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- Beets, G., et al.
(författare)
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ECCO Essential Requirements for Quality Cancer Care: Colorectal Cancer. A critical review
- 2017
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Ingår i: Critical Reviews in Oncology Hematology. - : Elsevier BV. - 1040-8428. ; 110, s. 81-93
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Tidskriftsartikel (refereegranskat)abstract
- Background: ECCO essential requirements for quality cancer care (ERQCC) are checklists and explanations of organisation and actions that are necessary to give high-quality care to patients who have a specific tumour type. They are written by European experts representing all disciplines involved in cancer care. ERQCC papers give oncology teams, patients, policymakers and managers an overview of the elements needed in any healthcare system to provide high quality of care throughout the patient journey. References are made to clinical guidelines and other resources where appropriate, and the focus is on care in Europe. Colorectal cancer (CRC) is the second most common cause of cancer death in Europe and has wide variation in outcomes among countries. Increasing numbers of older people are contracting the disease, and treatments for advanced stages are becoming more complex. A growing number of survivors also require specialist support. High-quality care can only be a carried out in specialised CRC units or centres which have both a core multidisciplinary team and an extended team of allied professionals, and which are subject to quality and audit procedures. Such units or centres are far from universal in all European countries. It is essential that, to meet European aspirations for comprehensive cancer control, healthcare organisations implement the essential requirements in this paper, paying particular attention to multidisciplinarity and patient-centred pathways from diagnosis, to treatment, to survivorship. Conclusion: Taken together, the information presented in this paper provides a comprehensive description of the essential requirements for establishing a high-quality CRC service. The ECCO expert group is aware that it is not possible to propose a 'one size fits all' system for all countries, but urges that access to multidisciplinary units or centres must be guaranteed for all those with CRC. (C) 2016 The Authors. Published by Elsevier Ireland Ltd.
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| 2. |
- Bokhorst, J. M., et al.
(författare)
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Assessment of individual tumor buds using keratin immunohistochemistry: moderate interobserver agreement suggests a role for machine learning
- 2020
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Ingår i: Modern Pathology. - : NATURE PUBLISHING GROUP. - 0893-3952 .- 1530-0285. ; 33:5, s. 825-833
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Tidskriftsartikel (refereegranskat)abstract
- Tumor budding is a promising and cost-effective biomarker with strong prognostic value in colorectal cancer. However, challenges related to interobserver variability persist. Such variability may be reduced by immunohistochemistry and computer-aided tumor bud selection. Development of computer algorithms for this purpose requires unequivocal examples of individual tumor buds. As such, we undertook a large-scale, international, and digital observer study on individual tumor bud assessment. From a pool of 46 colorectal cancer cases with tumor budding, 3000 tumor bud candidates were selected, largely based on digital image analysis algorithms. For each candidate bud, an image patch (size 256 x 256 mu m) was extracted from a pan cytokeratin-stained whole-slide image. Members of an International Tumor Budding Consortium (n = 7) were asked to categorize each candidate as either (1) tumor bud, (2) poorly differentiated cluster, or (3) neither, based on current definitions. Agreement was assessed with Cohens and Fleiss Kappa statistics. Fleiss Kappa showed moderate overall agreement between observers (0.42 and 0.51), while Cohens Kappas ranged from 0.25 to 0.63. Complete agreement by all seven observers was present for only 34% of the 3000 tumor bud candidates, while 59% of the candidates were agreed on by at least five of the seven observers. Despite reports of moderate-to-substantial agreement with respect to tumor budding grade, agreement with respect to individual pan cytokeratin-stained tumor buds is moderate at most. A machine learning approach may prove especially useful for a more robust assessment of individual tumor buds.
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