Related Experiment Video
Updated: Feb 12, 2026
07:55
Establishment of a Primary Culture of Patient-derived Soft Tissue Sarcoma
Published on: April 11, 2018
15.2K
Improved survival for extremity soft tissue sarcoma treated in high-volume facilities
Tyler Abarca1, Yubo Gao2, Varun Monga3
1Carver College of Medicine, University of Iowa, Iowa City, Iowa.
Journal of Surgical Oncology
|April 11, 2018
Summary
Patients treated at high-volume hospitals for extremity soft tissue sarcoma had better survival rates and fewer positive margins. This highlights the importance of experienced centers for optimal patient outcomes.
Area of Science:
- Oncology
- Surgical Oncology
- Health Services Research
Background:
- Extremity soft tissue sarcoma (STS) is a rare malignancy.
- Treatment decisions and outcomes may vary based on hospital expertise.
- Hospital volume is a potential indicator of center experience and resource allocation.
Purpose of the Study:
- To investigate the impact of hospital volume on treatment strategies for extremity STS.
- To evaluate the effect of hospital volume on surgical outcomes and patient survival.
- To compare treatment results between high-volume and low-volume centers for STS.
Main Methods:
- Utilized the National Cancer Database to identify adult patients with non-metastatic extremity STS.
- Matched patients from high- and low-volume centers using propensity score analysis.
- Created two comparable groups of 2437 patients each for analysis.
Main Results:
- High-volume centers utilized chemotherapy more frequently (22% vs 17%).
- Lower rates of positive surgical margins were observed in high-volume centers (12% vs 17%).
- No significant differences in limb salvage or readmission rates were found; however, low-volume centers showed diminished overall survival (HR 1.24 at 5 years).
Conclusions:
- Treatment at high-volume hospitals is associated with improved overall survival and reduced positive margins for extremity STS.
- These findings underscore the importance of centralizing care for complex conditions like STS.
- Balancing patient access with specialized center experience is crucial for optimizing outcomes.
Related Concept Videos
01:18Survival Curves
732
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...
732
01:19Survival Tree
436
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
Building a Survival Tree
Constructing a...
Building a Survival Tree
Constructing a...
436
01:22Absolute and Local Extreme Values
86
The highest and lowest values of a function, relative to a reference axis, are known as extreme values. These include absolute maximum and absolute minimum values, which represent the highest and lowest points the function reaches across its entire domain. Within a restricted portion of the function, the highest and lowest values are referred to as local maximum and local minimum values, respectively.Periodic functions, such as sine and cosine, show extreme values at infinitely many points due...
86
01:18Introduction To Survival Analysis
830
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...
The primary goal of survival analysis is to estimate survival time—the time...
830
01:20Comparing the Survival Analysis of Two or More Groups
617
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...
617
01:09Truncation in Survival Analysis
631
Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are...
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are...
631