Evidence map›Paper›PMID 38463234›Full record

ArticleFrontiers in oncology2024

A study on the radiomic correlation between CBCT and pCT scans based on modified 3D-RUnet image segmentation.

Yanjuan Yu, Guanglu Gao, Xiang Gao, Zongkai Zhang, Yipeng He, Liwan Shi, Zheng Kang

Abstract read
In one paragraph

Article in Frontiers in oncology, 2024. 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. Article
4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Yanjuan Yu *College of Electronic Engineering, Zhangzhou Institute of Technology, Zhangzhou, Fujian, China.
Guanglu Gao *Department of Radiation Oncology, the First Affiliated Hospital of Xiamen University, Xiamen, Fujian, China.
Xiang GaoDepartment of Radiation Oncology, the First Affiliated Hospital of Xiamen University, Xiamen, Fujian, China.
Zongkai ZhangDepartment of Radiation Oncology, the First Affiliated Hospital of Xiamen University, Xiamen, Fujian, China.
Yipeng HeDepartment of Radiation Oncology, the First Affiliated Hospital of Xiamen University, Xiamen, Fujian, China.
Liwan ShiDepartment of Radiation Oncology, the First Affiliated Hospital of Xiamen University, Xiamen, Fujian, China.
Zheng KangDepartment of Radiation Oncology, the First Affiliated Hospital of Xiamen University, Xiamen, Fujian, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: The present study is based on evidence indicating a potential correlation between cone-beam CT (CBCT) measurements of tumor size, shape, and the stage of locally advanced rectal cancer. To further investigate this relationship, the study quantitatively assesses the correlation between positioning CT (pCT) and CBCT in the radiomics features of these cancers, and examines their potential for substitution. Methods: In this study, 103 patients diagnosed with locally advanced rectal cancer and undergoing neoadjuvant chemoradiotherapy were selected as participants. Their CBCT and pCT images were used to divide the participants into two groups: a training set and a validation set, with a 7:3 ratio. An improved conventional 3D-RUNet (CLA-UNet) deep learning model was trained on the training set data and then applied to the validation set. The DSC, HD95 and ASSD were calculated for quantitative evaluation purposes. Then, radiomics features were extracted from 30 patients of the test set. Results: The experiments demonstrate that, the modified model achieves an average DSC score 0.792 for pCT and 0.672 for CBCT scans. 1037 features were extracted from each patient's CBCT and pCT images, 73 image features were found to have R values greater than 0.9, including three features related to the staging and prognosis of rectal cancer. Conclusion: In this study, we proposed an automatic, fast, and consistent method for rectal cancer GTV segmentation for pCT and CBCT scans. The findings of radiomic results indicate that CBCT images have significant research value in the field of radiomics.

Indexed as

3D-RUnetCLAHEcone-beam CTradiomicsrectal cancer

Identifiers

PMID38463234
PMCPMC10921553

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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.