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IntroductionComparable indicators on complete cancer prevalence are increasingly needed in Europe to support survivorship care planning. Direct measures can be biased by limited registration time and estimates are needed to recover long term survivors. The completeness index method, based on incidence and survival modelling, is the standard most validated approach.MethodsWithin this framework, we consider two alternative approaches that do not require any direct modelling activity: i) empirical indices derived from long established European registries; ii) pre-calculated indices derived from US-SEER cancer registries. Relying on the EUROCARE-6 study dataset we compare standard vs alternative complete prevalence estimates using data from 62 registries in 27 countries by sex, cancer type and registration time.ResultsFor tumours mostly diagnosed in the elderly the empirical estimates differ little from standard estimates (on average less than 5% after 10-15 years of registration), especially for low prognosis cancers. For early-onset cancers (bone, brain, cervix uteri, testis, Hodgkin disease, soft tissues) the empirical method may produce substantial underestimations of complete prevalence (up to 20%) even when based on 35-year observations. SEER estimates are comparable to the standard ones for most cancers, including many early-onset tumours, even when derived from short time series (10-15 years). Longer observations are however needed when cancer-specific incidence and prognosis differ remarkably between US and European populations (endometrium, thyroid or stomach).DiscussionThese results may facilitate the dissemination of complete prevalence estimates across Europe and help bridge the current information gaps.

Tyrosine kinase inhibitors (TKIs) have been improving the prognosis of patients with chronic myeloid leukemia (CML), but there are still large differences in survival among European countries. This raises questions on the added value of results from population-based studies, which use real-world data, compared to results of randomized controlled trials (RCTs) involving patients with CML. There are also questions about the extent of the findings on RCTs effectiveness for patients in the general population. We compare survival data extracted from our previous systematic review and meta-analysis of CML RCTs with the latest updated population-based survival data of EUROCARE-6, the widest collaborative study on cancer survival in Europe. The EUROCARE-6 CML survival estimated in patients (15–64 years) diagnosed in 2000–2006 vs. 2007–2013 revealed that the prognostic improvement highlighted by RCTs was confirmed in real-world settings, too. The study shows, evaluating for the first time all European regions, that the optimal outcome figures obtained in controlled settings for CML are also achievable (and indeed achieved) in real-world settings with prompt introduction of TKIs in daily clinical practice. However, some differences still persist, particularly in Eastern European countries, where overall survival values are lower than elsewhere, probably due to a delayed introduction of TKIs. Our results suggest an insufficient adoption of adequate protocols in daily clinical practice in those countries where CML survival values remain lower in real life than the values obtained in RCTs. New high-resolution population-based studies may help to identify failures in the clinical pathways followed there.

Abstract  A collection of radiative emissions by several flames is analyzed. Such flames have been obtained in a number of burners, either premixed or not, fed with different fuels, while radiative emission is collected by means of a photo-diode, whose sampled signal is able to carry a large quantity of information about chemistry of flames and its interaction with turbulence. All the spectra computed show a decaying trend towards high frequencies with a slope of  −5/3, that is known to be the inertial scaling of kinetic energy for homogeneous, isotropic, nonreacting turbulent flows. The aim of this work is to propose a physical model for the interpretation of the radiant energy scaling law coming from this simple instrument. To this purpose radiative emission of the flame has been splitted into two contributions, and its dynamics analyzed. The first contribution is the chemiluminescence effect, whereas the other is to be accounted for thermal emission of a gray/black body, ruled by Planck radiative equation. The time fluctuation of radiative emission is proposed to be linked to a thin reacting surface fluctuating in time and space under the constraint of turbulent fluid dynamic field. The thin reacting surface should be considered as the local flame front in premixed combustion, and as the local stoichiometric mixture fraction front in nonpremixed case. Taking into account such emission mechanisms, a physical interpretation of the  −5/3 slope is proposed. [ABSTRACT FROM AUTHOR]

Background: Anxiety and depression represent prevalent yet frequently undetected mental health concerns within the older population. The challenge of identifying these conditions presents an opportunity for artificial intelligence (AI)-driven, remotely available, tools capable of screening and monitoring mental health. A critical criterion for such tools is their cultural adaptability to ensure effectiveness across diverse populations., Objective: This study aims to illustrate the preliminary transferability of two established AI models designed to detect high depression and anxiety symptom scores. The models were initially trained on data from a nonverbal working memory game (1- and 2-back tasks) in a dataset by thymia, a company that develops AI solutions for mental health and well-being assessments, encompassing over 6000 participants from the United Kingdom, United States, Mexico, Spain, and Indonesia. We seek to validate the models' performance by applying it to a new dataset comprising older Brazilian adults, thereby exploring its transferability and generalizability across different demographics and cultures., Methods: A total of 69 Brazilian participants aged 51-92 years old were recruited with the help of Laços Saúde, a company specializing in nurse-led, holistic home care. Participants received a link to the thymia dashboard every Monday and Thursday for 6 months. The dashboard had a set of activities assigned to them that would take 10-15 minutes to complete, which included a 5-minute game with two levels of the n-back tasks. Two Random Forest models trained on thymia data to classify depression and anxiety based on thresholds defined by scores of the Patient Health Questionnaire (8 items) (PHQ-8) ≥10 and those of the Generalized Anxiety Disorder Assessment (7 items) (GAD-7) ≥10, respectively, were subsequently tested on the Laços Saúde patient cohort., Results: The depression classification model exhibited robust performance, achieving an area under the receiver operating characteristic curve (AUC) of 0.78, a specificity of 0.69, and a sensitivity of 0.72. The anxiety classification model showed an initial AUC of 0.63, with a specificity of 0.58 and a sensitivity of 0.64. This performance surpassed a benchmark model using only age and gender, which had AUCs of 0.47 for PHQ-8 and 0.53 for GAD-7. After recomputing the AUC scores on a cross-sectional subset of the data (the first n-back game session), we found AUCs of 0.79 for PHQ-8 and 0.76 for GAD-7., Conclusions: This study successfully demonstrates the preliminary transferability of two AI models trained on a nonverbal working memory task, one for depression and the other for anxiety classification, to a novel sample of older Brazilian adults. Future research could seek to replicate these findings in larger samples and other cultural contexts., (© Alexandra Livia Georgescu, Nicholas Cummins, Emilia Molimpakis, Eduardo Giacomazzi, Joana Rodrigues Marczyk, Stefano Goria. Originally published in JMIR Formative Research (https://formative.jmir.org).)

Background: Although the immune checkpoint inhibitors, nivolumab and pembrolizumab, were found to be promising in patients with advanced NSCLC, some of them either do not respond or have recurrence after an initial response. It is still unclear who will benefit from these therapies, and, hence, there is an unmet clinical need to build robust biomarkers., Methods: Patients with advanced NSCLC (N = 323) who were treated with pembrolizumab or nivolumab were retrospectively identified from two institutions. Radiomics features extracted from baseline pretreatment computed tomography scans along with the clinical variables were used to build the predictive models for overall survival (OS), progression-free survival (PFS), and programmed death-ligand 1 (PD-L1). To develop the imaging and integrative clinical-imaging predictive models, we used the XGBoost learning algorithm with ReliefF feature selection method and validated them in an independent cohort. The concordance index for OS, PFS, and area under the curve for PD-L1 was used to evaluate model performance., Results: We developed radiomics and the ensemble radiomics-clinical predictive models for OS, PFS, and PD-L1 expression. The concordance indices of the radiomics model were 0.60 and 0.61 for predicting OS and PFS and area under the curve was 0.61 for predicting PD-L1 in the validation cohort, respectively. The combined radiomics-clinical model resulted in higher performance with 0.65, 0.63, and 0.68 to predict OS, PFS, and PD-L1 in the validation cohort, respectively., Conclusions: We found that pretreatment computed tomography imaging along with clinical data can aid as predictive biomarkers for PD-L1 and survival end points. These imaging-driven approaches may prove useful to expand the therapeutic options for nonresponders and improve the selection of patients who would benefit from immune checkpoint inhibitors., (© 2023 Published by the Elsevier Inc. on behalf of the International Association for the Study of Lung Cancer.)

Background: Immune checkpoint inhibitors (ICIs) are a great breakthrough in cancer treatments and provide improved long-term survival in a subset of non-small cell lung cancer (NSCLC) patients. However, prognostic and predictive biomarkers of immunotherapy still remain an unmet clinical need. In this work, we aim to leverage imaging data and clinical variables to develop survival risk models among advanced NSCLC patients treated with immunotherapy., Methods: This retrospective study includes a total of 385 patients from two institutions who were treated with ICIs. Radiomics features extracted from pretreatment CT scans were used to build predictive models. The objectives were to predict overall survival (OS) along with building a classifier for short- and long-term survival groups. We employed the XGBoost learning method to build radiomics and integrated clinical-radiomics predictive models. Feature selection and model building were developed and validated on a multicenter cohort., Results: We developed parsimonious models that were associated with OS and a classifier for short- and long-term survivor groups. The concordance indices (C-index) of the radiomics model were 0.61 and 0.57 to predict OS in the discovery and validation cohorts, respectively. While the area under the curve (AUC) values of the radiomic models for short- and long-term groups were found to be 0.65 and 0.58 in the discovery and validation cohorts. The accuracy of the combined radiomics-clinical model resulted in 0.63 and 0.62 to predict OS and in 0.77 and 0.62 to classify the survival groups in the discovery and validation cohorts, respectively., Conclusions: We developed and validated novel radiomics and integrated radiomics-clinical survival models among NSCLC patients treated with ICIs. This model has important translational implications, which can be used to identify a subset of patients who are not likely to benefit from immunotherapy. The developed imaging biomarkers may allow early prediction of low-group survivors, though additional validation of these radiomics models is warranted.

With the increasing use of immune checkpoint inhibitors (ICIs), there is an urgent need to identify biomarkers to stratify responders and non-responders using programmed death-ligand (PD-L1) expression, and to predict patient-specific outcomes such as progression free survival (PFS). The current study is aimed to determine the feasibility of building imaging-based predictive biomarkers for PD-L1 and PFS through systematically evaluating a combination of several machine learning algorithms with different feature selection methods. A retrospective, multicenter study of 385 advanced NSCLC patients amenable to ICIs was undertaken in two academic centers. Radiomic features extracted from pretreatment CT scans were used to build predictive models for PD-L1 and PFS (short-term vs. long-term survivors). We first employed the LASSO methodology followed by five feature selection methods and seven machine learning approaches to build the predictors. From our analyses, we found several combinations of feature selection methods and machine learning algorithms to achieve a similar performance. Logistic regression with ReliefF feature selection (AUC = 0.64, 0.59 in discovery and validation cohorts) and SVM with Anova F-test feature selection (AUC = 0.64, 0.63 in discovery and validation datasets) were the best-performing models to predict PD-L1 and PFS. This study elucidates the application of suitable feature selection approaches and machine learning algorithms to predict clinical endpoints using radiomics features. Through this study, we identified a subset of algorithms that should be considered in future investigations for building robust and clinically relevant predictive models., (© 2023. The Author(s).)

Objectives: 1. to evaluate mortality risk excess in the population residing in nursing care homes (NCHs) compared to non-NCHs before the COVID-19 outbreak; 2. to verify if the outbreak modified risk excess; 3. to estimate the COVID-19 impact; 4. to ascertain incidence-mortality relationship., Design: cohort study., Setting and Population: Mantua and Cremona provinces (Lombardy Region, Northern Italy) - included in ATS Val Padana - with COVID-19 incidence rate 7.5‰ and 16.9‰, respectively. Inhabitants aged >= 75 years as of 1st January 2018, 2019, and 2020 (three cohorts), stratified in NCH or not. The indicators calculated were: 1. rate ratio (RR) for NCH vs non-NCH, adjusted by gender, age, chronic diseases number, at least 1 hospitalisation, at least 1 Emergency room access in the previous year, for 2018, 2019, and 2020; 2. adjusted RR, 2019 and 2020 vs 2018, both sub-cohorts (i.e., NCH and non-NCH)., Main Outcome Measures: first four-month period mortality of the considered years., Results: aproximately 100,000 inhabitants by year, 7% in NCH. In the 2020 first four-month period, 4,343 deaths occurred of which 45% in NCH. RR in NCH population vs non-NCH for the year 2018 was 2.13 (95%CI 1.94-2.34); for the year 2019 was 2.70 (95%CI 2.43-3.00); for the year 2020 was 6.98 (95%CI 6,49-7,50). Adjusted RR for NCH population in 2020 vs 2018 was 2.22 (95%CI 2.05-2.42) in the whole ATS Val Padana; 1.58 (95%CI 1.40-1.77) in Mantua Province; 2.93 (2.62-3.27) in Cremona Province. Adjusted RR in non-NCH population in the year 2020 vs 2018 was 1.59 (95%CI 1.48-1.70) in the whole ATS; 1.34 (95%CI 1.23-1.46) in Mantua Province; 1.89 (95%CI 1.73-2.07) in Cremona Province., Conclusions: the NCH population experienced an excess risk mortality compared to non-NCH before the COVID-19; this excess increased during the outbreak. In 2020, in NCHs the risk was more than double compared to the 2018 risk, while in non-NCHs it rose approximately by 60%. The gap between NCHs/non-NCHs COVID-19 impact was higher in Cremona than in Mantua.

The aim of this cohort study was to investigate the relationship between crestal bone levels and crown-to-implant ratio of ultra-short implants, after functional loading. Sixty patients with single or partial edentulism and alveolar bone atrophy were enrolled and treated between December 2009 and January 2016. Without using bone-grafting procedures, patients were rehabilitated with ultra-short implants characterized by a microrough surface and a 6-mm length. Clinical and anatomical crown-to-implant (C/I) ratios and crestal bone levels (CBL) were measured after a follow-up period ranging from 12 to 72 months; all peri-implant and prosthetic parameters were recorded. The data collected were statistically analyzed ( P = .05). A total of 47 patients with 66 ultra-short implants were completely followed up according to described protocol. The mean follow-up was 48.5 ± 19.1 months. The mean anatomical C/I ratio was 2.2, while the mean clinical C/I ratio was 2.6 ± 0.6 at baseline and 2.8 ± 0.6 at the last follow-up appointment. Mean CBL as calculated at the baseline was 0.7 ± 0.5 mm, while at the last appointment it measured 1.0 ± 0.5 mm. The overall implant-based success rate was 96.9%, and the mean peri-implant bone loss (PBL) was 0.3 ± 0.3 mm. No statistically significant relationship was found between anatomical or clinical C/I ratio and PBL. Ultra-short implants appear to offer a predictable solution for implant-prosthetic rehabilitation in patients with edentulism and bone atrophy. A high percentage of implants were successful, with minimal crestal bone loss. The high C/I ratio did not appear to influence either peri-implant bone loss or prosthetic complication rates.