Erectile dysfunction and its management in patients with diabetes mellitus

Diabetes can be described as a syndrome of multiple closely related conditions induced by a chronic state of hyperglycaemia resulting from defective insulin secretion, insulin action or both. Chronic complications associated with diabetes (including neuropathy, vascular disease, nephropathy and retinopathy) are common, and of these, erectile dysfunction (ED) deserves special attention. ED and its correlation with cardiovascular disease require careful evaluation and appropriate treatment. PDE5 inhibitors (PDE5is) are an important tool for the treatment of ED, with new drugs coming onto the market since the late 90s. This review offers an overview of PDE5is and their use in treating ED in diabetes. We underline the differences between different types of PDE5i, focusing on available doses, duration of action, T ½, side effects and selectivity profiles in relation to patients with diabetes. We also discuss the link between diabetes and ED in presence of various associated cofactors (obesity, hypertension and its pharmacological treatments, atherosclerosis, hyperhomocysteinaemia, neuropathy, nephropathy, hypogonadism and depression). Finally a number of past and ongoing clinical trials on the use of PDE5is in patients with diabetes are presented to offer an overview of the appropriate treatment of ED in this condition

Normalization strategies in multi-center radiomics abdominal MRI: systematic review and meta-analyses

Goal: Artificial intelligence applied to medical image analysis has been extensively used to develop non-invasive diagnostic and prognostic signatures. However, these imaging biomarkers should be largely validated on multi-center datasets to prove their robustness before they can be introduced into clinical practice. The main challenge is represented by the great and unavoidable image variability which is usually addressed using different pre-processing techniques including spatial, intensity and feature normalization. The purpose of this study is to systematically summarize normalization methods and to evaluate their correlation with the radiomics model performances through meta-analyses. This review is carried out according to the PRISMA statement: 4777 papers were collected, but only 74 were included. Two meta-analyses were carried out according to two clinical aims: characterization and prediction of response. Findings of this review demonstrated that there are some commonly used normalization approaches, but not a commonly agreed pipeline that can allow to improve performance and to bridge the gap between bench and bedside

Virtual biopsy in abdominal pathology: where do we stand?

In recent years, researchers have explored new ways to obtain information from pathological tissues, also exploring non-invasive techniques, such as virtual biopsy (VB). VB can be defined as a test that provides promising outcomes compared to traditional biopsy by extracting quantitative information from radiological images not accessible through traditional visual inspection. Data are processed in such a way that they can be correlated with the patient’s phenotypic expression, or with molecular patterns and mutations, creating a bridge between traditional radiology, pathology, genomics, and artificial intelligence (AI). Radiomics is the backbone of VB, since it allows the extraction and selection of features from radiological images, feeding them into AI models in order to derive lesions' pathological characteristics and molecular status. Presently, the output of VB provides only a gross approximation of the findings of tissue biopsy. However, in the future, with the improvement of imaging resolution and processing techniques, VB could partially substitute the classical surgical or percutaneous biopsy, with the advantage of being non-invasive, comprehensive, accounting for lesion heterogeneity, and low cost. In this review, we investigate the concept of VB in abdominal pathology, focusing on its pipeline development and potential benefits

A Deep Learning model to segment liver metastases on CT images acquired at different time-points during chemotherapy

The aim of this study is to present a fully automatic deep learning algorithm to segment liver Colorectal cancer metastases (lmCRC) on CT images, based on a U-Net structure, comparing nets with and without the transfer learning approach. This is a bi-centric study, enrolling patients who underwent CT exam before (baseline) and after first-line therapy (TP1). Patients were divided into training (using a portion of baseline sequences from both centers) to train the DL model, and two validation sets: one with baseline (valB), and one with TP1 (valTP1) sequences. The reference standard for the automatic segmentations was defined by the manual segmentations performed by an experienced radiologist on the portal phase of the baseline and TP1 CT exam. The best performing model obtained Dice Similarity Coefficient (DSC) of 0.68±0.24, Precision (Pr) of 0.74±0.27, Recall (Re) of 0.73±0.26, Detection Rate (DR) of 93% on the valB, and DSC of 0.61±0.28, Pr of 0.68±0.31, Re of 0.65±0.29 and DR of 88% on the valTP1. These encouraging results, if confirmed on larger dataset, might provide a reliable and robust tool that can be used as first step of future radiomics analyses aimed at predicting response to therapy, improving the management of lmCRC patients

An innovative radiomics approach to predict response to chemotherapy of liver metastases based on CT images

Liver metastases (mts) from colorectal cancer (CRC) can have different responses to chemotherapy in the same patient. The aim of this study is to develop and validate a machine learning algorithm to predict response of individual liver mts. 22 radiomic features (RF) were computed on pretreatment portal CT scans following a manual segmentation of mts. RFs were extracted from 7x7 Region of Interests (ROIs) that moved across the image by step of 2 pixels. Liver mts were classified as non-responder (R-) if their largest diameter increased more than 3 mm after 3 months of treatment and responder (R+), otherwise. Features selection (FS) was performed by a genetic algorithm and classification by a Support Vector Machine (SVM) classifier. Sensitivity, specificity, negative (NPV) and positive (PPV) predictive values were evaluated for all lesions in the training and validation sets, separately. On the training set, we obtained sensitivity of 86%, specificity of 67%, PPV of 89% and NPV of 61%, while, on the validation set, we reached a sensitivity of 73%, specificity of 47%, PPV of 64% and NPV of 57%. Specificity was biased by the low number of R- lesions on the validation set. The promising results obtained in the validation dataset should be extended to a larger cohort of patient to further validate our method.Clinical Relevance— to personalize treatment of patients with metastastic colorectal cancer, based on the likelihood of response to chemotherapy of each liver metastasis

Virtual biopsy in prostate cancer: can machine learning distinguish low and high aggressive tumors on MRI?

In the last decades, MRI was proven a useful tool for the diagnosis and characterization of Prostate Cancer (PCa). In the literature, many studies focused on characterizing PCa aggressiveness, but a few have distinguished between low-aggressive (Gleason Grade Group (GG) =3) PCas based on biparametric MRI (bpMRI). In this study, 108 PCas were collected from two different centers and were divided into training, testing, and validation set. From Apparent Diffusion Coefficient (ADC) map and T2-Weighted Image (T2WI), we extracted texture features, both 3D and 2D, and we implemented three different methods of Feature Selection (FS): Minimum Redundance Maximum Relevance (MRMR), Affinity Propagation (AP), and Genetic Algorithm (GA). From the resulting subsets of predictors, we trained Support Vector Machine (SVM), Decision Tree, and Ensemble Learning classifiers on the training set, and we evaluated their prediction ability on the testing set. Then, for each FS method, we chose the best classifier, based on both training and testing performances, and we further assessed their generalization capability on the validation set. Between the three best models, a Decision Tree was trained using only two features extracted from the ADC map and selected by MRMR, achieving, on the validation set, an Area Under the ROC (AUC) equal to 81%, with sensitivity and specificity of 77% and 93%, respectively.Clinical Relevance- Our best model demonstrated to be able to distinguish low-aggressive from high-aggressive PCas with high accuracy. Potentially, this approach could help clinician to noninvasively distinguish between PCas that might need active treatment and those that could potentially benefit from active surveillance, avoiding biopsy-related complications

Deep learning to segment liver metastases on CT images: Impact on a radiomics method to predict response to chemotherapy

Predicting response to neo-adjuvant chemotherapy of liver metastases (mts) using CT images is of key importance to provide personalized treatments. However, manual segmentation of mts should be avoid to develop methods that could be integrated into the clinical practice. The aim of this study is to evaluate if and how much automatic segmentation can affect a radiomics-based method to predict response to neoadjuvant chemotherapy of individual liver mts. To this scope, we developed an automatic deep learning method to segment liver mts, based on the U-net architecture, and we compared the classification results of a classifier fed with manual and automatic masks. In the validation set composed of 39 liver mts, the automatic deeplearning algorithm was able to detect 82% of mts, with a median precision of 67%. Using manual and automatic masks, we obtained the same classification in 19/32 mts. In case of mts with largest diameter > 20 mm, the precision of the segmentation does not impact the classification results and we obtained the same classification with both masks. Conversely, with smaller mts, we showed that a Dice coefficient of at least 0.5 should be obtained to extract the same information from the two segmentations. This are very important results in the perspective of using radiomics-based approach to predict response to therapy into clinical practice. Indeed, either precisely manually segment all lesions or refine them after automatic segmentation is a time-consuming task that cannot be performed on a daily basis

A Convolutional Neural Network based system for Colorectal cancer segmentation on MRI images

The aim of the study is to present a new Convolutional Neural Network (CNN) based system for the automatic segmentation of the colorectal cancer. The algorithm implemented consists of several steps: a pre-processing to normalize and highlights the tumoral area, the classification based on CNNs, and a post-processing aimed at reducing false positive elements. The classification is performed using three CNNs: each of them classifies the same regions of interest acquired from three different MR sequences. The final segmentation mask is obtained by a majority voting. Performances were evaluated using a semi-automatic segmentation revised by an experienced radiologist as reference standard. The system obtained Dice Similarity Coefficient (DSC) of 0.60, Precision (Pr) of 0.76 and Recall (Re) of 0.55 on the testing set. After applying the leave-one-out validation, we obtained a median DSC=0.58, Pr=0.74, Re=0.54. The promising results obtained by this system, if validated on a larger dataset, could strongly improve personalized medicine

Diabetes and erectile dysfunction. The relationships with health literacy, treatment adherence, unrealistic optimism, and glycaemic control

Purpose: The aim of this study was to evaluate the relationships between health literacy, unrealistic optimism, and adherence to glycometabolic disease management related to erectile dysfunction (ED) in male patients with type 2 diabetes (T2D) or preDM.Materials and Methods: This prospective observational study enroled 167 consecutive patients with T2D and ED. All patients underwent the following examinations: (a) medical history collection; (b) Body Mass Index (BMI) determination; (c) hormonal and biochemical assessment; (d) duration of T2D, complications and treatment; (e) International Index of Erectile Function-5 questionnaire to assess ED; and (f) validated questionnaire to evaluate health literacy, unrealistic optimism, and treatment adherence.Results: Overall, mean age was 62.5 +/- 9.4 years (range: 20-75) and mean BMI was 28.4 +/- 4.8 kg/m(2) (range: 18.4-46.6). The mean IIEF-5 score was 15.4 +/- 5.2 (range: 5-25). The majority of patients showed high health literacy. However, low health literacy was found in patients with higher IIEF-5 scores and high BMI. Unrealistic optimism was low in most patients. Higher adherence to treatment was found in patients who reported regular physical activity, who followed a diet, and in patients with a family history of T2D. Regarding anti-diabetic treatment, patients treated with insulin showed higher health literacy than patients not treated with other medications, whereas higher adherence was found in patients using SGLT2-i.Conclusions: This study highlighted the close relationship between metabolic compensation, BMI, ED, and psychological attitudes, including health literacy and unrealistic optimism

Diabetes and erectile dysfunction: The relationships with health literacy, treatment adherence, unrealistic optimism, and glycaemic control

Purpose: The aim of this study was to evaluate the relationships between health literacy, unrealistic optimism, and adherence to glycometabolic disease management related to erectile dysfunction (ED) in male patients with type 2 diabetes (T2D) or preDM. Materials and methods: This prospective observational study enroled 167 consecutive patients with T2D and ED. All patients underwent the following examinations: (a) medical history collection; (b) Body Mass Index (BMI) determination; (c) hormonal and biochemical assessment; (d) duration of T2D, complications and treatment; (e) International Index of Erectile Function-5 questionnaire to assess ED; and (f) validated questionnaire to evaluate health literacy, unrealistic optimism, and treatment adherence. Results: Overall, mean age was 62.5 ± 9.4 years (range: 20-75) and mean BMI was 28.4 ± 4.8 kg/m2 (range: 18.4-46.6). The mean IIEF-5 score was 15.4 ± 5.2 (range: 5-25). The majority of patients showed high health literacy. However, low health literacy was found in patients with higher IIEF-5 scores and high BMI. Unrealistic optimism was low in most patients. Higher adherence to treatment was found in patients who reported regular physical activity, who followed a diet, and in patients with a family history of T2D. Regarding anti-diabetic treatment, patients treated with insulin showed higher health literacy than patients not treated with other medications, whereas higher adherence was found in patients using SGLT2-i. Conclusions: This study highlighted the close relationship between metabolic compensation, BMI, ED, and psychological attitudes, including health literacy and unrealistic optimism