Age-Specific Cardiovascular Risk Factors for Major Adverse Cardiac Events in Patients Undergoing Myocardial Perfusion Imaging.

BACKGROUND: The prevalence of traditional cardiovascular risk factors shows different age-specific patterns. It is not known whether the prognostic impact of risk factors is similarly age-specific. We evaluated the profiles of cardiovascular risk factors and their prognostic impact on coronary artery disease (CAD) in relation to age. METHODS: We included 3667 patients with suspected or known CAD undergoing stress myocardial perfusion imaging (MPI). We evaluated the risk for major adverse cardiac events (MACE) within three years from the index MPI in patients belonging to three groups according to age tertile distribution: <59, 59-68, and >68 years. Gender, body mass index, diabetes, hypertension, dyslipidemia, family history of CAD, smoking, angina, dyspnea, previous CAD, and MPI outcome were assessed as risk factors by a multivariable Cox's regression. RESULTS: The three-year risk of MACE increased progressively with age and was 9%, 13%, and 18% for each group, respectively (p < 0.0001). Dyspnea and abnormal MPI outcome were significant risk factors for all age groups. Diabetes and smoking were significant from the age of 59 onwards, while hypertension resulted significant for patients older than 68 years. CONCLUSIONS: The number of risk factors was significantly associated with the occurrence of MACE increase with age. It is noteworthy that a personal history of CAD was not useful for risk stratification, while MPI results were.

External validation and update of the J-ACCESS model in an Italian cohort of patients undergoing stress myocardial perfusion imaging.

BACKGROUND: Cardiovascular risk models are based on traditional risk factors and investigations such as imaging tests. External validation is important to determine reproducibility and generalizability of a prediction model. We performed an external validation of t the Japanese Assessment of Cardiac Events and Survival Study by Quantitative Gated SPECT (J-ACCESS) model, developed from a cohort of patients undergoing stress myocardial perfusion imaging. METHODS: We included 3623 patients with suspected or known coronary artery disease undergoing stress single-photon emission computer tomography (SPECT) myocardial perfusion imaging at our academic center between January 2001 and December 2019. RESULTS: In our study population, the J-ACCESS model underestimated the risk of major adverse cardiac events (cardiac death, nonfatal myocardial infarction, and severe heart failure requiring hospitalization) within three-year follow-up. The recalibrations and updated of the model slightly improved the initial performance: C-statistics increased from 0.664 to 0.666 and Brier score decreased from 0.075 to 0.073. Hosmer-Lemeshow test indicated a logistic regression fit only for the calibration slope (P = .45) and updated model (P = .22). In the update model, the intercept, diabetes, and severity of myocardial perfusion defects categorized coefficients were comparable with J-ACCESS. CONCLUSION: The external validation of the J-ACCESS model as well as recalibration models have a limited value for predicting of three-year major adverse cardiac events in our patients. The performance in predicting risk of the updated model resulted superimposable to the calibration slope model.

Diagnostic value of clinical risk scores for predicting normal stress myocardial perfusion imaging in subjects without coronary artery calcium.

BACKGROUND: We evaluated if risk scores commonly used to predict the absence of significant stenosis at coronary computed tomography (CT) angiography are useful to predict a normal stress myocardial perfusion imaging (MPI) study. METHODS: Our cohort included a total of 1422 consecutive patients with zero coronary artery calcium score (ZCS) who underwent 82Rb PET/CT for evaluation of suspected coronary artery disease (CAD). Predictive models were constructed as reported by Genders et al. and Alshahrani et al., and the probability of abnormal summed stress score (SSS) and of reduced myocardial perfusion reserve (MPR) based on these risk scores was assessed. RESULTS: In the overall population, the prevalence of abnormal SSS was 0.10 and the prevalence of reduced MPR was 0.17 (both P < .001).The observed frequencies of abnormal SSS and reduced MPR vs the probabilities predicted by the Genders and Alshahrani models were above the diagonal identity line, highlighting an underestimation of the observed occurrence by these models. The areas under the receiver operating characteristic curve of the Genders and Alshahrani models indicated lack of discriminative ability for predicting abnormal SSS (0.547 and 0.527) and reduced MPR (0.509 and 0.538). The Hosmer-Lemeshow test revealed that both models underestimated the observed occurrence of abnormal SSS and reduced MPR. CONCLUSIONS: Available models were unable to identify among patients with ZCS those with a low probability of a normal stress MPI study. Thus, an optimal approach to rule out from MPI patients without detectable coronary calcium still needs to be improved.

External validation of the CRAX2MACE model in an Italian cohort of patients with suspected coronary artery disease undergoing stress myocardial perfusion imaging.

BACKGROUND: Prevention and development of diagnostic and therapeutic techniques reduced morbidity and mortality for coronary artery disease (CAD). In this context, the cardiovascular risk assessment for major adverse cardiac events (MACE) at 2-year (CRAX2MACE) model for prediction of 2-year major adverse cardiac events was developed. We performed an external validation of this model. METHODS: We included 1003 patients with suspected CAD undergoing stress-rest single-photon emission computed tomography myocardial perfusion imaging at our academic center between March 2015 and April 2019. RESULTS: Considering the occurrence of MACE (death from any cause, acute myocardial infarction, or late coronary revascularization), for the CRAX2MACE model the area under the receiver operating characteristic curve was 0.612 and the Brier score was 0.061. The Hosmer-Lemeshow test estimated a non-optimal fit (χ2 28, P < .001). Considering only hard events (cardiac death, acute myocardial infarction), the external validation of the CRAX2MACE model revealed a Brier score of 0.053 and an area under the receiver operating characteristic curve of 0.621. Hosmer-Lemeshow test was calculated by deciles and showed a poor fit (χ2 31, P < .001). CONCLUSION: CRAX2MACE model had a limited value for predicting 2-year major adverse cardiovascular events in an external validation cohort of patients with suspected CAD.

Machine learning analysis: general features, requirements and cardiovascular applications.

Artificial intelligence represents the science which will probably change the future of medicine by solving actually challenging issues. In this special article, the general features of machine learning are discussed. First, a background explanation regarding the division of artificial intelligence, machine learning and deep learning is given and a focus on the structure of machine learning subgroups is shown. The traditional process of a machine learning analysis is described, starting from the collection of data, across features engineering, modelling and till the validation and deployment phase. Due to the several applications of machine learning performed in literature in the last decades and the lack of some guidelines, the need of a standardization for reporting machine learning analysis results emerged. Some possible standards for reporting machine learning results are identified and discussed deeply; these are related to study population (number of subjects), repeatability of the analysis, validation, results, comparison with current practice. The way to the use of machine learning in clinical practice is open and the hope is that, with emerging technology and advanced digital and computational tools, available from hospitalization and subsequently after discharge, it will also be possible, with the help of increasingly powerful hardware, to build assistance strategies useful in clinical practice.

A Comparison among Different Machine Learning Pretest Approaches to Predict Stress-Induced Ischemia at PET/CT Myocardial Perfusion Imaging.

Traditional approach for predicting coronary artery disease (CAD) is based on demographic data, symptoms such as chest pain and dyspnea, and comorbidity related to cardiovascular diseases. Usually, these variables are analyzed by logistic regression to quantifying their relationship with the outcome; nevertheless, their predictive value is limited. In the present study, we aimed to investigate the value of different machine learning (ML) techniques for the evaluation of suspected CAD; having as gold standard, the presence of stress-induced ischemia by 82Rb positron emission tomography/computed tomography (PET/CT) myocardial perfusion imaging (MPI) ML was chosen on their clinical use and on the fact that they are representative of different classes of algorithms, such as deterministic (Support vector machine and Naïve Bayes), adaptive (ADA and AdaBoost), and decision tree (Random Forest, rpart, and XGBoost). The study population included 2503 consecutive patients, who underwent MPI for suspected CAD. To testing ML performances, data were split randomly into two parts: training/test (80%) and validation (20%). For training/test, we applied a 5-fold cross-validation, repeated 2 times. With this subset, we performed the tuning of free parameters for each algorithm. For all metrics, the best performance in training/test was observed for AdaBoost. The Naïve Bayes ML resulted to be more efficient in validation approach. The logistic and rpart algorithms showed similar metric values for the training/test and validation approaches. These results are encouraging and indicate that the ML algorithms can improve the evaluation of pretest probability of stress-induced myocardial ischemia.

Inferring a cause-effect relationship between lockdown restrictions and COVID-19 pandemic trend during the first wave.

The large number of infected persons due to the COVID-19 pandemic and the need of hospital care for many of them induced the majority of world governments to implement lockdown measures. We developed an analytical model to evaluate the trend of the SARS-CoV-2 pandemic. This model was applied to the first four months of the epidemiological data of the most affected countries in Europe and Russia, in order to evaluate the effect of the lockdown on the epidemic curves during the first wave. According to our model, the difference between the beginning of the lockdown and the slope change of the curve representing the daily distribution of counts was: Germany and Spain 6 days, France 7 days, the United Kingdom 9 days, Italy 21 days, and Russia 30 days. On the basis of these results, we infer a possible cause-effect relationship between the lockdown imposed in countries taken into account and the curve representing the daily distribution of new cases. Lockdown measures imposed by governments slowed the spread of the pandemic and reduced the number of infected persons. In economic terms, the damage was considerable, with entire production sectors in crisis. On the other hand, the efforts and innovations implemented to produce vaccines and effective treatments against the pandemic could be applied also in other fields of public health.

Comparison of three freeware software packages for 18F-FDG PET texture feature calculation.

PURPOSE: To compare texture feature estimates obtained from 18F-FDG-PET images using three different software packages. METHODS: PET images from 15 patients with head and neck cancer were processed with three different freeware software: CGITA, LIFEx, and Metavol. For each lesion, 38 texture features were extracted from each software package. To evaluate the statistical agreement among the features across packages a non-parametric Kruskal-Wallis test was used. Differences in the features between each couple of software were assessed using a subsequent Dunn test. Correlation between texture features was evaluated via the Spearman coefficient. RESULTS: Twenty-three of 38 features showed a significant agreement across the three software (P < 0.05). The agreement was better between LIFEx vs. Metavol (36 of 38) and worse between CGITA and Metavol (24 of 38), and CGITA vs. LIFEx (23 of 38). All features resulted correlated (ρ > = 0.70, P < 0.001) in comparing LIFEx vs. Metavol. Seven of 38 features were found not in agreement and slightly or not correlated (ρ < 0.70, P < 0.001) in comparing CGITA vs. LIFEx, and CGITA vs. Metavol. CONCLUSION: Some texture discrepancies across software packages exist. Our findings reinforce the need to continue the standardization process, and to succeed in building a reference dataset to be used for comparisons.

Cardiac magnetic resonance imaging during the COVID-19 pandemic: A southern Italian single-center experience.

PURPOSE: We aimed to assess the impact of COVID-19 pandemic on cardiac magnetic resonance (CMR) imaging studies performed during the lockdown imposed by the Italian Government from March 2020 to May 2020. MATERIALS AND METHOD: We reviewed the number and the findings of CMR scans performed during the COVID-19 pandemic between March and May 2020 at University of Naples Federico II. The number and the findings of CMR studies acquired in the corresponding months of 2017, 2018 and 2019 were also assessed for direct comparison. RESULTS: A total of 117 CMR studies was considered, including the procedures performed during the pandemic (n = 18) and those performed in the corresponding months of the prior 3 years (n = 99). The number of CMR studies performed during the COVID-19 pandemic was significantly (P < .01) lower compared to the mean number (n = 33) of the procedures performed in the corresponding months of 2017-2019. The percentage of abnormal CMR studies was similar (P = 0.73) during the pandemic (67 %) compared to that found in the corresponding months of 2017-2019 (70 %) suggesting that many abnormal tests were missed due to the lockdown. CONCLUSION: The number of CMR studies was significantly reduced during the COVID-19 pandemic compared to the corresponding period of the previous three years. The lack of difference in the prevalence of abnormal CMR studies between the two study time intervals strongly suggests that many patients with potentially abnormal imaging test have been missed during the pandemic.

Effects of the COVID-19 pandemic on myocardial perfusion imaging for ischemic heart disease.

PURPOSE: We assessed the effects of the COVID-19 pandemic on myocardial perfusion imaging (MPI) for ischemic heart disease during the lockdown imposed by the Italian Government. METHODS: We retrospectively reviewed the number and the findings of stress single-photon emission computed tomography (SPECT)-MPI performed between February and May 2020 during the COVID-19 pandemic at the University of Napoli Federico II. The number and the findings of stress SPECT-MPI studies acquired in the corresponding months of the years 2017, 2018, and 2019 were also evaluated for direct comparison. RESULTS: The number of stress SPECT-MPI studies performed during the COVID-19 pandemic (n = 123) was significantly lower (P < 0.0001) compared with the mean yearly number of procedures performed in the corresponding months of the years 2017, 2018, and 2019 (n = 413). Yet, the percentage of abnormal stress SPECT-MPI studies was similar (P = 0.65) during the pandemic (36%) compared with the mean percentage value of the corresponding period of the years 2017, 2018, and 2019 (34%). CONCLUSION: The number of stress SPECT-MPI studies was significantly reduced during the COVID-19 pandemic compared with the corresponding months of the previous 3 years. The lack of difference in the prevalence of abnormal SPECT-MPI studies between the two study periods strongly suggests that many patients with potentially abnormal imaging test have been missed during the pandemic.

Pretest models for predicting abnormal stress single-photon emission computed tomography myocardial perfusion imaging.

BACKGROUND: The frequency of abnormal stress SPECT myocardial perfusion imaging (MPS) decreased over the past decades despite an increase in the prevalence of cardiovascular risk factors. These findings strengthen the need to develop more effective strategies for appropriately referring patients with suspected coronary artery disease (CAD) to cardiac imaging. The aim of this study was to develop pretest assessment models for predicting abnormal stress MPS. METHODS: We included 5,601 consecutive patients with suspected CAD, who underwent stress MPS at our academic center. Two different models were considered: a basic model including age, gender, and anginal symptoms; and a clinical model including also diabetes, hypertension, hypercholesterolemia, smoking, and family history of CAD. RESULTS: In patients with abnormal MPS, the basic model classified more than 75% of patients as intermediate risk, whereas only 23% were incorrectly classified as low risk. In patients with normal MPS, 45% were correctly classified as low risk and none as high risk. Basic and clinical models had a limited discriminating capacity (area under the receiver operating characteristic curve 0.644 for basic model and 0.647 for clinical model) between individuals with and without abnormal stress MPS. The decision curve analysis demonstrates a high net benefit across a range of threshold probabilities from ~ 15% to ~30% for both models. CONCLUSIONS: A pretest risk stratification based on traditional cardiovascular risk factors has a limited value for predicting an abnormal stress MPS in patients with suspected CAD. However, selecting a proper threshold probability enhances the appropriateness of referral to stress MPS.

First month of the epidemic caused by COVID-19 in Italy: current status and real-time outbreak development forecast.

Background: The first outbreak of COVID-19 in Italy occurred during the second half of February 2020 in some areas in the North of the country. Due to the high contagiousness of the infection, further spread by asymptomatic people, Italy has become in a few weeks the country with the greatest number of infected people in the world. The large number of severe cases among infected people in Italy led to the hospitalization of thousands of patients, with a heavy burden on the National Health Service. Methods: We analyzed data provided daily by Italian Authorities for the period from 24 February 2020 to 30 March 2020. Considering such information, we developed a forecast model in real-time, based on the cumulative log-logistic distribution. Results: A total of 101,739 infected individuals were confirmed until 30 March 2020, of which 14,620 recovered or discharged, and 11,591 deaths. Until the same date patients quarantined at home were 43,752, whereas hospitalized patients were 31,776, of which 3981 in intensive care. The active cases (i.e. the number of patients not yet recovered until that date) were 75,528. The forecast model estimated a number of infected persons for Italy of 234,000 about, and a duration of the epidemic of approximately 4 months. Conclusions: One month after the first outbreaks there seemed to be the first signs of a decrease in the number of infections, showing that we could be now facing the descending phase of the epidemic. The forecast obtained thanks to our model could be used by decision-makers to implement coordinative and collaborative efforts in order to control the epidemic. The pandemic due to novel Coronavirus must be a warning for all countries worldwide, regarding a rapid and complete dissemination of information, surveillance, health organization, and cooperation among the states.

Voxel-based analysis of gray matter relaxation rates shows different correlation patterns for cognitive impairment and physical disability in relapsing-remitting multiple sclerosis.

BACKGROUND: Regional analyses of markers of microstructural gray matter (GM) changes, including relaxation rates, have shown inconsistent correlations with physical and cognitive impairment in MS. OBJECTIVE: To assess voxelwise the correlation of the R1 and R2 relaxation rates with the physical and cognitive impairment in MS. METHODS: GM R1 and R2 relaxation rate maps were obtained in 241 relapsing-remitting MS patients by relaxometric segmentation of MRI studies. Correlations with the Expanded Disability Status Scale (EDSS) and the percentage of impaired cognitive test (Brief Repeatable Battery and Stroop Test, available in 186 patients) were assessed voxelwise, including voxel GM content as nuisance covariate to remove the effect of atrophy on the correlations. RESULTS: Extensive clusters of inverse correlation between EDSS and R2 were detected throughout the brain, while inverse correlations with R1 were mostly limited to perirolandic and supramarginal cortices. Cognitive impairment correlated negatively with R1, and to a lesser extent with R2, in the middle frontal, mesial temporal, midcingulate and medial parieto-occipital cortices. CONCLUSION: In relapsing-remitting MS patients, GM microstructural changes correlate diffusely with physical disability, independent of atrophy, with a preferential role of the sensorimotor cortices. Neuronal damage in the limbic system and dorsolateral prefrontal cortices correlates with cognitive dysfunction.

Temporal trends of abnormal myocardial perfusion imaging in a cohort of Italian subjects: Relation with cardiovascular risk factors.

BACKGROUND: The frequency of abnormal stress single-photon emission computed tomography myocardial perfusion imaging (MPS) has decreased over the past decades despite an increase in the prevalence of cardiovascular risk factors. This study evaluated the temporal trend of abnormal stress MPS and its relationship with risk factors in a cohort of Italian subjects. METHODS: We included all patients who underwent clinically indicated stress MPS at our academic center between January 2006 and December 2017. Patients were assessed for change in demographics, clinical symptoms, risk factors, and frequency of abnormal and ischemic MPS. RESULTS: A total of 8,886 stress MPS studies were performed (3,350 abnormal). Age, male gender, diabetes, smoking, and angina were independent predictors of abnormal MPS. There was a slight decline in the frequency of abnormal (from 39 to 36%, P < 0.05) and ischemic (from 25 to 22%, P < 0.01) MPS during the study period, while the percentage of patients with hypertension, hypercholesterolemia, smoking, and angina increased. The Cochran-Mantel-Haenszel test indicates that the likelihood of having an abnormal MPS did not change over time for age, diabetes, smoking, and a history of coronary artery disease (CAD), increased for hypertension and hypercholesterolemia and decreased for male compared to female gender. CONCLUSIONS: In our cohort of Italian subjects, there was a slight temporal decline in the frequency of abnormal and ischemic MPS despite an increase over time in the prevalence of many cardiac risk factors. These results strengthen the need to develop more effective strategies for appropriately referring patients to cardiac imaging procedures.

A New Relational Database Including Clinical Data and Myocardial Perfusion Imaging Findings in Coronary Artery Disease.

BACKGROUND: The aim of this study was to test a relational database including clinical data and imaging findings in a large cohort of subjects with suspected or known Coronary Artery Disease (CAD) undergoing stress single-photon emission computed tomography (SPECT) myocardial perfusion imaging. METHODS: We developed a relational database including clinical and imaging data of 7995 subjects with suspected or known CAD. The software system was implemented by PostgreSQL 9.2, an open source object-relational database, and managed from remote by pgAdmin III. Data were arranged according to a logic of aggregation and stored in a schema with twelve tables. Statistical software was connected to the database directly downloading data from server to local personal computer. RESULTS: There was no problem or anomaly for database implementation and user connections to the database. The epidemiological analysis performed on data stored in the database demonstrated abnormal SPECT findings in 46% of male subjects and 19% of female subjects. Imaging findings suggest that the use of SPECT imaging in our laboratory is appropriate. CONCLUSION: The development of a relational database provides a free software tool for the storage and management of data in line with the current standard.

Brain tissue volumes and relaxation rates in multiple sclerosis: implications for cognitive impairment.

OBJECTIVE: Both normal gray matter atrophy and brain tissue relaxation rates, in addition to total lesion volume, have shown significant correlations with cognitive test scores in multiple sclerosis (MS). Aim of the study was to assess the relative contributions of macro- and microstructural changes of both normal and abnormal brain tissues, probed, respectively, by their volumes and relaxation rates, to the cognitive status and physical disability of MS patients. METHODS: MRI studies from 241 patients with relapsing-remitting MS were retrospectively analyzed by fully automated multiparametric relaxometric segmentation. Ordinal backward regression analysis was applied to the resulting volumes and relaxation rates of both normal (gray matter, normal-appearing white matter and CSF) and abnormal (T2-weighted lesions) brain tissues, controlling for age, sex and disease duration, to identify the main independent contributors to the cognitive status, as measured by the percentage of failed tests at a cognitive test battery (Rao's Brief Repeatable Battery and Stroop test, available in 186 patients), and to the physical disability, as assessed by the Expanded Disability Status Scale (EDSS). RESULTS: The R1 relaxation rate (a putative marker of tissue disruption) of the MS lesions appeared the single most significant contributor to cognitive impairment (p < 0.001). On the contrary, the EDSS appeared mainly affected by the decrease in R2 of the gray matter (p < 0.0001), (possibly influenced by cortical plaques, edema and inflammation). CONCLUSIONS: In RR-MS the tissue damage in white matter lesions appears the single main determinant of the cognitive status of patients, likely through disconnection phenomena, while the physical disability appears related to the involvement of gray matter.

Combined microcomputed tomography, biomechanical and histomorphometric analysis of the peri-implant bone: a pilot study in minipig model.

OBJECTIVES: To present a practical approach that combines biomechanical tests, microcomputed tomography (µCT) and histomorphometry, providing quantitative results on bone structure and mechanical properties in a minipig model, in order to investigate the specific response to an innovative dental biomaterial. METHODS: Titanium implants with innovative three-dimensional scaffolds were inserted in the tibias of 4 minipigs. Primary stability and osseointegration were investigated by means of insertion torque (IT) values, resonance frequency analysis (RFA), bone-to-implant contact (BIC), bone mineral density (BMD) and stereological measures of trabecular bone. RESULTS: A significant positive correlation was found between IT and RFA (r=0.980, p=0.0001). BMD at the implant sites was 18% less than the reference values (p=0.0156). Peri-implant Tb.Th was 50% higher, while Tb.N was 50% lower than the reference zone (p<0.003) and they were negatively correlated (r=-0.897, p=0.006). SIGNIFICANCE: µCT increases evaluation throughput and offers the possibility for qualitative three-dimensional recording of the bone-implant system as well as for non-destructive evaluation of bone architecture and mineral density, in combination with conventional analysis methods. The proposed multimodal approach allows to improve accuracy and reproducibility for peri-implant bone measurements and could support future investigations.

Evaluation of growth patterns and body composition in C57Bl/6J mice using dual energy X-ray absorptiometry.

The normal growth pattern of female C57BL/6J mice, from 5 to 30 weeks of age, has been investigated in a longitudinal study. Weight, body surface area (BS), and body mass index (BMI) were evaluated in forty mice. Lean mass and fat mass, bone mineral content (BMC), and bone mineral density (BMD) were monitored by dual energy X-ray absorptiometry (DEXA). Weight and BS increased linearly (16.15 ± 0.64-27.64 ± 1.42 g; 51.13 ± 0.74-79.57 ± 2.15 cm(2), P < 0.01), more markedly from 5 to 9 weeks of age (P < 0.001). BMD showed a peak at 17 weeks (0.0548 ± 0.0011 g/cm(2) ∗ m, P < 0.01). Lean mass showed an evident gain at 9 (15.8 ± 0.8 g, P < 0.001) and 25 weeks (20.5 ± 0.3 g, P < 0.01), like fat mass from 13 to 17 weeks (2.0 ± 0.4-3.6 ± 0.7 g, P < 0.01). BMI and lean mass index (LMI) reached the highest value at 21 weeks (3.57 ± 0.02-0.284 ± 0.010 g/cm(2), resp.), like fat mass index (FMI) at 17 weeks (0.057 ± 0.009 g/cm(2)) (P < 0.01). BMI, weight, and BS showed a moderate positive correlation (0.45-0.85) with lean mass from 5 to 21 weeks. Mixed linear models provided a good prediction for lean mass, fat mass, and BMD. This study may represent a baseline reference for a future comparison of wild-type C57BL/6J mice with models of altered growth.

Combination of computer-aided detection algorithms for automatic lung nodule identification.

PURPOSE: The aim of this work is to evaluate the potential of combining different computer-aided detection (CADe) methods to increase the actual support for radiologists of automated systems in the identification of pulmonary nodules in CT scans. METHODS: The outputs of three different CADe systems developed by researchers of the Italian MAGIC-5 collaboration were combined. The systems are: the CAMCADe (based on a Channeler-Ant-Model which segments vessel tree and nodule candidates and a neural classifier), the RGVPCADe (a Region-Growing- Volume-Plateau algorithm detects nodule candidates and a neural network reduces false positives); the VBNACADe (two dedicated procedures, based respectively on a 3D dot-enhancement algorithm and on intersections of pleura surface normals, identifies internal and juxtapleural nodules, and a Voxel-Based-Neural-Approach reduces false positives. A dedicated OsiriX plugin implemented with the Cocoa environments of MacOSX allows annotating nodules and visualizing singles and combined CADe findings. RESULTS: The combined CADe has been tested on thin slice (lower than 2 mm) CTs of the LIDC public research database and the results have been compared with those obtained by the single systems. The FROC (Free Receiver Operating Characteristic) curves show better results than the best of the single approaches. CONCLUSIONS: Has been demonstrated that the combination of different approaches offers better results than each single CADe system. A clinical validation of the combined CADe as second reader is being addressed by means of the dedicated OsiriX plugin.