Machine Learning-Based Survival Prediction Models for Progression-Free and Overall Survival in Advanced-Stage Hodgkin Lymphoma.

PURPOSE: Patients diagnosed with advanced-stage Hodgkin lymphoma (aHL) have historically been risk-stratified using the International Prognostic Score (IPS). This study investigated if a machine learning (ML) approach could outperform existing models when it comes to predicting overall survival (OS) and progression-free survival (PFS). PATIENTS AND METHODS: This study used patient data from the Danish National Lymphoma Register for model development (development cohort). The ML model was developed using stacking, which combines several predictive survival models (Cox proportional hazard, flexible parametric model, IPS, principal component, penalized regression) into a single model, and was compared with two versions of IPS (IPS-3 and IPS-7) and the newly developed aHL international prognostic index (A-HIPI). Internal model validation was performed using nested cross-validation, and external validation was performed using patient data from the Swedish Lymphoma Register and Cancer Registry of Norway (validation cohort). RESULTS: In total, 707 and 760 patients with aHL were included in the development and validation cohorts, respectively. Examining model performance for OS in the development cohort, the concordance index (C-index) for the ML model, IPS-7, IPS-3, and A-HIPI was found to be 0.789, 0.608, 0.650, and 0.768, respectively. The corresponding estimates in the validation cohort were 0.749, 0.700, 0.663, and 0.741. For PFS, the ML model achieved the highest C-index in both cohorts (0.665 in the development cohort and 0.691 in the validation cohort). The time-varying AUCs for both the ML model and the A-HIPI were consistently higher in both cohorts compared with the IPS models within the first 5 years after diagnosis. CONCLUSION: The new prognostic model for aHL on the basis of ML techniques demonstrated a substantial improvement compared with the IPS models, but yielded a limited improvement in predictive performance compared with the A-HIPI.

Temporal changes in survival among adult patients with acute myeloid leukaemia in the period 2000-2016: a Danish population-based study.

In the present study, we quantify the progress in overall survival (OS) during the period 2000-2016 among Danish patients with acute myeloid leukaemia (AML). This population-based study, including 3820 adult patients with AML, demonstrates a significantly improved OS over time with the 2-year age-standardised OS increasing from 22% in 2002 to 31% in 2016. The improvement in OS was exclusively seen in patients with AML aged ≥50 years, with absolute improvements in 2-year OS from 2002 to 2016 of ≥10% among patients aged 50-75 years and a small absolute increase in those aged >75 years.

Outcome of peripheral T-cell lymphoma in first complete remission: a Danish-Swedish population-based study.

In the present study, we investigate the outcome of 109 Danish and 123 Swedish patients with nodal PTCL in first complete remission (CR), and examine the impact of imaging-based follow-up (FU) strategies. The patients were selected by the following criteria: (a) newly diagnosed nodal PTCL from 2007 to 2012, (b) age ≥18 years, and (c) CR after CHOP or CHOEP therapy. FU guidelines in Sweden included symptom assessment, clinical examinations and blood tests at 3-4-month intervals for 2 years. FU strategies in Denmark was similar but included routine imaging, usually every 6 months for 2 years. Patients had fully comparable characteristics. Overall survival (OS) estimates for patients in CR were similar for all patients (p = .6) and in PTCL subtypes. In multivariate analysis, country of follow-up had no impact on OS. However, despite continuous CR for ≥2 years, the OS of PTCL remained inferior to a matched general population.

hemaClass.org: Online One-By-One Microarray Normalization and Classification of Hematological Cancers for Precision Medicine.

BACKGROUND: Dozens of omics based cancer classification systems have been introduced with prognostic, diagnostic, and predictive capabilities. However, they often employ complex algorithms and are only applicable on whole cohorts of patients, making them difficult to apply in a personalized clinical setting. RESULTS: This prompted us to create hemaClass.org, an online web application providing an easy interface to one-by-one RMA normalization of microarrays and subsequent risk classifications of diffuse large B-cell lymphoma (DLBCL) into cell-of-origin and chemotherapeutic sensitivity classes. Classification results for one-by-one array pre-processing with and without a laboratory specific RMA reference dataset were compared to cohort based classifiers in 4 publicly available datasets. Classifications showed high agreement between one-by-one and whole cohort pre-processsed data when a laboratory specific reference set was supplied. The website is essentially the R-package hemaClass accompanied by a Shiny web application. The well-documented package can be used to run the website locally or to use the developed methods programmatically. CONCLUSIONS: The website and R-package is relevant for biological and clinical lymphoma researchers using affymetrix U-133 Plus 2 arrays, as it provides reliable and swift methods for calculation of disease subclasses. The proposed one-by-one pre-processing method is relevant for all researchers using microarrays.