Azacitidine as epigenetic priming for chemotherapy is safe and well-tolerated in infants with newly diagnosed KMT2A-rearranged acute lymphoblastic leukemia: Children's Oncology Group trial AALL15P1.

Infants less than 1 year old diagnosed with KMT2A-rearranged (KMT2A-r) acute lymphoblastic leukemia (ALL) are at high risk of remission failure, relapse, and death due to leukemia, despite intensive therapies. Infant KMT2A-r ALL blasts are characterized by DNA hypermethylation. Epigenetic priming with DNA methyltransferase inhibitors increases the cytotoxicity of chemotherapy in preclinical studies. The Children's Oncology Group trial AALL15P1 tested the safety and tolerability of five days of azacitidine immediately prior to the start of chemotherapy on day six, in four post-induction chemotherapy courses for infants with newly diagnosed KMT2A-r ALL. The treatment was welltolerated, with only two of 31 evaluable patients (6.5%) experiencing dose-limiting toxicity. Whole genome bisulfite sequencing of peripheral blood mononuclear cells (PBMCs) demonstrated decreased DNA methylation in 87% of samples tested following five days of azacitidine. Event-free survival was similar to prior studies of newly diagnosed infant ALL. Azacitidine is safe and results in decreased DNA methylation of PBMCs in infants with KMT2A-r ALL, but the incorporation of azacitidine to enhance cytotoxicity did not impact survival. Clinicaltrials.gov identifier: NCT02828358.

Radiomics analysis for distinctive identification of COVID-19 pulmonary nodules from other benign and malignant counterparts.

This observational study investigated the potential of radiomics as a non-invasive adjunct to CT in distinguishing COVID-19 lung nodules from other benign and malignant lung nodules. Lesion segmentation, feature extraction, and machine learning algorithms, including decision tree, support vector machine, random forest, feed-forward neural network, and discriminant analysis, were employed in the radiomics workflow. Key features such as Idmn, skewness, and long-run low grey level emphasis were identified as crucial in differentiation. The model demonstrated an accuracy of 83% in distinguishing COVID-19 from other benign nodules and 88% from malignant nodules. This study concludes that radiomics, through machine learning, serves as a valuable tool for non-invasive discrimination between COVID-19 and other benign and malignant lung nodules. The findings suggest the potential complementary role of radiomics in patients with COVID-19 pneumonia exhibiting lung nodules and suspicion of concurrent lung pathologies. The clinical relevance lies in the utilization of radiomics analysis for feature extraction and classification, contributing to the enhanced differentiation of lung nodules, particularly in the context of COVID-19.