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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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Tumor Progression02:07

Tumor Progression

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Tumor progression is a phenomenon where the pre-formed tumor acquires successive mutations to become clinically more aggressive and malignant. In the 1950s, Foulds first described the stepwise progression of cancer cells through successive stages.
Colon cancer is one of the best-documented examples of tumor progression. Early mutation in the APC gene in colon cells causes a small growth on the colon wall called a polyp. With time, this polyp grows into a benign, pre-cancerous tumor. Further...
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  1. Home
  3. Research Domains
  5. Biomedical And Clinical Sciences
  7. Oncology And Carcinogenesis
  9. Predictive And Prognostic Markers
  11. Advances In Prognostic Models For Osteosarcoma Risk.
  1. Home
  3. Research Domains
  5. Biomedical And Clinical Sciences
  7. Oncology And Carcinogenesis
  9. Predictive And Prognostic Markers
  11. Advances In Prognostic Models For Osteosarcoma Risk.

Related Experiment Video

Intratibial Osteosarcoma Cell Injection to Generate Orthotopic Osteosarcoma and Lung Metastasis Mouse Models
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Advances in prognostic models for osteosarcoma risk.

Yi Yao1,2,3, Dapeng Wang1, Li Zheng1,2,3

  • 1Guangxi Engineering Center in Biomedical Materials for Tissue and Organ Regeneration, The First Affiliated Hospital of Guangxi Medical University, Guangxi Medical University, Nanning, 530021, China.

Heliyon
|April 8, 2024

View abstract on PubMed

Summary
This summary is machine-generated.

New prognosis models for osteosarcoma are emerging due to advances in sequencing technology and data availability. This review evaluates their predictive performance and reliability for clinical application.

Keywords:
OsteosarcomaRisk-prognosis model

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Area of Science:

  • Oncology
  • Genomics
  • Biostatistics

Background:

  • Risk prognosis models are statistical tools used to predict disease development or clinical outcomes.
  • Osteosarcoma prognosis models are increasingly developed with advancements in large-scale second-generation sequencing.
  • Abundant clinical and biological data fuels the expansion of these predictive models.

Purpose of the Study:

  • To present the predictive effects and reliability of various osteosarcoma prognosis models.
  • To serve as a reference for the evaluation and application of these models in clinical practice.

Main Methods:

  • Review and analysis of existing osteosarcoma prognosis models.
  • Evaluation of predictive performance and reliability based on available data.
  • Assessment of candidate genes and their clinical utility.

    • Main Results:

    • Significant increase in the number of available osteosarcoma prognostic models.
    • Identification of numerous candidate genes for prognostic prediction.
    • Variability in the predictive effects and reliability across different models.

    Conclusions:

    • Osteosarcoma prognosis models show promise for clinical stratification and risk assessment.
    • Further evaluation is needed to determine the optimal models for clinical application.
    • Advancements in sequencing and data integration are crucial for developing robust prognostic tools.