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Record W2771033857 · doi:10.7150/thno.19703

Early Assessment of Tumor Response to Radiation Therapy using High-Resolution Quantitative Microvascular Ultrasound Imaging

2017· erratum· en· W2771033857 on OpenAlexfundno aff
Sandeep K. Kasoji, Judith N. Rivera, Ryan C. Gessner, S Chang, Paul A. Dayton

Bibliographic record

VenueTheranostics · 2017
Typeerratum
Languageen
FieldEngineering
TopicUltrasound and Hyperthermia Applications
Canadian institutionsnot available
FundersNational Center for Advancing Translational SciencesNational Cancer InstituteNational Institutes of HealthSunnybrook Research Institute
KeywordsUltrasoundUltrasound imagingRadiation therapyMedicineRadiologyBiomedical engineeringMedical physics

Abstract

fetched live from OpenAlex

Measuring changes in tumor volume using anatomical imaging weeks to months post radiation therapy (RT) is currently the clinical standard for indicating treatment response to RT.For patients whose tumors do not respond successfully to treatment, this approach is suboptimal as timely modification of the treatment approach may lead to better clinical outcomes.We propose to use tumor microvasculature as a biomarker for early assessment of tumor response to RT. Acoustic angiography is a novel contrast ultrasound imaging technique that enables high-resolution microvascular imaging and has been shown to detect changes in microvascular structure due to cancer growth.Data suggest that acoustic angiography can detect longitudinal changes in the tumor microvascular environment that correlate with RT response.Methods: Three cohorts of Fisher 344 rats were implanted with rat fibrosarcoma tumors and were treated with a single fraction of RT at three dose levels (15 Gy, 20 Gy, and 25 Gy) at a dose rate of 300 MU/min.A simple treatment condition was chosen for testing the feasibility of our imaging technique.All tumors were longitudinally imaged immediately prior to and after treatment and then every 3 days after treatment for a total of 30 days.Both acoustic angiography (using in-house produced microbubble contrast agents) and standard b-mode imaging was performed at each imaging time point using a pre-clinical Vevo770 scanner and a custom modified dual-frequency transducer.Results: Results show that all treated tumors in each dose group initially responded to treatment between days 3-15 as indicated by decreased tumor growth accompanied with decreased vascular density.Untreated tumors continued to increase in both volume and vascular density until they reached the maximum allowable size of 2 cm in diameter.Tumors that displayed complete control (no tumor recurrence) continued to decrease in size and vascular density, while tumors that progressed after the initial response presented an increase in tumor volume and volumetric vascular density.The increase in tumor volumetric vascular density in recurring tumors can be detected 10.25 ± 1.5 days, 6 ± 0 days, and 4 ± 1.4 days earlier than the measurable increase in tumor volume in the 15, 20, and 25 Gy dose groups, respectively.A dose-dependent growth rate for tumor recurrence was also observed.Conclusions: In this feasibility study we have demonstrated the ability of acoustic angiography to detect longitudinal changes in vascular density, which was shown to be a potential biomarker for tumor response to RT.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.018
GPT teacher head0.286
Teacher spread0.268 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations46
Published2017
Admission routes1
Has abstractyes

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