism.
Young patients with dense breasts have a relatively low-positive biopsy rate for breast cancer (∼1 in 7). South Korean women have higher breast density than Westerners. We investigated the benefit of using a functional and metabolic imaging technique, diffuse optical spectroscopic imaging (DOSI), to help the standard of care imaging tools to distinguish benign from malignant lesions in premenopausal Korean women. DOSI uses near-infrared light to measure breast tissue composition by quantifying tissue concentrations of water (ctH2O), bulk lipid (ctLipid), deoxygenated (ctHHb), and oxygenated (ctHbO2) hemoglobin. DOSI spectral signatures specific to abnormal tissue and absent in healthy tissue were also used to form a malignancy index. This study included 19 premenopausal subjects (average age 41±9), corresponding to 11 benign and 10 malignant lesions. Elevated lesion to normal ratio of ctH2O, ctHHb, ctHbO2, total hemoglobin (THb=ctHHb+ctHbO2), and tissue optical index (ctHHb×ctH2O/ctLipid) were observed in the malignant lesions compared to the benign lesions (p<0.02). THb and malignancy index were the two best single predictors of malignancy, with >90% sensitivity and specificity. Malignant lesions showed significantly higher metabolism and perfusion than benign lesions. DOSI spectral features showed high discriminatory power for distinguishing malignant and benign lesions in dense breasts of the Korean population.
Instrument equivalence and quality control are critical elements of multi-center clinical trials. We currently have five identical Diffuse Optical Spectroscopic Imaging (DOSI) instruments enrolled in the American College of Radiology Imaging Network (ACRIN, #6691) trial located at five academic clinical research sites in the US. The goal of the study is to predict the response of breast tumors to neoadjuvant chemotherapy in 60 patients. In order to reliably compare DOSI measurements across different instruments, operators and sites, we must be confident that the data quality is comparable. We require objective and reliable methods for identifying, correcting, and rejecting low quality data. To achieve this goal, we developed and tested an automated quality control algorithm that rejects data points below the instrument noise floor, improves tissue optical property recovery, and outputs a detailed data quality report. Using a new protocol for obtaining dark-noise data, we applied the algorithm to ACRIN patient data and successfully improved the quality of recovered physiological data in some cases.
We previously developed a self-referencing differential spectroscopic (SRDS) method to detect lesions by identifying a spectroscopic biomarker of breast cancer, i.e., the specific tumor component (STC). The SRDS method is based on the assumption of the exclusive presence of this spectroscopic biomaker in malignant disease. Although clinical results using this method have already been published, the dependence of the STC spectra on the choice of reference tissue has not yet been addressed. In this study, we explore the impact of the selection of the reference region size and location on the STC spectrum in 10 subjects with malignant breast tumors. Referencing from both contralateral and ipsilateral sides was performed. Regardless of the referencing, we are able to obtain consistent high contrast images of malignant lesions using the STC with less than 13% deviation. These results suggest that the STC measurements are independent of any type, location, and amount of normal breast tissue used for referencing. This confirms the initial assumption of the SRDS analysis, that there are specific tumor components in cancer that do not exist in normal tissue. This also indicates that bilateral measurements are not required for lesion identification using the STC method.
We describe an algorithm to calculate an index that characterizes spatial differences in broadband near-infrared [(NIR), 650-1000 nm] absorption spectra of tumor-containing breast tissue. Patient-specific tumor spatial heterogeneities are visualized through a heterogeneity spectrum function (HS). HS is a biomarker that can be attributed to different molecular distributions within the tumor. To classify lesion heterogeneities, we built a heterogeneity index (HI) derived from the HS by weighing the HS in specific NIR absorption bands. It is shown that neoadjuvant chemotherapy (NAC) response is potentially related to the tumor heterogeneity. Therefore, we correlate the heterogeneity index obtained prior to treatment with the final response to NAC. From a pilot study of 15 cancer patients treated with NAC, pathological complete responders (pCR) were separated from non-pCR according to their HI (-44 ± 12 and 43 ± 17, p = 3 × 10−8, respectively). We conclude that the HS function is a biomarker that can be used to visualize spatial heterogeneities in lesions, and the baseline HI prior to therapy correlates with chemotherapy pathological response.
We present the first clinical results of a novel fully automated 3D breast ultrasound system. This system was designed to match a Philips diffuse optical mammography system to enable straightforward coregistration of optical and ultrasound images. During a measurement, three 3D transducers scan the breast at 4 different views. The resulting 12 datasets are registered together into a single volume using spatial compounding.
In a pilot study, benign and malignant masses could be identified in the 3D images, however lesion visibility is less compared to conventional breast ultrasound. Clear breast shape visualization suggests that ultrasound could support the reconstruction and interpretation of diffuse optical tomography images.
We present a method to enhance tumor detectability in breasts imaged with our optical fluorescence mammography
system. During a measurement, transmission data at 4 wavelengths and fluorescence data for excitation at 1 wavelength
are collected after injection of an optical contrast agent. The data are reconstructed into 3D images of the absorption and
fluorescence distributions. Combining those images enables the identification of various breast tissue compounds. Here,
we investigate the relevance of our method in phantom experiments.
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