Publications by authors named "P J van Lugtenburg"

Patients with relapsed or refractory (R/R) diffuse large B-cell lymphoma (DLBCL) have poor outcomes. Gemcitabine + oxaliplatin (GemOx) with rituximab, a standard salvage therapy, yields complete response (CR) rates of approximately 30% and median overall survival (OS) of 10-13 months. Patients with refractory disease fare worse, with a CR rate of 7% for subsequent therapies and median OS of 6 months.

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Next Generation Sequencing-based subtyping and interim- and end of treatment positron emission tomography (i/eot-PET) monitoring have high potential for upfront and on-treatment risk assessment of diffuse large B-cell lymphoma patients. We performed Dana Farber Cancer Institute (DFCI) and LymphGen genetic subtyping for the HOVON84 (n = 208, EudraCT-2006-005174-42) and PETAL (n = 204, EudraCT-2006-001641-33) trials retrospectively combined with DFCI genetic data (n = 304). For all R-CHOP treated patients (n = 592), C5/MCD- and C2/A53-subtypes show significantly worse outcome independent of the international prognostic index.

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Background: Bleomycin is an oncolytic and antibiotic agent used to treat various human cancers because of its antitumor activity. Unfortunately, up to 46% of the patients treated with bleomycin develop drug-induced interstitial lung disease (DIILD) and potentially life-threatening interstitial pulmonary fibrosis. Tools and biomarkers for predicting and detecting DIILD are limited.

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Article Synopsis
  • The study aimed to validate a deep learning model for predicting treatment outcomes in diffuse large B-cell lymphoma patients across 5 clinical trials, comparing it to the international prognostic index (IPI) and radiomic models.
  • The deep learning model, trained on PET/CT scans, demonstrated a higher predictive performance (AUC of 0.66) than IPI (AUC of 0.60) and performed well across all trials.
  • While the deep learning and clinical PET models showed similar performance (AUC of 0.69), the PET model achieved the highest AUC (0.71), although the deep learning model provided outcomes without requiring tumor delineation.
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