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Related Concept Videos

Cancer Survival Analysis01:21

Cancer Survival Analysis

329
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 Immunotherapy01:27

Tumor Immunotherapy

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Immunotherapy is a treatment that boosts or manipulates the immune system to fight diseases, including cancer. For instance, by stimulating an immune response through vaccinations against viruses that cause cancers, like hepatitis B virus and human papillomavirus, these diseases can be prevented. Nonetheless, some cancer cells can avoid the immune system due to their rapid mutation and division. The immune response to many cancers involves three phases: elimination, equilibrium, and escape.
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Kaplan-Meier Approach01:24

Kaplan-Meier Approach

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The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
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  7. Oncology And Carcinogenesis
  9. Predictive And Prognostic Markers
  11. Development And Validation Of A New Tool To Estimate Early Mortality In Patients With Advanced Cancer Treated With Immunotherapy.
  1. Home
  3. Research Domains
  5. Biomedical And Clinical Sciences
  7. Oncology And Carcinogenesis
  9. Predictive And Prognostic Markers
  11. Development And Validation Of A New Tool To Estimate Early Mortality In Patients With Advanced Cancer Treated With Immunotherapy.

Related Experiment Video

Predictive Immune Modeling of Solid Tumors
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Predictive Immune Modeling of Solid Tumors

Published on: February 25, 2020

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Development and validation of a new tool to estimate early mortality in patients with advanced cancer treated with immunotherapy.

Andrea De Giglio1,2, Alessandro Leonetti3, Francesca Comito4

  • 1Department of Medical and Surgical Sciences, Alma Mater Studiorum University of Bologna, Via Massarenti, 9, 40138, Bologna, Italy. andrea.degiglio2@unibo.it.

Cancer Immunology, Immunotherapy : CII
|October 2, 2024

View abstract on PubMed

Summary
This summary is machine-generated.

The Lung Immune Prognostic Index (LIPI) and ECOG Performance Status (PS) can predict early mortality in advanced cancer patients treated with immune checkpoint inhibitors (ICIs). These factors help identify patients unlikely to benefit from ICI therapy.

Keywords:
Early mortalityImmunotherapyPrognostic predictionSolid tumors

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Area of Science:

  • Oncology
  • Immunotherapy
  • Cancer Research

Background:

  • Immune checkpoint inhibitors (ICIs) are standard treatments for advanced solid cancers.
  • Resistance to ICIs leads to early mortality (EM), posing a significant challenge.
  • Prognostic factors for EM, including the Lung Immune Prognostic Index (LIPI), are underexplored.

Purpose of the Study:

  • To identify prognostic factors for early mortality (EM) in patients with advanced solid tumors treated with ICIs.
  • To develop and validate a predictive model for 90-day mortality.
  • To assess the prognostic value of LIPI and ECOG PS for EM and early progression.

Main Methods:

  • Retrospective, observational study of 637 patients with advanced solid tumors treated with ICIs.
  • Logistic regression models to identify factors associated with EM and 90-day progression.
  • Development and external validation of a nomogram for predicting 90-day mortality.

    • Main Results:

    • 21.3% of patients died within 90 days; 8.4% died within 30 days; 34.5% experienced early progression.
    • ECOG PS 2 and high/intermediate LIPI score were independently associated with 90-day mortality.
    • A nomogram combining LIPI and ECOG PS achieved an AUC of 0.76 for 90-day mortality prediction, validated externally (AUC 0.72).

    Conclusions:

    • LIPI and ECOG PS are independent predictors of 90-day mortality in advanced cancer patients receiving ICIs.
    • LIPI also demonstrates prognostic validity for 30-day mortality and early progression.
    • These findings aid in identifying patients who may not benefit from ICI therapy.