Evidence map›Paper›PMID 42174085›Full record

ArticleScientific reports2026

Deep learning and radiomics models in patients with advanced non-small cell lung cancer treated with immunotherapy combined with stereotactic radiotherapy.

G Kothari, N Hardcastle, R Perera, P Jackson, S Lee, J D Silver, M Gaudreault, J Li, C Brown, P L R Mitchell and 4 more

2 registry-linked trialsAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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.

NCT02444741 phase1 / phase2completednot on this map

Phase I/II Trial of MK-3475 and Hypofractionated Stereotactic Radiation Therapy in Patients With NSCLC

TypeinterventionalSponsorM.D. Anderson Cancer CenterRan2015 to 2026Enrolled124ConditionsMalignant Solid Neoplasm, Metastatic Lung Non-Small Cell Carcinoma, Stage IV Lung Non-Small Cell Cancer AJCC v7Arms3-Dimensional Conformal Radiation Therapy, Intensity-Modulated Radiation Therapy, Laboratory Biomarker Analysis, Pembrolizumab, Proton Beam Radiation Therapy
NCT02492568 phase2completednot on this map

Randomized Phase II, 2-arm Study of Pembrolizumab After High Dose Radiation (SBRT) Versus Pembrolizumab Alone in Patients With Advanced Non-small Cell Lung Cancer

TypeinterventionalSponsorThe Netherlands Cancer InstituteRan2015 to 2018Enrolled92ConditionsNon-Small Cell Lung CancerArmspembrolizumab, Stereotactic Body Radiation Therapy
3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

14 authors.

G KothariDivision of Radiation Oncology, Peter MacCallum Cancer Centre, 305 Grattan St, Melbourne, VIC, 3000, Australia. Gargi.Kothari@petermac.org.
N HardcastleThe Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia.
R PereraSchool of Electrical, Mechanical and Infrastructure Engineering, University of Melbourne, Melbourne, VIC, 3010, Australia.
P JacksonThe Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia.
S LeeDepartment of Cancer Imaging, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia.
J D SilverStatistical Consulting Centre, School of Mathematics and Statistics, University of Melbourne, Melbourne, VIC, Australia.
M GaudreaultThe Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia.
J LiThe Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia.
C BrownNorthern Sydney Cancer Centre, Radiation Oncology Unit, Royal North Shore Hospital, St Leonards, Sydney, NSW, Australia.
P L R MitchellDepartment of Medical Oncology, Olivia Newton John Cancer Centre, Austin Health, VIC, Australia.
T KronThe Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia.
J W WelshDepartment of Thoracic Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
W S M E TheelenDepartment of Thoracic Oncology, Netherlands Cancer Institute, Amsterdam, The Netherlands.
S SivaDivision of Radiation Oncology, Peter MacCallum Cancer Centre, 305 Grattan St, Melbourne, VIC, 3000, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Quantitative imaging is an emerging field that may allow prediction of oncological outcomes. We investigate whether radiomics and deep learning can predict outcomes in metastatic non-small cell lung cancer utilizing randomized trials of PD-1 inhibitors + /- stereotactic ablative body radiotherapy: PEMBRO-RT(NCT02492568), NIVORAD(ACTRN12616000352404) and MDACC(NCT02444741). A random forest model developed on PEMBRO-RT using radiomics features had an AUC of 0.57 for prediction of per-lesion progressive disease on immunotherapy compared to an AUC of 0.92 for a deep learning model. A random forest survival model using radiomics features for overall survival (progression free survival) had a concordance index of 0.63(0.59) and improved to 0.67(0.65) by adding clinical features, including PD-L1 and treatment arm. Validation on NIVORAD and MDACC revealed reduced AUCs. Overall, a deep learning compared to a radiomics model demonstrated excellent predictive value for per-lesion progressive disease for patients on immunotherapy. Models had reduced performance on external validation. Research improving generalizability is required for clinical translation.

Indexed as

Carcinoma, Non-Small-Cell LungDeep LearningImmunotherapyLung NeoplasmsRadiosurgeryAgedCombined Modality TherapyFemaleHumansMaleRadiomicsRandom ForestRandomized Controlled Trials as TopicDeep learningImmunotherapyLung cancerNon-small cell lung cancerRadiomicsStereotactic ablative body radiotherapy

Identifiers

PMID42174085
PMCPMC13408920

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.