Publications by authors named "N G Mikhaeel"
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.
Total metabolic tumor volume (TMTV) is prognostic in lymphoma. However, cutoff values for risk stratification vary markedly, according to the tumor delineation method used. We aimed to create a standardized TMTV benchmark dataset allowing TMTV to be tested and applied as a reproducible biomarker.
View Article and Find Full Text PDFRadiotherapy (RT) has potential synergistic effects with chimeric antigen receptor (CAR) T but is not widely used as bridging therapy due to logistical challenges and lack of standardised protocols. We analysed RT bridging in a multicentre national cohort of large B-cell lymphoma patients approved for 3L axicabtagene ciloleucel or tisagenlecleucel across 12 UK centres. Of 763 approved patients, 722 were leukapheresed, 717 had data available on bridging therapy.
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