Increasing specimen coverage using digital whole-mount breast pathology: Implementation, clinical feasibility and application in research

Multifocal and diffusely infiltrating breast cancers are highly fatal subgroups needing further improvement in diagnostic and therapeutic strategies

Physicians treating breast cancer patients often wonder why this dreaded disease is still fatal in some women despite our best diagnostic and therapeutic efforts. Our own studies on prospectively documented cases spanning several decades have given us new insights for approaching this problem. By using imaging biomarkers to classify breast cancer subtypes according to their apparent site of origin, we found that a majority of breast cancer deaths (71%) occur in a minority of breast cancers (45%). Breast cancer deaths are significantly more likely to occur in women with multifocal acinar adenocarcinoma of the breast, AAB (13.1%), diffusely invasive breast cancers of ductal origin, DAB (24 %) and breast malignancies of mesenchymal hybrid cell origin, BCMO (33.7%) compared with women having unifocal invasive breast cancers (6.1%). Preventing more of these fatal events will require a re-evaluation of the current imperfect histopathologic terminology of breast cancer with special attention to the diffuse breast cancer subtypes, intensification of multimodality imaging and multidisciplinary management, as well as application of image guided large format histopathology.

Can we improve breast cancer management using an image-guided histopathology workup supported by larger histopathology sections?

PURPOSE

Breast radiologists examine the entire breast in full-size images, while breast pathologists examine small tissue samples at high magnification. The diagnostic information from these complementary imaging approaches can be difficult to integrate for a more clinically relevant evaluation of malignancies spanning several centimetres. We have explored the advantages and disadvantages of imaging guided larger section pathology techniques compared with the standard 2 × 2.5 cm. small section technique.

METHODS

We compared the ability of conventional small section histopathology with larger section histopathology techniques to examine surgical resection margins and full disease extent. We evaluated the pre-surgical imaging workup and use of microfocus magnification radiography of sliced surgical specimens in the histopathologic evaluation of disease extent and status of surgical margins.

RESULTS

Image assisted large section histopathology of excised breast tissue enables comprehensive examination of an approximately tenfold larger contiguous tissue area than is provided by conventional small section technology. Attempting to cover the full area of each consecutive slice of resected tissue is more labour-intensive and expensive with the small section approach and poses challenges in reconstituting three-dimensional tumour architecture after morcellation and sectioning. Restricting histopathologic examination to a limited number of samples provides an incomplete evaluation of surgical margins.

CONCLUSIONS

A considerably improved documentation of breast cancer and a more reliable assessment of tissue margins is provided by using larger sized histopathology samples to correlate with breast imaging findings. These in turn can enable more appropriate treatment planning, improved surgical performance, fewer recurrences, and better patient outcome. Uncertainty of surgical margin evaluation inherent to the standard small section technique can lead to inappropriate decisions in surgical management and adjunctive therapy. Progress in breast diagnosis and treatment will largely depend on whether histopathology terminology and technique will undergo a revolution similar to the one that has already occurred in breast imaging.

Targeted labeling with tissue marking dyes guided by magnifying endoscopy of endoscopic submucosal dissection specimen improves the accuracy of endoscopic and histopathological diagnosis of early gastric cancer: a before–after study

BACKGROUND

Although histopathological evaluation after endoscopic submucosal dissection (ESD) is critical to assess the accuracy of endoscopic diagnosis, it is still challenging to perform precise endoscopic to pathological evaluation. We evaluated the importance of tissue marking dye (TMD)-targeted marking for post-ESD specimen guided by magnificent endoscope on histopathological accuracy and endoscopic-to-histopathological reconstruction.

STUDY DESIGN

A total of 81 specimens resected by ESD [43 without TMD marking (N-TMD group), and 38 specimens with TMD-targeted cancerous areas marking guided by post-procedural magnifying endoscopy on resected specimens (TMD group)] between January 31, 2019, and January 31, 2022 at the Renmin Hospital of Wuhan University were included in the study. The baseline characteristics of patients, discrepancies between endoscopic and histopathological diagnosis, and the impact of TMD on histopathological diagnosis and reconstruction were analyzed.

RESULTS

Discrepancies between endoscopic (pre-ESD) and histopathological (post-ESD) diagnosis increased significantly in TMD group (68.4% (26/38) for tumor areas, 26.3% (10/38) for tumor margins, and 26.3% (10/38) for tumor differentiations) when compared with N-TMD group (p < 0.0001). Deeper sections were achieved in all TMD-marked resected lesions and 27.9% (12/43) lesions in the N-TMD group (p < 0.001). More pathological evaluations in TMD group were changed from curative resection to non-curative resection [6/38(15.8%) vs 1/43(2.3%)] compared with N-TMD group (p < 0.0001). TMD-targeted marking also improved the efficiency of histopathological reconstruction on pre-procedural endoscopic images and benefit endoscopists training.

CONCLUSION

TMD-targeted labeling on resected specimens could improve precise endoscopic-to-pathological diagnosis, reconstruction by point-to-point marking and benefit endoscopists training.

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.

Feasibility of Micro-Computed Tomography Imaging for Direct Assessment of Surgical Resection Margins During Breast-Conserving Surgery

BACKGROUND

To analyze the feasibility and accuracy of micro-computed tomography (micro-CT) for surgical margin assessment in breast excision specimen.

MATERIALS AND METHODS

Two data sets of 30 micro-CT scans were retrospectively evaluated for positive resection margins by four observers in two phases, using pathology as a gold standard. Results of phase 1 were evaluated to define micro-CT evaluation guidelines for phase 2. Interobserver agreement was also assessed (kappa). In addition, a prospective study was conducted in which 40 micro-CT scans were directly acquired, reconstructed, and evaluated for positive resection margins by one observer. A suspect positive resection margin on micro-CT was annotated onto the specimen with ink, enabling local validation by pathology. Main outcome measures were accuracy, sensitivity, specificity, and positive predictive value (PPV).

RESULTS

Average accuracy, sensitivity, specificity, and PPV for the four observers were 63%, 38%, 70%, and 22%, respectively, in phase 1 and 72%, 40%, 78%, and 26%, respectively, in phase 2. The interobserver agreement was fair [kappa (range), 0.31 (0.12-0.80) in phase 1 and 0.23 (0-0.43) in phase 2]. In the prospective study 70% of the surgical resection margins were correctly evaluated. Ten specimens were annotated for positive resection margins, which correlated with three positive and three close (<1 mm) margins on pathology. Sensitivity, specificity, and PPV were 38%, 78%, and 30%, respectively.

CONCLUSIONS

Micro-CT imaging of breast excision specimen has moderate accuracy and considerable interobserver variation for analysis of surgical resection margins. Especially sensitivity and PPV need to be improved before micro-CT-based margin assessment can be introduced in clinical practice.

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.

Application of Radiomics and Decision Support Systems for Breast MR Differential Diagnosis

Over the years, MR systems have evolved from imaging modalities to advanced computational systems producing a variety of numerical parameters that can be used for the noninvasive preoperative assessment of breast pathology. Furthermore, the combination with state-of-the-art image analysis methods provides a plethora of quantifiable imaging features, termed radiomics that increases diagnostic accuracy towards individualized therapy planning. More importantly, radiomics can now be complemented by the emerging deep learning techniques for further process automation and correlation with other clinical data which facilitate the monitoring of treatment response, as well as the prediction of patient's outcome, by means of unravelling of the complex underlying pathophysiological mechanisms which are reflected in tissue phenotype. The scope of this review is to provide applications and limitations of radiomics towards the development of clinical decision support systems for breast cancer diagnosis and prognosis.

A Cluster-then-label Semi-supervised Learning Approach for Pathology Image Classification

Completely labeled pathology datasets are often challenging and time-consuming to obtain. Semi-supervised learning (SSL) methods are able to learn from fewer labeled data points with the help of a large number of unlabeled data points. In this paper, we investigated the possibility of using clustering analysis to identify the underlying structure of the data space for SSL. A cluster-then-label method was proposed to identify high-density regions in the data space which were then used to help a supervised SVM in finding the decision boundary. We have compared our method with other supervised and semi-supervised state-of-the-art techniques using two different classification tasks applied to breast pathology datasets. We found that compared with other state-of-the-art supervised and semi-supervised methods, our SSL method is able to improve classification performance when a limited number of labeled data instances are made available. We also showed that it is important to examine the underlying distribution of the data space before applying SSL techniques to ensure semi-supervised learning assumptions are not violated by the data.

An Image Analysis Resource for Cancer Research: PIIP-Pathology Image Informatics Platform for Visualization, Analysis, and Management

Pathology Image Informatics Platform (PIIP) is an NCI/NIH sponsored project intended for managing, annotating, sharing, and quantitatively analyzing digital pathology imaging data. It expands on an existing, freely available pathology image viewer, Sedeen. The goal of this project is to develop and embed some commonly used image analysis applications into the Sedeen viewer to create a freely available resource for the digital pathology and cancer research communities. Thus far, new plugins have been developed and incorporated into the platform for out of focus detection, region of interest transformation, and IHC slide analysis. Our biomarker quantification and nuclear segmentation algorithms, written in MATLAB, have also been integrated into the viewer. This article describes the viewing software and the mechanism to extend functionality by plugins, brief descriptions of which are provided as examples, to guide users who want to use this platform. PIIP project materials, including a video describing its usage and applications, and links for the Sedeen Viewer, plug-ins, and user manuals are freely available through the project web page: http://pathiip.org Cancer Res; 77(21); e83-86. ©2017 AACR.

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].

Triaging Diagnostically Relevant Regions from Pathology Whole Slides of Breast Cancer: A Texture Based Approach

PURPOSE

Pathologists often look at whole slide images (WSIs) at low magnification to find potentially important regions and then zoom in to higher magnification to perform more sophisticated analysis of the tissue structures. Many automated methods of WSI analysis attempt to preprocess the down-sampled image in order to select salient regions which are then further analyzed by a more computationally intensive step at full magnification. Although it can greatly reduce processing times, this process may lead to small potentially important regions being overlooked at low magnification. We propose a texture analysis technique to ease the processing of H&E stained WSIs by triaging clinically important regions.

METHOD

Image patches randomly selected from the whole tissue area were divided into smaller tiles and Gaussian-like texture filters were applied to them. Texture filter responses from each tile were combined together and statistical measures were derived from their histograms of responses. Bag of visual words pipeline was then employed to combine extracted features from tiles to form one histogram of words per every image patch. A support vector machine classifier was trained using the calculated histograms of words to be able to distinguish between clinically relevant and irrelevant patches.

RESULT

Experimental analysis on 5151 image patches from 10 patient cases (65 tissue slides) indicated that our proposed texture technique out-performed two previously proposed colour and intensity based methods with an area under the ROC curve of 0.87.

CONCLUSION

Texture features can be employed to triage clinically important areas within large WSIs.

Real-time whole slide mosaicing for non-automated microscopes in histopathology analysis

Context:

Mosaics of Whole Slides (WS) are a valuable resource for pathologists to have the whole sample available at high resolution. The WS mosaic provides pathologists with an overview of the whole sample at a glance, helping them to make a reliable diagnosis. Despite recent solutions exist for creating WS mosaics based, for instance, on automated microscopes with motorized stages or WS scanner, most of the histopathology analysis are still performed in laboratories endowed with standard manual stage microscopes. Nowadays, there are lots of dedicated devices and hardware to achieve WS automatically and in batch, but only few of them are conceived to work tightly connected with a microscope and none of them is capable of working in real-time with common light microscopes. However, there is a need of having low-cost yet effective mosaicing applications even in small laboratories to improve routine histopathological analyses or to perform remote diagnoses.

Aims:

The purpose of this work is to study and develop a real-time mosaicing algorithm working even using non-automated microscopes, to enable pathologists to achieve WS while moving the holder manually, without exploiting any dedicated device. This choice enables pathologists to build WS in real-time, while browsing the sample as they are accustomed to, helping them to identify, locate, and digitally annotate lesions fast.

Materials and Methods:

Our method exploits fast feature tracker and frame to frame registration that we implemented on common graphics processing unit cards. The system work with common light microscopes endowed with a digital camera and connected to a commodity personal computer.

Result and Conclusion:

The system has been tested on several histological samples to test the effectiveness of the algorithm to work with mosaicing having different appearances as far as brightness, contrast, texture, and detail levels are concerned, attaining sub-pixel registration accuracy at real-time interactive rates.

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

Tumour size, most commonly measured by maximum linear extent, remains a strong predictor of survival in breast cancer. Tumour volume, proportional to the number of tumour cells, may be a more accurate surrogate for size. We describe a novel “3D pathology volumetric technique” for lumpectomies and compare it with 2D measurements. Volume renderings and total tumour volume are computed from digitized whole-mount serial sections using custom software tools. Results are presented for two lumpectomy specimens selected for tumour features which may challenge accurate measurement of tumour burden with conventional, sampling-based pathology: (1) an infiltrative pattern admixed with normal breast elements; (2) a localized invasive mass separated from the in situ component by benign tissue. Spatial relationships between key features (tumour foci, close or involved margins) are clearly visualized in volume renderings. Invasive tumour burden can be underestimated using conventional pathology, compared to the volumetric technique (infiltrative pattern: 30% underestimation; localized mass: 3% underestimation for invasive tumour, 44% for in situ component). Tumour volume approximated from 2D measurements (i.e., maximum linear extent), assuming elliptical geometry, was seen to overestimate volume compared to the 3D volumetric calculation (by a factor of 7x for the infiltrative pattern; 1.5x for the localized invasive mass).