Evidence map›Paper›PMID 30135549›Full record

ArticleScientific reports2018

Deep Learning and Radiomics predict complete response after neo-adjuvant chemoradiation for locally advanced rectal cancer.

Jean-Emmanuel Bibault, Philippe Giraud, Martin Housset, Catherine Durdux, Julien Taieb, Anne Berger, Romain Coriat, Stanislas Chaussade, Bertrand Dousset, Bernard Nordlinger and 1 more

Erratum issued Registry-linked trialOpen access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. It is linked to trial NCT06108310 (ATTRACT - ArTificial inTelligence-based RAdiogenomics in Colon Tumors), which is not on this map. Cited by 108 papers, 8 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
108citing papers in PubMed, 8 pooled it
18.5field-weighted citation impact, top 1% 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.

NCT06108310 unknown statusnot on this mapstarted 2021, after this paper: background citation

ATTRACT - ArTificial inTelligence-based RAdiogenomics in Colon Tumors

TypeobservationalSponsorUniversity of Roma La SapienzaRan2021 to 2025Enrolled500ConditionsColon Cancer Stage II, Colon Cancer Stage III
3 · Its place in the literature

Who cites it

108 citing papers in PubMed, 8 syntheses or guidelines pooled it, 226 citations in OpenAlex.

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  20. Predicting early recurrence in resectable rectal cancer.World journal of gastrointestinal surgery · 2025
    Article

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

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors at 3 institutions in 1 country.

Jean-Emmanuel BibaultRadiation Oncology Department, Georges Pompidou European Hospital, Assistance Publique - Hôpitaux de Paris, Paris Descartes University, Paris Sorbonne Cité, Paris, France. jean-emmanuel.bibault@aphp.fr.ORCID 0000-0002-1728-6776
Philippe GiraudRadiation Oncology Department, Georges Pompidou European Hospital, Assistance Publique - Hôpitaux de Paris, Paris Descartes University, Paris Sorbonne Cité, Paris, France.
Martin HoussetRadiation Oncology Department, Georges Pompidou European Hospital, Assistance Publique - Hôpitaux de Paris, Paris Descartes University, Paris Sorbonne Cité, Paris, France.
Catherine DurduxRadiation Oncology Department, Georges Pompidou European Hospital, Assistance Publique - Hôpitaux de Paris, Paris Descartes University, Paris Sorbonne Cité, Paris, France.
Julien TaiebDepartment of gastroenterology and digestive oncology, Georges Pompidou European Hospital, Assistance Publique - Hôpitaux de Paris, Paris Descartes University, Paris Sorbonne Cité, Paris, France.
Anne BergerDepartment of General Surgery and Surgical Oncology, Georges Pompidou European Hospital, Assistance Publique - Hôpitaux de Paris, Paris Descartes University, Paris Sorbonne Cité, Paris, France.
Romain CoriatGastroenterology and digestive oncology unit, Cochin University Hospital, Assistance Publique - Hôpitaux de Paris, Paris, France.
Stanislas ChaussadeGastroenterology and digestive oncology unit, Cochin University Hospital, Assistance Publique - Hôpitaux de Paris, Paris, France.
Bertrand DoussetDepartment of Digestive, Hepato-biliary and Endocrine Surgery, Cochin Hospital, Assistance Publique - Hôpitaux de Paris, Paris, France.
Bernard NordlingerDepartment of General Surgery and Surgical Oncology, Hôpital Ambroise Paré, Assistance Publique - Hôpitaux de Paris, Boulogne-Billancourt, France.
Anita BurgunINSERM UMR 1138 Team 22: Information Sciences to support Personalized Medicine, Paris Descartes University, Sorbonne Paris Cité, Paris, France.
Délégation Paris 5 · FRAssistance Publique – Hôpitaux de Paris · FRHôpital Cochin · FR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Treatment of locally advanced rectal cancer involves chemoradiation, followed by total mesorectum excision. Complete response after chemoradiation is an accurate surrogate for long-term local control. Predicting complete response from pre-treatment features could represent a major step towards conservative treatment. Patients with a T2-4 N0-1 rectal adenocarcinoma treated between June 2010 and October 2016 with neo-adjuvant chemoradiation from three academic institutions were included. All clinical and treatment data was integrated in our clinical data warehouse, from which we extracted the features. Radiomics features were extracted from the tumor volume from the treatment planning CT Scan. A Deep Neural Network (DNN) was created to predict complete response, as a methodological proof-of-principle. The results were compared to a baseline Linear Regression model using only the TNM stage as a predictor and a second model created with Support Vector Machine on the same features used in the DNN. Ninety-five patients were included in the final analysis. There were 49 males (52%) and 46 females (48%). Median tumour size was 48 mm (15-130). Twenty-two patients (23%) had pathologic complete response after chemoradiation. One thousand six hundred eighty-three radiomics features were extracted. The DNN predicted complete response with an 80% accuracy, which was better than the Linear Regression model (69.5%) and the SVM model (71.58%). Our model correctly predicted complete response after neo-adjuvant rectal chemoradiotherapy in 80% of the patients of this multicenter cohort. Our results may help to identify patients who would benefit from a conservative treatment, rather than a radical resection.

Indexed as

AdenocarcinomaAgedBiomarkers, PharmacologicalChemoradiotherapy, AdjuvantCohort StudiesCombined Modality TherapyDeep LearningFemaleHumansMaleMiddle AgedNeoadjuvant TherapyPrognosisProof of Concept StudyRectal NeoplasmsRectumBiomarkers, Pharmacological

Identifiers

PMID30135549
PMCPMC6105676
OpenAlexW2886551281

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Registered trials

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.