Journal Article

Peritumoral radiomics features predict distant metastasis in locally advanced NSCLC

Coroller, Thibaud P., Aerts, Hugo J. W. L., van Griethuysen, Joost J. M., Mak, Raymond H., Dou, Tai H.

2018

Overview

Radiomics provides quantitative tissue heterogeneity profiling and is an exciting approach to developing imaging biomarkers in the context of precision medicine. Normal-appearing parenchymal tissues surrounding primary tumors can harbor microscopic disease that leads to increased risk of distant metastasis (DM). This study assesses whether computed-tomography (CT) imaging features of such peritumoral tissues can predict DM in locally advanced non-small cell lung cancer (NSCLC). 200 NSCLC patients of histological adenocarcinoma were included in this study. The investigated lung tissues were tumor rim, defined to be 3mm of tumor and parenchymal tissue on either side of the tumor border and the exterior region extended from 3 to 9mm outside of the tumor. Fifteen stable radiomic features were extracted and evaluated from each of these regions on pre-treatment CT images. For comparison, features from expert-delineated tumor contours were similarly prepared. The patient cohort was separated into training and validation datasets for prognostic power evaluation. Both univariable and multivariable analyses were performed for each region using concordance index (CI). Univariable analysis reveals that six out of fifteen tumor rim features were significantly prognostic of DM (p-value < 0.05), as were ten features from the visible tumor, and only one of the exterior features was. Multivariablely, a rim radiomic signature achieved the highest prognostic performance in the independent validation sub-cohort (CI = 0.64, p-value = 2.4×10-5) significantly over a multivariable clinical model (CI = 0.53), a visible tumor radiomics model (CI = 0.59), or an exterior tissue model (CI = 0.55). Furthermore, patient stratification by the combined rim signature and clinical predictor led to a significant improvement on the clinical predictor alone and also outperformed stratification using the combined tumor signature and clinical predictor. We identified peritumoral rim radiomic features significantly associated with DM. This study demonstrated that peritumoral imaging characteristics may provide additional valuable information over the visible tumor features for patient risk stratification due to cancer metastasis.

• Publisher: Public Library of Science,Public Library of Science (PLoS)
• Subject: Adenocarcinoma / Bioindicators / Bioinformatics / Biology and Life Sciences / Biomarkers / Cancer / Cancer therapies / Chemotherapy / Computed tomography / Feature extraction / Health risks / Heterogeneity / Lung cancer / Lung diseases / Mathematical models / Medical imaging / Medical research / Medical schools / Medicine and Health Sciences / Metastases / Metastasis / Non-small cell lung carcinoma / Oncology / Patients / Physical Sciences / Precision medicine / Radiation therapy / Radiomics / Research and Analysis Methods / Studies / Tissues / Tumors