ArticleBMC cancer2023
Risk stratification and prognostic value of multi-modal MRI-based radiomics for extranodal nasal-type NK/T-cell lymphoma.
Article in BMC cancer, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed, 3 citations in OpenAlex.
- Development and validation of the ultrasound-based radiomics and deep learning prognostic models for diffuse large B-cell lymphoma.Annals of hematology · 2026Article
- Robust and interpretable deep learning system for prognostic stratification of extranodal natural killer/T-cell lymphoma.European journal of nuclear medicine and molecular imaging · 2025Article
- Distinguishing diffuse large B-cell lymphoma from Hodgkin's lymphoma in children using an enhanced computed tomography radiomics approach.Translational pediatrics · 2024Article
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Authors and funding
25 authors at 4 institutions in 2 countries.
Funding
Abstract
backgroundMagnetic resonance imaging (MRI) performs well in the locoregional assessment of extranodal nasal-type NK/T-cell lymphoma (ENKTCL). It's important to assess the value of multi-modal MRI-based radiomics for estimating overall survival (OS) in patients with ENKTCL.
methodsPatients with ENKTCL in a prospectively cohort were systemically reviewed and all the pretreatment MRI were acquisitioned. An unsupervised spectral clustering method was used to identify risk groups of patients and radiomic features. A nomogram-revised risk index (NRI) plus MRI radiomics signature (NRI-M) was developed, and compared with the NRI.
resultsThe 2 distinct type I and II groups of the MRI radiomics signatures were identified. The 5-year OS rates between the type I and type II groups were 87.2% versus 67.3% (P = 0.002) in all patients, and 88.8% versus 69.2% (P = 0.003) in early-stage patients. The discrimination and calibration of the NRI-M for OS prediction demonstrated a better performance than that of either MRI radiomics or NRI, with a mean area under curve (AUC) of 0.748 and 0.717 for predicting the 5-year OS in all-stages and early-stage patients.
conclusionsThe NRI-M model has good performance for predicting the prognosis of ENKTCL and may help design clinical trials and improve clinical decision making.
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