Dataset for reporting of the invasive carcinoma of the breast: recommendations from the International Collaboration on Cancer Reporting (ICCR).

BACKGROUND AND OBJECTIVES: Current national or regional guidelines for the pathology reporting on invasive breast cancer differ in certain aspects, resulting in divergent reporting practice and a lack of comparability of data. Here we report on a new international dataset for the pathology reporting of resection specimens with invasive cancer of the breast. The dataset was produced under the auspices of the International Collaboration on Cancer Reporting (ICCR), a global alliance of major (inter-)national pathology and cancer organizations. METHODS AND RESULTS: The established ICCR process for dataset development was followed. An international expert panel consisting of breast pathologists, a surgeon, and an oncologist prepared a draft set of core and noncore data items based on a critical review and discussion of current evidence. Commentary was provided for each data item to explain the rationale for selecting it as a core or noncore element, its clinical relevance, and to highlight potential areas of disagreement or lack of evidence, in which case a consensus position was formulated. Following international public consultation, the document was finalized and ratified, and the dataset, which includes a synoptic reporting guide, was published on the ICCR website. CONCLUSIONS: This first international dataset for invasive cancer of the breast is intended to promote high-quality, standardized pathology reporting. Its widespread adoption will improve consistency of reporting, facilitate multidisciplinary communication, and enhance comparability of data, all of which will help to improve the management of invasive breast cancer patients.

Polyp segmentation with consistency training and continuous update of pseudo-label.

Polyp segmentation has accomplished massive triumph over the years in the field of supervised learning. However, obtaining a vast number of labeled datasets is commonly challenging in the medical domain. To solve this problem, we employ semi-supervised methods and suitably take advantage of unlabeled data to improve the performance of polyp image segmentation. First, we propose an encoder-decoder-based method well suited for the polyp with varying shape, size, and scales. Second, we utilize the teacher-student concept of training the model, where the teacher model is the student model's exponential average. Third, to leverage the unlabeled dataset, we enforce a consistency technique and force the teacher model to generate a similar output on the different perturbed versions of the given input. Finally, we propose a method that upgrades the traditional pseudo-label method by learning the model with continuous update of pseudo-label. We show the efficacy of our proposed method on different polyp datasets, and hence attaining better results in semi-supervised settings. Extensive experiments demonstrate that our proposed method can propagate the unlabeled dataset's essential information to improve performance.

Mapping single-cell data to reference atlases by transfer learning.

Large single-cell atlases are now routinely generated to serve as references for analysis of smaller-scale studies. Yet learning from reference data is complicated by batch effects between datasets, limited availability of computational resources and sharing restrictions on raw data. Here we introduce a deep learning strategy for mapping query datasets on top of a reference called single-cell architectural surgery (scArches). scArches uses transfer learning and parameter optimization to enable efficient, decentralized, iterative reference building and contextualization of new datasets with existing references without sharing raw data. Using examples from mouse brain, pancreas, immune and whole-organism atlases, we show that scArches preserves biological state information while removing batch effects, despite using four orders of magnitude fewer parameters than de novo integration. scArches generalizes to multimodal reference mapping, allowing imputation of missing modalities. Finally, scArches retains coronavirus disease 2019 (COVID-19) disease variation when mapping to a healthy reference, enabling the discovery of disease-specific cell states. scArches will facilitate collaborative projects by enabling iterative construction, updating, sharing and efficient use of reference atlases.

Dataset for Pathology Reporting of Colorectal Cancer: Recommendations From the International Collaboration on Cancer Reporting (ICCR).

OBJECTIVE: The aim of this study to describe a new international dataset for pathology reporting of colorectal cancer surgical specimens, produced under the auspices of the International Collaboration on Cancer Reporting (ICCR). BACKGROUND: Quality of pathology reporting and mutual understanding between colorectal surgeon, pathologist and oncologist are vital to patient management. Some pathology parameters are prone to variable interpretation, resulting in differing positions adopted by existing national datasets. METHODS: The ICCR, a global alliance of major pathology institutions with links to international cancer organizations, has developed and ratified a rigorous and efficient process for the development of evidence-based, structured datasets for pathology reporting of common cancers. Here we describe the production of a dataset for colorectal cancer resection specimens by a multidisciplinary panel of internationally recognized experts. RESULTS: The agreed dataset comprises eighteen core (essential) and seven non-core (recommended) elements identified from a review of current evidence. Areas of contention are addressed, some highly relevant to surgical practice, with the aim of standardizing multidisciplinary discussion. The summation of all core elements is considered to be the minimum reporting standard for individual cases. Commentary is provided, explaining each element's clinical relevance, definitions to be applied where appropriate for the agreed list of value options and the rationale for considering the element as core or non-core. CONCLUSIONS: This first internationally agreed dataset for colorectal cancer pathology reporting promotes standardization of pathology reporting and enhanced clinicopathological communication. Widespread adoption will facilitate international comparisons, multinational clinical trials and help to improve the management of colorectal cancer globally.

Dataset for the reporting of carcinoma of the esophagus in resection specimens: recommendations from the International Collaboration on Cancer Reporting.

BACKGROUND AND OBJECTIVES: A standardized data set for esophageal carcinoma pathology reporting was developed based on the approach of the International Collaboration on Cancer Reporting (ICCR) for the purpose of improving cancer patient outcomes and international benchmarking in cancer management. MATERIALS AND METHODS: The ICCR convened a multidisciplinary international expert panel to identify the best evidence-based clinical and pathological parameters for inclusion in the data set for esophageal carcinoma. The data set incorporated the current edition of the World Health Organization Classification of Tumours of the Digestive System, and Tumour-Node-Metastasis staging systems. RESULTS: The scope of the data set encompassed resection specimens of the esophagus and esophagogastric junction with tumor epicenter ≤20 mm into the proximal stomach. Core reporting elements included information on neoadjuvant therapy, operative procedure used, tumor focality, tumor site, tumor dimensions, distance of tumor to resection margins, histological tumor type, presence and type of dysplasia, tumor grade, extent of invasion in the esophagus, lymphovascular invasion, response to neoadjuvant therapy, status of resection margin, ancillary studies, lymph node status, distant metastases, and pathological staging. Additional non-core elements considered useful to report included clinical information, specimen dimensions, macroscopic appearance of tumor, and coexistent pathology. CONCLUSIONS: This is the first international peer-reviewed structured reporting data set for surgically resected specimens of the esophagus. The ICCR carcinoma of the esophagus data set is recommended for routine use globally and is a valuable tool to support standardized reporting, to benefit patient care by providing diagnostic and prognostic best-practice parameters.

Probabilistic linkage without personal information successfully linked national clinical datasets.

BACKGROUND: Probabilistic linkage can link patients from different clinical databases without the need for personal information. If accurate linkage can be achieved, it would accelerate the use of linked datasets to address important clinical and public health questions. OBJECTIVE: We developed a step-by-step process for probabilistic linkage of national clinical and administrative datasets without personal information, and validated it against deterministic linkage using patient identifiers. STUDY DESIGN AND SETTING: We used electronic health records from the National Bowel Cancer Audit and Hospital Episode Statistics databases for 10,566 bowel cancer patients undergoing emergency surgery in the English National Health Service. RESULTS: Probabilistic linkage linked 81.4% of National Bowel Cancer Audit records to Hospital Episode Statistics, vs. 82.8% using deterministic linkage. No systematic differences were seen between patients that were and were not linked, and regression models for mortality and length of hospital stay according to patient and tumour characteristics were not sensitive to the linkage approach. CONCLUSION: Probabilistic linkage was successful in linking national clinical and administrative datasets for patients undergoing a major surgical procedure. It allows analysts outside highly secure data environments to undertake linkage while minimizing costs and delays, protecting data security, and maintaining linkage quality.

Determining lines of therapy in patients with solid cancers: a proposed new systematic and comprehensive framework.

The complexity of neoplasia and its treatment are a challenge to the formulation of general criteria that are applicable across solid cancers. Determining the number of prior lines of therapy (LoT) is critically important for optimising future treatment, conducting medication audits, and assessing eligibility for clinical trial enrolment. Currently, however, no accepted set of criteria or definitions exists to enumerate LoT. In this article, we seek to open a dialogue to address this challenge by proposing a systematic and comprehensive framework to determine LoT uniformly across solid malignancies. First, key terms, including LoT and 'clinical progression of disease' are defined. Next, we clarify which therapies should be assigned a LoT, and why. Finally, we propose reporting LoT in a novel and standardised format as LoT N (CLoT + PLoT), where CLoT is the number of systemic anti-cancer therapies (SACT) administered with curative intent and/or in the early setting, PLoT is the number of SACT given with palliative intent and/or in the advanced setting, and N is the sum of CLoT and PLoT. As a next step, the cancer research community should develop and adopt standardised guidelines for enumerating LoT in a uniform manner.

Defining treatment regimens and lines of therapy using real-world data in oncology.

Retrospective observational research relies on databases that do not routinely record lines of therapy or reasons for treatment change. Standardized approaches to estimate lines of therapy were developed and evaluated in this study. A number of rules were developed, assumptions varied and macros developed to apply to large datasets. Results were investigated in an iterative process to refine line of therapy algorithms in three different cancers (lung, colorectal and gastric). Three primary factors were evaluated and included in the estimation of lines of therapy in oncology: defining a treatment regimen, addition/removal of drugs and gap periods. Algorithms and associated Statistical Analysis Software (SAS®) macros for line of therapy identification are provided to facilitate and standardize the use of real-world databases for oncology research.

Lay abstract Most, if not all, real-world healthcare databases do not contain data explaining treatment changes, requiring that rules be applied to estimate when treatment changes may reflect advancement of underlying disease. This study investigated three tumor types (lung, colorectal and gastric cancer) to develop and provide rules that researchers can apply to real-world databases. The resulting algorithms and associated SAS^® macros from this work are provided for use in the Supplementary data.

Collaborative data familiarisation and quality assessment: Reflections from use of a national dataset to investigate palliative care for Indigenous Australians.

BACKGROUND: Data quality is fundamental to the integrity of quantitative research. The role of external researchers in data quality assessment (DQA) remains ill-defined in the context of secondary use for research of large, centrally curated health datasets. In order to investigate equity of palliative care provided to Indigenous Australian patients, researchers accessed a now-historical version of a national palliative care dataset developed primarily for the purpose of continuous quality improvement. OBJECTIVES: (i) To apply a generic DQA framework to the dataset and (ii) to report the process and results of this assessment and examine the consequences for conducting the research. METHOD: The data were systematically examined for completeness, consistency and credibility. Data quality issues relevant to the Indigenous identifier and framing of research questions were of particular interest. RESULTS: The dataset comprised 477,518 records of 144,951 patients (Indigenous N = 1515; missing Indigenous identifier N = 4998) collected from participating specialist palliative care services during a period (1 January 2010-30 June 2015) in which data-checking systems underwent substantial upgrades. Progressive improvement in completeness of data over the study period was evident. The data were error-free with respect to many credibility and consistency checks, with anomalies detected reported to data managers. As the proportion of missing values remained substantial for some clinical care variables, multiple imputation procedures were used in subsequent analyses. CONCLUSION AND IMPLICATIONS: In secondary use of large curated datasets, DQA by external researchers may both influence proposed analytical methods and contribute to improvement of data curation processes through feedback to data managers.

A unique data analysis framework and open source benchmark data set for the analysis of comprehensive two-dimensional gas chromatography software.

Comprehensive two-dimensional gas chromatography (GC × GC) is amongst the most powerful separation technologies currently existing. Since its advent in early 1990, it has become an established method which is readily available. However, one of its most challenging aspects, especially in hyphenation with mass spectrometry is the high amount of chemical information it provides for each measurement. The GC × GC community agrees that there, the highest demand for action is found. In response, the number of software packages allowing for in-depth data processing of GC × GC data has risen over the last couple of years. These packages provide sophisticated tools and algorithms allowing for more streamlined data evaluation. However, these tools/algorithms and their respective specific functionalities differ drastically within the available software packages and might result in various levels of findings if not appropriately implemented by the end users. This study focuses on two main objectives. First, to propose a data analysis framework and second to propose an open-source dataset for benchmarking software options and their specificities. Thus, allowing for an unanimous and comprehensive evaluation of GC × GC software. Thereby, the benchmark data includes a set of standard compound measurements and a set of chocolate aroma profiles. On this foundation, eight readily available GC × GC software packages were anonymously investigated for fundamental and advanced functionalities such as retention and detection device derived parameters, revealing differences in the determination of e.g. retention times and mass spectra.

Should analyses of large, national palliative care data sets with patient reported outcomes (PROs) be restricted to services with high patient participation? A register-based study.

BACKGROUND: There is an increased interest in the analysis of large, national palliative care data sets including patient reported outcomes (PROs). No study has investigated if it was best to include or exclude data from services with low response rates in order to obtain the patient reported outcomes most representative of the national palliative care population. Thus, the aim of this study was to investigate whether services with low response rates should be excluded from analyses to prevent effects of possible selection bias. METHODS: Data from the Danish Palliative Care Database from 24,589 specialized palliative care admittances of cancer patients was included. Patients reported ten aspects of quality of life using the EORTC QLQ-C15-PAL-questionnaire. Multiple linear regression was performed to test if response rate was associated with the ten aspects of quality of life. RESULTS: The score of six quality of life aspects were significantly associated with response rate. However, in only two cases patients from specialized palliative care services with lower response rates (< 20.0%, 20.0-29.9%, 30.0-39.9%, 40.0-49.9% or 50.0-59.9) were feeling better than patients from services with high response rates (≥60%) and in both cases it was less than 2 points on a 0-100 scale. CONCLUSIONS: The study hypothesis, that patients from specialized palliative care services with lower response rates were reporting better quality of life than those from specialized palliative care services with high response rates, was not supported. This suggests that there is no reason to exclude data from specialized palliative care services with low response rates.

Problems in variation interpretation guidelines and in their implementation in computational tools.

BACKGROUND: ACMG/AMP and AMP/ASCO/CAP have released guidelines for variation interpretation, and ESHG for diagnostic sequencing. These guidelines contain recommendations including the use of computational prediction methods. The guidelines per se and the way they are implemented cause some problems. METHODS: Logical reasoning based on domain knowledge. RESULTS: According to the guidelines, several methods have to be used and they have to agree. This means that the methods with the poorest performance overrule the better ones. The choice of the prediction method(s) should be made by experts  based on systematic benchmarking studies reporting all the relevant performance measures. Currently variation interpretation methods have been applied mainly to amino acid substitutions and splice site variants; however, predictors for some other types of variations are available and there will be tools for new application areas in the near future. Common problems in prediction method usage are discussed. The number of features used for method training or the number of variation types predicted by a tool are not indicators of method performance. Many published gene, protein or disease-specific benchmark studies suffer from too small dataset rendering the results useless. In the case of binary predictors, equal number of positive and negative cases is beneficial for training, the imbalance has to be corrected for performance assessment. Predictors cannot be better than the data they are based on and used for training and testing. Minor allele frequency (MAF) can help to detect likely benign cases, but the recommended MAF threshold is apparently too high. The fact that many rare variants are disease-causing or -related does not mean that rare variants in general would be harmful. How large a portion of the tested variants a tool can predict (coverage) is not a quality measure. CONCLUSION: Methods used for variation interpretation have to be carefully selected. It should be possible to use only one predictor, with proven good performance or a limited number of complementary predictors with state-of-the-art performance. Bear in mind that diseases and pathogenicity have a continuum and variants are not dichotomic i.e. either pathogenic or benign, either.

Dataset for the reporting of renal biopsy for tumour: recommendations from the International Collaboration on Cancer Reporting (ICCR).

The International Collaboration on Cancer Reporting (ICCR) has developed a suite of detailed datasets for international implementation. These datasets are based on the reporting protocols developed by the Royal College of Pathologists (UK), The Royal College of Pathologists of Australasia and the College of American Pathologists, with modifications undertaken by international expert groups appointed according to ICCR protocols. The dataset for the reporting of renal biopsy for tumour is designed to provide a structured reporting template containing minimum data recording key elements suitable for international use. In formulating the dataset, the ICCR panel incorporated recommendations from the 2012 Vancouver Consensus Conference of the International Society of Urological Pathology (ISUP) and the 2016 edition of the WHO Bluebook on tumours of the urinary and male genital systems. Reporting elements were divided into Required (Core) and Recommended (Non-core) components of the report. Required elements are as follows: specimen laterality, histological tumour type, WHO/ISUP histological tumour grade, sarcomatoid morphology, rhabdoid morphology, necrosis, lymphovascular invasion and coexisting pathology in non-neoplastic kidney. Recommended reporting elements are as follows: operative procedure, tumour site(s), histological tumour subtype and details of ancillary studies. In particular, it is noted that fluorescence in situ hybridisation studies may assist in diagnosing translocation renal cell carcinoma (RCC) and in distinguishing oncocytoma and eosinophilic chromophobe RCC. It is anticipated that the implementation of this dataset into routine clinical practice will facilitate uniformity of pathology reporting worldwide. This, in turn, should have a positive impact on patient treatment and the quality of demographic information held by cancer registries.

Data Set for the Reporting of Carcinoma of the Renal Pelvis and Ureter-Nephroureterectomy and Ureterectomy Specimens: Recommendations From the International Collaboration on Cancer Reporting (ICCR).

Cancer reporting guidelines have been developed and utilized in many countries throughout the world. The International Collaboration on Cancer Reporting (ICCR), through an alliance of colleges and other pathology organizations in Australasia, United Kingdom, Ireland, Europe, USA, and Canada, has developed comprehensive standardized data sets to provide for global usage and promote uniformity in cancer reporting. Structured reporting facilitates provision of all necessary information, which ensures accurate and comprehensive data collection, with the ultimate aim of improving cancer diagnostics and treatment. The data set for primary carcinoma of the renal pelvis and ureter treated with nephroureterectomy or ureterectomy had input from an expert panel of international uropathologists. This data set was based on current evidence-based practice and incorporated information from the 2016 fourth edition of the World Health Organization (WHO) Bluebook on tumors of the urinary and male genital systems and the 2017 American Joint Committee on Cancer (AJCC) TNM staging eighth edition. This protocol applies to both noninvasive and invasive carcinomas in these locations. Reporting elements are considered to be essential (required) or nonessential (recommended). Required elements include operative procedure, specimens submitted, tumor location, focality and size, histologic tumor type, subtype/variant of urothelial carcinoma, WHO grade, extent of invasion, presence or absence of vascular invasion, status of the resection margins and lymph nodes and pathologic stage. The data set provides a detailed template for the collection of data and it is anticipated that this will facilitate appropriate patient management with the potential to foster collaborative research internationally.

Data set for the reporting of carcinoma of renal tubular origin: recommendations from the International Collaboration on Cancer Reporting (ICCR).

AIMS: The International Collaboration on Cancer Reporting (ICCR) has provided detailed data sets based upon the published reporting protocols of the Royal College of Pathologists, the Royal College of Pathologists of Australasia and the College of American Pathologists. METHODS AND RESULTS: The data set for carcinomas of renal tubular origin treated by nephrectomy was developed to provide a minimum structured reporting template suitable for international use, and incorporated recommendations from the 2012 Vancouver Consensus Conference of the International Society of Urological Pathology (ISUP) and the fourth edition of the World Health Organisation Bluebook on tumours of the urinary and male genital systems published in 2016. Reporting elements were divided into those, which are required and recommended components of the report. Required elements are: specimen laterality, operative procedure, attached structures, tumour focality, tumour dimension, tumour type, WHO/ISUP grade, sarcomatoid/rhabdoid morphology, tumour necrosis, extent of invasion, lymph node status, surgical margin status, AJCC TNM staging and co-existing pathology. Recommended reporting elements are: pre-operative treatment, details of tissue removed for experimental purposes prior to submission, site of tumour(s) block identification key, extent of sarcomatoid and/or rhabdoid component, extent of necrosis, presence of tumour in renal vein wall, lymphovascular invasion and lymph node status (size of largest focus and extranodal extension). CONCLUSIONS: It is anticipated that the implementation of this data set in routine clinical practice will inform patient treatment as well as provide standardised information relating to outcome prediction. The harmonisation of data reporting should also facilitate international research collaborations.

Data Set for the Reporting of Ear and Temporal Bone Tumors: Explanations and Recommendations of the Guidelines From the International Collaboration on Cancer Reporting.

The International Collaboration on Cancer Reporting (ICCR) was established to internationally unify and standardize the pathologic reporting of cancers based on collected evidence, as well as to allow systematic multi-institutional intercountry data collection to guide cancer care in the future. Such collaborative efforts are particularly essential for developing an evidence base for rare neoplasms or those with marked geographic variation in incidence, such as the tumors of the ear and the temporal bone. The ear and the temporal bone, including the external auditory canal and the middle and inner ear, with the closely associated facial nerve, internal carotid artery, and internal jugular vein, is one of the most complex anatomic structures in the head and neck. A wide range of benign and malignant neoplasms arise in this region. The management of these neoplasms involves complex surgery because of the anatomic confines, and as such, both benign and malignant tumors are included in this data set, as the oncologically equivalent management requires a multidisciplinary approach and standardized nomenclature and terminology. Surgical procedures at this site result in multifaceted 3-dimensional specimens that can be difficult to handle at macroscopic exam. A comprehensive macroscopic examination is important for identifying critical prognostic factors and often requires clinical and radiologic correlation. Histologic examination is straightforward for basal cell or squamous cell carcinoma but can be quite challenging for other neoplasms. A summary of the ICCR guidelines for ear tumors is presented, along with discussion of the salient evidence and practical issues.

Data Set for the Reporting of Malignant Odontogenic Tumors: Explanations and Recommendations of the Guidelines From the International Collaboration on Cancer Reporting.

A data set has been developed for the reporting of excisional biopsies and resection specimens for malignant odontogenic tumors by members of an expert panel working on behalf of the International Collaboration on Cancer Reporting, an international organization established to unify and standardize reporting of cancers. Odontogenic tumors are rare, which limits evidence-based support for designing a scientifically sound data set for reporting them. Thus, the selection of reportable elements within the data set and considering them as either core or noncore is principally based on evidence from malignancies affecting other organ systems, limited case series, expert opinions, and/or anecdotal reports. Nevertheless, this data set serves as the initial step toward standardized reporting on malignant odontogenic tumors that should evolve over time as more evidence becomes available and functions as a prompt for further research to provide such evidence.

Data Set for the Reporting of Nodal Excisions and Neck Dissection Specimens for Head and Neck Tumors: Explanations and Recommendations of the Guidelines From the International Collaboration on Cancer Reporting.

Standardized, synoptic pathologic reporting for tumors greatly improves communication among clinicians, patients, and researchers, supporting prognostication and comparison about patient outcomes across institutions and countries. The International Collaboration on Cancer Reporting is a nonprofit organization whose mission is to develop evidence-based, universally available surgical pathology reporting data sets. Within the head and neck region, lymph node excisions and neck dissections are frequently performed as part of the management of head and neck cancers arising from the mucosal sites (sinonasal tract, nasopharynx, oropharynx, hypopharynx, oral cavity, and larynx) along with bone tumors, skin cancers, melanomas, and other tumor categories. The type of specimen, exact location (lymph node level), laterality, and orientation (by suture or diagram) are essential to accurate classification. There are significant staging differences for each anatomic site within the head and neck when lymph node sampling is considered, most importantly related to human papillomavirus-associated oropharyngeal carcinomas and mucosal melanomas. Number, size, and site of affected lymph nodes, including guidelines on determining the size of tumor deposits and the presence of extranodal extension and soft tissue metastasis, are presented in the context of prognostication. This review elaborates on each of the elements included in the data set for Nodal Excisions and Neck Dissection Specimens for Head & Neck Tumours.