Evidence map›Paper›PMID 31431368›Full record

ArticleRadiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology2020

Predicting the tumor response to chemoradiotherapy for rectal cancer: Model development and external validation using MRI radiomics.

Philippe Bulens, Alice Couwenberg, Martijn Intven, Annelies Debucquoy, Vincent Vandecaveye, Eric Van Cutsem, André D'Hoore, Albert Wolthuis, Pritam Mukherjee, Olivier Gevaert and 1 more

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

Article in Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers, 7 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
57citing papers in PubMed, 7 pooled it
9.1field-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

57 citing papers in PubMed, 7 syntheses or guidelines pooled it, 89 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Pooled it
  6. Pooled it
  7. Pooled it
  8. Trial
  9. Trial
  10. Multi-parametric MRI radiomics to predict sensitivity to neoadjuvant chemoradiotherapy based on multi-regions of interest in locally advanced rectal cancer: a multicenter study.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026
    Article
  11. Article
  12. Review
  13. Article
  14. Article
  15. Article
  16. Observational
  17. Article
  18. Review
  19. Article
  20. 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

11 authors at 3 institutions in 3 countries.

Philippe BulensDepartment of Radiation Oncology, University Hospitals Leuven, Belgium.
Alice CouwenbergDepartment of Radiation Oncology, University Medical Center Utrecht, The Netherlands.
Martijn IntvenDepartment of Radiation Oncology, University Medical Center Utrecht, The Netherlands.
Annelies DebucquoyDepartment of Radiation Oncology, University Hospitals Leuven, Belgium.
Vincent VandecaveyeDepartment of Radiology, University Hospitals Leuven, Belgium.
Eric Van CutsemDepartment of Digestive Oncology, University Hospitals Leuven, Belgium.
André D'HooreDepartment of Abdominal Surgery, University Hospitals Leuven, Belgium.
Albert WolthuisDepartment of Abdominal Surgery, University Hospitals Leuven, Belgium.
Pritam MukherjeeStanford Center for Biomedical Informatics Research, Department of Medicine and Biomedical Data Science, Stanford University, USA.
Olivier GevaertStanford Center for Biomedical Informatics Research, Department of Medicine and Biomedical Data Science, Stanford University, USA.
Karin HaustermansDepartment of Radiation Oncology, University Hospitals Leuven, Belgium. Electronic address: karin.haustermans@uzleuven.be.
KU Leuven · BEStanford University · USUniversity Medical Center Utrecht · NL

Funding

Radiogenomics Framework for Non-Invasive Personalized MedicineR01EB020527 · NIBIB · STANFORD UNIVERSITY · PI GEVAERT, OLIVIER · 2015 to 2018
$2.3M
NIBIB NIH HHS R01 EB020527
6 · The paper itself

Abstract

backgroundIn well-responding patients to chemoradiotherapy for locally advanced rectal cancer (LARC), a watch-and-wait strategy can be considered. To implement organ-sparing strategies, accurate patient selection is needed. We investigate the use of MRI-based radiomics models to predict tumor response to improve patient selection. MATERIALS AND

methodsModels were developed in a cohort of 70 patients and validated in an external cohort of 55 patients. Patients received chemoradiation followed by surgery and underwent T2-weighted and diffusion-weighted MRI (DW-MRI) before and after chemoradiation. The outcome measure was (near-)complete pathological tumor response (ypT0-1N0). Tumor segmentation was done on T2-images and transferred to b800-images and ADC maps, after which quantitative and four semantic features were extracted. We combined features using principal component analysis and built models using LASSO regression analysis. The best models based on precision and performance were selected for validation.

results21/70 patients (30%) achieved ypT0-1N0 in the development cohort versus 13/55 patients (24%) in the validation cohort. Three models (t2_dwi_pre_post, semantic_dwi_adc_pre, semantic_dwi_post) were identified with an area-under-the-curve (AUC) of 0.83 (95% CI 0.70-0.95), 0.86 (95% CI 0.75-0.98) and 0.84 (95% CI 0.75-0.94) respectively. Two models (t2_dwi_pre_post, semantic_dwi_post) validated well in the external cohort with AUCs of 0.83 (95% CI 0.70-0.95) and 0.86 (95% CI 0.76-0.97). These models however did not outperform a previously established four-feature semantic model.

conclusionPrediction models based on MRI radiomics non-invasively predict tumor response after chemoradiation for rectal cancer and can be used as an additional tool to identify patients eligible for an organ-preserving treatment.

Indexed as

Models, StatisticalAgedChemoradiotherapyClinical Trials as TopicCohort StudiesDiffusion Magnetic Resonance ImagingFemaleHumansMaleMiddle AgedNeoplasm StagingPredictive Value of TestsRectal NeoplasmsTreatment OutcomeMagnetic resonance imagingRadiomicsRectal cancerResponse prediction

Identifiers

PMID31431368
PMCPMC6997038
OpenAlexW2968739673

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.