Incidence, Survival Analysis and Future Perspective of Primary Peritoneal Mesothelioma (PPM): A Population-Based

Asad Ullah1, Abdul Waheed2, Jaffar Khan3

  • 1Georgia Cancer Center, Medical College of Georgia, Augusta University, Augusta, GA 30912, USA.

Cancers
|February 25, 2022
PubMed
Abstract

Insights

Primary peritoneal mesothelioma (PPM) is a rare cancer. Poor differentiation, larger tumors, Caucasian race, and advanced stage significantly increase mortality risk in PPM patients.

Area of Science:

  • Oncology
  • Surgical Pathology

Background:

  • Primary peritoneal mesothelioma (PPM) is a rare, aggressive malignancy of the peritoneum.
  • Delayed diagnosis due to non-specific symptoms contributes to poorer prognoses.

Purpose of the Study:

  • To investigate demographic, clinical, and pathological factors influencing prognosis and survival in primary peritoneal mesothelioma.
  • To analyze treatment modalities and their impact on patient outcomes.

Main Methods:

  • Retrospective analysis of 1998 primary peritoneal mesothelioma patients from the SEER database (1975-2016).
  • Statistical analysis including chi-square test, paired t-test, and multivariate analysis.

Main Results:

  • The majority of patients were male (56.2%) and Caucasian (90.4%), with a mean age at diagnosis of 69 years.
  • Poorly differentiated tumors, sizes > 4 cm, Caucasian race, and distant SEER stage were associated with increased mortality.
  • Five-year overall survival was 20.3%; surgery (43.5%) and chemotherapy (18.7%) showed varied survival impacts.

Conclusions:

  • Primary peritoneal mesothelioma is challenging to diagnose and aggressive.
  • Surgery and chemotherapy are primary treatment options; radiation therapy appears to have limited efficacy.
  • Establishing a nationwide registry is crucial for understanding PPM pathogenesis and survival factors.

Related Concept Videos

Cancer Survival Analysis01:21

Cancer Survival Analysis

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...
473
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

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,...
292
Actuarial Approach01:20

Actuarial Approach

The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
143
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
329
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
425
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
209