P2X7 mRNA expression in non-small cell lung cancer: MicroRNA regulation and prognostic value

The human P2X7 receptor is significant and exhibits several functions in neoplasia. At present, little is known with regard to its regulation. P2X7 expression may be regulated post-transcriptionally and putative microRNA (miRNA) binding sites are considered to be involved. The aim of this study was to determine whether miRNAs (miR-21, let-7 g and miR-205) regulate P2X7 mRNA stability. In addition, the impact of P2X7 expression in patients with non-small cell lung cancer (NSCLC) was investigated. P2X7 mRNA and mature Let-7 g, miR-21, and miR-205 expression levels were quantified in 96 NSCLC cases using quantitative reverse transcription polymerase chain reaction. In all samples, epidermal growth factor receptor and K-Ras mutational analysis was also performed. Samples with low P2X7 expression were found to exhibit a higher fold change in miR-21 expression when compared with samples exhibiting high P2X7 expression. Significantly higher miR-21 expression was observed in the tumors of NSCLC patients with a K-Ras mutation when compared with patients who had K-Ras wild-type tumors (P=0.003). Additionally, to evaluate the association between P2X7 expression and prognosis in NSCLC patients, survival analysis was performed using the Kaplan-Meier method. A significant difference in the progression-free survival and overall survival in the NSCLC patients with high P2X7 expression was identified, when compared with that of patients with low expression (P=0.03 and P=0.02, respetively). Therefore, we hypothesized that high levels of miR-21 expression in NSCLC patients with K-Ras mutations may be regulated by a complex circuit, including P2X7 downregulation and together these processes may promote tumor progression

Stereotactic Radiotherapy for Brain Metastases: Imaging Tools and Dosimetric Predictive Factors for Radionecrosis

Radionecrosis (RN) is the most important side effect after stereotactic radiotherapy (SRT) for brain metastases, with a reported incidence ranging from 3% to 24%. To date, there are no unanimously accepted criteria for iconographic diagnosis of RN, as well as no definitive dose-constraints correlated with the onset of this late effect. We reviewed the current literature and gave an overview report on imaging options for the diagnosis of RN and on dosimetric parameters correlated with the onset of RN. We performed a PubMed literature search according to the preferred reporting items and meta-analysis (PRISMA) guidelines, and identified articles published within the last ten years, up to 31 December 2019. When analyzing data on diagnostic tools, perfusion magnetic resonance imaging (MRI) seems to be very useful allowing evaluation of the blood flow in the lesion using the relative cerebral blood volume (rCBV) and blood vessel integrity using relative peak weight (rPH). It is necessary to combine morphological with functional imaging in order to match information about lesion morphology, metabolism and blood-flow. Eventually, serial imaging follow-up is needed. Regarding dosimetric parameters, in radiosurgery (SRS) V12 < 8 cm3 and V10 < 10.5 cm3 of normal brain are the most reliable prognostic factors, whereas in hypo-fractionated stereotactic radiotherapy (HSRT) V18 and V21 are considered the main predictive independent risk factors of RN

Biparametric prostate MRI: impact of a deep learning-based software and of quantitative ADC values on the inter-reader agreement of experienced and inexperienced readers

Objective To investigate the impact of an artificial intelligence (AI) software and quantitative ADC (qADC) on the inter-reader agreement, diagnostic performance, and reporting times of prostate biparametric MRI (bpMRI) for experienced and inexperienced readers. Materials and methods A total of 170 multiparametric MRI (mpMRI) of patients with suspicion of prostate cancer (PCa) were retrospectively reviewed by one experienced and one inexperienced reader three times, following a wash-out period. First, only the bpMRI sequences, including T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI) sequences, and apparent diffusion coefficient (ADC) maps, were used. Then, bpMRI and quantitative ADC values were used. Lastly, bpMRI and the AI software were used. Inter-reader agreement between the two readers and between each reader and the mpMRI original reports was calculated. Detection rates and reporting times were calculated for each group. Results Inter-reader agreement with respect to mpMRI was moderate for bpMRI, Quantib, and qADC for both the inexperienced (weighted k of 0.42, 0.45, and 0.41, respectively) and the experienced radiologists (weighted k of 0.44, 0.46, and 0.42, respectively). Detection rate of PCa was similar between the inexperienced (0.24, 0.26, and 0.23) and the experienced reader (0.26, 0.27 and 0.27), for bpMRI, Quantib, and qADC, respectively. Reporting times were lower for Quantib (8.23, 7.11, and 9.87 min for the inexperienced reader and 5.62, 5.07, and 6.21 min for the experienced reader, for bpMRI, Quantib, and qADC, respectively). Conclusions AI and qADC did not have a significant impact on the diagnostic performance of both readers. The use of Quantib was associated with lower reporting times

Efficacy of somatostatine analogues in survival and quality of life of a frail patient with poorly differentiated biliary neuroendocrine tumour

We describe the case of a 71-year-old woman with poorly differentiated neuroendocrine tumor of third distal biliary duct and osteolytic metastasis. The patient was evaluated as a third stage of Balducci’s criteria for the recognition of frailty. The patient received radiotherapy and octreotide LAR. This treatment allowed a good tumour progression rate (18 months), a good quality of life and a good survival (35 months). The case report describes the role of octreotide in the therapy of neuroendocrine tumours, and underlines the importance of a multidisciplinary management of cancer in frail patients

Let-7g and miR-21 expression in non-small cell lung cancer: correlation with clinicopathological and molecular features

MicroRNAs (miRNAs) play a key role in cancer pathogenesis and are involved in several human cancers, including non-small cell lung cancer (NSCLC). This study evaluated Let-7g and miR-21 expression by quantitative real-time PCR in 80 NSCLC patients and correlated the results with their main clinicopathological and molecular features. MiR-21 expression was significantly higher in NSCLC tissues compared to non-cancer lung tissues (p&lt;0.0001), while no significant changes in Let-7g expression were observed between the tumor and normal lung tissues. Target prediction analysis led to the identification of 26 miR-21 and 24 Let-7g putative target genes that play important roles in cancer pathogenesis and progression. No significant association was observed between the analysed miRNAs and the main clinicopathological or molecular characteristics of the NSCLC patients, although both miRNAs were downregulated in squamous cell carcinomas compared to adenocarcinomas. Noteworthy, we observed a significant association between low Let-7g expression and metastatic lymph nodes at diagnosis (p=0.046), as well as between high miR-21 expression and K-Ras mutations (p=0.0003). Survival analysis did not show any significant correlation between prognosis and the analysed miRNAs, although the patients with a high Let-7g and miR-21 expression showed a significantly lower short-term progression-free survival (p=0.01 and p=0.0003, respectively) and overall survival (p=0.023 and p=0.0045, respectively). In conclusion, we showed that Let-7g and miR-21 expression was deregulated in NSCLC and we demonstrated a strong relationship between miR-21 overexpression and K-Ras mutations. Our data indicate that Let-7g and miR-21 profiling combined with the determination of K-Ras mutational status may be considered a useful biomarker for a more effective molecular characterization and clinical management of NSCLC patients

Amplifying the Effects of Contrast Agents on Magnetic Resonance Images Using a Deep Learning Method Trained on Synthetic Data

OBJECTIVES: Artificial intelligence (AI) methods can be applied to enhance contrast in diagnostic images beyond that attainable with the standard doses of contrast agents (CAs) normally used in the clinic, thus potentially increasing diagnostic power and sensitivity. Deep learning-based AI relies on training data sets, which should be sufficiently large and diverse to effectively adjust network parameters, avoid biases, and enable generalization of the outcome. However, large sets of diagnostic images acquired at doses of CA outside the standard-of-care are not commonly available. Here, we propose a method to generate synthetic data sets to train an "AI agent" designed to amplify the effects of CAs in magnetic resonance (MR) images. The method was fine-tuned and validated in a preclinical study in a murine model of brain glioma, and extended to a large, retrospective clinical human data set. MATERIALS AND METHODS: A physical model was applied to simulate different levels of MR contrast from a gadolinium-based CA. The simulated data were used to train a neural network that predicts image contrast at higher doses. A preclinical MR study at multiple CA doses in a rat model of glioma was performed to tune model parameters and to assess fidelity of the virtual contrast images against ground-truth MR and histological data. Two different scanners (3 T and 7 T, respectively) were used to assess the effects of field strength. The approach was then applied to a retrospective clinical study comprising 1990 examinations in patients affected by a variety of brain diseases, including glioma, multiple sclerosis, and metastatic cancer. Images were evaluated in terms of contrast-to-noise ratio and lesion-to-brain ratio, and qualitative scores. RESULTS: In the preclinical study, virtual double-dose images showed high degrees of similarity to experimental double-dose images for both peak signal-to-noise ratio and structural similarity index (29.49 dB and 0.914 dB at 7 T, respectively, and 31.32 dB and 0.942 dB at 3 T) and significant improvement over standard contrast dose (ie, 0.1 mmol Gd/kg) images at both field strengths. In the clinical study, contrast-to-noise ratio and lesion-to-brain ratio increased by an average 155% and 34% in virtual contrast images compared with standard-dose images. Blind scoring of AI-enhanced images by 2 neuroradiologists showed significantly better sensitivity to small brain lesions compared with standard-dose images (4.46/5 vs 3.51/5). CONCLUSIONS: Synthetic data generated by a physical model of contrast enhancement provided effective training for a deep learning model for contrast amplification. Contrast above that attainable at standard doses of gadolinium-based CA can be generated through this approach, with significant advantages in the detection of small low-enhancing brain lesions.</p

AIforCOVID: predicting the clinical outcomes in patients with COVID-19 applying AI to chest-X-rays. An Italian multicentre study

Recent epidemiological data report that worldwide more than 53 million people have been infected by SARS-CoV-2, resulting in 1.3 million deaths. The disease has been spreading very rapidly and few months after the identification of the first infected, shortage of hospital resources quickly became a problem. In this work we investigate whether chest X-ray (CXR) can be used as a possible tool for the early identification of patients at risk of severe outcome, like intensive care or death. CXR is a radiological technique that compared to computed tomography (CT) it is simpler, faster, more widespread and it induces lower radiation dose. We present a dataset including data collected from 820 patients by six Italian hospitals in spring 2020 during the first COVID-19 emergency. The dataset includes CXR images, several clinical attributes and clinical outcomes. We investigate the potential of artificial intelligence to predict the prognosis of such patients, distinguishing between severe and mild cases, thus offering a baseline reference for other researchers and practitioners. To this goal, we present three approaches that use features extracted from CXR images, either handcrafted or automatically by convolutional neuronal networks, which are then integrated with the clinical data. Exhaustive evaluation shows promising performance both in 10-fold and leave-one-centre-out cross-validation, implying that clinical data and images have the potential to provide useful information for the management of patients and hospital resources

Indoor positioning con tecniche Ultra Wide Band: funzionamento, test e risultati

Oggigiorno, i ricevitori GNSS sono strumenti quotidianamente utilizzati da un numero di utenti elevato, per svariate applicazioni in ambito “outdoor”: dalla navigazione alla posizionamento di alta precisione. Un tema di ricerca molto attuale è la ricerca di una valida alternativa alla tecnica GNSS per il posizionamento indoor. Nei primi anni 2000 si era avanzata la possibilità di utilizzare le tecniche di “pseudoliti”, vale a dire delle antenne a terra in grado di replicare un segnale GNSS. Il tentativo svanì dopo alcuni anni, a seguito delle complessità e fragilità del sistema. Recentemente, è stata proposta la tecnologia ultra wide band (UWB) (Toth et al, 2017), vale a dire un sistema sempre basato sulle radio frequenze, costituito da antenne fisse (anchor) e antenne mobili (TAG) che sfrutta sempre il principio di trilaterazione, in grado di consentire un posizionamento indoor anche senza GNSS. Questo tipo di soluzione, inoltre, può essere in grado di stimare una soluzione di navigazione completa, vale a dire sia di posizione che di assetto. In questo contributo si è voluto testare il sistema commerciale “low cost” di Pozyx®, che si basa proprio sul posizionamento UWB, in grado di fornire una soluzione di navigazione completa: posizione e assetto (Dardari et al, 2017). Il sistema è costituito da una rete di moduli a radiofrequenza su banda f=500MHz, che permettono di raggiungere precisioni dell’ordine dei 10-15cm. Il sistema è composto da un TAG (mobile) che trasmette il pacchetto di dati e una serie di “anchor” (fissi) con posizione nota, installati nell’ambiente. I TAG hanno al loro interno anche un sistema IMU, costituito da tre accelerometri, giroscopi e magnetometri. Al fine di ottenere la stima della posizione e dell’assetto è necessario collegare il dispositivo TAG ad un sistema di controllo, quale computer o sistemi integrati quali Raspberry o Arduino. Si sono svolti diversi test in ambiente indoor, al fine di analizzare le precisioni e accuratezze del posizionamento e delle misure dei range. Si è realizzata una rete composta da 4 “anchor”, coprendo un’area di 6.44m x 4.91m. Il posizionamento è stato svolto utilizzando due algoritmi: UWB-only e tracking (Alarifi et al, 2016). Il posizionamento è stato effettuato sia in modalità “statica” che “cinematica”. In questo contributo si presenteranno solo i risultati della prova cinematica, in cui il rover è stato spostato sui punti contrassegnati per valutare il sistema di posizionamento cinematico

Stereotactic Radiotherapy for Brain Metastases: Imaging Tools and Dosimetric Predictive Factors for Radionecrosis

Radionecrosis (RN) is the most important side effect after stereotactic radiotherapy (SRT) for brain metastases, with a reported incidence ranging from 3% to 24%. To date, there are no unanimously accepted criteria for iconographic diagnosis of RN, as well as no definitive dose-constraints correlated with the onset of this late effect. We reviewed the current literature and gave an overview report on imaging options for the diagnosis of RN and on dosimetric parameters correlated with the onset of RN. We performed a PubMed literature search according to the preferred reporting items and meta-analysis (PRISMA) guidelines, and identified articles published within the last ten years, up to 31 December 2019. When analyzing data on diagnostic tools, perfusion magnetic resonance imaging (MRI) seems to be very useful allowing evaluation of the blood flow in the lesion using the relative cerebral blood volume (rCBV) and blood vessel integrity using relative peak weight (rPH). It is necessary to combine morphological with functional imaging in order to match information about lesion morphology, metabolism and blood-flow. Eventually, serial imaging follow-up is needed. Regarding dosimetric parameters, in radiosurgery (SRS) V12 &lt; 8 cm3 and V10 &lt; 10.5 cm3 of normal brain are the most reliable prognostic factors, whereas in hypo-fractionated stereotactic radiotherapy (HSRT) V18 and V21 are considered the main predictive independent risk factors of RN