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Objectives: To determine if radiomic features, alone or combined with clinical data, are associated with residual tumour (RT) at surgery, and predict the risk of disease progression within 12 months (PD12) in ovarian cancer (OC) patients.Methods: This retrospective study enrolled 101 patients according to the following inclusion parameters: cytoreductive surgery performed at our institution (9 May 2007-23 February 2016), assessment of BRCA mutational status, preoperative CT available. Radiomic features of the ovarian masses were extracted from 3D structures drawn on CT images. A phantom experiment was performed to assess the reproducibility of radiomic features. The final radiomic features included in the analysis (n = 516) were grouped into clusters using a hierarchical clustering procedure. The association of each cluster's representative radiomic feature with RT and PD12 was assessed by chi-square test. Multivariate analysis was performed using logistic regression models. P values < 0.05 were considered significant.Results: Patients with values of F2-Shape/Compactness1 below the median, of F1- GrayLevelCooccurenceMatrix25/0-1InformationMeasureCorr2 below the median and of F1-GrayLevelCooccurenceMatrix25/-333-1InverseVariance above the median showed higher risk of RT (36%, 36% and 35%, respectively, as opposed to 18%, 18% and 18%). Patients with values of F4-GrayLevelRunLengthMatrix25/-333RunPercentage above the median, of F2 shape/Max3DDiameter below the median and F1-GrayLevelCooccurenceMatrix25/45-1InverseVariance above the median showed higher risk of PD12 (22%, 24% and 23%, respectively, as opposed to 6%, 5% and 6%). At multivariate analysis F2-Shape/Max3DDiameter remained significant (odds ratio (95% CI) = 11.86 (1.41-99.88)). To predict PD12, a clinical radiomics model performed better than a base clinical model.Conclusion: This study demonstrated significant associations between radiomic features and prognostic factors such as RT and PD12.Key Points: • No residual tumour (RT) at surgery is the most important prognostic factor in OC. • Radiomic features related to mass size, randomness and homogeneity were associated with RT. • Progression of disease within 12 months (PD12) indicates worse prognosis in OC. • A model including clinical and radiomic features performed better than only-clinical model to predict PD12. [ABSTRACT FROM AUTHOR]

Objective: To test the ability of high-performance machine learning (ML) models employing clinical, radiological, and radiomic variables to improve non-invasive prediction of the pathological status of prostate cancer (PCa) in a large, single-institution cohort.Patients who underwent multiparametric MRI and prostatectomy in our institution in 2015–2018 were considered; a total of 949 patients were included. Gradient-boosted decision tree models were separately trained using clinical features alone and in combination with radiological reporting and/or prostate radiomic features to predict pathological T, pathological N, ISUP score, and their change from preclinical assessment. Model behavior was analyzed in terms of performance, feature importance, Shapley additive explanation (SHAP) values, and mean absolute error (MAE). The best model was compared against a naïve model mimicking clinical workflow.The model including all variables was the best performing (AUC values ranging from 0.73 to 0.96 for the six endpoints). Radiomic features brought a small yet measurable boost in performance, with the SHAP values indicating that their contribution can be critical to successful prediction of endpoints for individual patients. MAEs were lower for low-risk patients, suggesting that the models find them easier to classify. The best model outperformed (p ≤ 0.0001) clinical baseline, resulting in significantly fewer false negative predictions and overall was less prone to under-staging.Our results highlight the potential benefit of integrative ML models for pathological status prediction in PCa. Additional studies regarding clinical integration of such models can provide valuable information for personalizing therapy offering a tool to improve non-invasive prediction of pathological status.The best machine learning model was less prone to under-staging of the disease. The improved accuracy of our pathological prediction models could constitute an asset to the clinical workflow by providing clinicians with accurate pathological predictions prior to treatment.• Currently, the most common strategies for pre-surgical stratification of prostate cancer (PCa) patients have shown to have suboptimal performances.• The addition of radiological features to the clinical features gave a considerable boost in model performance. Our best model outperforms the naïve model, avoiding under-staging and resulting in a critical advantage in the clinic.•Machine learning models incorporating clinical, radiological, and radiomics features significantly improved accuracy of pathological prediction in prostate cancer, possibly constituting an asset to the clinical workflow.Methods: To test the ability of high-performance machine learning (ML) models employing clinical, radiological, and radiomic variables to improve non-invasive prediction of the pathological status of prostate cancer (PCa) in a large, single-institution cohort.Patients who underwent multiparametric MRI and prostatectomy in our institution in 2015–2018 were considered; a total of 949 patients were included. Gradient-boosted decision tree models were separately trained using clinical features alone and in combination with radiological reporting and/or prostate radiomic features to predict pathological T, pathological N, ISUP score, and their change from preclinical assessment. Model behavior was analyzed in terms of performance, feature importance, Shapley additive explanation (SHAP) values, and mean absolute error (MAE). The best model was compared against a naïve model mimicking clinical workflow.The model including all variables was the best performing (AUC values ranging from 0.73 to 0.96 for the six endpoints). Radiomic features brought a small yet measurable boost in performance, with the SHAP values indicating that their contribution can be critical to successful prediction of endpoints for individual patients. MAEs were lower for low-risk patients, suggesting that the models find them easier to classify. The best model outperformed (p ≤ 0.0001) clinical baseline, resulting in significantly fewer false negative predictions and overall was less prone to under-staging.Our results highlight the potential benefit of integrative ML models for pathological status prediction in PCa. Additional studies regarding clinical integration of such models can provide valuable information for personalizing therapy offering a tool to improve non-invasive prediction of pathological status.The best machine learning model was less prone to under-staging of the disease. The improved accuracy of our pathological prediction models could constitute an asset to the clinical workflow by providing clinicians with accurate pathological predictions prior to treatment.• Currently, the most common strategies for pre-surgical stratification of prostate cancer (PCa) patients have shown to have suboptimal performances.• The addition of radiological features to the clinical features gave a considerable boost in model performance. Our best model outperforms the naïve model, avoiding under-staging and resulting in a critical advantage in the clinic.•Machine learning models incorporating clinical, radiological, and radiomics features significantly improved accuracy of pathological prediction in prostate cancer, possibly constituting an asset to the clinical workflow.Results: To test the ability of high-performance machine learning (ML) models employing clinical, radiological, and radiomic variables to improve non-invasive prediction of the pathological status of prostate cancer (PCa) in a large, single-institution cohort.Patients who underwent multiparametric MRI and prostatectomy in our institution in 2015–2018 were considered; a total of 949 patients were included. Gradient-boosted decision tree models were separately trained using clinical features alone and in combination with radiological reporting and/or prostate radiomic features to predict pathological T, pathological N, ISUP score, and their change from preclinical assessment. Model behavior was analyzed in terms of performance, feature importance, Shapley additive explanation (SHAP) values, and mean absolute error (MAE). The best model was compared against a naïve model mimicking clinical workflow.The model including all variables was the best performing (AUC values ranging from 0.73 to 0.96 for the six endpoints). Radiomic features brought a small yet measurable boost in performance, with the SHAP values indicating that their contribution can be critical to successful prediction of endpoints for individual patients. MAEs were lower for low-risk patients, suggesting that the models find them easier to classify. The best model outperformed (p ≤ 0.0001) clinical baseline, resulting in significantly fewer false negative predictions and overall was less prone to under-staging.Our results highlight the potential benefit of integrative ML models for pathological status prediction in PCa. Additional studies regarding clinical integration of such models can provide valuable information for personalizing therapy offering a tool to improve non-invasive prediction of pathological status.The best machine learning model was less prone to under-staging of the disease. The improved accuracy of our pathological prediction models could constitute an asset to the clinical workflow by providing clinicians with accurate pathological predictions prior to treatment.• Currently, the most common strategies for pre-surgical stratification of prostate cancer (PCa) patients have shown to have suboptimal performances.• The addition of radiological features to the clinical features gave a considerable boost in model performance. Our best model outperforms the naïve model, avoiding under-staging and resulting in a critical advantage in the clinic.•Machine learning models incorporating clinical, radiological, and radiomics features significantly improved accuracy of pathological prediction in prostate cancer, possibly constituting an asset to the clinical workflow.Conclusions: To test the ability of high-performance machine learning (ML) models employing clinical, radiological, and radiomic variables to improve non-invasive prediction of the pathological status of prostate cancer (PCa) in a large, single-institution cohort.Patients who underwent multiparametric MRI and prostatectomy in our institution in 2015–2018 were considered; a total of 949 patients were included. Gradient-boosted decision tree models were separately trained using clinical features alone and in combination with radiological reporting and/or prostate radiomic features to predict pathological T, pathological N, ISUP score, and their change from preclinical assessment. Model behavior was analyzed in terms of performance, feature importance, Shapley additive explanation (SHAP) values, and mean absolute error (MAE). The best model was compared against a naïve model mimicking clinical workflow.The model including all variables was the best performing (AUC values ranging from 0.73 to 0.96 for the six endpoints). Radiomic features brought a small yet measurable boost in performance, with the SHAP values indicating that their contribution can be critical to successful prediction of endpoints for individual patients. MAEs were lower for low-risk patients, suggesting that the models find them easier to classify. The best model outperformed (p ≤ 0.0001) clinical baseline, resulting in significantly fewer false negative predictions and overall was less prone to under-staging.Our results highlight the potential benefit of integrative ML models for pathological status prediction in PCa. Additional studies regarding clinical integration of such models can provide valuable information for personalizing therapy offering a tool to improve non-invasive prediction of pathological status.The best machine learning model was less prone to under-staging of the disease. The improved accuracy of our pathological prediction models could constitute an asset to the clinical workflow by providing clinicians with accurate pathological predictions prior to treatment.• Currently, the most common strategies for pre-surgical stratification of prostate cancer (PCa) patients have shown to have suboptimal performances.• The addition of radiological features to the clinical features gave a considerable boost in model performance. Our best model outperforms the naïve model, avoiding under-staging and resulting in a critical advantage in the clinic.•Machine learning models incorporating clinical, radiological, and radiomics features significantly improved accuracy of pathological prediction in prostate cancer, possibly constituting an asset to the clinical workflow.Clinical relevance statement: To test the ability of high-performance machine learning (ML) models employing clinical, radiological, and radiomic variables to improve non-invasive prediction of the pathological status of prostate cancer (PCa) in a large, single-institution cohort.Patients who underwent multiparametric MRI and prostatectomy in our institution in 2015–2018 were considered; a total of 949 patients were included. Gradient-boosted decision tree models were separately trained using clinical features alone and in combination with radiological reporting and/or prostate radiomic features to predict pathological T, pathological N, ISUP score, and their change from preclinical assessment. Model behavior was analyzed in terms of performance, feature importance, Shapley additive explanation (SHAP) values, and mean absolute error (MAE). The best model was compared against a naïve model mimicking clinical workflow.The model including all variables was the best performing (AUC values ranging from 0.73 to 0.96 for the six endpoints). Radiomic features brought a small yet measurable boost in performance, with the SHAP values indicating that their contribution can be critical to successful prediction of endpoints for individual patients. MAEs were lower for low-risk patients, suggesting that the models find them easier to classify. The best model outperformed (p ≤ 0.0001) clinical baseline, resulting in significantly fewer false negative predictions and overall was less prone to under-staging.Our results highlight the potential benefit of integrative ML models for pathological status prediction in PCa. Additional studies regarding clinical integration of such models can provide valuable information for personalizing therapy offering a tool to improve non-invasive prediction of pathological status.The best machine learning model was less prone to under-staging of the disease. The improved accuracy of our pathological prediction models could constitute an asset to the clinical workflow by providing clinicians with accurate pathological predictions prior to treatment.• Currently, the most common strategies for pre-surgical stratification of prostate cancer (PCa) patients have shown to have suboptimal performances.• The addition of radiological features to the clinical features gave a considerable boost in model performance. Our best model outperforms the naïve model, avoiding under-staging and resulting in a critical advantage in the clinic.•Machine learning models incorporating clinical, radiological, and radiomics features significantly improved accuracy of pathological prediction in prostate cancer, possibly constituting an asset to the clinical workflow.Key Points: To test the ability of high-performance machine learning (ML) models employing clinical, radiological, and radiomic variables to improve non-invasive prediction of the pathological status of prostate cancer (PCa) in a large, single-institution cohort.Patients who underwent multiparametric MRI and prostatectomy in our institution in 2015–2018 were considered; a total of 949 patients were included. Gradient-boosted decision tree models were separately trained using clinical features alone and in combination with radiological reporting and/or prostate radiomic features to predict pathological T, pathological N, ISUP score, and their change from preclinical assessment. Model behavior was analyzed in terms of performance, feature importance, Shapley additive explanation (SHAP) values, and mean absolute error (MAE). The best model was compared against a naïve model mimicking clinical workflow.The model including all variables was the best performing (AUC values ranging from 0.73 to 0.96 for the six endpoints). Radiomic features brought a small yet measurable boost in performance, with the SHAP values indicating that their contribution can be critical to successful prediction of endpoints for individual patients. MAEs were lower for low-risk patients, suggesting that the models find them easier to classify. The best model outperformed (p ≤ 0.0001) clinical baseline, resulting in significantly fewer false negative predictions and overall was less prone to under-staging.Our results highlight the potential benefit of integrative ML models for pathological status prediction in PCa. Additional studies regarding clinical integration of such models can provide valuable information for personalizing therapy offering a tool to improve non-invasive prediction of pathological status.The best machine learning model was less prone to under-staging of the disease. The improved accuracy of our pathological prediction models could constitute an asset to the clinical workflow by providing clinicians with accurate pathological predictions prior to treatment.• Currently, the most common strategies for pre-surgical stratification of prostate cancer (PCa) patients have shown to have suboptimal performances.• The addition of radiological features to the clinical features gave a considerable boost in model performance. Our best model outperforms the naïve model, avoiding under-staging and resulting in a critical advantage in the clinic.•Machine learning models incorporating clinical, radiological, and radiomics features significantly improved accuracy of pathological prediction in prostate cancer, possibly constituting an asset to the clinical workflow. [ABSTRACT FROM AUTHOR]

Objectives: Radiomic involves testing the associations of a large number of quantitative imaging features with clinical characteristics. Our aim was to extract a radiomic signature from axial T2-weighted (T2-W) magnetic resonance imaging (MRI) of the whole prostate able to predict oncological and radiological scores in prostate cancer (PCa). Methods: This study included 65 patients with localized PCa treated with radiotherapy (RT) between 2014 and 2018. For each patient, the T2-W MRI images were normalized with the histogram intensity scale standardization method. Features were extracted with the IBEX software. The association of each radiomic feature with risk class, T-stage, Gleason score (GS), extracapsular extension (ECE) score, and Prostate Imaging Reporting and Data System (PI-RADS v2) score was assessed by univariate and multivariate analysis. Results: Forty-nine out of 65 patients were eligible. Among the 1702 features extracted, 3 to 6 features with the highest predictive power were selected for each outcome. This analysis showed that texture features were the most predictive for GS, PI-RADS v2 score, and risk class; intensity features were highly associated with T-stage, ECE score, and risk class, with areas under the receiver operating characteristic curve (ROC AUC) ranging from 0.74 to 0.94. Conclusions: MRI-based radiomics is a promising tool for prediction of PCa characteristics. Although a significant association was found between the selected features and all the mentioned clinical/radiological scores, further validations on larger cohorts are needed before these findings can be applied in the clinical practice. Key Points: • A radiomic model was used to classify PCa aggressiveness. • Radiomic analysis was performed on T2-W magnetic resonance images of the whole prostate gland. • The most predictive features belong to the texture (57%) and intensity (43%) domains. [ABSTRACT FROM AUTHOR]

Objectives: Pulmonary nodules and masses are the typical presentations of lung cancer. However, a spectrum of focal opacities cannot be defined as either "pulmonary nodule" or "mass," despite representing cancer. We aimed to assess the morphology of screening-detected lung cancers at low-dose computed tomography LDTC and to evaluate inter-observer agreement in their classification.Methods: Four radiologists with different experiences in thoracic imaging retrospectively reviewed 273 screening-detected lung cancers. Readers were asked to assess if morphology at the time of diagnosis was consistent with the Fleischner Society definition of pulmonary "nodule" or "mass." Cancers not consistent were defined as "non-nodular/non-mass" (NN/NM) and sub-classified as follows: associated with cystic airspaces, stripe-like, scar-like, endobronchial, or not otherwise defined (NOD). Inter-observer agreement was evaluated using Cohen's K statistic among pairs of readers and modified Fleiss' kappa statistic for overall agreement.Results: Two hundred forty-one of the 273 (88%) lesions were defined as pulmonary nodule or mass by complete agreement, while 20/273 (7.3%) were defined as NN/NM. Six (2.2%) of 273 were sub-classified as lesions associated with cystic airspace, six (2.2%) as scar-like, five (1.8%) as endobronchial, and one (0.7%) as NOD by complete agreement. The concordance in defining morphology was excellent (261/273; 96%, 95%CI 92-98%; k 0.85, 95%CI 0.75-0.92) and also in the sub-classification (18/20; 90%, 95%CI 68-99%, k 0.93, 95%CI 0.86-1.00). There was incomplete agreement regarding lesion morphology in 4.4% (12/273) of cases.Conclusions: A non-negligible percentage of screening-detected lung cancers has a NN/NM appearance at LDCT. The concordance in defining lesion morphology was excellent. The awareness of various presentations can avoid missed or delayed diagnosis.Key Points: • A non-negligible percentage of screening-detected lung cancers have neither nodular nor mass appearance at low-dose CT. • The awareness of various LDCT presentations of lung cancer can avoid missed or delayed diagnosis. • Optimal protocol management in CT screening should take into consideration lung nodules as well as various other focal abnormalities. [ABSTRACT FROM AUTHOR]

Objectives: To assess the diagnostic accuracy of MRI performed using surface coils, with and without contrast medium, in predicting thyroid and cricoid cartilage infiltration in laryngeal tumours, and to investigate whether the radiologist's experience influences diagnostic accuracy.Methods: We retrospectively enrolled patients with biopsy-proven laryngeal cancer who had undergone preoperative staging MRI and open surgery. Two radiologists with different experience (senior vs. junior) reviewed the MR images without (session A1) and with contrast medium (session A2) separately. We calculated the accuracy of MRI with and without contrast medium in detecting infiltration of the thyroid and cricoid cartilages. Interobserver agreement was calculated by Cohen's Kappa (k).Results: Forty-two patients were enrolled, for a total of 62 cartilages. In session A1 the senior and junior radiologists showed an accuracy of 85% and 71%, respectively, with k = 0.53 (0.33-0.72). In session A2 the senior and junior radiologists showed an accuracy of 84% and 77%, respectively, with k = 0.68 (0.49-0.86).Conclusions: Staging of laryngeal tumours with surface coil MRI showed good diagnostic accuracy in assessing cartilaginous infiltration. We observed similar values of diagnostic accuracy for the analysis performed with and without contrast medium for the senior radiologist.Key Points: • Surface coil MRI demonstrated good accuracy in assessing laryngeal cartilage invasion. • The radiologist's experience can influence the diagnostic accuracy. • Gadolinium administration may increase interobserver concordance. [ABSTRACT FROM AUTHOR]

Purpose: To investigate whether there is an increased signal intensity (SI) of dentate nucleus (DN) and globus pallidus (GP) on unenhanced T1-weighted magnetic resonance imaging (MRI), in patients who had undergone multiple administrations of gadoxetate disodium.Materials and Methods: We retrospectevely included stage III melanoma patients, who had been previously enrolled in a trial of adjuvant therapy and who had undergone whole-body contrast-enhanced MRIs with gadoxetate disodium every three months for their follow-up. The SI ratios of DN-to-pons and GP-to-thalamus on unenhanced T1-weighted images were calculated. The difference in SI ratios between the first and the last MRI examinations was assessed and a linear mixed model was performed to detect how SI ratios varied with the number of administrations.Results: Eighteen patients were included in our study. The number of gadoxetate disodium administrations ranged from 2 to 18. Paired t-test did not show any significant difference in DN-to-pons (p=0.21) and GP-to-thalamus (p=0.09) SI ratios by the end of the study. DN-to-pons SI ratio and GP-to-thalamus SI ratio did not significantly increase with increasing the number of administrations (p=0.14 and p=0.06, respectively).Conclusion: Multiple administrations of gadoxetate disodium are not associated with increased SI in DN and GP in the brain.Key Points: • Gadolinium may deposit in the human brain after multiple GBCA administrations. • Gadolinium deposition is associated with increased T1W signal intensity • Increase in signal intensity is most apparent within the DN and GP • Multiple administrations of gadoxetate disodium do not increase T1W signal. [ABSTRACT FROM AUTHOR]

Objectives: To assess the independent prognostic value of standardized uptake value (SUV) and apparent diffusion coefficient (ADC), separately and combined, in order to evaluate if the combination of these two variables allows further prognostic stratification of patients with head and neck squamous cell carcinomas (HNSCC).Methods: Pretreatment SUV and ADC were calculated in 57 patients with HNSCC. Mean follow-up was 21.3 months. Semiquantitative analysis of primary tumours was performed using SUVmaxT/B, ADCmean, ADCmin and ADCmax. The prognostic value of SUVmaxT/B, ADCmean, ADCmin and ADCmax in predicting disease-free survival (DFS) was evaluated with log-rank test and Cox regression models.Results: Patients with SUVmaxT/B ≥5.75 had an overall worse prognosis (p = 0.003). After adjusting for lymph node status and diameter, SUVmaxT/B and ADCmin were both significant predictors of DFS with hazard ratio (HR) = 10.37 (95 % CI 1.22-87.95) and 3.26 (95 % CI 1.20-8.85) for SUVmaxT/B ≥5.75 and ADCmin ≥0.58 × 10-3 mm2/s, respectively. When the analysis was restricted to subjects with SUVmaxT/B ≥5.75, high ADCmin significantly predicted a worse prognosis, with adjusted HR = 3.11 (95 % CI 1.13-8.55).Conclusions: The combination of SUVmaxT/B and ADCmin improves the prognostic role of the two separate parameters; patients with high SUVmaxT/B and high ADCmin are associated with a poor prognosis.Key Points: • High SUV maxT/B is a poor prognostic factor in HNSCC • High ADC min is a poor prognostic factor in HNSCC • In patients with high SUV maxT/B , high ADC min identified those with worse prognosis. [ABSTRACT FROM AUTHOR]

Objectives: To assess the association between CT features and EGFR, ALK, KRAS mutations in non-small cell lung cancer.Methods: Patients undergoing chest CT and testing for the above gene mutations were included. Qualitative evaluation of CTs included: lobe; lesion diameter; shape; margins; ground-glass opacity; density; cavitation; air bronchogram; pleural thickening; intratumoral necrosis; nodules in tumour lobe; nodules in non-tumour lobes; pleural retraction; location; calcifications; emphysema; fibrosis; pleural contact; pleural effusion. Statistical analysis was performed to assess association of features with each gene mutation. ROC curves for gene mutations were drawn; the corresponding area under the curve was calculated. P-values <0.05 were considered significant.Results: Of 285 patients, 60/280 (21.43 %) were positive for EGFR mutation; 31/270 (11.48 %) for ALK rearrangement; 64/240 (26.67 %) for KRAS mutation. EGFR mutation was associated with air bronchogram, pleural retraction, females, non-smokers, small lesion size, and absence of fibrosis. ALK rearrangements were associated with age and pleural effusion. KRAS mutation was associated with round shape, nodules in non-tumour lobes, and smoking.Conclusions: This study disclosed associations between CT features and alterations of EGFR (air bronchogram, pleural retraction, small lesion size, absence of fibrosis), ALK (pleural effusion) and KRAS (round lesion shape, nodules in non-tumour lobes).Key Points: Air bronchogram, pleural retraction, small size relate to EGFR mutation in NSCLC. Pleural effusion and younger age relate to ALK mutation. Round lesion shape, nodules in non-tumour lobes relate to KRAS mutation. [ABSTRACT FROM AUTHOR]

Objectives: To investigate whether whole-body diffusion-weighted imaging (WB-DWI) alone is adequate for detecting metastases in melanoma patients, or if standard WB contrast-enhanced magnetic resonance imaging (WB-ceMRI) is required.Methods: Seventy-one WB-DWI studies were performed quarterly along with whole-body MRI including contrast-enhanced sequences (WB-ceMRI) in 19 patients with advanced melanoma. The reference standard was biopsy, other imaging investigations, or changes observed on follow-up. Findings of metastasis in separate WB-DWI and WB-DWI + WB-ceMRI readings were compared using κ statistics. Additionally, the distribution of findings was examined and calculated per body region (brain, neck, chest, abdomen, liver, pelvis, subcutaneous tissues, bones) and diagnostic accuracy (DA), sensitivity, specificity, negative predictive value, and positive predictive value were calculated per patient.Results: The eight examinations that were positive by the reference standard contained a total of 14 metastatic findings. With almost perfect agreement between techniques (κ = 85 %, 95 % CI 70-100 %) for detection of examinations with metastatic findings, and complete agreement in extracranial metastasis detection, 10 metastases were detected using WB-DWI and 13 using WB-DWI + WB-ceMRI. WB-DWI and WB-DWI + WB-MRI had equivalent per patient DA (79 %).Conclusions: WB-DWI without additional WB-ceMRI sequences is promising for the detection of extracranial metastases in melanoma patients, but contrast-enhanced MRI is required for evaluating the brain. [ABSTRACT FROM AUTHOR]

A response by Massimo Bellomi and colleagues to a letter to the editor about their article which discusses emphysema that is associated with smoking is presented.

Objective: To assess the safety of the non-ionic iso-osmolar contrast agent iodixanol on renal function in patients with monoclonal gammopathies undergoing CT.Methods: We explored the effect of iodixanol on renal function in 30 patients with monoclonal gammopathies and 20 oncological patients with a normal electrophoretic profile (control group). The parameters used to estimate renal function were: serum creatinine, eGFR (determined 24 h before and 48 h after the administration of iodixanol), and urinary excretion of Neutrophil Gelatinase-Associated Lipocalin (NGAL) determined 2 h and 24 h after. Serum creatinine was also determined 1 month after the administration of iodixanol.Results: No significant increase in serum creatinine values were observed in the monoclonal gammopathies group and in 19/20 patients in the control group. Only 1 patient in the control group developed a transient contrast agent-induced nephropathy. We found no statistically significant difference between the two groups regarding the percentage variation from baseline values of serum creatinine, creatinine clearance, NGAL 2 h after, and eGFR. Whereas NGAL at 24 h showed a statistically significant increase in patients with Monoclonal gammopathies.Conclusion: The use of iodixanol appears to be safe in patients with monoclonal gammopathies and an eGFR≥ 60 ml/min/1.73 mq. [ABSTRACT FROM AUTHOR]

Purpose: We have little knowledge about the evolution of emphysema, and relatively little is understood about its evolution in relation to smoking habits. This study aims to assess the evolution of emphysema in asymptomatic current and former smokers over 2 years and to investigate the association with subjects' characteristics. The study was approved by our Ethics Committee and all participants provided written informed consent.Materials and Methods: We measured emphysema by automatic low-dose computed tomography densitometry in 254 current and 282 former smokers enrolled in a lung-cancer screening. The measures were repeated after 2 years. The association between subjects' characteristics, smoking habits and emphysema were assessed by chi-squared and Wilcoxon tests. Univariate and multivariate odds ratios (OR) with 95% confidence intervals (CI) were calculated for the risk of emphysema worsening according to subjects' characteristics. We assessed the trend of increasing risk of emphysema progression by smoking habits using the Mantel-Haenszel chi-squared test.Results: The median percentage increase in emphysema over a 2-year period was significantly higher in current than in former smokers (OR 1.8; 95% CI 1.3-2.6; p < 0.0001). The risk of worsening emphysema (by 30% in 2 years) in current smokers increased with smoking duration (p for trend <0.02).Conclusion: As emphysema is a known risk factor for lung cancer, its evaluation could be used as a potential factor for identification of a high-risk population. The evaluation of emphysema progression can be added to low-dose CT screening programmes to inform and incite participants to stop smoking. [ABSTRACT FROM AUTHOR]