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

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*

• 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