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Metastatic melanoma treated by immunotherapy: discovering prognostic markers from radiomics analysis of pretreatment CT with feature selection and classification

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Abstract

Purpose

Immunotherapy has dramatically improved the prognosis of patients with metastatic melanoma (MM). Yet, there is a lack of biomarkers to predict whether a patient will benefit from immunotherapy. Our aim was to create radiomics models on pretreatment computed tomography (CT) to predict overall survival (OS) and treatment response in patients with MM treated with anti-PD-1 immunotherapy.

Methods

We performed a monocentric retrospective analysis of 503 metastatic lesions in 71 patients with 46 radiomics features extracted following lesion segmentation. Predictive accuracies for OS < 1 year versus > 1 year and treatment response versus no response was compared for five feature selection methods (sequential forward selection, recursive, Boruta, relief, random forest) and four classifiers (support vector machine (SVM), random forest, K-nearest neighbor, logistic regression (LR)) used with or without SMOTE data augmentation. A fivefold cross-validation was performed at the patient level, with a tumour-based classification.

Results

The highest accuracy level for OS predictions was obtained with 3D lesions (0.91) without clinical data integration when combining Boruta feature selection and the LR classifier, The highest accuracy for treatment response prediction was obtained with 3D lesions (0.88) without clinical data integration when combining Boruta feature selection, the LR classifier and SMOTE data augmentation. The accuracy was significantly higher concerning OS prediction with 3D segmentation (0.91 vs 0.86) while clinical data integration led to improved accuracy notably in 2D lesions (0.76 vs 0.87) regarding treatment response prediction. Skewness was the only feature found to be an independent predictor of OS (HR (CI 95%) 1.34, p-value 0.001).

Conclusion

This is the first study to investigate CT texture parameter selection and classification methods for predicting MM prognosis with treatment by immunotherapy. Combining pretreatment CT radiomics features from a single tumor with data selection and classifiers may accurately predict OS and treatment response in MM treated with anti-PD-1.

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Abbreviations

CAD:

Computer-aided diagnosis

CI:

Confidence interval

CTLA-4:

Cytotoxic T-lymphocyte-associated protein 4

HR:

Hazard ratio

KNN:

K-nearest neighbor

LDH:

Serum lactate dehydrogenase

MM:

Metastatic melanoma

OS:

Overall survival

PD-1:

Program cell death 1

PFS:

Progression-free survival

RECIST:

Response Evaluation Criteria In Solid Tumours

RF:

Random forest

ROI:

Region of interest

SFS:

Sequential forward selection

SMOTE:

Synthetic Minority Oversampling TEchnique

SVM:

Support vector machine

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Acknowledgements

The authors would like to thank Helen Braund for language editing.

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Authors and Affiliations

  1. EnCoV, Institut Pascal, UMR 6602 CNRS, Université Clermont Auvergne, 28 place Henri Dunant, 63000, Clermont-Ferrand, France

    Gulnur Ungan, Adrien Bartoli & Benoit Magnin

  2. Department of Medical Imaging, CHU Clermont Ferrand, 1 place Lucie Aubrac, 63100, Clermont-Ferrand, France

    Anne-Flore Lavandier, Constance Hordonneau, Benoit Chauveau, Louis Boyer, Jean-Marc Garcier & Benoit Magnin

  3. Dermatology Department, CHU Clermont Ferrand, 1 place Lucie Aubrac, 63100, Clermont-Ferrand, France

    Jacques Rouanet & Sandrine Mansard

  4. Biostatistics Unit, DRCI, CHU Clermont Ferrand, 58 rue Montalembert, 63000, Clermont-Ferrand, France

    Bruno Pereira

  5. Anatomy Department, Université Clermont Auvergne, 28 place Henri Dunant, 63000, Clermont-Ferrand, France

    Jean-Marc Garcier & Benoit Magnin

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  1. Gulnur Ungan
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Ungan, G., Lavandier, AF., Rouanet, J. et al. Metastatic melanoma treated by immunotherapy: discovering prognostic markers from radiomics analysis of pretreatment CT with feature selection and classification. Int J CARS 17, 1867–1877 (2022). https://doi.org/10.1007/s11548-022-02662-8

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