Publications by authors named "M M Biancalana"
Surgery remains the primary treatment modality in the management of early-stage invasive breast cancer. Artificial intelligence (AI)-powered visualization platforms offer the compelling potential to aid surgeons in evaluating the tumor's location and morphology within the breast and accordingly optimize their surgical approach. We sought to validate an AI platform that employs dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to render three-dimensional (3D) representations of the tumor and 5 additional chest tissues, offering clear visualizations as well as functionalities for quantifying tumor morphology, tumor-to-landmark structure distances, excision volumes, and approximate surgical margins.
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- Cancers share common hallmarks, especially in their metabolism, which significantly impacts tumor behavior and treatment responses.
- Researchers analyzed metabolic patterns in 10,915 patients from 34 cancer types to understand how different metabolic modes relate to patient outcomes.
- Their findings suggest certain metabolic pathways are useful indicators of prognosis and highlight the potential for repurposing therapies across different cancer types based on metabolic modeling.
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- Generalizable studies on population-level cancer treatment often overlook variations in individual tumors, making it challenging to predict how a patient will respond to neoadjuvant therapy (NAT) for breast cancer.
- This research evaluates an existing biophysical simulation platform, TumorScope Predict (TS), using data from early-stage and locally advanced breast cancer patients to forecast their response to NAT.
- Among the study cohort of 80 patients, the platform demonstrated a significant correlation between predicted tumor volumes and actual MRI-assessed volumes after treatment, highlighting its potential utility in clinical practice.
Article Synopsis
- Immuno-oncology therapies, particularly focusing on the PD-1/PD-L1 axis, show promise for treating early-stage breast cancer, but currently only benefit a small number of patients due to limited predictive accuracy and tumor variability.
- A new computational biomarker combines biophysical simulations with AI-driven analysis of DCE-MRI images to predict immune therapy responses across the entire tumor, enhancing understanding beyond traditional biopsies.
- The developed biomarker achieved an 88.2% accuracy rate in predicting complete pathologic responses in a small group of patients, and future virtual clinical trials suggest significant potential for improved outcomes with immune therapy addition in breast cancer treatments.
The Rel proteins of the NF-κB complex comprise one of the most investigated transcription factor families, forming a variety of hetero- or homodimers. Nevertheless, very little is known about the fundamental kinetics of NF-κB complex assembly, or the inter-conversion potential of dimerised Rel subunits. Here, we examined an unexplored aspect of NF-κB dynamics, focusing on the dissociation and reassociation of the canonical p50 and p65 Rel subunits and their ability to form new hetero- or homodimers.
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