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Related Concept Videos

Introduction to Test of Independence01:21

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In statistics, the term independence means that one can directly obtain the probability of any event involving both variables by multiplying their individual probabilities. Tests of independence are chi-square tests involving the use of a contingency table of observed (data) values.
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While Mendel’s Law of Segregation states that the two alleles for one gene are separated into different gametes, a different question of how different genes are inherited remains. For example, is the gene for tall plants inherited with the gene for green peas? Mendel asked this question by experimenting with a dihybrid cross; a cross in which both parents are homozygous for two distinct traits resulting in an F1 generation that are heterozygous for both traits.
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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.
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In electrical circuits, sources play a crucial role in providing power for the operation of the circuit. These sources can be broadly categorized into two types: independent and dependent.
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Related Experiment Video

Updated: Jan 26, 2026

Establishment of a Primary Culture of Patient-derived Soft Tissue Sarcoma
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MRI Radiomic Features Are Independently Associated With Overall Survival in Soft Tissue Sarcoma.

Matthew B Spraker1, Landon S Wootton2, Daniel S Hippe3

  • 1Department of Radiation Oncology, Washington University in St. Louis, St. Louis, Missouri.

Advances in Radiation Oncology
|April 24, 2019
PubMed
Summary

Radiomic features from MRI independently predict overall survival in soft tissue sarcomas (STS). Incorporating these features with clinical data improves prognostic models for personalized STS treatment selection.

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Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics
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Area of Science:

  • Oncology
  • Radiology
  • Medical Imaging

Background:

  • Soft tissue sarcomas (STS) are a diverse group of cancers.
  • Individualized treatment selection for STS remains challenging.
  • Predictive biomarkers for overall survival (OS) are crucial.

Purpose of the Study:

  • To investigate if radiomic features from MRI are independently associated with OS in STS.
  • To develop and validate prognostic models for OS using radiomics and clinical data.

Main Methods:

  • Analysis of two independent cohorts (N=165 and N=61) of adult patients with stage II-III STS.
  • Extraction of 30 radiomic features from pretreatment T1-weighted contrast-enhanced MRI.
  • Construction and validation of clinical-only, radiomics-only, and combined clinical-radiomic prognostic models.

Main Results:

  • Tumor volume and texture features were selected in the radiomics-only model.
  • Age, grade, and radiomic features were selected in the combined model.
  • Radiomic features showed independent association with OS in the validation cohort (HR=2.4, P=.009).

Conclusions:

  • Radiomic features from MRI are independently associated with OS in STS, even after accounting for age and grade.
  • A combined clinical-radiomic model demonstrated robust predictive performance for 3-year OS in an independent cohort.
  • Integrating radiomic features may enhance personalized therapy selection for STS patients.