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A proliferation saturation index to predict radiation response and personalize radiotherapy fractionation

Overview of attention for article published in Radiation Oncology, July 2015
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • One of the highest-scoring outputs from this source (#5 of 1,811)
  • High Attention Score compared to outputs of the same age (96th percentile)

Mentioned by

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69 tweeters
facebook
1 Facebook page
googleplus
1 Google+ user

Citations

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81 Dimensions

Readers on

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75 Mendeley
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Title
A proliferation saturation index to predict radiation response and personalize radiotherapy fractionation
Published in
Radiation Oncology, July 2015
DOI 10.1186/s13014-015-0465-x
Pubmed ID
Authors

Sotiris Prokopiou, Eduardo G. Moros, Jan Poleszczuk, Jimmy Caudell, Javier F. Torres-Roca, Kujtim Latifi, Jae K. Lee, Robert Myerson, Louis B. Harrison, Heiko Enderling

Abstract

Although altered protocols that challenge conventional radiation fractionation have been tested in prospective clinical trials, we still have limited understanding of how to select the most appropriate fractionation schedule for individual patients. Currently, the prescription of definitive radiotherapy is based on the primary site and stage, without regard to patient-specific tumor or host factors that may influence outcome. We hypothesize that the proportion of radiosensitive proliferating cells is dependent on the saturation of the tumor carrying capacity. This may serve as a prognostic factor for personalized radiotherapy (RT) fractionation. We introduce a proliferation saturation index (PSI), which is defined as the ratio of tumor volume to the host-influenced tumor carrying capacity. Carrying capacity is as a conceptual measure of the maximum volume that can be supported by the current tumor environment including oxygen and nutrient availability, immune surveillance and acidity. PSI is estimated from two temporally separated routine pre-radiotherapy computed tomography scans and a deterministic logistic tumor growth model. We introduce the patient-specific pre-treatment PSI into a model of tumor growth and radiotherapy response, and fit the model to retrospective data of four non-small cell lung cancer patients treated exclusively with standard fractionation. We then simulate both a clinical trial hyperfractionation protocol and daily fractionations, with equal biologically effective dose, to compare tumor volume reduction as a function of pretreatment PSI. With tumor doubling time and radiosensitivity assumed constant across patients, a patient-specific pretreatment PSI is sufficient to fit individual patient response data (R(2) = 0.98). PSI varies greatly between patients (coefficient of variation >128 %) and correlates inversely with radiotherapy response. For this study, our simulations suggest that only patients with intermediate PSI (0.45-0.9) are likely to truly benefit from hyperfractionation. For up to 20 % uncertainties in tumor growth rate, radiosensitivity, and noise in radiological data, the absolute estimation error of pretreatment PSI is <10 % for more than 75 % of patients. Routine radiological images can be used to calculate individual PSI, which may serve as a prognostic factor for radiation response. This provides a new paradigm and rationale to select personalized RT dose-fractionation.

Twitter Demographics

The data shown below were collected from the profiles of 69 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 75 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 3%
United Kingdom 1 1%
Unknown 72 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 20 27%
Researcher 14 19%
Student > Master 8 11%
Other 5 7%
Student > Doctoral Student 4 5%
Other 15 20%
Unknown 9 12%
Readers by discipline Count As %
Medicine and Dentistry 15 20%
Mathematics 13 17%
Physics and Astronomy 8 11%
Agricultural and Biological Sciences 5 7%
Biochemistry, Genetics and Molecular Biology 5 7%
Other 14 19%
Unknown 15 20%

Attention Score in Context

This research output has an Altmetric Attention Score of 45. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 30 June 2021.
All research outputs
#625,931
of 18,974,311 outputs
Outputs from Radiation Oncology
#5
of 1,811 outputs
Outputs of similar age
#9,567
of 243,460 outputs
Outputs of similar age from Radiation Oncology
#1
of 1 outputs
Altmetric has tracked 18,974,311 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,811 research outputs from this source. They receive a mean Attention Score of 2.5. This one has done particularly well, scoring higher than 99% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 243,460 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 96% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them