On the prediction of Hodgkin lymphoma treatment response

Purpose. The cure rate in Hodgkin lymphoma is high, but the response along with treatment is still unpredictable and highly variable among patients. Detecting those patients who do not respond to treatment at early stages could bring improvements in their treatment. This research tries to identify the main biological prognostic variables currently gathered at diagnosis and design a simple machine learning methodology to help physicians improve the treatment response assessment. Methods. We carried out a retrospective analysis of the response to treatment of a cohort of 263 Caucasians who were diagnosed with Hodgkin lymphoma in Asturias (Spain). For that purpose, we used a list of 35 clinical and biological variables that are currently measured at diagnosis before any treatment begins. To establish the list of most discriminatory prognostic variables for treatment response, we designed a machine learning approach based on two different feature selection methods (Fisher’s ratio and maximum percentile distance) and backwards recursive feature elimination using a nearest-neighbor classifier (k-NN). The weights of the k-NN classifier were optimized using different terms of the confusion matrix (true- and false-positive rates) to minimize risk in the decisions. Results and conclusions. We found that the optimum strategy to predict treatment response in Hodgkin lymphoma consists in solving two different binary classification problems, discriminating first if the patient is in progressive disease; if not, then discerning among complete and partial remission. Serum ferritin turned to be the most discriminatory variable in predicting treatment response, followed by alanine aminotransferase and alkaline phosphatase. The importance of these prognostic variables suggests a close relationship between inflammation, iron overload, liver damage and the extension of the disease (AU)

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Lymphomatoid granulomatosis of central nervous system and lung driven by epstein barr virus proliferation: successful treatment with rituximab-containing chemotherapy.

Lymphomatoid granulomatosis (LYG) is a very rare Epstein-Barr virus (EBV) associated B-cell lymphoproliferative disorder. We report the case of a 41-year-old man who presented with fever and respiratory symptoms. Computed tomography showed multiple nodules in both lung fields. Polymerase chain reaction (PCR) analysis for EBV was positive in bronchoalveolar lavage and biopsy of lung node yielded a diagnosis of LYG, grade III. Shortly after initiation of treatment with agressive chemotherapy, neurological deterioration appeared. Neuroimaging findings revealed hydrocephalus and PCR analysis of the cerebrospinal fluid (CSF) was positive for EBV. Treatment with intravenous rituximab led to rapid reduction of EBV load in CSF, along with clinical and radiological improvement. After completion of treatment with immunochemotherapy, an autologous stem cell transplantation was performed. Patient stays in remission 18 months after diagnosis.