Related Experiment Video
Updated: May 10, 2026
09:57
Comprehensive Protocol to Sample and Process Bone Marrow for Measuring Measurable Residual Disease and Leukemic Stem Cells in Acute Myeloid Leukemia
Published on: March 5, 2018
Current survival measures reliably reflect modern sequential treatment in CML: correlation with prognostic
Tomas Pavlik1, Eva Janousova, Jiri Mayer
1Institute of Biostatistics and Analyses, Masaryk University, Brno, Czech Republic.
American Journal of Hematology
|June 14, 2013
Summary
Early BCR-ABL1 transcript levels in chronic myeloid leukemia (CML) patients treated with imatinib predict long-term outcomes. Patients with low BCR-ABL1 transcripts at 3 months show better survival, aiding early risk stratification.
Area of Science:
- Hematology
- Oncology
- Clinical Research
Background:
- Chronic myeloid leukemia (CML) management relies on prognostic scores and response monitoring.
- Imatinib therapy has revolutionized CML treatment, necessitating refined prognostic tools.
Purpose of the Study:
- To evaluate the prognostic value of established scores (Sokal, Euro, EUTOS) and early treatment response markers.
- To assess the predictive power of BCR-ABL1 transcript levels and complete cytogenetic response (CCgR) at 3 months for survival outcomes.
Main Methods:
- Analysis of data from 723 chronic phase CML patients.
- Comparison of prognostic scores, BCR-ABL1 levels, and CCgR achievement.
- Evaluation using current cumulative incidence (CCI), current leukemia-free survival (CLFS), and overall survival.
Main Results:
- BCR-ABL1 transcript levels at 3 months significantly predicted 5-year CCI, with levels ≤10% associated with 94.3% CCI versus >10% with 57.1% (P=0.005).
- Early BCR-ABL1 levels effectively identified patients with poorer outcomes on imatinib.
- CLFS was not significantly impacted by the evaluated prognostic factors once CCgR was achieved.
Conclusions:
- BCR-ABL1 transcript levels at 3 months are a crucial early predictor of imatinib therapy response and long-term survival in CML.
- Achieving CCgR indicates a favorable prognosis, regardless of initial risk stratification.
- This finding supports early identification of high-risk CML patients for intensified monitoring or alternative therapies.
Related Concept Videos
01:21Cancer 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...
01:20Comparing 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 Cox...
01:15Assumptions 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.
01:24Kaplan-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,...
01:20Actuarial 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,...
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
01:18Survival Curves
Survival curves are graphical representations that depict the survival experience of a population over time, offering an intuitive way to track the proportion of individuals who remain event-free at each time point. These curves are widely used in fields such as medicine, public health, and reliability engineering to visualize and compare survival probabilities across different groups or conditions.
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...