Publications by authors named "Cristina A Tentori"
Article Synopsis
- Advancements in understanding myelodysplastic neoplasms (MDS) have revealed important cellular and molecular factors that influence disease progression, highlighting the significance of immune dysregulation in the bone marrow during MDS evolution.
- Despite these advancements, immunotherapy for MDS has lagged due to a lack of effective immune classifications for patient stratification and no widely accepted immune panels for clinical use.
- To address these challenges, the i4MDS consortium proposes standardized immune monitoring approaches, including flow cytometry panels and cytokine assays, aiming to improve patient stratification and develop predictive markers for treatment response in MDS.
Personalized Timing for Allogeneic Stem-Cell Transplantation in Hematologic Neoplasms: A Target Trial Emulation Approach Using Multistate Modeling and Microsimulation.
Caterina Gregorio Marta Spreafico Saverio D'Amico Elisabetta Sauta Gianluca Asti Cristina Astrid Tentori
JCO Clin Cancer Inform
May 2024
Purpose: Decision about the optimal timing of a treatment procedure in patients with hematologic neoplasms is critical, especially for cellular therapies (most including allogeneic hematopoietic stem-cell transplantation [HSCT]). In the absence of evidence from randomized trials, real-world observational data become beneficial to study the effect of the treatment timing. In this study, a framework to estimate the expected outcome after an intervention in a time-to-event scenario is developed, with the aim of optimizing the timing in a personalized manner.
View Article and Find Full Text PDFArticle Synopsis
- Allogeneic hematopoietic stem-cell transplantation is the only curative option for patients with myelodysplastic syndromes, and the timing of this treatment is crucial for maximizing benefits and minimizing risks.
- A decision support system was developed to identify the optimal timing for HSCT based on clinical and genomic data from a large study of over 7,000 patients, comparing outcomes using the Molecular International Prognostic Scoring System (IPSS-M) against traditional scoring methods.
- The findings suggest that patients with lower risk can benefit from delaying transplantation, while those at higher risk should undergo it immediately, indicating that the IPSS-M strategy significantly improves life expectancy and supports personalized treatment plans.
Article Synopsis
- Synthetic data are artificial datasets generated using algorithms that learn from real patient data, aiming to preserve privacy while accelerating research in life sciences, specifically in hematologic neoplasms.
- The study utilized a conditional generative adversarial network to create high-fidelity synthetic data for conditions like myelodysplastic syndromes (MDS) and acute myeloid leukemia (AML), validating the data's accuracy and privacy through a robust framework.
- Results showed that synthetic data could significantly augment existing clinical data, enhance research capabilities, and allow for the development of new molecular classification and scoring systems, ultimately benefiting clinicians through a user-friendly website for data generation.
Article Synopsis
- Myelodysplastic syndromes (MDS) require a specialized treatment approach, and the new Molecular International Prognostic Scoring System (IPSS-M) aims to enhance predictions for patient outcomes compared to the older IPSS-R model.
- A study of 2,876 patients revealed that IPSS-M significantly improved survival predictions and shifted risk classifications in nearly half of the patients, even those without detectable gene mutations.
- The findings suggest IPSS-M could better identify patients suitable for hematopoietic stem cell transplantation, although its effectiveness in certain treatment responses remains limited; further research on other influencing factors is necessary.
Article Synopsis
- * In a study of 1,794 people aged 80+, about one-third had mutations that correlated with lower survival rates and the likelihood of developing myeloid neoplasms, especially with specific mutations (like JAK2, DNMT3A, TET2).
- * A predictive model based on mutation profiles and red blood cell index abnormalities categorized individuals into three risk groups for developing myeloid neoplasms; additionally, unexplained cytopenia in this age group could indicate underlying myeloid ne