Mapping the cited evidence of ductal carcinoma in situ from the 5th edition of the World Health Organisation classification of tumours of the breast.

AIMS: Ductal carcinoma in situ (DCIS) is recognised by the World Health Organisation (WHO) Classification of Tumours (WCT) as a non-invasive neoplastic epithelial proliferation confined to the mammary ducts and lobules. This report categorises the references cited in the DCIS chapter of the 5th edition of the WCT (Breast Tumours) according to prevailing evidence levels for evidence-based medicine and the Hierarchy of Evidence for Tumour Pathology (HETP), identifying potential gaps that can inform subsequent editions of the WCT for this tumour. METHODS AND RESULTS: We included all citations from the DCIS chapter of the WCT (Breast Tumours, 5th edition). Each citation was appraised according to its study design and evidence level. We developed our map of cited evidence, which is a graphical matrix of tumour type (column) and tumour descriptors (rows). Spheres were used to represent the evidence, with size and colour corresponding to their number and evidence level respectively. Thirty-six publications were retrieved. The cited literature in the DCIS chapter comprised mainly case series and were regarded as low-level. We found an unequal distribution of citations among tumour descriptors. 'Pathogenesis' and 'prognosis and prediction' contained the most references, while 'clinical features', 'aetiology' and 'diagnostic molecular pathology' had only a single citation each. 'Prognosis and prediction' had the greatest proportion of moderate- and high-levels of evidence. CONCLUSION: Our findings align with the disposition for observational studies inherent in the field of pathology. Our map is a springboard for future efforts in mapping all available evidence on DCIS, potentially augmenting the editorial process and future editions of WCTs.

Saliency Map and Deep Learning in Binary Classification of Brain Tumours.

The paper was devoted to the application of saliency analysis methods in the performance analysis of deep neural networks used for the binary classification of brain tumours. We have presented the basic issues related to deep learning techniques. A significant challenge in using deep learning methods is the ability to explain the decision-making process of the network. To ensure accurate results, the deep network being used must undergo extensive training to produce high-quality predictions. There are various network architectures that differ in their properties and number of parameters. Consequently, an intriguing question is how these different networks arrive at similar or distinct decisions based on the same set of prerequisites. Therefore, three widely used deep convolutional networks have been discussed, such as VGG16, ResNet50 and EfficientNetB7, which were used as backbone models. We have customized the output layer of these pre-trained models with a softmax layer. In addition, an additional network has been described that was used to assess the saliency areas obtained. For each of the above networks, many tests have been performed using key metrics, including statistical evaluation of the impact of class activation mapping (CAM) and gradient-weighted class activation mapping (Grad-CAM) on network performance on a publicly available dataset of brain tumour X-ray images.

The International Collaboration for Cancer Classification and Research.

Gaps in the translation of research findings to clinical management have been recognized for decades. They exist for the diagnosis as well as the management of cancer. The international standards for cancer diagnosis are contained within the World Health Organization (WHO) Classification of Tumours, published by the International Agency for Research on Cancer (IARC) and known worldwide as the WHO Blue Books. In addition to their relevance to individual patients, these volumes provide a valuable contribution to cancer research and surveillance, fulfilling an important role in scientific evidence synthesis and international standard setting. However, the multidimensional nature of cancer classification, the way in which the WHO Classification of Tumours is constructed, and the scientific information overload in the field pose important challenges for the translation of research findings to tumour classification and hence cancer diagnosis. To help address these challenges, we have established the International Collaboration for Cancer Classification and Research (IC3 R) to provide a forum for the coordination of efforts in evidence generation, standard setting and best practice recommendations in the field of tumour classification. The first IC3 R meeting, held in Lyon, France, in February 2019, gathered representatives of major institutions involved in tumour classification and related fields to identify and discuss translational challenges in data comparability, standard setting, quality management, evidence evaluation and copyright, as well as to develop a collaborative plan for addressing these challenges.

Time to Classify Tumours of the Stomach and the Kidneys According to Cell of Origin.

Malignant tumours are traditionally classified according to their organ of origin and whether they are of epithelial (carcinomas) or mesenchymal (sarcomas) origin. By histological appearance the site of origin may often be confirmed. Using same treatment for tumours from the same organ is rational only when there is no principal heterogeneity between the tumours of that organ. Organ tumour heterogeneity is typical for the lungs with small cell and non-small cell tumours, for the kidneys where clear cell renal carcinoma (CCRCC) is the dominating type among other subgroups, and in the stomach with adenocarcinomas of intestinal and diffuse types. In addition, a separate type of neuroendocrine tumours (NETs) is found in most organs. Every cell type able to divide may develop into a tumour, and the different subtypes most often reflect different cell origin. In this article the focus is on the cells of origin in tumours arising in the stomach and kidneys and the close relationship between normal neuroendocrine cells and NETs. Furthermore, that the erythropoietin producing cell may be the cell of origin of CCRCC (a cancer with many similarities to NETs), and that gastric carcinomas of diffuse type may originate from the ECL cell, whereas the endodermal stem cell most probably gives rise to cancers of intestinal type.

Authentication of differential gene expression in oral squamous cell carcinoma using machine learning applications.

BACKGROUND: Recently, the possibility of tumour classification based on genetic data has been investigated. However, genetic datasets are difficult to handle because of their massive size and complexity of manipulation. In the present study, we examined the diagnostic performance of machine learning applications using imaging-based classifications of oral squamous cell carcinoma (OSCC) gene sets. METHODS: RNA sequencing data from SCC tissues from various sites, including oral, non-oral head and neck, oesophageal, and cervical regions, were downloaded from The Cancer Genome Atlas (TCGA). The feature genes were extracted through a convolutional neural network (CNN) and machine learning, and the performance of each analysis was compared. RESULTS: The ability of the machine learning analysis to classify OSCC tumours was excellent. However, the tool exhibited poorer performance in discriminating histopathologically dissimilar cancers derived from the same type of tissue than in differentiating cancers of the same histopathologic type with different tissue origins, revealing that the differential gene expression pattern is a more important factor than the histopathologic features for differentiating cancer types. CONCLUSION: The CNN-based diagnostic model and the visualisation methods using RNA sequencing data were useful for correctly categorising OSCC. The analysis showed differentially expressed genes in multiwise comparisons of various types of SCCs, such as KCNA10, FOSL2, and PRDM16, and extracted leader genes from pairwise comparisons were FGF20, DLC1, and ZNF705D.

Invited Review: DNA methylation-based classification of paediatric brain tumours.

DNA methylation-based machine learning algorithms represent powerful diagnostic tools that are currently emerging for several fields of tumour classification. For various reasons, paediatric brain tumours have been the main driving forces behind this rapid development and brain tumour classification tools are likely further advanced than in any other field of cancer diagnostics. In this review, we will discuss the main characteristics that were important for this rapid advance, namely the high clinical need for improvement of paediatric brain tumour diagnostics, the robustness of methylated DNA and the consequential possibility to generate high-quality molecular data from archival formalin-fixed paraffin-embedded pathology specimens, the implementation of a single array platform by most laboratories allowing data exchange and data pooling to an unprecedented extent, as well as the high suitability of the data format for machine learning. We will further discuss the four most central output qualities of DNA methylation profiling in a diagnostic setting (tumour classification, tumour sub-classification, copy number analysis and guidance for additional molecular testing) individually for the most frequent types of paediatric brain tumours. Lastly, we will discuss DNA methylation profiling as a tool for the detection of new paediatric brain tumour classes and will give an overview of the rapidly growing family of new tumours identified with the aid of this technique.

Interobserver variation in the classification of thymic lesions including biopsies and resection specimens in an international digital microscopy panel.

AIMS: Thymic tumours are rare in routine pathology practice. Although the World Health Organization (WHO) classification describes a number of well-defined categories, the classification remains challenging. The aim of this study was to investigate the reproducibility of the WHO classification among a large group of international pathologists with expertise in thymic pathology and by using whole slide imaging to facilitate rapid diagnostic turnover. METHODS AND RESULTS: Three hundred and five tumours, consisting of 90 biopsies and 215 resection specimens, were reviewed with a panel-based virtual microscopy approach by a group of 13 pathologists with expertise in thymic tumours over a period of 6 years. The specimens were classified according to the WHO 2015 classification. The data were subjected to statistical analysis, and interobserver concordance (Fleiss kappa) was calculated. All cases were diagnosed within a time frame of 2 weeks. The overall level of agreement was substantial (κ = 0.6762), and differed slightly between resection specimens (κ = 0.7281) and biopsies (κ = 0.5955). When analysis was limited to thymomas only, and they were grouped according to the European Society for Medical Oncology Clinical Practice Guidelines into B2, B3 versus A, AB, B1 and B3 versus A, AB, B1, B2, the level of agreement decreased slightly (κ = 0.5506 and κ = 0.4929, respectively). Difficulties arose in distinguishing thymoma from thymic carcinoma. Within the thymoma subgroup, difficulties in distinction were seen within the B group. CONCLUSIONS: Agreement in diagnosing thymic lesions is substantial when they are assessed by pathologists with experience of these rare tumours. Digital pathology decreases the turnaround time and facilitates access to what is essentially a multinational resource. This platform provides a template for dealing with rare tumours for which expertise is sparse.

Clinical consequences of controversies in gastric physiology.

Studies on the regulation of gastric acid secretion started more than 100 years ago at an early phase of experimental physiology. In nearly the whole last century there were disputes about the interpretation of the findings: the interaction between the three principle gastric acid secretagogues acetylcholine, gastrin and histamine, the cell producing the relevant histamine which turned out to be the ECL cell, the ability of the ECL cell to divide and thus develop into tumours, the classification of gastric carcinomas and the mechanism for Helicobacter pylori carcinogenesis. The elucidation of the central role of the ECL cell and thus its main regulator, gastrin, solve all these controversies, and gives a solid base for handling upper gastrointestinal diseases.

Datasets for the reporting of neoplasia of the testis: recommendations from the International Collaboration on Cancer Reporting.

We here describe the development of an evidence-based cancer dataset by an International Collaboration on Cancer Reporting expert panel for the reporting of primary testicular neoplasia, and present the 'required' and 'recommended' elements to be included in the pathology report, as well as a commentary. This dataset encompasses the updated 2016 World Health Organisation classification of urological tumours, the results of an International Society of Urological Pathology consultation, and also staging with our preferred method: the American Joint Committee on Cancer version 8. Implementation of this dataset will facilitate consistent and accurate data collection between different cohorts, facilitate research, and hopefully result in improved patient management.

Advances in the clinicopathological and molecular classification of cutaneous mesenchymal neoplasms.

In recent years, there have been several important refinements in the classification of cutaneous mesenchymal neoplasms, including the description of new tumour types, along with the identification of novel and recurrent molecular genetic findings. In addition to providing new insights into tumour biology, many of these advances have had significant clinical consequences with regard to diagnostics, management, and prognostication. Newly described entities include pseudomyogenic haemangioendothelioma, haemosiderotic fibrolipomatous tumour, and fibroblastic connective tissue naevus, which are reviewed in the context of the principal differential diagnoses and significant clinical implications. Genetic characterization of several soft tissue tumour types that occur in the skin has resulted in the identification of diagnostically useful markers: ALK gene rearrangement with corresponding ALK protein expression by immunohistochemistry in epithelioid fibrous histiocytoma; the WWTR1-CAMTA1 fusion gene with CAMTA1 protein expression in epithelioid haemangioendothelioma; MYC amplification and overexpression in radiation-associated angiosarcoma; and EWSR1 gene rearrangement in cutaneous myoepithelial tumours. Finally, the classification of intradermal smooth muscle tumours and unclassified/pleomorphic dermal sarcoma has been refined, resulting in both improved classification and improved prognostication. Many of the tumour types listed above are encountered not only by specialist dermatopathologists, but also by practising general surgical pathologists, and this review should therefore provide a widely applicable update on the histological and molecular classification of cutaneous mesenchymal neoplasms, along with the appropriate use of ancillary diagnostic tests, in particular immunohistochemistry, in the evaluation of such lesions and their histological mimics.

Discrimination between glioblastoma multiforme and solitary metastasis using morphological features derived from the p:q tensor decomposition of diffusion tensor imaging.

The management and treatment of high-grade glioblastoma multiforme (GBM) and solitary metastasis (MET) are very different and influence the prognosis and subsequent clinical outcomes. In the case of a solitary MET, diagnosis using conventional radiology can be equivocal. Currently, a definitive diagnosis is based on histopathological analysis on a biopsy sample. Here, we present a computerised decision support framework for discrimination between GBM and solitary MET using MRI, which includes: (i) a semi-automatic segmentation method based on diffusion tensor imaging; (ii) two-dimensional morphological feature extraction and selection; and (iii) a pattern recognition module for automated tumour classification. Ground truth was provided by histopathological analysis from pre-treatment stereotactic biopsy or at surgical resection. Our two-dimensional morphological analysis outperforms previous methods with high cross-validation accuracy of 97.9% and area under the receiver operating characteristic curve of 0.975 using a neural networks-based classifier.

Canine ovarian epithelial tumours: histopathological and immunohistochemical evaluation with proposed histopathological classification system.

Canine ovarian epithelial tumours (OETs) are currently divided into ovarian adenomas and carcinomas, which are further inconsistently subclassified as papillary or cystic, whereas in human medicine, OETs are subdivided into several subtypes. This study aimed to establish clear morphological features enabling more consistent distinction between benign OETs and ovarian carcinomas (OvCas) as well as defining different histopathological patterns of canine OvCas. Analysis revealed a mitotic count threshold of >2 as a potential criterion for differentiating OvCas from benign OETs. Alongside ovarian adenomas, ovarian borderline tumours were introduced as a distinct category among benign OETs. OvCas exhibited five different histopathological patterns, namely papillary, solid with tubular differentiation, micropapillary, cystic and sarcomatous. Since some OvCas can morphologically overlap with other ovarian tumours, the expression of cytokeratin 7, a cytokeratin expressed in ovarian epithelium, was assessed and proved helpful, although it was not expressed in all cases. Furthermore, we investigated the expression of 14-3-3σ and cyclooxygenase 2 (COX-2). Based on the frequent expression of 14-3-3σ, this marker appears to have a role in canine OETs since it is not expressed in normal canine ovaries. The infrequent expression of COX-2 suggests that it is a poor candidate as a potential therapeutic target in canine OvCas.

Towards Understanding of Gastric Cancer Based upon Physiological Role of Gastrin and ECL Cells.

The stomach is an ideal organ to study because the gastric juice kills most of the swallowed microbes and, thus, creates rather similar milieu among individuals. Combined with a rather easy access to gastric juice, gastric physiology was among the first areas to be studied. During the last century, a rather complete understanding of the regulation of gastric acidity was obtained, establishing the central role of gastrin and the histamine producing enterochromaffin-like (ECL) cell. Similarly, the close connection between regulation of function and proliferation became evident, and, furthermore, that chronic overstimulation of a cell with the ability to proliferate, results in tumour formation. The ECL cell has long been acknowledged to give rise to neuroendocrine tumours (NETs), but not to play any role in carcinogenesis of gastric adenocarcinomas. However, when examining human gastric adenocarcinomas with the best methods presently available (immunohistochemistry with increased sensitivity and in-situ hybridization), it became clear that many of these cancers expressed neuroendocrine markers, suggesting that some of these tumours were of neuroendocrine, and more specifically, ECL cell origin. Thus, the ECL cell and its main regulator, gastrin, are central in human gastric carcinogenesis, which make new possibilities in prevention, prophylaxis, and treatment of this cancer.

Histologic margins and the residual tumour classification scheme: Is it time to use a validated scheme in human oncology to standardise margin assessment in veterinary oncology?

There is no consensus on the definition of a complete histologic excision in veterinary oncology; many definitions have been used in various studies, but these have been arbitrarily selected with no apparent justification. The residual tumour classification scheme, where a complete histologic excision is defined as a histologic tumour-free margin >0 mm, has been used for >40 years in human oncology by all of the major clinical staging organizations and is considered highly prognostic for the vast majority of malignant tumours in people. Because of the widespread use of the residual tumour classification scheme both clinically and in research studies, this standardized approach permits better communication between clinicians, an evidence-based decision-making process for adjuvant treatment options following surgical resection, minimizes exposing patients to unnecessary adjuvant treatments and a better ability to compare local tumour control for specific tumours between different studies. The adoption of the residual tumour classification scheme in veterinary oncology would likely achieve similar outcomes and minimize the prevalent confusion within the veterinary community, amongst both general practitioners and specialists, regarding the definition of what constitutes a complete histologic excision.

Dataset for the reporting of prostate carcinoma in core needle biopsy and transurethral resection and enucleation specimens: recommendations from the International Collaboration on Cancer Reporting (ICCR).

The International Collaboration on Cancer Reporting (ICCR) is a project which issues datasets and guidelines for international standardisation of cancer reporting. This review summarises the required and recommended elements of the datasets for prostate core needle biopsies and transurethral resection (TURP) and enucleation specimens of the prostate. To obtain as much information as possible from needle biopsies there should be only one core in each specimen jar with the exception of saturation biopsies. The gross description of the specimens should include core lengths of needle biopsies and weight of resection specimens. The tumours should be classified according to the 4th World Health Organization (WHO) classification and graded both by Gleason scores and the grouping of these in International Society of Urological Pathology (ISUP) grades (Grade groups). Percent high-grade cancer is an optional component of the report. Tumour extent in needle biopsies should be reported both by number of cores positive for cancer and the linear extent measured in either millimetre or percent core involvement by tumour. In needle biopsies where low-grade cancer is discontinuous and seen in few cores, it is recommended that the tumour extent should be reported both by including and subtracting intervening benign tissue. For resection specimens, the percentage of the tissue area (or percentage of number of TURP chips) involved with cancer should be estimated. Extraprostatic extension should be reported when seen, while the reporting of perineural, seminal vesicle/ejaculatory duct and lymphovascular invasion is only recommended. Intraductal carcinoma of the prostate (IDC-P) should be reported when present, because of its strong link with aggressive cancer. The current recommendation is that the IDC-P component should not be graded. The structured and standardised reporting of prostate cancer contributes to safer and more efficient patient care and facilitates the compilation and understanding of multiparametric diagnostic and prognostic data.

An extensive study for binary characterisation of adrenal tumours.

On adrenal glands, benign tumours generally change the hormone equilibrium, and malign tumours usually tend to spread to the nearby tissues and to the organs of the immune system. These features can give a trace about the type of adrenal tumours; however, they cannot be observed all the time. Different tumour types can be confused in terms of having a similar shape, size and intensity features on scans. To support the evaluation process, biopsy process is applied that includes injury and complication risks. In this study, we handle the binary characterisation of adrenal tumours by using dynamic computed tomography images. Concerning this, the usage of one more imaging modalities and biopsy process is wanted to be excluded. The used dataset consists of 8 subtypes of adrenal tumours, and it seemed as the worst-case scenario in which all handicaps are available against tumour classification. Histogram, grey level co-occurrence matrix and wavelet-based features are investigated to reveal the most effective one on the identification of adrenal tumours. Binary classification is proposed utilising four-promising algorithms that have proven oneself on the task of binary-medical pattern classification. For this purpose, optimised neural networks are examined using six dataset inspired by the aforementioned features, and an efficient framework is offered before the use of a biopsy. Accuracy, sensitivity, specificity, and AUC are used to evaluate the performance of classifiers. Consequently, malign/benign characterisation is performed by proposed framework, with success rates of 80.7%, 75%, 82.22% and 78.61% for the metrics, respectively. Graphical abstract.

Multinodular and vacuolating neuronal tumour of the cerebrum: a rare neuroimaging incidentaloma or a potentially treatable cause of focal epilepsy?

Multinodular and vacuolating neuronal tumour (MVNT) of the cerebrum is a relatively new, well defined histopathological and neuroradiological entity, in many cases associated with an early adult-onset epilepsy. These lesions have an indolent course and resemble both malformative and neoplastic processes, combining a focal developmental anomaly and a low-grade tumour. Herein, we report a case of a 48-year-old female patient with left temporal lobe epilepsy associated with MVNT. In addition, a comprehensive review of all the previously published cases is provided with a focus on seizure-related cases, surgical treatment, and postoperative outcome.

Methylation array profiling of adult brain tumours: diagnostic outcomes in a large, single centre.

The introduction of the classification of brain tumours based on their DNA methylation profile has significantly changed the diagnostic approach for cases with ambiguous histology, non-informative or contradictory molecular profiles or for entities where methylation profiling provides useful information for patient risk stratification, for example in medulloblastoma and ependymoma. We present our experience that combines a conventional molecular diagnostic approach with the complementary use of a DNA methylation-based classification tool, for adult brain tumours originating from local as well as national referrals. We report the frequency of IDH mutations in a large cohort of nearly 1550 patients, EGFR amplifications in almost 1900 IDH-wildtype glioblastomas, and histone mutations in 70 adult gliomas. We demonstrate how additional methylation-based classification has changed and improved our diagnostic approach. Of the 325 cases referred for methylome testing, 179 (56%) had a calibrated score of 0.84 and higher and were included in the evaluation. In these 179 samples, the diagnosis was changed in 45 (25%), refined in 86 (48%) and confirmed in 44 cases (25%). In addition, the methylation arrays contain copy number information that usefully complements the methylation profile. For example, EGFR amplification which is 95% concordant with our Real-Time PCR-based copy number assays. We propose here a diagnostic algorithm that integrates histology, conventional molecular tests and methylation arrays.

TNM-O: ontology support for staging of malignant tumours.

BACKGROUND: Objectives of this work are to (1) present an ontological framework for the TNM classification system, (2) exemplify this framework by an ontology for colon and rectum tumours, and (3) evaluate this ontology by assigning TNM classes to real world pathology data. METHODS: The TNM ontology uses the Foundational Model of Anatomy for anatomical entities and BioTopLite 2 as a domain top-level ontology. General rules for the TNM classification system and the specific TNM classification for colorectal tumours were axiomatised in description logic. Case-based information was collected from tumour documentation practice in the Comprehensive Cancer Centre of a large university hospital. Based on the ontology, a module was developed that classifies pathology data. RESULTS: TNM was represented as an information artefact, which consists of single representational units. Corresponding to every representational unit, tumours and tumour aggregates were defined. Tumour aggregates consist of the primary tumour and, if existing, of infiltrated regional lymph nodes and distant metastases. TNM codes depend on the location and certain qualities of the primary tumour (T), the infiltrated regional lymph nodes (N) and the existence of distant metastases (M). Tumour data from clinical and pathological documentation were successfully classified with the ontology. CONCLUSION: A first version of the TNM Ontology represents the TNM system for the description of the anatomical extent of malignant tumours. The present work demonstrates its representational power and completeness as well as its applicability for classification of instance data.