Exploration and validation of radiomics signature as an independent prognostic biomarker in stage III-IVb nasopharyngeal carcinoma

Abstract

There is no consensus on specific prognostic biomarkers potentially improving survival of nasopharyngeal carcinoma (NPC), especially in advanced-stage disease. The prognostic value of MRI-based radiomics signature is unclear. A total of 970 quantitative features were extracted from the tumor of 100 untreated NPC patients (stage III-IVb) (discovery set: n = 70, validation set: n = 30). We then applied least absolute shrinkage and selection operator (lasso) regression to select features that were most associated with progression-free survival (PFS). Candidate prognostic biomarkers included age, gender, overall stage, hemoglobin, platelet counts and radiomics signature. We developed model 1 (without radiomics signature) and model 2 (with radiomics signature) in the discovery set and then tested in the validation set. Multivariable Cox regression analysis was used to yield hazard ratio (HR) of each potential biomarker. We found the radiomics signature stratified patients in the discovery set into a low or high risk group for PFS (HR = 5.14, p < 0.001) and was successfully validated for patients in the validation set (HR = 7.28, p = 0.015). However, the other risk factors showed no significantly prognostic value (all p-values for HR, > 0.05). Accordingly, pretreatment MRI-based radiomics signature is a non-invasive and cost-effective prognostic biomarker in advanced NPC patients, which would improve decision-support in cancer care.

Investigation of thoracic four-dimensional CT-based dimension reduction technique for extracting the robust radiomic features

“…Quantitative analysis of medical images using various softwares reportedly offers more and better information than a physician alone does [1]. Moreover, the prognostic power of radiomics has been recently established for various cancer types [2][3][4][5][6][7][8][9]. Nevertheless, radiomic features are susceptible to various factors; for example, CT protocols include scanning methods and parameter settings [10][11][12][13], multiple segmentation with several persons [14,15], and respiratory motion [16][17][18].…”

Exploring Applications of Radiomics in Magnetic Resonance Imaging of Head and Neck Cancer: A Systematic Review

“…All six NPC studies investigated prognostic or predictive biomarkers. Although they contained varying sample sizes (100–118), four studies ( 42 , 47 – 49 ) selected from the same number of extracted radiomic features (970), subsequently constructing radiomic signatures from contrast-enhanced T1-weighted or T2-weighted feature categories. Among these studies, three investigated progression (either dichotomized yes/no or analyzed continuously) or a construct of prognostic performance.…”

Exploring MRI based radiomics analysis of intratumoral spatial heterogeneity in locally advanced nasopharyngeal carcinoma treated with intensity modulated radiotherapy

“…While the results from this approach supported the potential of radiomics in improving patient stratification for therapy, it assumes that the tumor is heterogenous but well mixed and ignores the regional variations within a tumor. [32,35,36] Studies from Ouyang et al and Zhang et al described their radiomics model based on pre-treatment contrast enhanced T1-and T2-weighted MRI which could predict disease progression in patients with NPC. [42] Zhang et al found textural features correlate not just to local failure but also to distant treatment failure.…”

“…[30,31] Magnetic resonance imaging (MRI) is the imaging of choice in the diagnosis and local staging in NPC due to its superior soft tissue contrast and allows for accurate delineation of target volumes for purposes of radiotherapy. [32][33][34][35][36][37] Whilst some research has been carried out on the application of radiomics in nasopharyngeal cancer, an approach that utilizes MRI radiomics as a predictive signature for intra-tumoral radio-resistance has not yet been developed. Comprehensive image analysis using radiomics that can identify radio-resistant tumor sub-volumes from pre-treatment MRI scans could guide individualized radiation therapy by suggesting target volumes in which a higher dose of radiation is needed for better tumor control.…”