Evidence map›Paper›PMID 31059768›Full record

ArticleMagnetic resonance imaging2019

Machine learning for prediction of chemoradiation therapy response in rectal cancer using pre-treatment and mid-radiation multi-parametric MRI.

Liming Shi, Yang Zhang, Ke Nie, Xiaonan Sun, Tianye Niu, Ning Yue, Tiffany Kwong, Peter Chang, Daniel Chow, Jeon-Hor Chen and 1 more

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Magnetic resonance imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
61citing papers in PubMed, 5 pooled it
7.5field-weighted citation impact, top 2% 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

61 citing papers in PubMed, 5 syntheses or guidelines pooled it, 107 citations in OpenAlex.

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1 more citing papers are in PubMed but not listed here.

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 5 institutions in 3 countries.

Liming ShiDepartment of Radiation Oncology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Yang ZhangDepartment of Radiological Sciences, University of California, Irvine, CA, USA.
Ke NieDepartment of Radiation Oncology, Rutgers-The State University of New Jersey, New Brunswick, NJ, USA. Electronic address: kn231@cinj.rutgers.edu.
Xiaonan SunDepartment of Radiation Oncology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China. Electronic address: sunxiaonan@zju.edu.cn.
Tianye NiuDepartment of Radiation Oncology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Ning YueDepartment of Radiation Oncology, Rutgers-The State University of New Jersey, New Brunswick, NJ, USA.
Tiffany KwongDepartment of Radiological Sciences, University of California, Irvine, CA, USA.
Peter ChangDepartment of Radiological Sciences, University of California, Irvine, CA, USA.
Daniel ChowDepartment of Radiological Sciences, University of California, Irvine, CA, USA.
Jeon-Hor ChenDepartment of Radiological Sciences, University of California, Irvine, CA, USA; Department of Radiology, E-Da Hospital and I-Shou University, Kaohsiung, Taiwan.
Min-Ying SuDepartment of Radiological Sciences, University of California, Irvine, CA, USA. Electronic address: msu@uci.edu.
University of California, Irvine · USRutgers, The State University of New Jersey · USZhejiang University · CNE-Da Hospital · TWSir Run Run Shaw Hospital · CN

Funding

TRANSCRIPTIONAL PROFILINGP30CA072720 · NCI · UNIV OF MED/DENT NJ-R W JOHNSON MED SCH · PI Chang S Chan · 1997 to 2026
$94.5M
Univ.of Calif., Irvine Cancer Center Support GrantP30CA062203 · NCI · UNIVERSITY OF CALIFORNIA-IRVINE · PI Christine Hui · 1994 to 2026
$57.9M
Prediction of Neoadjuvant Chemo Pathological Response Using MRI MarkersR01CA127927 · NCI · UNIVERSITY OF CALIFORNIA-IRVINE · PI SU, MIN-YING L. · 2008 to 2017
$5.0M
Mammographic Density and Metabolic Genotyping for Predicting Cancer PrognosisR21CA208938 · NCI · UNIVERSITY OF CALIFORNIA-IRVINE · PI CHEN, JEON-HOR, SU, MIN-YING L. · 2017 to 2018
$370k
NCI NIH HHS P30 CA062203NCI NIH HHS P30 CA072720NCI NIH HHS R01 CA127927NCI NIH HHS R21 CA208938
6 · The paper itself

Abstract

purposeTo predict the neoadjuvant chemoradiation therapy (CRT) response in patients with locally advanced rectal cancer (LARC) using radiomics and deep learning based on pre-treatment MRI and a mid-radiation follow-up MRI taken 3-4 weeks after the start of CRT.

methodsA total of 51 patients were included, 45 with pre-treatment, 41 with mid-radiation therapy (RT), and 35 with both MRI sets. The multi-parametric MRI protocol included T2, diffusion weighted imaging (DWI) with b-values of 0 and 800 s/mm

resultsTumor volume decreased and ADC increased significantly in the mid-RT MRI compared to the pre-treatment MRI. For predicting pCR vs. non-pCR, combining ROI and radiomics features achieved an AUC of 0.80 for pre-treatment, 0.82 for mid-RT, and 0.86 for both MRI together. For predicting GR vs. non-GR, the AUC was 0.91 for pre-treatment, 0.92 for mid-RT, and 0.93 for both MRI together. In deep learning using CNN, combining pre-treatment and mid-RT MRI achieved a higher accuracy compared to using either dataset alone, with AUC of 0.83 for predicting pCR vs. non-pCR.

conclusionRadiomics based on pre-treatment and early follow-up multi-parametric MRI in LARC patients receiving CRT could extract comprehensive quantitative information to predict final pathologic response.

Indexed as

Machine LearningChemoradiotherapyContrast MediaDiffusion Magnetic Resonance ImagingFemaleHumansImage EnhancementMaleMiddle AgedMultiparametric Magnetic Resonance ImagingPredictive Value of TestsRectal NeoplasmsRectumReproducibility of ResultsTreatment OutcomeTumor BurdenContrast MediaConvolutional neural networkLocally advanced rectal cancerMulti-parametric MRINeoadjuvant chemoradiation therapyRadiomics

Identifiers

PMID31059768
PMCPMC7709818
OpenAlexW2942648398

What OpenQuestion holds

Textmetadata
LicenceTDM
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 OpenQuestion graph.