European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology
September 9, 2026 · via Europe PMC
Bridging gaps in surgical oncology research and training: the DISSECT COST action (CA24158).
Kaul P, Garg PK, Caballero C, +12 more, Stättner S.
AI summary — for doctors, generated from the abstract only
This article introduces DISSECT, a European collaborative initiative aiming to identify and address disparities in surgical oncology training across regions, rather than reporting new clinical outcome data.
What was studied
Not reported in the abstract as a research study with patient data — this article describes the rationale, scope, and planned activities of DISSECT (Disparities in Surgical Specialization and Education in Cancer Training), a European Collaboration in Science and Technology (COST) Action aimed at addressing disparities in surgical oncology training.
Key findings
- This is a program description rather than a study with outcome data, so no patient-level or training-outcome statistics are reported in the abstract.
- DISSECT aims to bring together institutions from high- and lower-resource settings to promote bidirectional knowledge exchange, strengthen multidisciplinary training, and enhance research capacity in surgical oncology.
- The initiative also plans to encourage responsible use of emerging technologies (including artificial intelligence) and integrate patient partnership into training efforts.
Clinical relevance
Not applicable in the usual clinical sense, as this describes an educational/training initiative rather than a clinical finding affecting patient management; it may be relevant to surgical training programs and institutions considering international collaboration.
Who should care
Limitations
This is a program announcement/rationale paper, not an empirical study; no outcomes of the initiative are yet available, as it is described as 'moving from approval to implementation.'
A new European initiative (DISSECT) aims to reduce disparities in surgical oncology training across institutions and countries, but no outcome data are yet available.
This is an AI-generated summary of the published abstract, not a clinical recommendation. It may not reflect the full text or subsequent updates — verify against the original source before acting on it.
Abstract
Cancer surgery remains a cornerstone of treatment for solid malignancies worldwide, yet substantial disparities persist in the training of surgical oncologists across regions and countries. Differences in training duration, accreditation, clinical exposure, multidisciplinary involvement, mentorship, and research activity contribute to variation in surgical outcomes and widen existing gaps in cancer morbidity and mortality. DISSECT (Disparities in Surgical Specialization and Education in Cancer Training) is a European Collaboration in Science and Technology (COST Action) aimed at systematically identifying and addressing these gaps in surgical oncology training through a structured, collaborative, and evidence-based approach. By bringing together institutions from high- and lower-resource settings, DISSECT promotes bidirectional knowledge exchange, strengthens multidisciplinary training, enhances research capacity, and supports sustainable mentorship. It also encourages responsible use of emerging technologies, including artificial intelligence, and integrates patient partnership. Here, we introduce the rationale, scope, and anticipated impact of DISSECT as it moves from approval to implementation.
DOI
10.1016/j.ejso.2026.112070
PMID
42753502
Research metadata (titles, abstracts, authors, publication dates) is sourced from Europe PMC, which aggregates content from PubMed/MEDLINE, PubMed Central, and other providers under the U.S. National Library of Medicine (NLM/NCBI). NLM does not claim copyright on abstracts; the original journal publishers or authors may hold rights to the full articles. AI-generated summaries are derived only from the abstract, never the full text, and are always labeled as AI-generated.
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