Hyperspectral dark-field microscopy of human breast lumpectomy samples for tumor margin detection in breast-conserving surgery.

Significance: Hyperspectral dark-field microscopy (HSDFM) and data cube analysis algorithms demonstrate successful detection and classification of various tissue types, including carcinoma regions in human post-lumpectomy breast tissues excised during breast-conserving surgeries. Aim: We expand the application of HSDFM to the classification of tissue types and tumor subtypes in pre-histopathology human breast lumpectomy samples. Approach: Breast tissues excised during breast-conserving surgeries were imaged by the HSDFM and analyzed. The performance of the HSDFM is evaluated by comparing the backscattering intensity spectra of polystyrene microbead solutions with the Monte Carlo simulation of the experimental data. For classification algorithms, two analysis approaches, a supervised technique based on the spectral angle mapper (SAM) algorithm and an unsupervised technique based on the K-means algorithm are applied to classify various tissue types including carcinoma subtypes. In the supervised technique, the SAM algorithm with manually extracted endmembers guided by H&E annotations is used as reference spectra, allowing for segmentation maps with classified tissue types including carcinoma subtypes. Results: The manually extracted endmembers of known tissue types and their corresponding threshold spectral correlation angles for classification make a good reference library that validates endmembers computed by the unsupervised K-means algorithm. The unsupervised K-means algorithm, with no a priori information, produces abundance maps with dominant endmembers of various tissue types, including carcinoma subtypes of invasive ductal carcinoma and invasive mucinous carcinoma. The two carcinomas' unique endmembers produced by the two methods agree with each other within <2% residual error margin. Conclusions: Our report demonstrates a robust procedure for the validation of an unsupervised algorithm with the essential set of parameters based on the ground truth, histopathological information. We have demonstrated that a trained library of the histopathology-guided endmembers and associated threshold spectral correlation angles computed against well-defined reference data cubes serve such parameters. Two classification algorithms, supervised and unsupervised algorithms, are employed to identify regions with carcinoma subtypes of invasive ductal carcinoma and invasive mucinous carcinoma present in the tissues. The two carcinomas' unique endmembers used by the two methods agree to <2% residual error margin. This library of high quality and collected under an environment with no ambient background may be instrumental to develop or validate more advanced unsupervised data cube analysis algorithms, such as effective neural networks for efficient subtype classification.

PRAME expression in cutaneous melanoma does not correlate with disease-specific survival.

BACKGROUND: Immunohistochemistry-based protein biomarkers can provide useful prognostic information in cutaneous melanoma. The independent prognostic value of Ki-67 has been studied with variable results. PReferentially expressed Antigen in MElanoma (PRAME) immunohistochemistry is a useful new ancillary tool for distinguishing cutaneous nevi from melanoma; however, its prognostic value has not been well studied. We evaluated PRAME as a prognostic marker in cutaneous melanoma, compared to Ki-67. METHODS: We analyzed the immunohistochemical expression of PRAME and Ki-67 in 165 melanocytic lesions, including 92 primary melanomas, 19 metastatic melanomas, and 54 melanocytic nevi using tissue microarrays. PRAME immunostaining was scored based on the percentage of positive nuclei: 0 <1%, 1+ 1%-25%, 2+ 26%-50%, 3+ 51%-75%, and 4+ >75%. The percentage of Ki-67-positive tumor nuclei was used to calculate the proliferation index. RESULTS: PRAME and Ki-67 both showed significantly increased expression in melanomas compared to nevi (p < 0.0001 and p < 0.001, respectively). There was no significant difference in PRAME expression in primary versus metastatic melanomas. By contrast, the Ki-67 proliferation index was higher in metastatic melanoma than in primary melanoma (p = 0.013). Increased Ki-67 index correlated with ulceration (p < 0.001), increased Breslow depth (p = 0.001), and higher mitotic rate (p < 0.0001), whereas increased PRAME expression correlated with higher mitotic rate (p = 0.047) and Ki-67 index (p = 0.007). Increased Ki-67 index correlated with worse disease-specific survival in patients with primary melanoma (p < 0.001), but PRAME expression did not show prognostic significance in disease-specific survival (p = 0.63). In a multivariable analysis of patients with primary melanoma, tumor Breslow depth, ulceration, mitotic rate, and Ki-67 index were each independent predictors of disease-specific survival (p = 0.006, 0.02, 0.001, and 0.04, respectively); however, PRAME expression was not predictive of disease-specific survival (p = 0.64). CONCLUSION: Ki-67 is an independent prognostic marker; although increased PRAME expression correlates with the Ki-67 proliferation index and mitotic rate, PRAME is not an independent prognostic marker for cutaneous melanoma. PRAME and Ki-67 are useful ancillary tools for distinguishing benign from malignant melanocytic lesions.

Evaluating Receptor-Specific Fresh Specimen Staining for Tumor Margin Detection in Clinical Breast Specimens.

PURPOSE: Reliable and rapid identification of tumor in the margins of breast specimens during breast-conserving surgery to reduce repeat surgery rates is an active area of investigation. Dual-stain difference imaging (DDSI) is one of many approaches under evaluation for this application. This technique aims to topically apply fluorescent stain pairs (one targeted to a receptor-of-interest and the other a spectrally distinct isotype), image both stains, and compute a normalized difference image between the two channels. Prior evaluation and optimization in a variety of preclinical models produced encouraging diagnostic performance. Herein, we report on a pilot clinical study which evaluated HER2-targeted DDSI on 11 human breast specimens. PROCEDURES: Gross sections from 11 freshly excised mastectomy specimens were processed using a HER2-receptor-targeted DDSI protocol shortly after resection. After staining with the dual-probe protocol, specimens were imaged on a fluorescence scanner, followed by tissue fixation for hematoxylin and eosin and anti-HER2 immunohistochemical staining. Receiver operator characteristic curves and area under the curve (AUC) analysis were used to assess diagnostic performance of the resulting images. Performance values were also compared to expression level determined from IHC staining. RESULTS: Eight of the 11 specimens presented with distinguishable invasive ductal carcinoma and/or were not affected by an imaging artifact. In these specimens, the DDSI technique provided an AUC = 0.90 ± 0.07 for tumor-to-adipose tissue and 0.81 ± 0.15 for tumor-to-glandular tissue, which was significantly higher than AUC values recovered from images of the targeted probe alone. DDSI values and diagnostic performance did not correlate with HER2 expression level, and tumors with low HER2 expression often produced high AUC, suggesting that even the low expression levels were enough to help distinguish tumor. CONCLUSIONS: The results from this preliminary study of rapid receptor-specific staining in human specimens were consistent with prior preclinical results and demonstrated promising diagnostic potential.

Breast-Conserving Surgery Margin Guidance Using Micro-Computed Tomography: Challenges When Imaging Radiodense Resection Specimens.

BACKGROUND: Breast-conserving surgery (BCS) is an integral component of early-stage breast cancer treatment, but costly reexcision procedures are common due to the high prevalence of cancer-positive margins on primary resections. A need exists to develop and evaluate improved methods of margin assessment to detect positive margins intraoperatively. METHODS: A prospective trial was conducted through which micro-computed tomography (micro-CT) with radiological interpretation by three independent readers was evaluated for BCS margin assessment. Results were compared to standard-of-care intraoperative margin assessment (i.e., specimen palpation and radiography [abbreviated SIA]) for detecting cancer-positive margins. RESULTS: Six hundred margins from 100 patients were analyzed. Twenty-one margins in 14 patients were pathologically positive. On analysis at the specimen-level, SIA yielded a sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 42.9%, 76.7%, 23.1%, and 89.2%, respectively. SIA correctly identified six of 14 margin-positive cases with a 23.5% false positive rate (FPR). Micro-CT readers achieved sensitivity, specificity, PPV, and NPV ranges of 35.7-50.0%, 55.8-68.6%, 15.6-15.8%, and 86.8-87.3%, respectively. Micro-CT readers correctly identified five to seven of 14 margin-positive cases with an FPR range of 31.4-44.2%. If micro-CT scanning had been combined with SIA, up to three additional margin-positive specimens would have been identified. DISCUSSION: Micro-CT identified a similar proportion of margin-positive cases as standard specimen palpation and radiography, but due to difficulty distinguishing between radiodense fibroglandular tissue and cancer, resulted in a higher proportion of false positive margin assessments.

Developing diagnostic assessment of breast lumpectomy tissues using radiomic and optical signatures.

High positive margin rates in oncologic breast-conserving surgery are a pressing clinical problem. Volumetric X-ray scanning is emerging as a powerful ex vivo specimen imaging technique for analyzing resection margins, but X-rays lack contrast between non-malignant and malignant fibrous tissues. In this study, combined micro-CT and wide-field optical image radiomics were developed to classify malignancy of breast cancer tissues, demonstrating that X-ray/optical radiomics improve malignancy classification. Ninety-two standardized features were extracted from co-registered micro-CT and optical spatial frequency domain imaging samples extracted from 54 breast tumors exhibiting seven tissue subtypes confirmed by microscopic histological analysis. Multimodal feature sets improved classification performance versus micro-CT alone when adipose samples were included (AUC = 0.88 vs. 0.90; p-value = 3.65e-11) and excluded, focusing the classification task on exclusively non-malignant fibrous versus malignant tissues (AUC = 0.78 vs. 0.85; p-value = 9.33e-14). Extending the radiomics approach to high-dimensional optical data-termed "optomics" in this study-offers a promising optical image analysis technique for cancer detection. Radiomic feature data and classification source code are publicly available.

Optical scatter imaging of resected breast tumor structures matches the patterns of micro-computed tomography.

In patients undergoing breast-conserving surgery (BCS), the rate of re-excision procedures to remove residual tumor left behind after initial resection can be high. Projection radiography, and recently, volumetric x-ray imaging are used to assess margin adequacy, but x-ray imaging lacks contrast between healthy, abnormal benign, and malignant fibrous tissues important for surgical decision making. The purpose of this study was to compare micro-CT and optical scatter imagery of surgical breast specimens and to demonstrate enhanced contrast-to intra-tumoral morphologies and tumor boundary features revealed by optical scatter imaging. A total of 57 breast tumor slices from 57 patients were imagedex vivoby spatially co-registered micro-CT and optical scatter scanning. Optical scatter exhibited greater similarity with micro-CT in 89% (51/57) of specimens versus diffuse white light (DWL) luminance using mutual information (mean ± standard deviation of 0.48 ± 0.21 versus 0.24 ± 0.12;p < 0.001) and in 81% (46/57) of specimens using the Sørensen-Dice coefficient (0.48 ± 0.21 versus 0.33 ± 0.18;p < 0.001). The coefficient of variation (CV) quantified the feature content in each image. Optical scatter exhibited the highest CV in every specimen (optical scatter: 0.70 ± 0.17; diffuse luminance: 0.24 ± 01; micro-CT: 0.15 ± 0.03 for micro-CT;p < 0.001). Optical scatter also exhibited the highest contrast ratios across representative tumor boundaries with adjacent healthy/benign fibrous tissues (1.5-3.7 for optical scatter; 1.0-1.1 for diffuse luminance; 1.0-1.1 for micro-CT). The two main findings from this study were: first, optical scatter contrast was in general similar to the radiological view of the tissue relative to DWL imaging; and second, optical scatter revealed additional features associated with fibrous tissue structures of similar radiodensity that may be relevant to diagnosis. The value of micro-CT lies in its rapid three-dimensional scanning of specimen morphology, and combined with optical scatter imaging with sensitivity to fibrous surface tissues, may be an attractive solution for margin assessment during BCS.

Modeling and Synthesis of Breast Cancer Optical Property Signatures With Generative Models.

Is it possible to find deterministic relationships between optical measurements and pathophysiology in an unsupervised manner and based on data alone? Optical property quantification is a rapidly growing biomedical imaging technique for characterizing biological tissues that shows promise in a range of clinical applications, such as intraoperative breast-conserving surgery margin assessment. However, translating tissue optical properties to clinical pathology information is still a cumbersome problem due to, amongst other things, inter- and intrapatient variability, calibration, and ultimately the nonlinear behavior of light in turbid media. These challenges limit the ability of standard statistical methods to generate a simple model of pathology, requiring more advanced algorithms. We present a data-driven, nonlinear model of breast cancer pathology for real-time margin assessment of resected samples using optical properties derived from spatial frequency domain imaging data. A series of deep neural network models are employed to obtain sets of latent embeddings that relate optical data signatures to the underlying tissue pathology in a tractable manner. These self-explanatory models can translate absorption and scattering properties measured from pathology, while also being able to synthesize new data. The method was tested on a total of 70 resected breast tissue samples containing 137 regions of interest, achieving rapid optical property modeling with errors only limited by current semi-empirical models, allowing for mass sample synthesis and providing a systematic understanding of dataset properties, paving the way for deep automated margin assessment algorithms using structured light imaging or, in principle, any other optical imaging technique seeking modeling. Code is available.

Plasma DNA as a "liquid biopsy" incompletely complements tumor biopsy for identification of mutations in a case series of four patients with oligometastatic breast cancer.

PURPOSE: Circulating tumor DNA in plasma may present a minimally invasive opportunity to identify tumor-derived mutations to inform selection of targeted therapies for individual patients, particularly in cases of oligometastatic disease where biopsy of multiple tumors is impractical. To assess the utility of plasma DNA as a "liquid biopsy" for precision oncology, we tested whether sequencing of plasma DNA is a reliable surrogate for sequencing of tumor DNA to identify targetable genetic alterations. METHODS: Blood and biopsies of 1-3 tumors were obtained from 4 evaluable patients with advanced breast cancer. One patient provided samples from an additional 7 tumors post-mortem. DNA extracted from plasma, tumor tissues, and buffy coat of blood were used for probe-directed capture of all exons in 149 cancer-related genes and massively parallel sequencing. Somatic mutations in DNA from plasma and tumors were identified by comparison to buffy coat DNA. RESULTS: Sequencing of plasma DNA identified 27.94 ± 11.81% (mean ± SD) of mutations detected in a tumor(s) from the same patient; such mutations tended to be present at high allelic frequency. The majority of mutations found in plasma DNA were not found in tumor samples. Mutations were also found in plasma that matched clinically undetectable tumors found post-mortem. CONCLUSIONS: The incomplete overlap of genetic alteration profiles of plasma and tumors warrants caution in the sole reliance of plasma DNA to identify therapeutically targetable alterations in patients and indicates that analysis of plasma DNA complements, but does not replace, tumor DNA profiling. TRIAL REGISTRATION: Subjects were prospectively enrolled in trial NCT01836640 (registered April 22, 2013).

Implementation of an Emergency Use Authorization Test During an Impending National Crisis.

The laboratory response to the current severe acute respiratory syndrome coronavirus 2 pandemic may be termed heroic. From the identification of the novel coronavirus to implementation of routine laboratory testing around the world to the development of potential vaccines, laboratories have played a critical role in the efforts to curtail this pandemic. In this brief report, we review our own effort at a midsized, rural, academic medical center to implement a molecular test for the virus; and we share insights and lessons learned from that process, which might be helpful in similar situations in the future.

Endocytosis of very low-density lipoproteins: an unexpected mechanism for lipid acquisition by breast cancer cells.

We previously described the expression of CD36 and LPL by breast cancer (BC) cells and tissues and the growth-promoting effect of VLDL observed only in the presence of LPL. We now report a model in which LPL is bound to a heparan sulfate proteoglycan motif on the BC cell surface and acts in concert with the VLDL receptor to internalize VLDLs via receptor-mediated endocytosis. We also demonstrate that gene-expression programs for lipid synthesis versus uptake respond robustly to triglyceride-rich lipoprotein availability. The literature emphasizes de novo FA synthesis and exogenous free FA uptake using CD36 as paramount mechanisms for lipid acquisition by cancer cells. We find that the uptake of intact lipoproteins is also an important mechanism for lipid acquisition and that the relative reliance on lipid synthesis versus uptake varies among BC cell lines and in response to VLDL availability. This metabolic plasticity has important implications for the development of therapies aimed at the lipid dependence of many types of cancer, in that the inhibition of FA synthesis may elicit compensatory upregulation of lipid uptake. Moreover, the mechanism that we have elucidated provides a direct connection between dietary fat and tumor biology.-.

Structured light imaging for breast-conserving surgery, part II: texture analysis and classification.

Subdiffuse spatial frequency domain imaging (sd-SFDI) data of 42 freshly excised, bread-loafed tumor resections from breast-conserving surgery (BCS) were evaluated using texture analysis and a machine learning framework for tissue classification. Resections contained 56 regions of interest (RoIs) determined by expert histopathological analysis. RoIs were coregistered with sd-SFDI data and sampled into ∼4 × 4 mm2 subimage samples of confirmed and homogeneous histological categories. Sd-SFDI reflectance textures were analyzed using gray-level co-occurrence matrix pixel statistics, image primitives, and power spectral density curve parameters. Texture metrics exhibited statistical significance (p-value < 0.05) between three benign and three malignant tissue subtypes. Pairs of benign and malignant subtypes underwent texture-based, binary classification with correlation-based feature selection. Classification performance was evaluated using fivefold cross-validation and feature grid searching. Classification using subdiffuse, monochromatic reflectance (illumination spatial frequency of fx = 1.37 mm − 1, optical wavelength of λ = 490 nm) achieved accuracies ranging from 0.55 (95% CI: 0.41 to 0.69) to 0.95 (95% CI: 0.90 to 1.00) depending on the benign­malignant diagnosis pair. Texture analysis of sd-SFDI data maintains the spatial context within images, is free of light transport model assumptions, and may provide an alternative, computationally efficient approach for wide field-of-view (cm2) BCS tumor margin assessment relative to pixel-based optical scatter or color properties alone.

Structured light imaging for breast-conserving surgery, part I: optical scatter and color analysis.

Structured light imaging (SLI) with high spatial frequency (HSF) illumination provides a method to amplify native tissue scatter contrast and better differentiate superficial tissues. This was investigated for margin analysis in breast-conserving surgery (BCS) and imaging gross clinical tissues from 70 BCS patients, and the SLI distinguishability was examined for six malignancy subtypes relative to three benign/normal breast tissue subtypes. Optical scattering images recovered were analyzed with five different color space representations of multispectral demodulated reflectance. Excluding rare combinations of invasive lobular carcinoma and fibrocystic disease, SLI was able to classify all subtypes of breast malignancy from surrounding benign tissues (p-value < 0.05) based on scatter and color parameters. For color analysis, HSF illumination of the sample generated more statistically significant discrimination than regular uniform illumination. Pathological information about lesion subtype from a presurgical biopsy can inform the search for malignancy on the surfaces of specimens during BCS, motivating the focus on pairwise classification analysis. This SLI modality is of particular interest for its potential to differentiate tissue classes across a wide field-of-view (∼100 cm2) and for its ability to acquire images of macroscopic tissues rapidly but with microscopic-level sensitivity to structural and morphological tissue constituents.

A Randomized Prospective Trial of Supine MRI-Guided Versus Wire-Localized Lumpectomy for Breast Cancer.

BACKGROUND: Wire-localized excision of non-palpable breast cancer is imprecise, resulting in positive margins 15-35% of the time. METHODS: Women with a confirmed diagnosis of non-palpable invasive breast cancer (IBC) or ductal carcinoma in situ (DCIS) were randomized to a new technique using preoperative supine magnetic resonance imaging (MRI) with intraoperative optical scanning and tracking (MRI group) or wire-localized (WL group) partial mastectomy. The main outcome measure was the positive margin rate. RESULTS: In this study, 138 patients were randomly assigned. Sixty-six percent had IBC and DCIS, 22% had IBC, and 12% had DCIS. There were no differences in patient or tumor characteristics between the groups. The proportion of patients with positive margins in the MRI-guided surgery group was half that observed in the WL group (12 vs. 23%; p = 0.08). The specimen volumes in the MRI and WL groups did not differ significantly (74 ± 33.9 mL vs. 69.8 ± 25.1 mL; p = 0.45). The pathologic tumor diameters were underestimated by 2 cm or more in 4% of the cases by MRI and in 9% of the cases by mammography. Positive margins were observed in 68% and 58% of the cases underestimated by 2 cm or more using MRI and mammography, respectively, and in 15% and 14% of the cases not underestimated using MRI and mammography, respectively. CONCLUSIONS: A novel system using supine MRI images co-registered with intraoperative optical scanning and tracking enabled tumors to be resected with a trend toward a lower positive margin rate compared with wire-localized partial mastectomy. Margin positivity was more likely when imaging underestimated pathologic tumor size.

Diagnostic and prognostic value of glucose transporters in melanocytic lesions.

We have previously reported increased glucose transporter 1 (GLUT1) expression in melanoma compared to benign nevi, associated with a significantly lower survival rate. GLUT1 upregulation was highly specific for distinguishing melanoma from benign nevi, yet poorly sensitive, likely because of expression of other GLUT isoforms. The purpose of this study was to evaluate GLUT2 and GLUT3, as melanoma biomarkers. A tissue microarray, consisting of 91 primary melanomas, 18 melanoma metastases, and 56 nevi, was examined using GLUT2 and GLUT3 immunohistochemistry. A semiquantitative scoring method was used to determine the percentage of positive tumor cells and staining intensity. GLUT2 was negative in all melanomas and benign nevi examined. Increased GLUT3 expression was more frequent in melanoma than in nevi (P < 0.0001), and in metastatic melanoma than in primary melanomas (P < 0.001). Of melanoma cases, 85.3% expressed either GLUT1 or GLUT3 or both, 39.4% of melanoma cases coexpressed GLUT1 and GLUT3, 17.4% of melanoma cases only expressed GLUT1, 28.4% of melanoma cases only expressed GLUT3, and 14.7% of melanoma cases were negative for both markers. Patients whose melanoma exhibited a high level of GLUT3 had significantly lower survival rates than those with low GLUT3 expression (P = 0.002). Evaluating both GLUT1 and GLUT3 increased the diagnostic value by increasing the sensitivity while the specificity remained high. In conclusion, GLUT2 was not expressed in melanocytes. GLUT3 expression was upregulated in melanoma compared with nevi, especially in those with worse prognosis. Similar to GLUT1, GLUT3 may serve as a useful diagnostic and prognostic marker.

Ductal Carcinoma in Situ Biomarkers in a Precision Medicine Era: Current and Future Molecular-Based Testing.

Historically, ductal carcinoma in situ (DCIS) of the breast has been managed aggressively with surgery and radiotherapy because of a risk of progression to invasive ductal carcinoma. However, this treatment paradigm has been challenged by overtreatment concerns and evidence that suggests that DCIS can be stratified according to risk of recurrence or risk of progression to invasive disease. Traditional methods of risk stratification include histologic grade and hormone receptor status. Recent technological advancements have enabled an era of precision medicine, where DCIS can be molecularly analyzed by tools, such as next-generation DNA and RNA sequencing, to identify molecular biomarkers for risk stratification. These findings have led to the development of tools such as the Oncotype DX Breast DCIS Score, a gene expression-based assay with the potential to prevent overtreatment in low-risk disease.

In Vivo and Ex Vivo Microscopy: Moving Toward the Integration of Optical Imaging Technologies Into Pathology Practice.

The traditional surgical pathology assessment requires tissue to be removed from the patient, then processed, sectioned, stained, and interpreted by a pathologist using a light microscope. Today, an array of alternate optical imaging technologies allow tissue to be viewed at high resolution, in real time, without the need for processing, fixation, freezing, or staining. Optical imaging can be done in living patients without tissue removal, termed in vivo microscopy, or also in freshly excised tissue, termed ex vivo microscopy. Both in vivo and ex vivo microscopy have tremendous potential for clinical impact in a wide variety of applications. However, in order for these technologies to enter mainstream clinical care, an expert will be required to assess and interpret the imaging data. The optical images generated from these imaging techniques are often similar to the light microscopic images that pathologists already have expertise in interpreting. Other clinical specialists do not have this same expertise in microscopy, therefore, pathologists are a logical choice to step into the developing role of microscopic imaging expert. Here, we review the emerging technologies of in vivo and ex vivo microscopy in terms of the technical aspects and potential clinical applications. We also discuss why pathologists are essential to the successful clinical adoption of such technologies and the educational resources available to help them step into this emerging role.

In Vivo and Ex Vivo Microscopy: A Business Plan to Justify the Introduction of Similar Emerging Technologies Into Pathology Practice.

CONTEXT.­: Our patients are now demanding value for their medical diagnoses and treatment in terms of optimal costs, quality, and outcomes. The financial justification for the introduction of new emerging technologies that may better meet these needs will depend on many factors, even if there is an established reimbursement code. In vivo and ex vivo microscopic technologies (IVM and EVM, respectively) will be used as examples of potentially transforming technologies. OBJECTIVE.­: To describe the components of a business plan that ensures all of the ramifications of introducing a new technology into pathology practice have been considered. As well as the financial justification, such a plan should include strategic vision and congruence, the advantages and drawbacks of introducing such technology, and how plans for marketing, implementation, and verification can be operationalized. DATA SOURCES.­: Unlike many pathologists, administrative directors in clinical laboratories already know the components of a financially sound business plan. In addition to the financial justifications, other considerations of such a plan include expense reductions, multiyear buildups in revenue generation, the replacement of other technologies, improved productivity and workflows, additional space, new capital, retrained personnel, and the impact on other departments. CONCLUSIONS.­: Pathologists will learn a business plan format to improve their confidence in making the sound financial justifications needed to consider the introduction of an emerging technology into pathology practice, even when there is initially no obvious revenue stream because formal reimbursement codes have not been established.

Review of methods for intraoperative margin detection for breast conserving surgery.

Breast conserving surgery (BCS) is an effective treatment for early-stage cancers as long as the margins of the resected tissue are free of disease according to consensus guidelines for patient management. However, 15% to 35% of patients undergo a second surgery since malignant cells are found close to or at the margins of the original resection specimen. This review highlights imaging approaches being investigated to reduce the rate of positive margins, and they are reviewed with the assumption that a new system would need high sensitivity near 95% and specificity near 85%. The problem appears to be twofold. The first is for complete, fast surface scanning for cellular, structural, and/or molecular features of cancer, in a lumpectomy volume, which is variable in size, but can be large, irregular, and amorphous. A second is for full, volumetric imaging of the specimen at high spatial resolution, to better guide internal radiologic decision-making about the spiculations and duct tracks, which may inform that surfaces are involved. These two demands are not easily solved by a single tool. Optical methods that scan large surfaces quickly are needed with cellular/molecular sensitivity to solve the first problem, but volumetric imaging with high spatial resolution for soft tissues is largely outside of the optical realm and requires x-ray, micro-CT, or magnetic resonance imaging if they can be achieved efficiently. In summary, it appears that a combination of systems into hybrid platforms may be the optimal solution for these two very different problems. This concept must be cost-effective, image specimens within minutes and be coupled to decision-making tools that help a surgeon without adding to the procedure. The potential for optical systems to be involved in this problem is emerging and clinical trials are underway in several of these technologies to see if they could reduce positive margin rates in BCS.

Light scattering measured with spatial frequency domain imaging can predict stromal versus epithelial proportions in surgically resected breast tissue.

This study aims to determine if light scatter parameters measured with spatial frequency domain imaging (SFDI) can accurately predict stromal, epithelial, and adipose fractions in freshly resected, unstained human breast specimens. An explicit model was developed to predict stromal, epithelial, and adipose fractions as a function of light scattering parameters, which was validated against a quantitative analysis of digitized histology slides for N = 31 specimens using leave-one-out cross-fold validation. Specimen mean stromal, epithelial, and adipose volume fractions predicted from light scattering parameters strongly correlated with those calculated from digitized histology slides (r = 0.90, 0.77, and 0.91, respectively, p-value <1 × 10 - 6). Additionally, the ratio of predicted epithelium to stroma classified malignant specimens with a sensitivity and specificity of 90% and 81%, respectively, and also classified all pixels in malignant lesions with 63% and 79%, at a threshold of 1. All specimens and pixels were classified as malignant, benign, or fat with 84% and 75% accuracy, respectively. These findings demonstrate how light scattering parameters acquired with SFDI can be used to accurately predict and spatially map stromal, epithelial, and adipose proportions in fresh unstained, human breast tissue, and suggest that these estimations could provide diagnostic value.