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

SUBMITTER: Trebeschi S 

PROVIDER: S-EPMC6594459 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature