S Sn no or ri in ng g: : a an na al ly ys si is s, , m me ea as su ur re em me en nt t, , c cl li in ni ic ca al l i im mp pl li ic ca at ti io on ns s a an nd d a ap pp pl li ic ca at ti io on ns s F. Dalmasso, R. Prota Snoring: analysis, measurement, clinical implications and applications. F. Dalmasso, R. Prota. © ERS Journals Ltd. 1996. ABSTRACT: Snoring was described in literature even before medicine. Common definitions do not consider acoustic measurements of snoring. In this paper we discuss the main pathophysiological aspects of snoring and the snoring-sleep relationship as the generating mechanisms. Snoring can be analysed and measured by the following methods: 1) LeqEquivalent Continuous Sound Level, which only quantifies noisiness, annoyance, and damage to the partner's and snorer's hearing; 2) Power Spectrum, with frequency values, formantic structure data and typical shape, which can help to distinguish simple snoring from heavy snoring with obstructive sleep apnoea syndrome (OSAS); 3) Linear Prediction Code (LPC) method, which can define the crosssectional area (CSA) of the upper airways and which locates sites of obstruction.Simulated snoring analysis with LPC and with simultaneous fluoroscopy permits the definition of CSA and the identification of three snoring patterns: nasal, oral and oronasal. Snoring is an important sign of sleep-related breathing disorders (SRBD), of the upper airway resistance syndrome (UARS), and of the OSAS. Snoring is a symptom of nasal obstruction and is associated with cardiovascular diseases and nocturnal asthma as a trigger or causative factor; however, its acoustic features in these disorders are not well-defined. Home monitoring of snoring is very useful for epidemiology and is mandatory, together with heart rate and arterial oxygen saturation (Sa,O 2 ), to screen SRBD.

Purpose: Advanced ion beam therapeutic techniques, such as hypofractionation, respiratory gating, or laser-based pulsed beams, have dose rate time structures which are substantially different 15 from those found in conventional approaches. The biological impact of the time structure is mediated through the β parameter in the linear quadratic (LQ) model. The aim of this study is to assess the impact of changes in the value of the β parameter on the treatment outcomes, also accounting for non instantaneous intra-fraction dose delivery or fractionation and comparing the effects of using different primary ions. with good results. Notably, in contrast to the original MKM formulation, the MCt-MKM explicitly predicts an ion and LET dependent β compatible with observations. The data from a split-dose experiment were used to experimentally determine the value of the parameter related to the cellular repair kinetics. Concerning the clinical case considered, an RBE decrease was observed, depending on the dose, ion and LET, exceeding up to 3% of the acute value in the case of a protraction in Conclusions:The present study provides a framework for exploiting the temporal effects of dose delivery. The results show the possibility of optimizing the treatment outcomes accounting for the 40 correlation between the specific dose rate time structure and the spatial characteristic of the LET distribution, depending on the ion type used.2 *

Few attempts have been made to include the oxygen enhancement ratio (OER) in treatment planning for ion beam therapy, and systematic studies to evaluate the impact of hypoxia in treatment with the beam of different ion species are sorely needed. The radiobiological models used to quantify the OER in such studies are mainly based on the dose-averaged LET estimates, and do not explicitly distinguish between the ion species and fractionation schemes. In this study, a new type of OER modelling, based on the microdosimetric kinetic model, taking into account the specificity of the different ions, LET spectra, tissues and fractionation schemes, has been developed. The model has been benchmarked with published in vitro data, HSG, V79 and CHO cells in aerobic and hypoxic conditions, for different ion irradiation. The model has been included in the simulation of treatments for a clinical case (brain tumour) using proton, lithium, helium, carbon and oxygen ion beams. A study of the tumour control probability (TCP) as a function of oxygen partial pressure, dose per fraction and primary ion type has been performed. The modelled OER depends on both the LET and ion type, also showing a decrease for an increased dose per fraction with a slope that depends on the LET and ion type, in good agreement with the experimental data. In the investigated clinical case, a significant increase in TCP has been found upon increasing the ion charge. Higher OER variations as a function of dose per fraction have also been found for low-LET ions (up to 15% varying from 2 to 8 Gy(RBE) for protons). This model could be exploited in the identification of treatment condition optimality in the presence of hypoxia, including fractionation and primary particle selection.

The evolution of the local effect model was implemented to assess cellular radiosensitization in the presence of GNPs and then validated with in vitro data. The model provides a useful framework to estimate the nanoparticle-driven radiosensitivity in treatment irradiations and could be applied to real clinical treatment predictions (described in a second part of this paper).

Objectives: within this investigation we investigated several approaches to enhance the positive predictive value (PPV) of chest digital tomosynthesis (DTS) in the lung cancer detection Methods: the investigation was carried out within the SOS clinical trial (NCT03645018) for lung cancer screening with DTS. Lung nodules were identified by visual analysis and then classified using the diameter and the radiological aspect of the nodule following a modified lung-RADS classification.Haralick texture features were extracted from the segmented nodules. Both semantic variables and radiomics features were used to build a predictive model using two approaches: logistic regression model on a sub-set of variables selected with backward feature selection or machine learning using the whole sub-set of variables. We used two machine learning methods: a Random Forest and a neural network. Machine learning methods were applied to a training set and validated on a test set. Methods were compared using diagnostic accuracy metrics.Results: binary visual analysis had a good sensitivity (0.95) but a low PPV (0.14). Lung-RADS classification increased the PPV (0.19) but with an unacceptable low sensitivity (0.65). Analogously, logistic regression showed a mildly increased PPV (0.22) and a low sensitivity (0.67). Random Forest demonstrated a low accuracy with a moderate PPV (0.40) but with a dramatically low sensitivity (0.30). Neural network demonstrated to be the best predictor with a nearly perfect PPV (0.95) and a high sensitivity (0.90).Conclusions: among the various technique to reduce the false positive rates of DTS the neural network demonstrated a very high PPV. The use of visual analysis along with neural network could help radiologists to depict a follow-up strategy after a positive DTS.

Functional parameters from positron emission tomography (PET) seem promising biomarkers in various lymphoma subtypes. This study investigated the prognostic value of PET radiomics in diffuse large B‐cell lymphoma (DLBCL) patients treated with R‐CHOP given either every 14 (testing set) or 21 days (validation set). Using the PyRadiomics Python package, 107 radiomics features were extracted from baseline PET scans of 133 patients enrolled in the Swiss Group for Clinical Cancer Research 38/07 prospective clinical trial (SAKK 38/07) [ClinicalTrial.gov identifier: NCT00544219]. The international prognostic indices, the main clinical parameters and standard PET metrics, together with 52 radiomics uncorrelated features (selected using the Spearman correlation test) were included in a least absolute shrinkage and selection operator (LASSO) Cox regression to assess their impact on progression‐free (PFS), cause‐specific (CSS), and overall survival (OS). A linear combination of the resulting parameters generated a prognostic radiomics score (RS) whose area under the curve (AUC) was calculated by receiver operating characteristic analysis. The RS efficacy was validated in an independent cohort of 107 DLBCL patients. LASSO Cox regression identified four radiomics features predicting PFS in SAKK 38/07. The derived RS showed a significant capability to foresee PFS in both testing (AUC, 0.709; p < 0.001) and validation (AUC, 0.706; p < 0.001) sets. RS was significantly associated also with CSS and OS in testing (CSS: AUC, 0.721; p < 0.001; OS: AUC, 0.740; p < 0.001) and validation (CSS: AUC, 0.763; p < 0.0001; OS: AUC, 0.703; p = 0.004) sets. The RS allowed risk classification of patients with significantly different PFS, CSS, and OS in both cohorts showing better predictive accuracy respect to clinical international indices. PET‐derived radiomics may improve the prediction of outcome in DLBCL patients.

The model provides a useful framework to estimate the nanoparticle-driven radiosensitivity in breast cancer treatment irradiation, accounting for the complex interplay between dose and GNP uptake distributions.

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