The international collaboration on cancer reporting (ICCR): development of evidence-based core data sets for pathology cancer reporting

Ellis, David W.; Dvořák, Jiří; Hirschowitz, Lynn; Judge, Meagan; Kwiatkowski, A; Srigley, John R.; Washington, Mary Kay; Wells, Megan

doi:10.1097/01.pat.0000426752.37463.b8

Cited by 2 publications

(2 citation statements)

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“…It initially piloted the development of datasets for pathology reporting of lung cancer, 2 endometrial cancer, 3 prostate cancer, 4 and melanoma, 5 which has been described previously in detail. 6 These were successfully completed and led to an overall reduction in the number of core (required) data points.…”

Section: Introductionmentioning

confidence: 99%

“…The ICCR also collaborated with other international cancer organizations including the International Agency for Research on Cancer (IARC), which is responsible for producing the World Health Organization (WHO) monographs or ‘Blue Books’ on tumour classification, the Union for International Cancer Control (UICC), and the American Joint Committee on Cancer (AJCC). It initially piloted the development of datasets for pathology reporting of lung cancer, 2 endometrial cancer, 3 prostate cancer, 4 and melanoma, 5 which has been described previously in detail 6 . These were successfully completed and led to an overall reduction in the number of core (required) data points.…”

Section: Introductionmentioning

confidence: 99%

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Dataset for pathology reporting of ductal carcinoma in situ, variants of lobular carcinoma in situ and low‐grade lesions: recommendations from the International Collaboration on Cancer Reporting (ICCR)

Fox

Webster²,

et al. 2022

Histopathology

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Aims: To describe a new international dataset for pathology reporting of ductal carcinoma in situ (DCIS), variants of lobular carcinoma in situ (LCIS) and low-grade lesions (encapsulated papillary carcinoma, solid papillary carcinoma in situ, Paget's disease) produced by the International Collaboration on Cancer Reporting (ICCR). Methods and results:The ICCR, a global alliance of pathology bodies, uses a rigorous and efficient process for the development of evidence-based, structured datasets for pathology reporting of common cancers. Their aim is to support quality pathology reporting and engender understanding between the breast surgeon, pathologist, and oncologist for optimal and uniform patient management globally. Here we describe the dataset for DCIS, some variants of LCIS (namely the pleomorphic and the florid variants), and lowgrade lesions by a multidisciplinary panel of

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dataset for pathology reporting of ductal carcinoma in situ, variants of lobular carcinoma in situ and low‐grade lesions: recommendations from the International Collaboration on Cancer Reporting (ICCR)

Fox

Webster²,

et al. 2022

Histopathology

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Applications of Machine Learning in Palliative Care: A Systematic Review

Steinmann

Schröder

et al. 2023

Cancers

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Objective: To summarize the available literature on using machine learning (ML) for palliative care practice as well as research and to assess the adherence of the published studies to the most important ML best practices. Methods: The MEDLINE database was searched for the use of ML in palliative care practice or research, and the records were screened according to PRISMA guidelines. Results: In total, 22 publications using machine learning for mortality prediction (n = 15), data annotation (n = 5), predicting morbidity under palliative therapy (n = 1), and predicting response to palliative therapy (n = 1) were included. Publications used a variety of supervised or unsupervised models, but mostly tree-based classifiers and neural networks. Two publications had code uploaded to a public repository, and one publication uploaded the dataset. Conclusions: Machine learning in palliative care is mainly used to predict mortality. Similarly to other applications of ML, external test sets and prospective validations are the exception.

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The international collaboration on cancer reporting (ICCR): development of evidence-based core data sets for pathology cancer reporting

Cited by 2 publications

References 1 publication

Dataset for pathology reporting of ductal carcinoma in situ, variants of lobular carcinoma in situ and low‐grade lesions: recommendations from the International Collaboration on Cancer Reporting (ICCR)

Dataset for pathology reporting of ductal carcinoma in situ, variants of lobular carcinoma in situ and low‐grade lesions: recommendations from the International Collaboration on Cancer Reporting (ICCR)

Applications of Machine Learning in Palliative Care: A Systematic Review

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