Evidence map›Paper›PMID 42823717›Full record

ArticleCancer imaging : the official publication of the International Cancer Imaging Society2026

CT radiomics showed no improvement beyond volume dynamics for early lesion-level size response to immunotherapy in metastatic melanoma.

Seval Akbal, Johan Constantin Schepers, Paul V Op Gen Oorth, Vincent Bleymehl, Moritz Schroth, Felix Winneknecht, Devayani Machiraju, Julius C Holzschuh, Kevin S Zhang, Sebastian Wohlfeil and 5 more

Abstract readMulticenter Study
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In one paragraph

Article in Cancer imaging : the official publication of the International Cancer Imaging Society, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

15 authors.

Seval AkbalDepartment of Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany.
Johan Constantin SchepersDepartment of Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany.
Paul V Op Gen OorthDepartment of Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany.
Vincent BleymehlDepartment of Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany.
Moritz SchrothDepartment of Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany.
Felix WinneknechtDepartment of Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany.
Devayani MachirajuDepartment of Dermatology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Ruprecht-Karls-University of Heidelberg, Heidelberg, Germany.
Julius C HolzschuhDepartment of Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany.
Kevin S ZhangDepartment of Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany.
Sebastian WohlfeilDepartment of Dermatology, Venereology and Allergology, University Medical Center Mannheim, Ruprecht-Karls-University of Heidelberg, Mannheim, Germany.
Isabelle AyxDepartment of Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany.
Stefan O SchoenbergDepartment of Radiology and Nuclear Medicine, University Medical Center Mannheim, Ruprecht-Karls-University of Heidelberg, Mannheim, Germany.
Heinz-Peter SchlemmerDepartment of Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany.
Jessica C HasselDepartment of Dermatology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Ruprecht-Karls-University of Heidelberg, Heidelberg, Germany.
Lukas T RotkopfDepartment of Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany. l.rotkopf@dkfz.de.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWe evaluated whether CT intensity and texture radiomics provide predictive value beyond simple volume dynamics for lesion-level response prediction in metastatic melanoma treated with immune checkpoint inhibitors.

methodsWe conducted a retrospective multicenter study including 158 patients (1,626 lesions) from an internal cohort (n = 129 patients with standardized protocols) and an external cohort (n = 29 patients with heterogeneous protocols). The primary endpoint was binary lesion-level size response (clinical benefit vs. progressive disease) at the second follow-up. We trained and compared Random Forest classifiers to predict this endpoint using clinical variables, volumetric features, radiomics, and combined predictors derived from baseline and first follow-up imaging. Models were trained on the internal cohort using patient-grouped cross-validation and applied unchanged to the external cohort. Paired differences in AUC were assessed against a ± 0.05 equivalence margin.

resultsBaseline-only prediction of first follow-up response was near chance for all models. For predicting response at second follow-up, the volumetric model achieved an internal AUC of 0.84 [95% CI 0.78-0.88] and the radiomics model 0.86 [0.81-0.90]. In the external cohort, with 12 progression events, the volumetric and radiomics models achieved AUCs of 0.68 [0.50-0.85] and 0.68 [0.51-0.88], with no significant difference between any models. Organ-specific analysis showed no significant improvement from adding radiomics for any lesion site. Across all 288 radiomic features, the median absolute correlation with lesion volume was 0.37. Sensitivity analyses contained small or borderline gains that did not replicate externally.

conclusionsVolumetric features capture the primary predictive information for early lesion-level response, and CT radiomics showed no conclusive improvement over volume dynamics. Confirmation in larger independent cohorts is required before radiomic signatures can be recommended for this task. CLINICAL TRIAL NUMBER: Not applicable.

Indexed as

Immune Checkpoint InhibitorsImmunotherapyMelanomaRadiomicsTomography, X-Ray ComputedAdultAgedFemaleHumansMaleMiddle AgedRetrospective StudiesTumor BurdenImmune Checkpoint InhibitorsImmune checkpoint inhibitorsMetastatic melanomaRadiomicsTreatment response predictionTumor volumetry

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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.