Balloon Aortic Valvuloplasty as Bridge-to-Decision in High Risk Patients with Severe Aortic Valve Stenosis

Background. A sizable group of patients with symptomatic aortic stenosis (AS) can undergo neither surgical aortic valve replacement (AVR) nor transcatheter aortic valve implantation (TAVI) because of clinical contraindications. The aim of this study was to assess the potential role of balloon aortic valvuloplasty (BAV) as a “bridge-to-decision” in selected patients with severe AS and potentially reversible contraindications to definitive treatment. Methods. We retrospectively enrolled 645 patients who underwent first BAV at our Institution between July 2007 and December 2012. Of these, the 202 patients (31.2%) who underwent BAV as bridge-to-decision (BTD) requiring clinical re-evaluation represented our study population. BTD patients were further subdivided in 5 groups: low left ventricular ejection fraction; mitral regurgitation grade ≥3; frailty; hemodynamic instability; comorbidity. The main objective of the study was to evaluate how BAV influenced the final treatment strategy in the whole BTD group and in its single specific subgroups. Results. Mean logistic EuroSCORE was 23.5±15.3%, mean age was 81±7 years. Mean transaortic gradient decreased from 47±17 mmHg to 33±14 mmHg. Of the 193 patients with BTD-BAV who received a second heart team evaluation, 72.5% were finally deemed eligible for definitive treatment (25.4%for AVR; 47.2% for TAVI): respectively, 96.7% of patients with left ventricular ejection fraction recovery; 70.5% of patients with mitral regurgitation reduction; 75.7% of patients who underwent BAV in clinical hemodynamic instability; 69.2% of frail patients and 68% of patients who presented relevant comorbidities. 27.5% of the study population was deemed ineligible for definitive treatment and treated with standard therapy/repeated BAV. In-hospital mortality was 4.5%, cerebrovascular accident occurred in 1% and overall vascular complications were 4% (0.5% major; 3.5% minor). Conclusions. Balloon aortic valvuloplasty should be considered as bridge-to-decision in high-risk patients with severe aortic stenosis who cannot be immediate candidates for definitive percutaneous or surgical treatment.

Harnessing the cellular and molecular complexity of high-risk neuroblastoma to investigate novel potential treatment approaches

Neuroblastoma (NB) is the most common type of tumor in infants and the third most common cancer in children. Current clinical practices employ a variety of strategies for NB treatment, ranging from standard chemotherapy to immunotherapy. Due to a lack of knowledge about the molecular mechanisms underlying the disease's onset, aggressive phenotype, and therapeutic resistance, these approaches are ineffective in the majority of instances. MYCN amplification is one of the most well-known genetic alterations associated with high risk in NB. The following work is divided into three sections and aims to provide new insights into the biology of NB and hypothetical new treatment strategies. First, we identified RUNX1T1 as a key gene involved in MYCN-driven NB onset in a transgenic mouse model. Our results suggested that that RUNX1T1 may recruit the Co-REST complex on target genes that regulate the differentiation of NB cells and that the interaction with RCOR3 is essential. Second, we provided insights into the role of MYCN in dysregulating the CDK/RB/E2F pathway controlling the G1/S transition of the cell cycle. We found that RB is dispensable in regulating MYCN amplified NB's cell cycle, providing the rationale for using cyclin/CDK complexes inhibitors in NBs carrying MYCN amplification and relatively high levels of RB1 expression. Third, we generated an M13 bacteriophage platform to target GD2-expressing cells in NB. Here, we generated a recombinant M13 phage capable of binding GD2-expressing cells selectively (M13GD2). Our results showed that M13GD2 chemically conjugated with the photosensitizer ECB04 preserves the retargeting capability, inducing cell death even at picomolar concentrations upon light irradiation. These results provided proof of concept for M13 phage employment in targeted photodynamic therapy for NB, an exciting strategy to overcome resistance to classical immunotherapy.

Developing a radiomic based machine learning model to identify patients with resected non-small-cell lung cancer at high risk of relapse

Background There is a wide variation of recurrence risk of Non-small-cell lung cancer (NSCLC) within the same Tumor Node Metastasis (TNM) stage, suggesting that other parameters are involved in determining this probability. Radiomics allows extraction of quantitative information from images that can be used for clinical purposes. The primary objective of this study is to develop a radiomic prognostic model that predicts a 3 year disease free-survival (DFS) of resected Early Stage (ES) NSCLC patients. Material and Methods 56 pre-surgery non contrast Computed Tomography (CT) scans were retrieved from the PACS of our institution and anonymized. Then they were automatically segmented with an open access deep learning pipeline and reviewed by an experienced radiologist to obtain 3D masks of the NSCLC. Images and masks underwent to resampling normalization and discretization. From the masks hundreds Radiomic Features (RF) were extracted using Py-Radiomics. Hence, RF were reduced to select the most representative features. The remaining RF were used in combination with Clinical parameters to build a DFS prediction model using Leave-one-out cross-validation (LOOCV) with Random Forest. Results and Conclusion A poor agreement between the radiologist and the automatic segmentation algorithm (DICE score of 0.37) was found. Therefore, another experienced radiologist manually segmented the lesions and only stable and reproducible RF were kept. 50 RF demonstrated a high correlation with the DFS but only one was confirmed when clinicopathological covariates were added: Busyness a Neighbouring Gray Tone Difference Matrix (HR 9.610). 16 clinical variables (which comprised TNM) were used to build the LOOCV model demonstrating a higher Area Under the Curve (AUC) when RF were included in the analysis (0.67 vs 0.60) but the difference was not statistically significant (p=0,5147).

Automatic risk evaluation in elderly patients based on Autonomic Nervous System assessment

Dysfunction of Autonomic Nervous System (ANS) is a typical feature of chronic heart failure and other cardiovascular disease. As a simple non-invasive technology, heart rate variability (HRV) analysis provides reliable information on autonomic modulation of heart rate. The aim of this thesis was to research and develop automatic methods based on ANS assessment for evaluation of risk in cardiac patients. Several features selection and machine learning algorithms have been combined to achieve the goals. Automatic assessment of disease severity in Congestive Heart Failure (CHF) patients: a completely automatic method, based on long-term HRV was proposed in order to automatically assess the severity of CHF, achieving a sensitivity rate of 93% and a specificity rate of 64% in discriminating severe versus mild patients. Automatic identification of hypertensive patients at high risk of vascular events: a completely automatic system was proposed in order to identify hypertensive patients at higher risk to develop vascular events in the 12 months following the electrocardiographic recordings, achieving a sensitivity rate of 71% and a specificity rate of 86% in identifying high-risk subjects among hypertensive patients. Automatic identification of hypertensive patients with history of fall: it was explored whether an automatic identification of fallers among hypertensive patients based on HRV was feasible. The results obtained in this thesis could have implications both in clinical practice and in clinical research. The system has been designed and developed in order to be clinically feasible. Moreover, since 5-minute ECG recording is inexpensive, easy to assess, and non-invasive, future research will focus on the clinical applicability of the system as a screening tool in non-specialized ambulatories, in order to identify high-risk patients to be shortlisted for more complex investigations.