{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T12:08:31Z","timestamp":1789474111235,"version":"build-2803163510"},"reference-count":36,"publisher":"American Society of Clinical Oncology (ASCO)","issue":"10","content-domain":{"domain":["ascopubs.org"],"crossmark-restriction":true},"short-container-title":["JCO Glob Oncol"],"published-print":{"date-parts":[[2024,10]]},"abstract":"<jats:sec>\n            <jats:title>PURPOSE<\/jats:title>\n            <jats:p>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.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>METHODS<\/jats:title>\n            <jats:p>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, na\u00efve Bayes, and support vector machine classifiers.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>RESULTS<\/jats:title>\n            <jats:p>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%.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>CONCLUSION<\/jats:title>\n            <jats:p>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.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1200\/go.23.00435","type":"journal-article","created":{"date-parts":[[2024,10,24]],"date-time":"2024-10-24T19:59:09Z","timestamp":1729799949000},"update-policy":"https:\/\/doi.org\/10.1200\/crossmark","source":"Crossref","is-referenced-by-count":3,"title":["Machine Learning to Predict Interim Response in Pediatric Classical Hodgkin Lymphoma Using Affordable Blood Tests"],"prefix":"10.1200","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8792-3251","authenticated-orcid":true,"given":"Jennifer A.","family":"Geel","sequence":"first","affiliation":[{"name":"Department of Paediatrics and Child Health, Paediatric Haematology-Oncology, Charlotte Maxeke Johannesburg Academic Hospital, Wits Donald Gordon Medical Centre, University of the Witwatersrand, Johannesburg, South 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