Abstract
Simple SummaryMore than 70% of patients with nasopharyngeal carcinoma (NPC) present with a locoregionally advanced state. Although the initial staging of NPC is primarily based on TNM staging, there is currently no well-established prognostic marker for NPC. Recently, radiomics has received considerable research attention as a potential prognostic biomarker for NPC. The aim of this systematic review and meta-analysis was to comprehensively evaluate the prognostic value of pretreatment magnetic resonance imaging (MRI)-based radiomics for NPC. The analyzed radiomic models demonstrated modest prognostic values, with a pooled mean estimated Harrell’s concordance index (C index) of 0.762. The prognostic models developed using more than eight radiomic features had significantly higher C-indices than those developed using fewer features. Our findings provide evidence that MRI-based radiomics may have a modest prognostic role in the treatment of NPC. However, more consistent study protocols are needed to verify the generalizability of radiomics. Advanced non-metastatic nasopharyngeal carcinoma (NPC) has variable treatment outcomes. However, there are no prognostic biomarkers for identifying high-risk patients with NPC. The aim of this systematic review and meta-analysis was to comprehensively assess the prognostic value of magnetic resonance imaging (MRI)-based radiomics for untreated NPC. The PubMed-Medline and EMBASE databases were searched for relevant articles published up to 12 August 2021. The Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) checklist was used to determine the qualities of the selected studies. Random-effects modeling was used to calculate the pooled estimates of Harrell’s concordance index (C-index) for progression-free survival (PFS). Between-study heterogeneity was evaluated using Higgins’ inconsistency index (I2). Among the studies reported in the 57 articles screened, 10 with 3458 patients were eligible for qualitative and quantitative data syntheses. The mean adherence rate to the TRIPOD checklist was 68.6 ± 7.1%. The pooled estimate of the C-index was 0.762 (95% confidence interval, 0.687–0.837). Substantial between-study heterogeneity was observed (I2 = 89.2%). Overall, MRI-based radiomics shows good prognostic performance in predicting the PFS of patients with untreated NPC. However, more consistent and robust study protocols are necessary to validate the prognostic role of radiomics for NPC.
Highlights
Nasopharyngeal carcinoma (NPC) is an endemic cancer in Southeast Asia and Southern China, with an annual incidence rate of 50–80 patients per 1,000,000 population [1]
This study indicated that magnetic resonance imaging (MRI)-based radiomics shows good prognostic performance in predicting the progression-free survival (PFS) of patients with untreated NPC
The studies reported in the included articles were evaluated using the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) checklist, which consists of 22 main criteria with 35 items [16,17]
Summary
Nasopharyngeal carcinoma (NPC) is an endemic cancer in Southeast Asia and Southern China, with an annual incidence rate of 50–80 patients per 1,000,000 population [1]. The prognosis of patients treated with CCRT is relatively fair, with a five-year overall survival and progression-free survival (PFS) rate of approximately 72% [4]. Radiomics is the analysis of medical images into high-throughput quantitative data. This field has recently gained significant attention in oncologic radiology research as an illustrative example of personalized precision medicine. The prognostic value of radiomics for untreated NPC has been previously established, further supporting its potential role as a prognostic imaging biomarker [12,13,14]. Clarifying the evidence on the role of radiomics in the prognostication of NPC will promote better clinical decision-making for precision medicine. This study indicated that MRI-based radiomics shows good prognostic performance in predicting the progression-free survival (PFS) of patients with untreated NPC
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