Evaluation of Response to Immune Checkpoint Inhibitors Using a Radiomics, Lesion-Level Approach

Abstract

Conventional methods to determine the response to immune checkpoint inhibitors (ICIs) are limited by the unique responses to an ICI. We performed a radiomics approach for all measurable lesions to identify radiomic variables that could distinguish hyperprogressive disease (HPD) on baseline CT scans and classify a dissociated response (DR). One hundred and ninety-six patients with advanced lung cancer, treated with ICI monotherapy, who underwent at least three CT scans, were retrospectively enrolled. For all 621 measurable lesions, HPDv was determined from baseline CT scans using the tumor growth kinetics (TGK) ratio, and radiomics features were extracted. Multivariable logistic regression analysis of radiomics features was performed to discriminate DR. Radiomics features that significantly discriminated HPDv on baseline CT differed according to organ. Of the 196 patients, 54 (27.6%) had a DR and 142 (72.4%) did not have a DR. Overall survival in the group with a DR was significantly inferior to that in the group without a DR (log rank test, p = 0.04). Our study shows that lesion-level analysis using radiomics features has great potential for discriminating HPDv and understanding heterogeneous tumor progression, including a DR, after ICI treatment.

PET/CT radiomics for prediction of hyperprogression in metastatic melanoma patients treated with immune checkpoint inhibitors

“…Furthermore, PD-HPD is a high-risk factor and was associated with significantly reduced OS compared to the other patients (median OS = 7 months vs. “not reached”), regardless of the type of ICI. Several other studies have reported similar results ( 7 , 24 ). An explanation might be that the affected patients experienced a negative effect upon treatment with ICI ( 41 ).…”

“…Unfortunately, no uncertainty estimates were provided. Additionally, Song et al evaluated CT-based radiomic features of hyperprogression at the lesion level in patients with advanced lung cancer treated with ICI (24). The authors demonstrated that radiomic features identifying hyperprogression differed among organs with AUC varying from 0.61 to 0.72, highlighting the potential of radiomics to differentiate hyperprogression and its organ-specific micro-environment.…”

“…As of today, there are no reliable, clinically validated biomarkers of PD-HPD. In recent years, a few studies were published investigating suitability of image-based biomarkers for PD-HPD prediction (24)(25)(26). Most of these studies, however, considered only CT-based radiomic signatures.…”

Hyperprogressive disease in non-small cell lung cancer after PD-1/PD-L1 inhibitors immunotherapy: underlying killer

“…Meanwhile, using radiomics features at the lesion-level analysis has the same effect. The novel radiomic models have translational implications to distinguish vulnerable NSCLC patients at risk of HPD (29)(30)(31).…”