Evidence map›Paper›PMID 39738216›Full record

ArticleScientific reports2024

Deep learning on CT scans to predict checkpoint inhibitor treatment outcomes in advanced melanoma.

Laurens S Ter Maat, Rob A J De Mooij, Isabella A J Van Duin, Joost J C Verhoeff, Sjoerd G Elias, Tim Leiner, Wouter A C van Amsterdam, Max F Troenokarso, Eran R A N Arntz, Franchette W P J Van den Berkmortel and 16 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–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

1 citing paper in PubMed.

  1. CT radiomics showed no improvement beyond volume dynamics for early lesion-level size response to immunotherapy in metastatic melanoma.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026
    Observational
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

26 authors.

Laurens S Ter MaatImage Sciences Institute, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
Rob A J De MooijMedical Image Analysis, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
Isabella A J Van DuinDepartment of Medical Oncology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
Joost J C VerhoeffDepartment of Radiotherapy, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
Sjoerd G EliasDepartment of Epidemiology, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
Tim LeinerDepartment of Radiology, Mayo Clinical, Rochester, MN, USA.
Wouter A C van AmsterdamDepartment of Data Science and Biostatistics, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
Max F TroenokarsoUtrecht University, Utrecht, The Netherlands.
Eran R A N ArntzUtrecht University, Utrecht, The Netherlands.
Franchette W P J Van den BerkmortelDepartment of Medical Oncology, Zuyderland Medical Center, Sittard-Geleen, The Netherlands.
Marye J Boers-SonderenDepartment of Medical Oncology, Radboudumc, Radboud University, Nijmegen, The Netherlands.
Martijn F BoomsmaDepartment of Radiology, Isala Zwolle, Zwolle, The Netherlands.
Fons J M Van den EertweghDepartment of Medical Oncology, Amsterdam UMC, Vrije Universiteit Amsterdam, Cancer Center Amsterdam, Boelelaan 1117, Amsterdam, The Netherlands.
Jan Willem de GrootIsala Oncology Center, Isala Zwolle, Zwolle, The Netherlands.
Geke A P HospersDepartment of Medical Oncology, UMC Groningen, University of Groningen, Groningen, The Netherlands.
Djura PiersmaDepartment of Medical Oncology, Medisch Spectrum Twente, Enschede, The Netherlands.
Art VreugdenhilDepartment of Medical Oncology, Maxima Medical Center, Veldhoven, The Netherlands.
Hans M WestgeestDepartment of Internal Medicine, Amphia Hospital, Breda, The Netherlands.
Ellen KapiteijnDepartment of Medical Oncology, Leiden University Medical Center, Leiden University, Leiden, The Netherlands.
Ardine A De WitDepartment of Public Health, Healthcare Innovation and Evaluation and Medical Humanities, Julius Center Research Program Methodology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
Willeke A M BlokxDepartment of Pathology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
Paul J Van DiestDepartment of Pathology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
Pim A De JongDepartment of Radiology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
Josien P W PluimImage Sciences Institute, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
Karijn P M Suijkerbuijk *Department of Medical Oncology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands. k.suijkerbuijk@umcutrecht.nl.
Mitko Veta *Medical Image Analysis, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.

Funding

The Netherlands Organization for Health Research and Development (ZonMW) 848101007
6 · The paper itself

Abstract

Immune checkpoint inhibitor (ICI) treatment has proven successful for advanced melanoma, but is associated with potentially severe toxicity and high costs. Accurate biomarkers for response are lacking. The present work is the first to investigate the value of deep learning on CT imaging of metastatic lesions for predicting ICI treatment outcomes in advanced melanoma. Adult patients that were treated with ICI for advanced melanoma were retrospectively identified from ten participating centers. A deep learning model (DLM) was trained on volumes of lesions on baseline CT to predict clinical benefit. The DLM was compared to and combined with a model of known clinical predictors (presence of liver and brain metastasis, level of lactate dehydrogenase, performance status and number of affected organs). A total of 730 eligible patients with 2722 lesions were included. The DLM reached an area under the receiver operating characteristic (AUROC) of 0.607 [95%CI 0.565-0.648]. In comparison, a model of clinical predictors reached an AUROC of 0.635 [95%CI 0.59 -0.678]. The combination model reached an AUROC of 0.635 [95% CI 0.595-0.676]. Differences in AUROC were not statistically significant. The output of the DLM was significantly correlated with four of the five input variables of the clinical model. The DLM reached a statistically significant discriminative value, but was unable to improve over known clinical predictors. The present work shows that the assessment over known clinical predictors is an essential step for imaging-based prediction and brings important nuance to the almost exclusively positive findings in this field.

Indexed as

Deep LearningImmune Checkpoint InhibitorsMelanomaTomography, X-Ray ComputedAdultAgedFemaleHumansMaleMiddle AgedRetrospective StudiesROC CurveTreatment OutcomeImmune Checkpoint Inhibitors

Identifiers

PMID39738216
PMCPMC11686296

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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.