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2024-05-15T13:41:07.000Z

Machine learning-based survival prediction models for advanced-stage Hodgkin lymphoma

May 15, 2024
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Learning objective: After reading this article, learners will be able to cite a new development in advanced-stage Hodgkin lymphoma.

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Accurate prognostication is important for the optimal treatment selection in patients with classical Hodgkin lymphoma.1 The International Prognostic Score (IPS)-7 is typically used to risk-stratify patients with advanced-stage Hodgkin lymphoma (aHL).1 The IPS-3 model was developed to simplify the IPS-7 while retaining its predictive capabilities. Recently, the aHL International Prognostic Index (A-HIPI) was developed to improve prognostication compared with the IPS models.1

New predictive models developed using machine learning (ML) have the potential to further improve prognostication. Jørgensen et al.1 recently developed and validated an ML-based survival prediction model for patients with aHL and published the findings in Journal of Clinical Oncology Clinical Cancer Informatics. Below, we summarize the key points.

Methods and patient population1

  • ML models were developed using data from adult patients with newly diagnosed aHL from the Danish National Lymphoma Register (development cohort).
  • The model was validated using data from adult patients with newly diagnosed aHL from the Cancer Registry of Norway and the Swedish Lymphoma Register (validation cohort).
  • Performance was measured by the integrated Brier score, the time-varying area under the curve (AUC), and the concordance index (C-index).

Key findings1

  • In total, 1,467 patients were included (development cohort, n = 707; validation cohort, n = 760).
  • Median follow-up was 7.2 years and 8.6 years in the development and validation cohorts, respectively.
  • The ML model achieved the highest C-index for overall survival (OS) and progression-free survival (PFS) in both the development and validation cohorts (Table 1).

Table 1. Performance of the ML, IPS-3, IPS-7, and A-HIPI models in the development and validation cohorts*

Model

Development cohort (n = 707)

Validation cohort (n = 760)

OS

PFS

OS

PFS

IBS

C-index

IBS

C-index

IBS

C-index

IBS

C-index

ML

0.073

0.789

0.141

0.665

0.049

0.749

0.085

0.691

IPS-3

0.085

0.650

0.149

0.576

0.054

0.663

0.078

0.64

IPS-7

0.087

0.608

0.151

0.549

0.052

0.700

0.077

0.672

A-HIPI

0.076

0.768

0.142

0.649

0.052

0.741

0.091

0.677

A-HIPI, advanced-stage Hodgkin lymphoma international prognostic index; C-index, concordance index; IBS, integrated Brier score; IPS, International Prognostic Score; ML, machine learning; OS, overall survival; PFS, progression-free survival.
*Adapted from Jørgensen, et al.1

  • The time-varying AUC for OS and PFS was consistently higher for the ML model vs IPS-7 and IPS-3 in both cohorts.
    • The time-varying AUC for the A-HIPI model was similar to the ML model for OS and PFS in the development cohort; this was similar for OS in the validation cohort.
    • However, time-varying AUC for PFS was similar to the IPS-7 in the validation cohort.
  • The 5-year OS and PFS rates for patients classified as high-risk by the ML and IPS-7 models are shown in Figure 1.

Figure 1. 5-year survival outcomes of high-risk patients in the ML model and IPS-7*

IPS, International Prognostic Score; ML, machine learning; OS, overall survival; PFS, progression-free survival.
*Data from Jørgensen, et al.1

 

Key learnings
  • The ML model showed improved prognostication for patients with aHL vs the IPS-7 and IPS-3.
  • A high-risk group was identified with worse OS than high-risk patients under the IPS-7 model.
  • The ML model was only slightly better than the recently developed A-HIPI model.

  1. Jørgensen RRK, Bergström F, Eloranta S, et al. Machine learning–based survival prediction models for progression-free and overall survival in advanced-stage Hodgkin lymphoma. JCO Clin Cancer Inform. 2024;8:e2300255. DOI: 1200/CCI.23.00255

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