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.
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.
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.
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 benign–malignant 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 (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.
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