0000000000008974
AUTHOR
Giorgio Russo
The Proton-Boron Reaction Increases the Radiobiological Effectiveness of Clinical Low- and High-Energy Proton Beams: Novel Experimental Evidence and Perspectives
Protontherapy is a rapidly expanding radiotherapy modality where accelerated proton beams are used to precisely deliver the dose to the tumor target but is generally considered ineffective against radioresistant tumors. Proton-Boron Capture Therapy (PBCT) is a novel approach aimed at enhancing proton biological effectiveness. PBCT exploits a nuclear fusion reaction between low-energy protons and 11B atoms, i.e. p+11B→ 3α (p-B), which is supposed to produce highly-DNA damaging α-particles exclusively across the tumor-conformed Spread-Out Bragg Peak (SOBP), without harming healthy tissues in the beam entrance channel. To confirm previous work on PBCT, here we report new in-vitro data obtained…
Radiomics Analysis of Brain [18F]FDG PET/CT to Predict Alzheimer’s Disease in Patients with Amyloid PET Positivity: A Preliminary Report on the Application of SPM Cortical Segmentation, Pyradiomics and Machine-Learning Analysis
Background: Early in-vivo diagnosis of Alzheimer’s disease (AD) is crucial for accurate management of patients, in particular, to select subjects with mild cognitive impairment (MCI) that may evolve into AD, and to define other types of MCI non-AD patients. The application of artificial intelligence to functional brain [18F]fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography(CT) aiming to increase diagnostic accuracy in the diagnosis of AD is still undetermined. In this field, we propose a radiomics analysis on advanced imaging segmentation method Statistical Parametric Mapping (SPM)-based completed with a Machine-Learning (ML) application to predict the diagnosi…
CHK1-targeted therapy to deplete DNA replication-stressed, p53-deficient, hyperdiploid colorectal cancer stem cells.
ObjectiveCancer stem cells (CSCs) are responsible for tumour formation and spreading, and their targeting is required for tumour eradication. There are limited therapeutic options for advanced colorectal cancer (CRC), particularly for tumours carrying RAS-activating mutations. The aim of this study was to identify novel CSC-targeting strategies.DesignTo discover potential therapeutics to be clinically investigated as single agent, we performed a screening with a panel of FDA-approved or investigational drugs on primary CRC cells enriched for CSCs (CRC-SCs) isolated from 27 patients. Candidate predictive biomarkers of efficacy were identified by integrating genomic, reverse-phase protein mic…
On Unsupervised Methods for Medical Image Segmentation: Investigating Classic Approaches in Breast Cancer DCE-MRI
Unsupervised segmentation techniques, which do not require labeled data for training and can be more easily integrated into the clinical routine, represent a valid solution especially from a clinical feasibility perspective. Indeed, large-scale annotated datasets are not always available, undermining their immediate implementation and use in the clinic. Breast cancer is the most common cause of cancer death in women worldwide. In this study, breast lesion delineation in Dynamic Contrast Enhanced MRI (DCE-MRI) series was addressed by means of four popular unsupervised segmentation approaches: Split-and-Merge combined with Region Growing (SMRG), k-means, Fuzzy C-Means (FCM), and spatial FCM (…
Cancer stem cell-based models of colorectal cancer reveal molecular determinants of therapy resistance
Abstract Colorectal cancer (CRC) therapy mainly relies on the use of conventional chemotherapeutic drugs combined, in a subset of patients, with epidermal growth factor receptor [EGFR]-targeting agents. Although CRC is considered a prototype of a cancer stem cell (CSC)-driven tumor, the effects of both conventional and targeted therapies on the CSC compartment are largely unknown. We have optimized a protocol for colorectal CSC isolation that allowed us to obtain CSC-enriched cultures from primary tumor specimens, with high efficiency. CSC isolation was followed by in vitro and in vivo validation, genetic characterization, and drug sensitivity analysis, thus generating panels of CSC lines w…
A New Preclinical Decision Support System Based on PET Radiomics: A Preliminary Study on the Evaluation of an Innovative 64Cu-Labeled Chelator in Mouse Models
The 64Cu-labeled chelator was analyzed in vivo by positron emission tomography (PET) imaging to evaluate its biodistribution in a murine model at different acquisition times. For this purpose, nine 6-week-old female Balb/C nude strain mice underwent micro-PET imaging at three different time points after 64Cu-labeled chelator injection. Specifically, the mice were divided into group 1 (acquisition 1 h after [64Cu] chelator administration, n = 3 mice), group 2 (acquisition 4 h after [64Cu]chelator administration, n = 3 mice), and group 3 (acquisition 24 h after [64Cu] chelator administration, n = 3 mice). Successively, all PET studies were segmented by means of registration with a standard te…
Artificial Intelligence Applications on Restaging [18F]FDG PET/CT in Metastatic Colorectal Cancer: A Preliminary Report of Morpho-Functional Radiomics Classification for Prediction of Disease Outcome
Featured Application Based on results defined in this study, new investigations might propose morpho-functional-based radiomics algorithms for risk stratification with possible impact on treatment management in colorectal cancer. The aim of this study was to investigate the application of [F-18]FDG PET/CT images-based textural features analysis to propose radiomics models able to early predict disease progression (PD) and survival outcome in metastatic colorectal cancer (MCC) patients after first adjuvant therapy. For this purpose, 52 MCC patients who underwent [F-18]FDGPET/CT during the disease restaging process after the first adjuvant therapy were analyzed. Follow-up data were recorded f…
NeXt for neuro-radiosurgery: A fully automatic approach for necrosis extraction in brain tumor MRI using an unsupervised machine learning technique
Stereotactic neuro-radiosurgery is a well-established therapy for intracranial diseases, especially brain metastases and highly invasive cancers that are difficult to treat with conventional surgery or radiotherapy. Nowadays, magnetic resonance imaging (MRI) is the most used modality in radiation therapy for soft-tissue anatomical districts, allowing for an accurate gross tumor volume (GTV) segmentation. Investigating also necrotic material within the whole tumor has significant clinical value in treatment planning and cancer progression assessment. These pathological necrotic regions are generally characterized by hypoxia, which is implicated in several aspects of tumor development and gro…