Abstract
First-order radiomic features, such as metabolic tumor volume (MTV) and total lesion glycolysis (TLG), are associated with disease progression in early-stage classical Hodgkin lymphoma (HL). We hypothesized that a model incorporating first- and second-order radiomic features would more accurately predict outcome than MTV or TLG alone. We assessed whether radiomic features extracted from baseline PET scans predicted relapsed or refractory disease status in a cohort of 251 patients with stage I-II HL who were managed at a tertiary cancer center. Models were developed and tested using a machine-learning algorithm. Features extracted from mediastinal sites were highly predictive of primary refractory disease. A model incorporating 5 of the most predictive features had an area under the curve (AUC) of 95.2% and total error rate of 1.8%. By comparison, the AUC was 78% for both MTV and TLG and was 65% for maximum standardize uptake value (SUVmax). Furthermore, among the patients with refractory mediastinal disease, our model distinguished those who were successfully salvaged from those who ultimately died of HL. We conclude that our PET radiomic model may improve upfront stratification of early-stage HL patients with mediastinal disease and thus contribute to risk-adapted, individualized management.
Highlights
The intensity of frontline therapy is dictated by the presence of risk factors at the time of diagnosis
Radiomic features extracted from mediastinal sites on the pre-treatment scans were highly predictive of primary refractory status
When considering mediastinal sites only (n = 169), the baseline radiomic features that were most predictive of primary refractory disease included the first-order features GlobalMax (i.e. SUVmax) and Volume, and the second-order features InformationMeasureCorr[1], InformationMeasureCorr[2], and InverseVariance derived from the GLCM2.5
Summary
The intensity of frontline therapy is dictated by the presence of risk factors at the time of diagnosis. Measurements that reflect both the 3-dimensional disease volume and metabolic activity, such as metabolic tumor volume (MTV) and total lesion glycolysis (TLG), have been associated with patient outcomes in HL13–15. These functional measurements of tumor volume provide additional prognostic information beyond the classical risk factors that include a unidimensional measurement of tumor bulk[13]. We hypothesized that a model incorporating first- and second-order radiomic features would more accurately predict refractory or relapsed disease status, when compared to MTV, TLG, or maximum SUV (SUVmax) alone, in a cohort of early-stage HL patients
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
