Publications by authors named "D Dall'olio"
Hematological malignancies are a diverse group of cancers developing in the peripheral blood, the bone marrow or the lymphatic system. Due to their heterogeneity, the identification of novel and advanced molecular signatures is essential for enhancing their characterization and facilitate its translation to new pharmaceutical solutions and eventually to clinical applications. In this study, we collected publicly available microarray data for more than five thousand subjects, across thirteen hematological malignancies.
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- Since 2017, combining targeted therapies with traditional chemotherapy has led to better outcomes for acute myeloid leukemia (AML) patients.
- A study of 5,359 AML patients over 20 years used data from the HARMONY Alliance to analyze treatment outcomes during four 5-year periods from 1997 to 2016.
- Results show significant improvements in 5-year survival rates and reduced 60-day mortality (from 13.0% to 4.7%), even across different genetic risk groups, indicating that the advancements in treatment have positively affected patient outcomes.
Purpose: Rare cancers constitute over 20% of human neoplasms, often affecting patients with unmet medical needs. The development of effective classification and prognostication systems is crucial to improve the decision-making process and drive innovative treatment strategies. We have created and implemented MOSAIC, an artificial intelligence (AI)-based framework designed for multimodal analysis, classification, and personalized prognostic assessment in rare cancers.
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- Quantitative computed tomography (QCT)-based models have shown better accuracy in predicting hip fractures compared to traditional bone mineral density methods, but manual femur segmentation from CT images is time-consuming and inconsistent.
- This study proposes a semi-automated segmentation method using bone and joint enhancement filters and graph-cut algorithms, which was tested on 10 subjects and compared to manual segmentation for accuracy.
- The results indicate that the semi-automated method significantly improves segmentation fidelity and reduces time while maintaining similar hip fracture risk assessments, suggesting its potential for large-scale applications and further development of automated techniques.
DNA methylation clocks presents advantageous characteristics with respect to the ambitious goal of identifying very early markers of disease, based on the concept that accelerated ageing is a reliable predictor in this sense. Such tools, being epigenomic based, are expected to be conditioned by sex and tissue specificities, and this work is about quantifying this dependency as well as that from the regression model and the size of the training set. Our quantitative results indicate that elastic-net penalization is the best performing strategy, and better so when-unsurprisingly-the data set is bigger; sex does not appear to condition clocks performances and tissue specific clocks appear to perform better than generic blood clocks.
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