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Related Concept Videos

Cancer Survival Analysis01:21

Cancer Survival Analysis

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Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
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Adaptive Mechanisms in Cancer Cells02:53

Adaptive Mechanisms in Cancer Cells

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Cancer cells accumulate genetic changes at an abnormally rapid rate due to the defects in the DNA repair mechanisms. From an evolutionary perspective, such genetic instability is advantageous for cancer development. Mutant cell lines accumulate a series of beneficial mutations that contribute to their progression into cancer.
Some of the advantages that cancer cells have on normal cells include - enhanced ability to divide without terminally differentiating, induce new blood vessel formation,...
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Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
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Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

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From Network Governance to Real-World-Time Learning: A High-Reliability Operating Model for Rare Cancers.

Bruno Fuchs1,2,3,4, Anna L Falkowski2, Ruben Jaeger2,3

  • 1Faculty of Health Sciences & Medicine, University of Lucerne, Frohburgstrasse 3, 6002 Luzern, Switzerland.

Cancers
|February 27, 2026
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Summary
This summary is machine-generated.

This study presents a rare-cancer Learning Health System (LHS) blueprint for continuous learning and improved care quality. It establishes a framework for auditable improvement science in rare cancers, ensuring valid benchmarking and reducing harm.

Keywords:
hub-and-spokelearning health systemmultidisciplinary tumor board/MDTrare cancersreal-world-time datasarcomavalue-based healthcare

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Generation of Heterogeneous Drug Gradients Across Cancer Populations on a Microfluidic Evolution Accelerator for Real-Time Observation
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Area of Science:

  • Healthcare Systems Science
  • Oncology
  • Health Services Research

Background:

  • Rare cancers present unique challenges due to low incidence, high heterogeneity, and fragmented multi-institutional care.
  • Pathway fragmentation in rare cancer care leads to preventable harm, variability, and waste.
  • Quality of care is best assessed by pathway integrity across the entire patient journey.

Purpose of the Study:

  • To define a pragmatic and transferable operating blueprint for a rare-cancer Learning Health System (LHS).
  • To enable continuous, auditable learning from routine care under explicit governance.
  • To maintain claims discipline and protect measurement validity within the LHS.

Main Methods:

  • Synthesized an implementation-oriented operating model using the Swiss Sarcoma Network (SSN) as an exemplar.
  • Coupled clinical governance (Integrated Practice Unit logic, hub-and-spoke routing, auditable multidisciplinary team decisions) with an interoperable data backbone.
  • Implemented a closed-loop control cycle: capture → harmonize → benchmark → learn → implement → re-measure, with defined owners and failure modes.

Main Results:

  • Specified minimal data primitives including time-stamped decisions, characteristics, treatments, outcomes, and PROMs/PREMs.
  • Developed a Value-Based Health Care (VBHC)-ready measurement backbone for outcomes, harms, timeliness, process fidelity, and resource stewardship.
  • Instituted validity guardrails: explicit applicability rules and mandatory case-mix/complexity stratification.

Conclusions:

  • The blueprint provides an operating model, not a platform, enabling credible improvement science and causal learning for rare cancers.
  • It distinguishes enabling infrastructure from the governed clinical system, supporting scalable excellence while preventing gaming and inequity.
  • Crucial validity gates (applicability rules, denominator integrity, anti-gaming safeguards, escalation governance) are specified for rare-cancer benchmarking, mitigating artifacts and unsafe inferences.