Automated reconstruction algorithm for identification of 3D architectures of cribriform ductal carcinoma in situ

Three-dimensional imaging and analysis of pathological tissue samples with de novo generation of citrate-based fluorophores

Two-dimensional (2D) histopathology based on the observation of thin tissue slides is the current paradigm in diagnosis and prognosis. However, labeling strategies in conventional histopathology are limited in compatibility with 3D imaging combined with tissue clearing techniques. Here, we present a rapid and efficient volumetric imaging technique of pathological tissues called 3D tissue imaging through de novo formation of fluorophores, or 3DNFC, which is the integration of citrate-based fluorogenic reaction DNFC and tissue clearing techniques. 3DNFC markedly increases the fluorescence intensity of tissues by generating fluorophores on nonfluorescent amino-terminal cysteine and visualizes the 3D structure of the tissues to provide their anatomical morphology and volumetric information. Furthermore, the application of 3DNFC to pathological tissue achieves the 3D reconstruction for the unbiased analysis of diverse features of the disorders in their natural context. We suggest that 3DNFC is a promising volumetric imaging method for the prognosis and diagnosis of pathological tissues.

Usefulness of X-ray dark-field imaging in the evaluation of local recurrence after nipple-sparing mastectomy

PURPOSE

Our previous study suggests that the cross-sectional morphology of ducts and branching of ducts in the nipple are associated with the presence of breast cancer. In this study, we evaluated whether cross-sectional morphology and duct branching of human nipple obtained by X-ray dark-field imaging tomographic technique (XDFI-CT) could predict the likelihood of the presence of intraductal cancer into the nipple.

METHODS

A total of 51 nipple specimens were obtained from consecutive total mastectomies performed for breast cancer in Nagoya Medical Center. After reconstructing 3D images of the nipple using XDFI-CT, the cross-sectional images and the 3D arrangement of ducts were extracted. These cross-sectional images of ducts were classified into four patterns based on the status of the lumen without being informed of pathology results.

RESULTS

Of the four patterns, the distended ducts with heterogenous content were highly correlated with the presence of ductal carcinoma in situ confirmed by histopathology. The total number of orifices identified in the 51 specimens was 1298, and 182 (14%) at the tip and 19 (1.5%) at least 5 mm depth from the tip were composed of two or more ducts.

CONCLUSIONS

Anatomy of nipple ducts is essential to evaluate risk of local recurrence after nipple-sparing mastectomy because cancerous spread occurs within the duct of the same segment of the mammary duct-lobular system in the in situ stage. The 3D microscale anatomy of nipple ducts revealed by XDFI-CT provides useful information to assess the risk of breast cancer involvement at the preserved portion in nipple-sparing mastectomy.

X-ray dark-field phase-contrast imaging: Origins of the concept to practical implementation and applications

The basic idea of X-ray dark-field imaging (XDFI), first presented in 2000, was based on the concepts used in an X-ray interferometer. In this article, we review 20 years of developments in our theoretical understanding, scientific instrumentation, and experimental demonstration of XDFI and its applications to medical imaging. We first describe the concepts underlying XDFI that are responsible for imparting phase contrast information in projection X-ray images. We then review the algorithms that can convert these projection phase images into three-dimensional tomographic slices. Various implementations of computed tomography reconstructions algorithms for XDFI data are discussed. The next four sections describe and illustrate potential applications of XDFI in pathology, musculoskeletal imaging, oncologic imaging, and neuroimaging. The sample applications that are presented illustrate potential use scenarios for XDFI in histopathology and other clinical applications. Finally, the last section presents future perspectives and potential technical developments that can make XDFI an even more powerful tool.

3D histopathology of human tumours by fast clearing and ultramicroscopy

Here, we describe a novel approach that allows pathologists to three-dimensionally analyse malignant tissues, including the tumour-host tissue interface. Our visualization technique utilizes a combination of ultrafast chemical tissue clearing and light-sheet microscopy to obtain virtual slices and 3D reconstructions of up to multiple centimetre sized tumour resectates. For the clearing of tumours we propose a preparation technique comprising three steps: (a) Fixation and enhancement of tissue autofluorescence with formalin/5-sulfosalicylic acid. (b) Ultrafast active chemical dehydration with 2,2-dimethoxypropane and (c) refractive index matching with dibenzyl ether at up to 56 °C. After clearing, the tumour resectates are imaged. The images are computationally post-processed for contrast enhancement and artefact removal and then 3D reconstructed. Importantly, the sequence a–c is fully reversible, allowing the morphological correlation of one and the same histological structures, once visualized with our novel technique and once visualized by standard H&E- and IHC-staining. After reverting the clearing procedure followed by standard H&E processing, the hallmarks of ductal carcinoma in situ (DCIS) found in the cleared samples could be successfully correlated with the corresponding structures present in H&E and IHC staining. Since the imaging of several thousands of optical sections is a fast process, it is possible to analyse a larger part of the tumour than by mechanical slicing. As this also adds further information about the 3D structure of malignancies, we expect that our technology will become a valuable addition for histological diagnosis in clinical pathology.

Three-dimensional histopathological reconstruction of bladder tumours

BACKGROUND

Histopathological analysis is the cornerstone in bladder cancer (BCa) diagnosis. These analysis suffer from a moderate observer agreement in the staging of bladder cancer. Three-dimensional reconstructions have the potential to support the pathologists in visualizing spatial arrangements of structures, which may improve the interpretation of specimen. The aim of this study is to present three-dimensional (3D) reconstructions of histology images.

METHODS

En-bloc specimens of transurethral bladder tumour resections were formalin fixed and paraffin embedded. Specimens were cut into sections of 4 μm and stained with Hematoxylin and Eosin (H&E). With a Phillips IntelliSite UltraFast scanner, glass slides were digitized at 20x magnification. The digital images were aligned by performing rigid and affine image alignment. The tumour and the muscularis propria (MP) were manually delineated to create 3D segmentations. In conjunction with a 3D display, the results were visualized with the Vesalius3D interactive visualization application for a 3D workstation.

RESULTS

En-bloc resection was performed in 21 BCa patients. Per case, 26-30 sections were included for the reconstruction into a 3D volume. Five cases were excluded due to export problems, size of the dataset or condition of the tissue block. Qualitative evaluation suggested an accurate registration for 13 out of 16 cases. The segmentations allowed full 3D visualization and evaluation of the spatial relationship of the BCa tumour and the MP.

CONCLUSION

Digital scanning of en-bloc resected specimens allows a full-fledged 3D reconstruction and analysis and has a potential role to support pathologists in the staging of BCa.

Histopathology: ditch the slides, because digital and 3D are on show

Due to the growing field of digital pathology, more and more digital histology slides are becoming available. This improves the accessibility, allows teleconsultations from specialized pathologists, improves education, and might give urologist the possibility to review the slides in patient management systems. Moreover, by stacking multiple two-dimensional (2D) digital slides, three-dimensional volumes can be created, allowing improved insight in the growth pattern of a tumor. With the addition of computer-aided diagnosis systems, pathologist can be guided to regions of interest, potentially reducing the workload and interobserver variation. Digital (3D) pathology has the potential to improve dialog between the pathologist and urologist, and, therefore, results in a better treatment selection for urologic patients.

CUBIC pathology: three-dimensional imaging for pathological diagnosis

The examination of hematoxylin and eosin (H&E)-stained tissues on glass slides by conventional light microscopy is the foundation for histopathological diagnosis. However, this conventional method has some limitations in x-y axes due to its relatively narrow range of observation area and in z-axis due to its two-dimensionality. In this study, we applied a CUBIC pipeline, which is the most powerful tissue-clearing and three-dimensional (3D)-imaging technique, to clinical pathology. CUBIC was applicable to 3D imaging of both normal and abnormal patient-derived, human lung and lymph node tissues. Notably, the combination of deparaffinization and CUBIC enabled 3D imaging of specimens derived from paraffin-embedded tissue blocks, allowing quantitative evaluation of nuclear and structural atypia of an archival malignant lymphoma tissue. Furthermore, to examine whether CUBIC can be applied to practical use in pathological diagnosis, we performed a histopathological screening of a lymph node metastasis based on CUBIC, which successfully improved the sensitivity in detecting minor metastatic carcinoma nodules in lymph nodes. Collectively, our results indicate that CUBIC significantly contributes to retrospective and prospective clinicopathological diagnosis, which might lead to the establishment of a novel field of medical science based on 3D histopathology.

Development of a 2D Image Reconstruction and Viewing System for Histological Images from Multiple Tissue Blocks: Towards High-Resolution Whole-Organ 3D Histological Images

High-resolution 3D histology image reconstruction of the whole brain organ starts from reconstructing the high-resolution 2D histology images of a brain slice. In this paper, we introduced a method to automatically align the histology images of thin tissue sections cut from the multiple paraffin-embedded tissue blocks of a brain slice. For this method, we employed template matching and incorporated an optimization technique to further improve the accuracy of the 2D reconstructed image. In the template matching, we used the gross image of the brain slice as a reference to the reconstructed 2D histology image of the slice, while in the optimization procedure, we utilized the Jaccard index as the metric of the reconstruction accuracy. The results of our experiment on the initial 3 different whole-brain tissue slices showed that while the method works, it is also constrained by tissue deformations introduced during the tissue processing and slicing. The size of the reconstructed high-resolution 2D histology image of a brain slice is huge, and designing an image viewer that makes particularly efficient use of the computing power of a standard computer used in our laboratories is of interest. We also present the initial implementation of our 2D image viewer system in this paper.

3-D Tissue Modelling and Virtual Pathology as New Approaches to Study Ductal Carcinoma In Situ

Widespread screening mammography programmes mean that ductal carcinoma in situ (DCIS), a pre-invasive breast lesion, is now more frequently diagnosed. However, not all diagnosed DCIS lesions progress to invasive breast cancer, which presents a dilemma for clinicians. As such, there is much interest in studying DCIS in the laboratory, in order to help understand more about its biology and determine the characteristics of those that progress to invasion. Greater knowledge would lead to targeted and better DCIS treatment. Here, we outline some of the models available to study DCIS, with a particular focus on animal-free systems.

Three-dimensional reconstruction of ductal carcinoma in situ with virtual slides

AIMS

This study aimed to assess the feasibility of using virtual slides to create 3D histopathological reconstructions to aid in the study of the biology of DCIS.

METHODS

Four μm thick serial sections of formalin fixed paraffin embedded tissue from three cases were cut and mounted onto glass slides, stained with haematoxylin and eosin, then scanned. The three image stacks comprised 30, 115 and 100 scanned sections creating a similar number of virtual slides. The virtual slides were registered using custom 3D software to create 3D tissue volumes. The volumes were annotated to highlight distinct features and 3D visualisations (segmentations) were created to study these features in 3D.

RESULTS

The most time-intensive step was the manual annotation of virtual slides 3D histopathological reconstructions were created of (i) DCIS surrounded by adjacent invasion; (ii) pure DCIS and (iii) a 'normal' lobule.

CONCLUSION

3D in silico reconstructions of DCIS were created and more extensive studies can now be done within a realistic timescale. We have identified structural similarities between a benign lobule and DCIS which support the view that much DCIS, apparently in a 'duct' is contained within and expanded lobule. This method has the potential to provide insights into the biology of DCIS.

Mathematical modeling for novel cancer drug discovery and development

INTRODUCTION

Mathematical modeling enables: the in silico classification of cancers, the prediction of disease outcomes, optimization of therapy, identification of promising drug targets and prediction of resistance to anticancer drugs. In silico pre-screened drug targets can be validated by a small number of carefully selected experiments.

AREAS COVERED

This review discusses the basics of mathematical modeling in cancer drug discovery and development. The topics include in silico discovery of novel molecular drug targets, optimization of immunotherapies, personalized medicine and guiding preclinical and clinical trials. Breast cancer has been used to demonstrate the applications of mathematical modeling in cancer diagnostics, the identification of high-risk population, cancer screening strategies, prediction of tumor growth and guiding cancer treatment.

EXPERT OPINION

Mathematical models are the key components of the toolkit used in the fight against cancer. The combinatorial complexity of new drugs discovery is enormous, making systematic drug discovery, by experimentation, alone difficult if not impossible. The biggest challenges include seamless integration of growing data, information and knowledge, and making them available for a multiplicity of analyses. Mathematical models are essential for bringing cancer drug discovery into the era of Omics, Big Data and personalized medicine.