Gonadal function in male patients after treatment for malignant lymphomas, with emphasis on chemotherapy

Gonadal function was assessed in male lymphoma survivors based on serum hormone levels (LH, FSH, testosterone, SHBG), and was related to treatment, age and observation time. Male patients ⩽50 years at diagnosis treated for Hodgkin's (HL) and/or non-Hodgkin's lymphoma (NHL) at the Norwegian Radium Hospital from 1 January 1980 to 31 December 2002 were included. Five treatment groups were defined: 1: radiotherapy only and/or low gonadotoxic chemotherapy (both HL and NHL)(‘No/low'), 2: medium gonadotoxicity chemotherapy for NHL (‘med-NHL'), 3: medium gonadotoxicity chemotherapy for HL (‘med-HL'), 4: highly gonadotoxic chemotherapy for NHL (‘high-NHL'), 5: highly gonadotoxic chemotherapy for HL (‘high-HL'). Gonadal hormone levels were categorised into three groups: 1: All gonadal hormones within normal range (normal), 2: Isolated elevated FSH, with LH, SHBG and testosterone within normal range (exocrine hypogonadism), 3: Testosterone below and/or LH above normal range (endocrine hypogonadism). One hundred and forty-four (49%) of the patients had normal gonadal hormones, 60 (20%) displayed exocrine hypogonadism and almost one-third (n=90, 30%) had endocrine hypogonadism. Compared to those treated with no/low gonadotoxic chemotherapy patients from all other treatment groups had significantly elevated risk for exocrine hypogonadism. Patients from the other treatment groups, except those in the med-NHL group, also had significantly elevated risk for endocrine hypogonadism compared with the group treated with no/low gonadotoxic chemotherapy. Men aged above 50 years at survey were about five times more likely to have endocrine hypogonadism compared with those less than 40 years. Because of the adverse health effects following long-lasting endocrine hypogonadism, gonadal hormones should be assessed regularly in male lymphoma survivors, especially after treatment with alkylating agents and high-dose chemotherapy with autologous stem cell support and in male patients who are 50 years and older

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