Editorial for “Differences in Radiomics Signatures Between Patients with Early and Advanced T‐Stage Nasopharyngeal Carcinoma Facilitate Prognostication”

Editorial for “Differences in Radiomics Signatures Between Patients with Early and Advanced T-Stage Nasopharyngeal Carcinoma Facilitate Prognostication” Multimodality imaging techniques are commonly used for tumor detection, grading, staging, treatment planning, and follow-up in nasopharyngeal carcinoma (NPC). This study utilized diagnostic magnetic resonance imaging (MRI) that provides multiplanar imaging capabilities with higher image resolution. Anatomical details in MRI are based on water protons’ relaxation properties, either the longitudinal (T1) or the transverse (T2) tissue relaxation times. Qualitative T1-weighted (T1w) and T2-weighted (T2w) images show excellent soft tissue contrast between normal and pathologic tissues and are used for the detection and delineation of tumor extent in an oncology setting, while quantitative dynamic contrast-enhanced (DCE)-and diffusion-weighted (DW)-MRI provide insight into physiological characteristics, including tumor tissue perfusion/permeability and cellularity, respectively, in NPC. Wu et al propose identifying prognostic factors from multiparametric (mp)MRI to predict NPC patient outcomes enabling precision medicine. The field of radiomics focuses on improvements of image analysis, using automated high-throughput extraction of quantitative image features including voxel intensity, statistical measures, shape/geometry, and tumor phenotype texture in the region of interest, capturing intratumoral heterogeneity. Notably, the stability of mpMRI radiomics features is dependent on tumor volume, MRI acquisition parameters, and magnetic field strengths. Previous studies demonstrated the potential utility of conventional MRI radiomics for patient stratification, staging, and treatment response in NPC. Although radiomics features have shown promise for prognosis and predicting treatment response, the accuracy and reproducibility of radiomic features require rigorous evaluation before inclusion in clinical practice. In this issue of JMRI, the article entitled “Differences in radiomics signatures between patients with early and advanced T-stage nasopharyngeal carcinoma facilitate prognostication” evaluated whether the MRI radiomics signatures can differentiate T-stages, i.e. T1–2 and T3–4. The study included data from 778 NPC patients (T1–2 = 298 and T3–4 = 480). The MRI acquisition protocol included standard T1w and T2w images and post contrast-enhanced (CE) T1w images acquired at 1.5 T (Signa CV/i; General Electric Healthcare, Chicago, USA) and 3.0 T (Magnetom Tim Trio; Siemens, Erlangen, Germany) MRI scanners. The MRI radiomic features were extracted using commercially available open-source PyRadiomics software. The authors tested three radiomic models on the entire dataset (training cohort [N = 525] and validation cohort [N = 253]) as well as on patient cohorts stratified by T-stages (T1–2 and T3–4) to investigate the predictive ability of prognostic models for progression-free survival (PFS). This study applied well-established radiomic analysis methods, including the least absolute shrinkage and selection operator (LASSO) Cox regression model that generates lowdimensional models. They also compared quantitative radiomic signature values between T-stages (T1–2 and T3–4) and for prognostication in NPC patients. Early T-stage tumors (T1–2) involved soft tissue only, and the MRI characteristics were more homogenous than in patients with advanced T-stages (T3–4), who had invasion of the tumor to either the bone or intracranial structures. A total of 4410 MRI features were extracted from seven feature classes, i.e. first-order statistics, shape descriptors, and texture classes (including gray level co-occurrence matrix [GLCM], gray level run length matrix [GLRLM], gray level size zone matrix [GLSZM], gray level dependence matrix [GLDM], and neighboring gray-tone difference matrix [NGTDM])). The key radiomics signature from first-order statistics, GLCM, GLSZM, GLDM, and NGTDM, could differentiate T-stages as demonstrated by Harrell’s concordance indices (C-indices). Radiomics nomogram, a combination of Radscore and clinical factors, achieved better prognostic performance for PFS than clinical nomograms alone. Despite LASSO parameter cross-validation, these nomograms will require further external testing/validation studies before potential clinical use.