Predicting Response to Immunotherapy in Patients with Metastatic Urothelial Cancer Using Radiomics - Expert Commentary

Imaging-based biomarker research is gaining momentum in oncology due to its non-invasive nature and the potential for monitoring tumor dynamics during treatment. The currently available biomarkers for immune checkpoint inhibitors (ICIs), such as programmed cell death ligand 1 (PD-L1) expression, tumor mutational burden, and tumor-infiltrating lymphocytes, have inherent limitations in predicting treatment outcomes. Different imaging-based biomarkers emerged as potential predictors of therapy response across various cancers. Through artificial intelligence radiomics tools, multiple quantitative imaging features are repeatedly extracted and analyzed. These non-invasive imaging features can be integrated with other predictive clinical markers to evaluate disease response.


Recently, Rundo et al. published a study in Clinical Genitourinary Cancer investigating a three-dimensional deep radiomics pipeline to predict disease control in patients with metastatic urothelial cancer (UC) treated with ICIs. They analyzed the computed tomography (CT) images of target lesions from 43 patients with metastatic UC. The imaging dataset was used for initial deep learning (70%) then testing and validation (30%). The deep learning pipeline was based on a 3-dimensional densely connected convolutional neural network (3D-DCNN) with separable convolutions and nonlocal blocks (NLBs). It was used to analyze target lesions before the initiation of ICIs.

Input data from segmented CT scan lesions was used to classify patients into two categories, patients with disease response to ICIs and patients with disease progression. The predictive accuracy of the 3D-DCNN architecture was 82.5%, with a sensitivity of 96% and a specificity of 60%. The addition of baseline clinical factors increased the predictive accuracy of the 3D-DCNN classifier to 92.5%, owing to an improvement of the specificity that increased to 87%.

This study shows the potential of radiomic approaches promising noninvasive biomarkers for predicting disease control on ICIs.

Written by: Bishoy M. Faltas, MD, Director of Bladder Cancer Research, Englander Institute for Precision Medicine, Weill Cornell Medicine, New York City, New York

References:

  1. Rundo F, Bersanelli M, Urzia V, Friedlaender A, Cantale O, Calcara G, et al. Three-Dimensional Deep Noninvasive Radiomics for the Prediction of Disease Control in Patients With Metastatic Urothelial Carcinoma treated With Immunotherapy. Clin Genitourin Cancer. 2021;doi.10.1016/j.clgc.2021.03.012. PMID: 33849811

Read the Abstract
email news signup