Evidence map›Paper›PMID 40998948›Full record

ArticleScientific reports2025

The improved features to dose correlation for dose volumes prediction in multidisease radiation therapy plans.

Peng Zhou, Jia Luo, Honghao Xu, Shengxiu Jiao, Chuan Chen

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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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

2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Peng Zhou *Department of Cancer Center, Daping Hospital, Army Medical University, Chongqing, China.
Jia Luo *Department of Cancer Center, Daping Hospital, Army Medical University, Chongqing, China.
Honghao XuDepartment of Nuclear Medicine, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, 250021, Shandong, China.
Shengxiu JiaoDepartment of Nuclear Medicine, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, 250021, Shandong, China. jiaoshx@mail2.sysu.edu.cn.
Chuan ChenDepartment of Cancer Center, Daping Hospital, Army Medical University, Chongqing, China. sinkriver@126.com.

Funding

Clinical Medical Research Project of Army Medical University project # 2023XLC12Medical Research Project of Artificial Intelligence Plus of Daping Hospital Key project # ZXAIZD004Natural Science Foundation of Shandong Province Youth Program, ZR2022QA081
6 · The paper itself

Abstract

Determining whether input features are consistently strongly correlated with organ-at-risk (OAR) dose across different diseases can improve the accuracy of dose-volume histogram (DVH) prediction in knowledge-based treatment planning. In this study, we utilized patient geometric and dosimetric data to establish correlations with OAR doses and investigated the differences between distance-to-dose and dosimetric-to-dose relationships. Ninety nasopharyngeal cancer radiotherapy plans, 66 esophageal cancer radiotherapy plans and 52 rectal cancer radiotherapy plans were selected as study objects. Geometric feature was quantified by the distance-to-target histogram (DTH), and dosimetric feature was quantified by the conformal-plan-dose-volume histogram (CPDVH). The distance-to-dose correlation and dosimetric feature-to-dose correlation were calculated. DTH-DVH and CPDVH-DVH correlations were calculated for fractional OAR volumes of 30%, 50% and 60%. The correlations were calculated using the Pearson product-moment correlation coefficient (R). Compared to the distance-to-dose correlation, dosimetric feature achieved a stronger correlation (R) with OAR doses for 10 out of 13 OARs, including those to the brainstem (- 0.83, 0.93) and lung (- 0.60, 0.92). Compared to the DTH-DVH correlation, the CPDVH dose-volume achieved a stronger correlation (R) with the DVH dose-volume for 10 out of 13 OARs, including the brainstem (mean, - 0.67 vs. 0.80) and lung (mean, - 0.64 vs. 0.76). Compared to the CPDVH-DVH correlation, the DTH distance-volume showed a stronger correlation with the DVH dose-volume at the parotid (mean, - 0.88 vs. 0.60) and bladder (mean, - 0.56 vs. 0.46). The results indicated that the dosimetric feature showed a stronger correlation with most OAR doses than the distance-to-dose correlation, which is helpful for accurate DVH prediction. Patient geometry influences dosimetric feature-to-dose correlation when many OARs voxels are located inside the C-shaped target region or when the OARs significantly overlap with the target, and using only dosimetric feature for the DVH prediction of these OARs is not appropriate.

Indexed as

Radiotherapy Planning, Computer-AssistedEsophageal NeoplasmsHumansNasopharyngeal NeoplasmsOrgans at RiskRadiometryRadiotherapy DosageCorrelation analysisDose-volume histogram predictionDosimetric featureEsophageal cancerNasopharyngeal cancerRectal cancer

Identifiers

PMID40998948
PMCPMC12464183

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