Evidence map›Paper›PMID 26355298›Full record

ArticlePloS one2015

Predicting Response to Neoadjuvant Chemotherapy with PET Imaging Using Convolutional Neural Networks.

Petros-Pavlos Ypsilantis, Musib Siddique, Hyon-Mok Sohn, Andrew Davies, Gary Cook, Vicky Goh, Giovanni Montana

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 73 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
73citing papers in PubMed, 5 pooled it
–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

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

73 citing papers in PubMed, 5 syntheses or guidelines pooled it.

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  12. Feasibility of UsingDiagnostics (Basel, Switzerland) · 2024
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13 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

7 authors.

Petros-Pavlos YpsilantisDepartment of Biomedical Engineering, King's College London, London SE1 7EH, United Kingdom.
Musib SiddiqueDepartment of Cancer Imaging, King's College London, London SE1 7EH, United Kingdom.
Hyon-Mok SohnDepartment of Cancer Imaging, King's College London, London SE1 7EH, United Kingdom.
Andrew DaviesDepartment of Cancer Imaging, King's College London, London SE1 7EH, United Kingdom.
Gary CookDepartment of Cancer Imaging, King's College London, London SE1 7EH, United Kingdom.
Vicky GohDepartment of Cancer Imaging, King's College London, London SE1 7EH, United Kingdom.
Giovanni MontanaDepartment of Biomedical Engineering, King's College London, London SE1 7EH, United Kingdom.

Funding

Cancer Research UK 16463
6 · The paper itself

Abstract

Imaging of cancer with 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) has become a standard component of diagnosis and staging in oncology, and is becoming more important as a quantitative monitor of individual response to therapy. In this article we investigate the challenging problem of predicting a patient's response to neoadjuvant chemotherapy from a single 18F-FDG PET scan taken prior to treatment. We take a "radiomics" approach whereby a large amount of quantitative features is automatically extracted from pretherapy PET images in order to build a comprehensive quantification of the tumor phenotype. While the dominant methodology relies on hand-crafted texture features, we explore the potential of automatically learning low- to high-level features directly from PET scans. We report on a study that compares the performance of two competing radiomics strategies: an approach based on state-of-the-art statistical classifiers using over 100 quantitative imaging descriptors, including texture features as well as standardized uptake values, and a convolutional neural network, 3S-CNN, trained directly from PET scans by taking sets of adjacent intra-tumor slices. Our experimental results, based on a sample of 107 patients with esophageal cancer, provide initial evidence that convolutional neural networks have the potential to extract PET imaging representations that are highly predictive of response to therapy. On this dataset, 3S-CNN achieves an average 80.7% sensitivity and 81.6% specificity in predicting non-responders, and outperforms other competing predictive models.

Indexed as

Neoadjuvant TherapyNeural Networks, ComputerPositron-Emission TomographyAdultAgedAged, 80 and overAlgorithmsEsophageal NeoplasmsFemaleFluorodeoxyglucose F18HumansImage Processing, Computer-AssistedKaplan-Meier EstimateMaleMiddle AgedTreatment OutcomeFluorodeoxyglucose F18

Identifiers

PMID26355298
PMCPMC4565716

What OpenQuestion holds

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Registered trials

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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.