Bayesian Inference-Based Gaussian Mixture Models With Optimal Components Estimation Towards Large-Scale Synthetic Data Generation for In Silico Clinical Trials

Boosting the performance of MALT lymphoma classification in patients with primary Sjögren's Syndrome through data augmentation: a case study

Sjögren's Syndrome (SS) patients with mucosa associated lymphoid tissue lymphomas (MALTLs) and diffuse large B-cell lymphomas (DLBCLs) have 10-year survival rates of 80% and 40%, respectively. This highlights the unique biologic burden of the two histologic forms, as well as, the need for early detection and thorough monitoring of these patients. The lack of MALTL patients and the fact that most studies are single cohort and combine patients with different lymphoma subtypes narrow the understanding of MALTL progression. Here, we propose a data augmentation pipeline that utilizes an advanced synthetic data generator which is trained on a Pan European data hub with primary SS (pSS) patients to yield a high-quality synthetic data pool. The latter is used for the development of an enhanced MALTL classification model. Four scenarios were defined to assess the reliability of augmentation. Our results revealed an overall improvement in the accuracy, sensitivity, specificity, and AUC by 7%, 6.3%, 9%, and 6.3%, respectively. This is the first case study that utilizes data augmentation to reflect the progression of MALTL in pSS.