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.

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.

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.

Micro-computed tomography enables rapid surgical margin assessment during breast conserving surgery (BCS): correlation of whole BCS micro-CT readings to final histopathology.

BACKGROUND: Roughly 23% of breast conserving surgery (BCS) patients undergo a second re-excision procedure due to pathologically positive surgical margins. We investigated the feasibility and potential value of micro-Computed Tomography (micro-CT) as a surgical margin guidance tool during BCS. METHODS: A cohort of 32 BCS specimens was prospectively imaged with a pre-clinical micro-CT system upon arrival in the surgical pathology laboratory. Reconstructed micro-CT scans were evaluated retrospectively by an experienced breast radiologist, who provided binary determinations whether lesions extended to the specimen margin. These readings were then compared to the final pathological diagnosis and to 2D specimen radiography readings. RESULTS: Of the 32 specimens imaged, 28 had malignant and four had benign pathological diagnoses. Overall five (four malignant, one benign) of the 32 specimens had lesion tissue extending to the margin. For all 32 specimens, micro-CT reconstructions were calculated (< 4 min. acquisition + reconstruction time) and each specimen was volumetrically analyzed by a radiologist. Of the 28 malignant specimen readings, 18 matched the final pathological diagnosis [64%, 95 CI (47%-81%)], with a negative predictive value of 89% [95 CI (74%-96%)]. Micro-CT readings revealed changes in the tumor location and margin status as compared to single-projection radiography readings. CONCLUSIONS: Micro-CT scanning of BCS specimens enabled margin status assessment over the entirety of the surgical surface in a clinically relevant time frame, provided additional spatial information over single-projection radiography, and may be a potentially useful BCS guidance tool.

Molecular dyes used for surgical specimen margin orientation allow for intraoperative optical assessment during breast conserving surgery.

A variety of optical techniques utilizing near-infrared (NIR) light are being proposed for intraoperative breast tumor margin assessment. However, immediately following a lumpectomy excision, the margins are inked, which preserves the orientation of the specimen but prevents optical interrogation of the tissue margins. Here, a workflow is proposed that allows for both NIR optical assessment following full specimen marking using molecular dyes which have negligible absorption and scattering in the NIR. The effect of standard surgical inks in contrast to molecular dyes for an NIR signal is shown. Further, the proposed workflow is demonstrated with full specimen intraoperative imaging on all margins directly after the lumpectomy has been excised and completely marked. This work is an important step in the path to clinical feasibility of intraoperative breast tumor margin assessment using NIR optical methods without having to compromise on the current clinical practice of inking resected specimens for margin orientation.

System analysis of spatial frequency domain imaging for quantitative mapping of surgically resected breast tissues.

The feasibility of spatial frequency domain imaging (SFDI) for breast surgical margin assessment was evaluated in tissue-simulating phantoms and in fully intact lumpectomy specimens at the time of surgery. Phantom data was evaluated according to contrast-detail resolution, quantitative accuracy and model-data goodness of fit, where optical parameters were estimated by minimizing the residual sum of squares between the measured modulation amplitude and its solutions, modeled according to diffusion and scaled-Monte Carlo simulations. In contrast-detail phantoms, a 1.25-mm-diameter surface inclusion was detectable for scattering contrast >28%; a fraction of this scattering contrast (7%) was detectable for a 10 mm surface inclusion and at least 33% scattering contrast was detected up to 1.5 mm below the phantom surface, a probing depth relevant to breast surgical margin assessment. Recovered hemoglobin concentrations were insensitive to changes in scattering, except for overestimation at visible wavelengths for total hemoglobin concentrations <15 µM. The scattering amplitude increased linearly with scattering concentration, but the scattering slope depended on both the particle size and number density. Goodness of fit was comparable for the diffusion and scaled-Monte Carlo models of transport in spatially modulated, near-infrared reflectance acquired from 47 lumpectomy tissues, but recovered absorption parameters varied more linearly with expected hemoglobin concentration in liquid phantoms for the scaled-Monte Carlo forward model. SFDI could potentially reduce the high secondary excision rate associated with breast conserving surgery; its clinical translation further requires reduced image reconstruction time and smart inking strategies.

Scatter spectroscopic imaging distinguishes between breast pathologies in tissues relevant to surgical margin assessment.

PURPOSE: A new approach to spectroscopic imaging was developed to detect and discriminate microscopic pathologies in resected breast tissues; diagnostic performance of the prototype system was tested in 27 tissues procured during breast conservative surgery. EXPERIMENTAL DESIGN: A custom-built, scanning in situ spectroscopy platform sampled broadband reflectance from a 150-µm-diameter spot over a 1 × 1 cm(2) field using a dark field geometry and telecentric lens; the system was designed to balance sensitivity to cellular morphology and imaging the inherent diversity within tissue subtypes. Nearly 300,000 broadband spectra were parameterized using light scattering models and spatially dependent spectral signatures were interpreted using a cooccurrence matrix representation of image texture. RESULTS: Local scattering changes distinguished benign from malignant pathologies with 94% accuracy, 93% sensitivity, 95% specificity, and 93% positive and 95% negative predictive values using a threshold-based classifier. Texture and shape features were important to optimally discriminate benign from malignant tissues, including pixel-to-pixel correlation, contrast and homogeneity, and the shape features of fractal dimension and Euler number. Analysis of the region-based diagnostic performance showed that spectroscopic image features from 1 × 1 mm(2) areas were diagnostically discriminant and enabled quantification of within-class tissue heterogeneities. CONCLUSIONS: Localized scatter-imaging signatures detected by the scanning spectroscopy platform readily distinguished benign from malignant pathologies in surgical tissues and showed new spectral-spatial signatures of clinical breast pathologies.