3D Pathology Volumetric Technique: A Method for Calculating Breast Tumour Volume from Whole-Mount Serial Section Images

An integrative web-based software tool for multi-dimensional pathology whole-slide image analytics

Objective.In the era of precision medicine, human tumor atlas-oriented studies have been significantly facilitated by high-resolution, multi-modal tissue based microscopic pathology image analytics. To better support such tissue-based investigations, we have developed Digital Pathology Laboratory (DPLab), a publicly available web-based platform, to assist biomedical research groups, non-technical end users, and clinicians for pathology whole-slide image visualization, annotation, analysis, and sharing via web browsers.Approach.A major advancement of this work is the easy-to-follow methods to reconstruct three-dimension (3D) tissue image volumes by registering two-dimension (2D) whole-slide pathology images of serial tissue sections stained by hematoxylin and eosin (H&E), and immunohistochemistry (IHC). The integration of these serial slides stained by different methods provides cellular phenotype and pathophysiologic states in the context of a 3D tissue micro-environment. DPLab is hosted on a publicly accessible server and connected to a backend computational cluster for intensive image analysis computations, with results visualized, downloaded, and shared via a web interface.Main results.Equipped with an analysis toolbox of numerous image processing algorithms, DPLab supports continued integration of community-contributed algorithms and presents an effective solution to improve the accessibility and dissemination of image analysis algorithms by research communities.Significance.DPLab represents the first step in making next generation tissue investigation tools widely available to the research community, enabling and facilitating discovery of clinically relevant disease mechanisms in a digital 3D tissue space.

Analysis of inferior nasal turbinate volume in subjects with nasal septum deviation: a retrospective cone beam tomography study

Background

The association of the linear dimensions of the inferior turbinate hypertrophy with nasal septal deviation has been studied recently. However, the volumetric dimensions provide a more accurate status of the turbinate hypertrophy compared to linear measurements. The aim of this study was to analyze the association of inferior nasal turbinate volume with the degree of nasal septal deviation (NSD).

Methods

A retrospective evaluation of the cone beam computed tomography (CBCT) scans of 412 patients was carried out to obtain 150 scans which were included in the study. The scans were categorized into three groups. Group 1 comprised of 50 scans of patients with no inferior turbinate hypertrophy (ITH) and no nasal septal deviation. Group 2 comprised of 50 scans of patients with ITH and no NSD; whereas Group 3 included 50 scans of patients with ITH and NSD. The total turbinate volume of inferior turbinates (bilateral) were determined by using Vesalius 3D software (PS-Medtech, Amsterdam, Netherlands).

Results

The intraclass correlation coefficient (ICC) between the volumetric estimations performed by the two radiologists was 0.82. There were no significant age and gender related changes in the total turbinate volume. Patients in Group 3 had significantly higher (p = 0.001) total turbinate volume compared to Group 2 and Group 1. There was a positive and significant correlation (r = 0.52, p = 0.002) between the degree of septal deviation and total turbinate volume. When the total turbinate volume of the patients with different types of septal deviation was compared in Group 3, a statistically significant difference (p = 0.001) was observed. Regression analysis revealed that the septal deviation angle (SDA) (p = 0.001) had a relationship with total turbinate volume. From the results of the study we can conclude that the total turbinate volume is higher in patients with nasal septal deviation. It can also be concluded that the septal deviation angle has a positive correlation with total turbinate volume. The data obtained from the study can be useful in post-surgical follow up and evaluation of patients with nasal septal deviation and hypertrophied inferior nasal turbinate.

Quantitative Predictors of Response to Neoadjuvant Chemotherapy on Dynamic Contrast-enhanced 3T Breast MRI

OBJECTIVE

To assess whether changes in quantitative parameters on breast MRI better predict pathologic complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer than change in volume.

METHODS

This IRB-approved retrospective study included women with newly diagnosed breast cancer who underwent 3T MRI before and during NAC from January 2013 to December 2019 and underwent surgery at our institution. Clinical data such as age, histologic diagnosis and grade, biomarker status, clinical stage, maximum index cancer dimension and volume, and surgical pathology (presence or absence of in-breast pCR) were collected. Quantitative parameters were calculated using software. Correlations between clinical features and MRI quantitative measures in pCR and non-pCR groups were assessed using univariate and multivariate logistic regression.

RESULTS

A total of 182 women with a mean age of 52 years (range, 26-79 years) and 187 cancers were included. Approximately 45% (85/182) of women had pCR at surgery. Stepwise multivariate regression analysis showed statistical significance for changes in quantitative parameters (increase in time to peak and decreases in peak enhancement, wash out, and Kep [efflux rate constant]) for predicting pCR. These variables in combination predicted pCR with 81.2% accuracy and an area under the curve (AUC) of 0.878. The AUCs of change in index cancer volume and maximum dimension were 0.767 and 0.613, respectively.

CONCLUSION

Absolute changes in quantitative MRI parameters between pre-NAC MRI and intra-NAC MRI could help predict pCR with excellent accuracy, which was greater than changes in index cancer volume and maximum dimension.

A Systematic Review of Cervical Ganglioneuromas

Objective

To perform a systematic review of the literature evaluating clinical characteristics and management of cervical ganglioneuromas (CGNs).

Data Sources

PubMed, Embase, and Cochrane Library databases were searched. Data such as patient demographics, imaging, and treatments were obtained.

Review Methods

Pertinent studies were downloaded, and the full text was reviewed by 4 authors (N.P., S.S., C.F., D.T.). Results were reported via the PRISMA guidelines (Preferred Reporting Items for Systematic Reviews and Meta-analyses).

Results

Fifty-two studies with 58 patients were identified in the literature. Of the 58 patients, 22 were adults and 36 were pediatric. The most common reported location of CGN was within the parapharyngeal space (76%), followed by the retropharyngeal (19%) and paravertebral/prevertebral (7%) spaces. The most common presenting symptoms included a nontender mass (29.3%), dysphagia (17.2%), and hoarseness (10.3%). Interestingly, the average tumor volume for patients with postoperative Horner’s syndrome was 183 mm3 (n = 21, 47.7%) vs 946 mm3 in patients without Horner’s syndrome (n = 23, 52.3%). This represents a statistically significant finding ( P = .018). There exists no significant difference in tumor volumes between adult and pediatric patients with Horner’s syndrome ( P = .645).

Conclusion

CGN is a rare tumor of the sympathetic nervous system. Management should involve complete surgical excision with biopsy. We found that patients with small-volume CGNs are significantly more likely to experience postoperative Horner’s syndrome. This finding is independent of age and should therefore be taken into consideration in any patient with suspected CGN.

Histo-genomics: digital pathology at the forefront of precision medicine

The toughest challenge OMICs face is that they provide extremely high molecular resolution but poor spatial information. Understanding the cellular/histological context of the overwhelming genetic data is critical for a full understanding of the clinical behavior of a malignant tumor. Digital pathology can add an extra layer of information to help visualize in a spatial and microenvironmental context the molecular information of cancer. Thus, histo-genomics provide a unique chance for data integration. In the era of a precision medicine, a four-dimensional (4D) (temporal/spatial) analysis of cancer aided by digital pathology can be a critical step to understand the evolution/progression of different cancers and consequently tailor individual treatment plans. For instance, the integration of molecular biomarkers expression into a three-dimensional (3D) image of a digitally scanned tumor can offer a better understanding of its subtype, behavior, host immune response and prognosis. Using advanced digital image analysis, a larger spectrum of parameters can be analyzed as potential predictors of clinical behavior. Correlation between morphological features and host immune response can be also performed with therapeutic implications. Radio-histomics, or the interface of radiological images and histology is another emerging exciting field which encompasses the integration of radiological imaging with digital pathological images, genomics, and clinical data to portray a more holistic approach to understating and treating disease. These advances in digital slide scanning are not without technical challenges, which will be addressed carefully in this review with quick peek at its future.

The subgross morphology of breast carcinomas: a single-institution series of 2033 consecutive cases documented in large-format histology slides

A large-format histology technique represents the most convenient method for documenting and assessing the subgross morphological prognostic parameters of breast cancer (i.e., the distribution of the tumor's invasive and in situ components, disease extent, and tumor size), especially when used in conjunction with systematic radiological-pathological correlation. Here we report a consecutive series of 2033 breast carcinomas operated on in Dalarna, Sweden, with a particular focus on these subgross parameters. We separately analyzed the distributions of the in situ and invasive components of the tumors and then combined these into an aggregate pattern when both components were present. We found that 40% of breast carcinomas had a simple (unifocal) subgross morphology, while 60% had a complex morphology presenting with multifocal or diffuse components. Extensive tumors (occupying a total volume of breast tissue with the greatest dimension being ≥ 40 mm) were more common in complex cases, occurring in 66% of multifocal cases and 88% of diffuse cases, compared with only 5% of unifocal cases. Compared with luminal A-like tumors, HER2-expressing tumors exhibited a significantly larger extent. Triple-negative and basal-like carcinomas tended to have a larger tumor size (based on the largest dimension of the largest invasive focus). In this report, we discuss the prognostic impact of these parameters and the necessity of their correct assessment in the diagnostic routine.

Three-dimensional tumor visualization of invasive breast carcinomas using whole-mount serial section histopathology: implications for tumor size assessment

PURPOSE

Linear tumor size (T-size) estimated with conventional histology informs breast cancer management. Previously we demonstrated significant differences in margin and focality estimates using conventional histology versus digital whole-mount serial sections (WMSS). Using WMSS we can measure T-size or volume. Here, we compare WMSS T-size with volume, and with T-size measured conventionally. We also compare the ellipsoid model for calculating tumor volume to direct, WMSS measurement.

METHODS

Two pathologists contoured regions of invasive carcinoma and measured T-size from both WMSS and (simulated) conventional sections in 55 consecutive lumpectomy specimens. Volume was measured directly from the contours. Measurements were compared using the paired t-test or Spearman's rank-order correlation. A five-point 'border index' was devised and assigned to each case to parametrize tumor shape considering 'compactness' or cellularity. Tumor volumes calculated assuming ellipsoid geometry were compared with direct, WMSS measurements.

RESULTS

WMSS reported significantly larger T-size than conventional histology in the majority of cases [61.8%, 34/55; means = (2.34 cm; 1.99 cm), p < 0.001], with a 16.4% (9/55) rate of 'upstaging'. The majority of discordances were due to undersampling. T-size and volume were strongly correlated (r = 0.838, p < 0.001). Significantly lower volume was obtained with WMSS versus ellipsoid modeling [means = (1.18 cm³; 1.45 cm³), p < 0.001].

CONCLUSIONS

Significantly larger T-size is measured with WMSS than conventionally, due primarily to undersampling in the latter. Volume and linear size are highly correlated. Diffuse tumors interspersed with normal or non-invasive elements may be sampled less extensively than more localized masses. The ellipsoid model overestimates tumor volume.

Emergence of "Big Data" and Its Potential and Current Limitations in Medical Imaging

Although electronic imaging was performed in the early 1950s in nuclear medicine, it was the introduction of computed tomography in 1972 that caused a revolution in medical imaging in that it marked the beginning of the inevitable transformation to digital imaging. This transformation is now more or less complete. While initially these CT images were relatively small, comprised of only about 6400 pixels per slice, the steady move toward higher spatial resolution, multislice imaging, digital radiography, and fluoroscopy rapidly increased the size of images and the amount of data required to be stored, processed, displayed, and moved about in a medical imaging department. The more recent introduction of digital pathology with submicron-sized pixels and the need for color further increases these demands. Rising work volumes in hospital, a push for cost containment, and a move toward greater precision in diagnosis and treatment of disease all work together to motivate the development of automated image analysis algorithms and techniques to improve efficiencies in in vivo imaging and pathology. This may require bringing together information from different imaging and nonimaging sources within the institution. While technological development has provided practical means for storage of the burgeoning data load and the use of multiple processors and high-speed networks has enabled more sophisticated analysis locally or in the cloud, challenges remain in terms of the ability to integrate data from different systems, the development of appropriately annotated image bases for training and testing of algorithms, and issues around privacy and ownership in obtaining access to patient-related data.

3D chemical imaging of the brain using quantitative IR spectro-microscopy

Three-dimensional (3D) histology is the next frontier for modern anatomo-pathology. Characterizing abnormal parameters in a tissue is essential to understand the rationale of pathology development. However, there is no analytical technique, in vivo or histological, that is able to discover such abnormal features and provide a 3D distribution at microscopic resolution. Here, we introduce a unique high-throughput infrared (IR) microscopy method that combines automated image correction and subsequent spectral data analysis for 3D-IR image reconstruction. We performed spectral analysis of a complete organ for a small animal model, a mouse brain with an implanted glioma tumor. The 3D-IR image is reconstructed from 370 consecutive tissue sections and corrected using the X-ray tomogram of the organ for an accurate quantitative analysis of the chemical content. A 3D matrix of 89 × 106 IR spectra is generated, allowing us to separate the tumor mass from healthy brain tissues based on various anatomical, chemical, and metabolic parameters. We demonstrate that quantitative metabolic parameters can be extracted from the IR spectra for the characterization of the brain vs. tumor metabolism (assessing the Warburg effect in tumors). Our method can be further exploited by searching for the whole spectral profile, discriminating tumor vs. healthy tissue in a non-supervised manner, which we call 'spectromics'.

3D volume reconstruction from serial breast specimen radiographs for mapping between histology and 3D whole specimen imaging

PURPOSE

In breast imaging, radiological in vivo images, such as x-ray mammography and magnetic resonance imaging (MRI), are used for tumor detection, diagnosis, and size determination. After excision, the specimen is typically sliced into slabs and a small subset is sampled. Histopathological imaging of the stained samples is used as the gold standard for characterization of the tumor microenvironment. A 3D volume reconstruction of the whole specimen from the 2D slabs could facilitate bridging the gap between histology and in vivo radiological imaging. This task is challenging, however, due to the large deformation that the breast tissue undergoes after surgery and the significant undersampling of the specimen obtained in histology. In this work, we present a method to reconstruct a coherent 3D volume from 2D digital radiographs of the specimen slabs.

METHODS

To reconstruct a 3D breast specimen volume, we propose the use of multiple target neighboring slices, when deforming each 2D slab radiograph in the volume, rather than performing pairwise registrations. The algorithm combines neighborhood slice information with free-form deformations, which enables a flexible, nonlinear deformation to be computed subject to the constraint that a coherent 3D volume is obtained. The neighborhood information provides adequate constraints, without the need for any additional regularization terms.

RESULTS

The volume reconstruction algorithm is validated on clinical mastectomy samples using a quantitative assessment of the volume reconstruction smoothness and a comparison with a whole specimen 3D image acquired for validation before slicing. Additionally, a target registration error of 5 mm (comparable to the specimen slab thickness of 4 mm) was obtained for five cases. The error was computed using manual annotations from four observers as gold standard, with interobserver variability of 3.4 mm. Finally, we illustrate how the reconstructed volumes can be used to map histology images to a 3D specimen image of the whole sample (either MRI or CT).

CONCLUSIONS

Qualitative and quantitative assessment has illustrated the benefit of using our proposed methodology to reconstruct a coherent specimen volume from serial slab radiographs. To our knowledge, this is the first method that has been applied to clinical breast cases, with the goal of reconstructing a whole specimen sample. The algorithm can be used as part of the pipeline of mapping histology images to ex vivo and ultimately in vivo radiological images of the breast.

Optimized SIFTFlow for registration of whole-mount histology to reference optical images

The registration of two-dimensional histology images to reference images from other modalities is an important preprocessing step in the reconstruction of three-dimensional histology volumes. This is a challenging problem because of the differences in the appearances of histology images and other modalities, and the presence of large nonrigid deformations which occur during slide preparation. This paper shows the feasibility of using densely sampled scale-invariant feature transform (SIFT) features and a SIFTFlow deformable registration algorithm for coregistering whole-mount histology images with blockface optical images. We present a method for jointly optimizing the regularization parameters used by the SIFTFlow objective function and use it to determine the most appropriate values for the registration of breast lumpectomy specimens. We demonstrate that tuning the regularization parameters results in significant improvements in accuracy and we also show that SIFTFlow outperforms a previously described edge-based registration method. The accuracy of the histology images to blockface images registration using the optimized SIFTFlow method was assessed using an independent test set of images from five different lumpectomy specimens and the mean registration error was [Formula: see text].

Cost-effectiveness analysis of whole-mount pathology processing for patients with early breast cancer undergoing breast conservation

BACKGROUND

Obtaining accurate histopathologic detail for breast lumpectomy specimens is challenging because of sampling and loss of three-dimensional conformational features with conventional processing. The whole-mount (wm) technique is a novel method of serial pathologic sectioning designed to optimize cross-sectional visualization of resected specimens and determination of margin status.

METHODS

Using a Markov chain cohort simulation cost-effectiveness model, we compared conventional processing with wm technique for breast lumpectomies. Cost-effectiveness was evaluated from the perspective of the Canadian health care system and compared using incremental cost-effectiveness ratios (icers) for cost per quality-adjusted life-year (qaly) over a 10-year time horizon. Deterministic and probabilistic sensitivity analyses were performed to test the robustness of the model with willingness-to-pay (wtp) thresholds of $0-$100,000. Costs are reported in adjusted 2014 Canadian dollars, discounted at a rate of 3%.

RESULTS

Compared with conventional processing, wm processing is more costly ($19,989 vs. $18,427) but generates 0.03 more qalys over 10 years. The icer is $45,414, indicating that this additional amount is required for each additional qaly obtained. The model was robust to all variance in parameters, with the prevalence of positive margins accounting for most of the model's variability.

CONCLUSIONS

After a wtp threshold of $45,414, wm processing becomes cost-effective and ultimately generates fewer recurrences and marginally more qalys over time. Excellent baseline outcomes for the current treatment of breast cancer mean that incremental differences in survival are small. However, the overall benefit of the wm technique should be considered in the context of achieving improved accuracy and not just enhancements in clinical effectiveness.