Evidence map›Paper›PMID 36925913›Full record

ArticleFrontiers in oncology2023

MRI-based pre-Radiomics and delta-Radiomics models accurately predict the post-treatment response of rectal adenocarcinoma to neoadjuvant chemoradiotherapy.

Likun Wang, Xueliang Wu, Ruoxi Tian, Hongqing Ma, Zekun Jiang, Weixin Zhao, Guoqing Cui, Meng Li, Qinsheng Hu, Xiangyang Yu and 1 more

Open access · goldFull text read
In one paragraph

Article in Frontiers in oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed, 2 pooled it
5.7field-weighted citation impact, top 3% of its field
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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

18 citing papers in PubMed, 2 syntheses or guidelines pooled it, 25 citations in OpenAlex.

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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

11 authors at 8 institutions in 1 country.

Likun WangTianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.
Xueliang WuGraduate School, Tianjin Medical University, Tianjin, China.
Ruoxi TianDepartment of Colorectal Surgery, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences, Beijing, China.
Hongqing MaDepartment of General Surgery, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China.
Zekun JiangCollege of Computer Science, Sichuan University, Chengdu, China.
Weixin ZhaoCollege of Computer Science, Sichuan University, Chengdu, China.
Guoqing CuiMedical Image Center, The First Affiliated Hospital of Hebei North University, Zhangjiakou, China.
Meng LiGraduate School, Hebei North University, Zhangjiakou, China.
Qinsheng HuDepartment of Orthopedics, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
Xiangyang YuDepartment of Gastrointestinal Surgery, Tianjin Medical University Nankai Hospital, Tianjin, China.
Wengui XuTianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.
Hebei North University · CNChengdu University · CNChinese Academy of Medical Sciences & Peking Union Medical College · CNHebei Medical University · CNTianjin Medical University · CNTianjin Medical University Cancer Institute and Hospital · CNTianjin Nankai Hospital · CNWest China Hospital of Sichuan University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: To develop and validate magnetic resonance imaging (MRI)-based pre-Radiomics and delta-Radiomics models for predicting the treatment response of local advanced rectal cancer (LARC) to neoadjuvant chemoradiotherapy (NCRT). Methods: Between October 2017 and August 2022, 105 LARC NCRT-naïve patients were enrolled in this study. After careful evaluation, data for 84 patients that met the inclusion criteria were used to develop and validate the NCRT response models. All patients received NCRT, and the post-treatment response was evaluated by pathological assessment. We manual segmented the volume of tumors and 105 radiomics features were extracted from three-dimensional MRIs. Then, the eXtreme Gradient Boosting algorithm was implemented for evaluating and incorporating important tumor features. The predictive performance of MRI sequences and Synthetic Minority Oversampling Technique (SMOTE) for NCRT response were compared. Finally, the optimal pre-Radiomics and delta-Radiomics models were established respectively. The predictive performance of the radionics model was confirmed using 5-fold cross-validation, 10-fold cross-validation, leave-one-out validation, and independent validation. The predictive accuracy of the model was based on the area under the receiver operator characteristic (ROC) curve (AUC). Results: There was no significant difference in clinical factors between patients with good and poor reactions. Integrating different MRI modes and the SMOTE method improved the performance of the radiomics model. The pre-Radiomics model (train AUC: 0.93 ± 0.06; test AUC: 0.79) and delta-Radiomcis model (train AUC: 0.96 ± 0.03; test AUC: 0.83) all have high NCRT response prediction performance by LARC. Overall, the delta-Radiomics model was superior to the pre-Radiomics model. Conclusion: MRI-based pre-Radiomics model and delta-Radiomics model all have good potential to predict the post-treatment response of LARC to NCRT. Delta-Radiomics analysis has a huge potential for clinical application in facilitating the provision of personalized therapy.

Indexed as

machine learningMRIneoadjuvant chemoradiotherapyradiomicsrectal adenocarcinoma

Identifiers

PMID36925913
PMCPMC10013156
OpenAlexW4321497274

What Socratic holds

Textfull text, public
LicenceCC BY
measurements read83
Read underepoch 390

Registered trials

None linked

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 Socratic graph.