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Development and validation of CT-based radiomics model of PET-negative residual CT masses: a potential biomarker for predicting relapse-free survival in non-Hodgkin lymphoma patients showing complete metabolic response

Authors
Cha, Seung HaKang, Ka-WonHan, Na YeonCho, YongwonSung, Deuk JaePark, Beom JinKim, Min JuSim, Ki ChoonHan, Yeo EunSung, Hwa Jung
Issue Date
Jan-2024
Publisher
Springer New York
Keywords
Radiomics; Tomography, X-ray computed; Positron-emission tomography; Non-Hodgkin lymphoma; Chemotherapy
Citation
Abdominal Radiology, v.49, no.1, pp 341 - 353
Pages
13
Indexed
SCIE
SCOPUS
Journal Title
Abdominal Radiology
Volume
49
Number
1
Start Page
341
End Page
353
URI
https://scholarworks.korea.ac.kr/kumedicine/handle/2021.sw.kumedicine/64324
DOI
10.1007/s00261-023-04083-w
ISSN
2366-004X
2366-0058
Abstract
Purpose PET-negative residual CT masses (PnRCMs) are usually dismissed as nonviable post-treatment lesions in non-Hodgkin lymphoma (NHL) patients showing complete metabolic response (CMR). We aimed to develop and validate computed tomography (CT)-based radiomics model of PET-negative residual CT mass (PnRCM) for predicting relapse-free survival (RFS) in NHL patients showing CMR.Methods A total of 224 patients who showed CMR after completing first-line chemotherapy for PET-avid NHL were recruited for model development. Patients with PnRCM were selected in accordance with the Lugano classification. Three-dimensional segmentation was done by two readers. Radiomic scores (RS) were constructed using features extracted using the Least-absolute shrinkage and selection operator analysis among radiomics features of PnRCMs showing more than substantial interobserver agreement (> 0.6). Cox regression analysis was performed with clinical and radiologic features. The performance of the model was evaluated using area under the curve (AUC). For validation, 153 patients from an outside hospital were recruited and analyzed in the same way.Results In the model development cohort, 68 (30.4%) patients had PnRCM. Kaplan-Meier analysis showed that patients with PnRCM had significantly (p = 0.005) shorter RFS than those without PnRCM. In Kaplan-Meier analysis, the high RS group showed significantly (p = 0.038) shorter RFS than the low-scoring group. Multivariate Cox regression analysis showed that high IPI score [hazard ratio (HR) 2.46; p = 0.02], treatment without rituximab (HR 3.821; p = 0.019) were factors associated with shorter RFS. In estimating RFS, combined model in both development and validation cohort showed AUC values of 0.81.Conclusion The combined model that incorporated both clinical parameters and CT-based RS showed good performance in predicting relapse in NHL patients with PnRCM. [GRAPHICS]
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Park, Beom jin
Anam Hospital (Department of Radiology, Anam Hospital)
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