Evidence map›Paper›PMID 39695801›Full record

ArticleRadiation oncology (London, England)2024

Estimation of adaptive radiation therapy requirements for rectal cancer: a two-center study.

Liyuan Chen, Lei Yu, Huanli Luo, Yanju Yang, Zhen Zhang, Fu Jin, Weigang Hu, Jiazhou Wang

Abstract readMulticenter Study
In one paragraph

Article in Radiation oncology (London, England), 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

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

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

8 authors.

Liyuan ChenDepartment of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.
Lei YuDepartment of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.
Huanli LuoDepartment of Oncology Radiation Physics Center, Chongqing University Cancer Hospital, Chongqing, 400030, China.
Yanju YangDepartment of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.
Zhen ZhangDepartment of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China. zhen_zhang@fudan.edu.cn.
Fu JinDepartment of Oncology Radiation Physics Center, Chongqing University Cancer Hospital, Chongqing, 400030, China. jfazj@126.com.
Weigang HuDepartment of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China. jackhuwg@gmail.com.
Jiazhou WangDepartment of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China. wjiazhou@gmail.com.

Funding

National Natural Science Foundation of China 12375339Shanghai Committee of Science and Technology Fund 19DZ1930902Xuhui District Artificial Intelligence Medical Hospital Cooperation Project 2021-012
6 · The paper itself

Abstract

backgroundRectal cancer patients are potential beneficiaries of adaptive radiotherapy (ART) which demands considerable resources. Currently, there is no definite guidance on what kind of patients and when will benefit from ART. This study aimed to develop and validate a methodology for estimating ART requirements in rectal cancer before treatment course. METHODS AND MATERIALS: This study involved 66 rectal cancer patients from center 1 and 27 patients from center 2. The ART requirements were evaluated by comparing 8 dose volume histogram (DVH) metrics of targets and organs at risk (OARs) between planning and treatment fractions. Tolerance ranges of deviation of DVH metrics were derived from 10 patients and applied to assess fractional variability. Eighteen features, encompassing diagnostic, dosimetric, and time-related information, were utilized to formulate a stepwise logistic regression model for fraction-level ART requirement estimation. The super parameters were determined through 5-fold cross-validation with 250 training fractions and the methodology was validated with 109 internal testing fractions and 134 external testing fractions.

resultsThe area under the curve (AUC) of training dataset was 0.74 (95% CI: 0.61 to 0.85), while in the internal and external testing, the AUC achieved 0.76 (95% CI: 0.60-0.90) and 0.68 (95% CI: 0.56-0.81). Using a best (or clinical applicable) cut-off value of 33.4% (11%), the predictive model achieved a sensitivity of 46.2% (69.2%) and specificity of 97.9% (68.7%). During the modeling, 5 features were retained: Homogeneity index (OR = 6.06, 95% CI: 2.93-14.8), planning target volume (OR = 1.77, 95% CI: 1.17-2.69), fraction dose (OR = 45.37, 95% CI: 5.74-469), accumulated dose (OR = 2.29, 95% CI: 1.35-4.14), and whether neoadjuvant chemoradiotherapy (OR > 1000).

conclusionART requirements are associated with target volume, target dose homogeneity, fraction dose, dose accumulation and whether neoadjuvant radiotherapy. The predictive model exhibited the capability to predict fraction-level ART requirements.

Indexed as

Organs at RiskRadiotherapy DosageRadiotherapy Planning, Computer-AssistedRectal NeoplasmsAdultAgedAged, 80 and overFemaleHumansMaleMiddle AgedRadiotherapy, Intensity-ModulatedART requirementsDVH metricsLogistic regressionMulti-institutional validationRectal cancer

Identifiers

PMID39695801
PMCPMC11657634

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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.