Predicting response to cancer immunotherapy using noninvasive radiomic biomarkers

Abstract

Introduction Immunotherapy is regarded as one of the major breakthroughs in cancer treatment. Despite its success, only a subset of patients responds—urging the quest for predictive biomarkers. We hypothesize that artificial intelligence (AI) algorithms can automatically quantify radiographic characteristics that are related to and may therefore act as noninvasive radiomic biomarkers for immunotherapy response. Patients and methods In this study, we analyzed 1055 primary and metastatic lesions from 203 patients with advanced melanoma and non-small-cell lung cancer (NSCLC) undergoing anti-PD1 therapy. We carried out an AI-based characterization of each lesion on the pretreatment contrast-enhanced CT imaging data to develop and validate a noninvasive machine learning biomarker capable of distinguishing between immunotherapy responding and nonresponding. To define the biological basis of the radiographic biomarker, we carried out gene set enrichment analysis in an independent dataset of 262 NSCLC patients. Results The biomarker reached significant performance on NSCLC lesions (up to 0.83 AUC, P < 0.001) and borderline significant for melanoma lymph nodes (0.64 AUC, P = 0.05). Combining these lesion-wide predictions on a patient level, immunotherapy response could be predicted with an AUC of up to 0.76 for both cancer types ( P < 0.001), resulting in a 1-year survival difference of 24% ( P = 0.02). We found highly significant associations with pathways involved in mitosis, indicating a relationship between increased proliferative potential and preferential response to immunotherapy. Conclusions These results indicate that radiographic characteristics of lesions on standard-of-care imaging may function as noninvasive biomarkers for response to immunotherapy, and may show utility for improved patient stratification in both neoadjuvant and palliative settings.

Hypoxia-related radiomics predict immunotherapy response: A multi-cohort study of NSCLC

“…The mechanism of serum albumin with respect to immunotherapy response is yet to be established however, it was used in The Gustave Roussy Immune Score as a prognostic marker in immunotherapy phase I trials 40 . Emerging evidence demonstrates the utility of radiomics as a non-invasive approach to quantify and predict lung cancer treatment response of tyrosine kinase inhibitors 41,42 , platinum-based chemotherapy 43 , neo-adjuvant chemo-radiation 44,45 , stereotactic body radiation therapy 46,47 , and immunotherapy 8,48,49 . With respect to immunotherapy treatment response, our group previously demonstrated that pre-treatment clinical covariates and radiomic features predicted rapid disease progression phenotypes, including hyperprogression (AUROCs ranging 0.804-0.865) among 228 NSCLC patients treated with single agent or double agent immunotherapy 8 .…”

“…However, only 22% of their cohort was NSCLC patients. Trebeschi et al 49 developed a machine learning based model that discriminates progressive disease from stable disease and responsive disease (AUC = 0.83) between 123 NSCLC patients treated with PD-1 checkpoint blockade immunotherapy. As such, the study presented here represents the single largest study population of NSCLC patients treated with immunotherapy.…”

Immunotherapy by Immune Checkpoint Inhibitors and Nuclear Medicine Imaging: Current and Future Applications

“…Despite the relatively low area under the curve of the score for this prediction (AUC = 0.67; 95% CI 0.57-0.77), the signature was able to predict an objective response to anti-PD-1 and PD-L1 therapy, notably at 3 months (p = 0.049), as well as overall survival in univariate (median overall survival was 24.3 months in the high radiomic score group versus 11.5 months in the low radiomic score group; p = 0.0081) and multivariate analyses [47]. Another study explored the interest of radiomics as a non-invasive biomarker for responses to cancer immunotherapy on 1055 primary and metastatic lesions from 203 contrast-enhanced CTs from patients with advanced melanoma and NSCLC, undergoing anti-PD1 therapy [48]. They found on a lesion-based approach, reflecting the metastatic condition, that lesions with heterogeneous density and more compact and spherical (high volume/surface ratio) were associated with a better response [48].…”

A Contrast-Enhanced Computed Tomography Based Radiomics Approach for Preoperative Differentiation of Pancreatic Cystic Neoplasm Subtypes: A Feasibility Study

“…Past decades had witnessed the rapid development of the field of medical image analysis, facilitating the development of the radiomics method which quantifies the tumor heterogeneity into high-dimension features (15). The radiomics approach can help clinicians make individualized decisions based on the quantitative radiomics features and machine-learning-based models (16). Chakraborty et al investigated the CT based radiomics features as markers for stratifying the high-risk IPMN patients (17).…”