Evidence map›Paper›PMID 41742522›Full record

ArticleJournal of veterinary internal medicine2026

Computed tomography perfusion in predicting radiation therapy response in dogs and a cat with head and neck tumors.

Soyeon Kim, Eunjee Kim, Min-Ok Ryu, Kyoungwon Seo, Junghee Yoon, Jihye Choi

Abstract read
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Article in Journal of veterinary internal medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Soyeon KimDepartment of Veterinary Medical Imaging, College of Veterinary Medicine, Seoul National University, Seoul 08826, South Korea.
Eunjee KimDepartment of Veterinary Medical Imaging, College of Veterinary Medicine, Seoul National University, Seoul 08826, South Korea.
Min-Ok RyuLaboratory of Veterinary Internal Medicine, Department of Veterinary Clinical Science, College of Veterinary Medicine, Seoul National University, Seoul 08826, South Korea.
Kyoungwon SeoLaboratory of Veterinary Internal Medicine, Department of Veterinary Clinical Science, College of Veterinary Medicine, Seoul National University, Seoul 08826, South Korea.
Junghee YoonDepartment of Veterinary Medical Imaging, College of Veterinary Medicine, Seoul National University, Seoul 08826, South Korea.
Jihye ChoiDepartment of Veterinary Medical Imaging, College of Veterinary Medicine, Seoul National University, Seoul 08826, South Korea.

Funding

Ministry of Science, ICT and Future Planning NRF-2021R1A2C200573011National Research Foundation of Korea
6 · The paper itself

Abstract

backgroundPredicting tumor response to radiation therapy (RT) is challenging, as conventional assessment primarily considers morphology. Computed tomography (CT) perfusion might provide early prediction by evaluating tumor oxygenation and vascularity. HYPOTHESIS/

objectivesTo evaluate the association between CT perfusion variables and tumor size reduction, and their potential to predict outcomes after RT. ANIMALS: Eleven dogs and 1 cat with malignant head and neck tumors, including squamous cell carcinoma, melanoma, and adenocarcinoma.

methodsThis retrospective study evaluated CT perfusion and tumor size before RT, and at 0, 30, and 90 days after RT. Perfusion variables, including blood flow (BF), blood volume (BV), and flow extraction (FE), were measured as absolute values and normalized to contralateral tissue (rc) and muscle (rm). Changes were compared with the previous (pΔ) and following (fΔ) examinations.

resultsMedian values before RT were BV 29.7 mL/100 mL, BF 229.9 mL/min/100 mL, and FE 39.5 mL/min/100 mL. Tumor volumes were generally smaller after RT. The perfusion variables increased at day 30, and subsequently declined by day 90. The rcBV and early increases in BF (pΔBF) were associated with change in tumor size. Higher BF or BV before RT correlated with greater tumor reduction. The change in rmBV relative to the previous scan (pΔrmBV) also correlated with sequent tumor size changes. CONCLUSIONS AND CLINICAL IMPORTANCE: Computed tomography perfusion could offer functional biomarkers for early RT response assessment. Higher BF and BV before RT were associated with better outcomes, and normalized variables, particularly rcBV and pΔBF more reliably reflected tumor size changes than absolute values.

Indexed as

catscomputed tomographydogsradiotherapytumor perfusion

Identifiers

PMID41742522
PMCPMC12881964

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.