Machine Learning to Predict Interim Response in Pediatric Classical Hodgkin Lymphoma Using Affordable Blood Tests

Abstract

Purpose: Response assessment of classical Hodgkin lymphoma (cHL) with positron emission tomography-computerized tomography (PET-CT) is standard of care in well-resourced settings but unavailable in most African countries. We aimed to investigate correlations between changes in PET-CT findings at interim analysis with changes in blood test results in pediatric patients with cHL in 17 South African centers.

Methods: Changes in ferritin, lactate dehydrogenase (LDH), erythrocyte sedimentation rate (ESR), albumin, total white cell count (TWC), absolute lymphocyte count (ALC), and absolute eosinophil count were compared with PET-CT Deauville scores (DS) after two cycles of doxorubicin, bleomycin, vinblastine, and dacarbazine in 84 pediatric patients with cHL. DS 1-3 denoted rapid early response (RER) while DS 4-5 denoted slow early response (SER). Missing values were imputed using the k-nearest neighbor algorithm. Baseline and follow-up blood test values were combined into a single difference variable. Data were split into training and testing sets for analysis using Python scikit-learn 1.2.2 with logistic regression, random forests, naïve Bayes, and support vector machine classifiers.

Results: Random forest analysis achieved the best validated test accuracy of 73% when predicting RER or SER from blood samples. When applied to the full data set, the optimal model had a predictive accuracy of 80% and a receiver operating characteristic AUC of 89%. The most predictive variable was the differences in ALC, contributing 21% to the model. Differences in ferritin, LDH, and TWC contributed 15%-16%. Differences in ESR, hemoglobin, and albumin contributed 11%-12%.

Conclusion: Changes in low-cost, widely available blood tests may predict chemosensitivity for pediatric cHL without access to PET-CT, identifying patients who may not require radiotherapy. Changes in these nonspecific blood tests should be assessed in combination with clinical findings and available imaging to avoid undertreatment.

FIG 1

Interim assessment and risk stratification of pediatric patients on SACCSG-HL-2018. PET-CT, positron emission tomography-computerized tomography.

FIG 2

Hematologic parameters and nonspecific markers of pediatric patients on SACCSG-HL-2018 at baseline and interim analysis: (A) TWC, (B) ALC, (C) hemoglobin, (D) AEC, (E) ESR, (F) copper, (G) ferritin, (H) albumin, and (I) LDH. AEC, absolute eosinophil count; ALC, absolute lymphocyte count; ESR, erythrocyte sedimentation rate; LDH, lactate dehydrogenase; TWC, total white cell count.

FIG 3

Prediction of chemosensitivity of pediatric patients with classical Hodgkin lymphoma. (A) Receiver operating characteristic curve. (B) Confusion matrix. RER, rapid early response; SER, slow early response.

FIG 4

Kaplan-Meier curve of progression-free survival comparing patients with RER and SER. RER, rapid early response; SER, slow early response.