PSMA-positive prostatic volume prediction with deep learning based on T2-weighted MRI.

PURPOSE: High PSMA expression might be correlated with structural characteristics such as growth patterns on histopathology, not recognized by the human eye on MRI images. Deep structural image analysis might be able to detect such differences and therefore predict if a lesion would be PSMA positive. Therefore, we aimed to train a neural network based on PSMA PET/MRI scans to predict increased prostatic PSMA uptake based on the axial T2-weighted sequence alone. MATERIAL AND METHODS: All patients undergoing simultaneous PSMA PET/MRI for PCa staging or biopsy guidance between April 2016 and December 2020 at our institution were selected. To increase the specificity of our model, the prostatic beds on PSMA PET scans were dichotomized in positive and negative regions using an SUV threshold greater than 4 to generate a PSMA PET map. Then, a C-ENet was trained on the T2 images of the training cohort to generate a predictive prostatic PSMA PET map. RESULTS: One hundred and fifty-four PSMA PET/MRI scans were available (133 [68Ga]Ga-PSMA-11 and 21 [18F]PSMA-1007). Significant cancer was present in 127 of them. The whole dataset was divided into a training cohort (n = 124) and a test cohort (n = 30). The C-ENet was able to predict the PSMA PET map with a dice similarity coefficient of 69.5 ± 15.6%. CONCLUSION: Increased prostatic PSMA uptake on PET might be estimated based on T2 MRI alone. Further investigation with larger cohorts and external validation is needed to assess whether PSMA uptake can be predicted accurately enough to help in the interpretation of mpMRI.

In Vivo Identification of H3K9me2/H3K79me3 as an Epigenetic Barrier to Carcinogenesis.

The highly dynamic nature of chromatin's structure, due to the epigenetic alterations of histones and DNA, controls cellular plasticity and allows the rewiring of the epigenetic landscape required for either cell differentiation or cell (re)programming. To dissect the epigenetic switch enabling the programming of a cancer cell, we carried out wide genome analysis of Histone 3 (H3) modifications during osteogenic differentiation of SH-SY5Y neuroblastoma cells. The most significant modifications concerned H3K27me2/3, H3K9me2, H3K79me1/2, and H3K4me1 that specify the process of healthy adult stem cell differentiation. Next, we translated these findings in vivo, assessing H3K27, H3K9, and H3K79 methylation states in biopsies derived from patients affected by basalioma, head and neck carcinoma, and bladder tumors. Interestingly, we found a drastic decrease in H3K9me2 and H3K79me3 in cancer specimens with respect to their healthy counterparts and also a positive correlation between these two epigenetic flags in all three tumors. Therefore, we suggest that elevated global levels of H3K9me2 and H3K79me3, present in normal differentiated cells but lost in malignancy, may reflect an important epigenetic barrier to tumorigenesis. This suggestion is further corroborated, at least in part, by the deranged expression of the most relevant H3 modifier enzymes, as revealed by bioinformatic analysis. Overall, our study indicates that the simultaneous occurrence of H3K9me2 and H3K79me3 is fundamental to ensure the integrity of differentiated tissues and, thus, their combined evaluation may represent a novel diagnostic marker and potential therapeutic target.

Choline PET/CT features to predict survival outcome in high-risk prostate cancer restaging: a preliminary machine-learning radiomics study.

BACKGROUND: Radiomic features are increasingly utilized to evaluate tumor heterogeneity in PET imaging but to date its role has not been investigated for Cho-PET in prostate cancer. The potential application of radiomics features analysis using a machine-learning radiomics algorithm was evaluated to select 18F-Cho PET/CT imaging features to predict disease progression in PCa. METHODS: We retrospectively analyzed high-risk PCa patients who underwent restaging 18F-Cho PET/CT from November 2013 to May 2018. 18F-Cho PET/CT studies and related structures containing volumetric segmentations were imported in the "CGITA" toolbox to extract imaging features from each lesion. A Machine-learning model has been adapted using NCA for feature selection, while DA was used as a method for feature classification and performance analysis. RESULTS: One hundred and six imaging features were extracted for 46 lesions for a total of 4876 features analyzed. No significant differences between the training and validating sets in terms of age, sex, PSA values, lesion location and size (P>0.05) were demonstrated by the machine-learning model. Thirteen features were able to discriminate FU disease status after NCA selection. Best performance in DA classification was obtained using the combination of the 13 selected features (sensitivity 74%, specificity 58% and accuracy 66%) compared to the use of all features (sensitivity 40%, specificity 52%, and accuracy 51%). Per-site performance of the 13 selected features in DA classification were as follows: T = sensitivity 63%, specificity 83%, accuracy 71%; N = sensitivity 87%, specificity 91% of and accuracy 90%; bone-M = sensitivity 33%, specificity 77% and accuracy 66%. CONCLUSIONS: An artificial intelligence model demonstrated to be feasible and able to select a panel of 18F-Cho PET/CT features with valuable association with PCa patients' outcome.

The prognostic value of subjective nasal symptoms and SNOT-22 score in middle ear surgery.

BACKGROUND: The relationship between nasal functions and middle ear surgery is still under debate. Nasal obstruction is considered as a negative prognostic factor in middle ear surgery. This is based on the theory that it may cause Eustachian tube dysfunction (ETD) by leading to reduced ventilation of the middle ear, as found in several patients with nasal septal deviation, chronic rhinitis and nasal polyps. OBJECTIVES: To assess how the subjective feeling of nasal function, evaluated by a preoperative questionnaire, may be predictive of surgical outcome and/or risk of failure in middle ear surgery. METHODS: We prospectively evaluated data of patients undergoing middle ear surgery for chronic otitis media with and without cholesteatoma. All patients completed the SNOT-22 and ETDQ-7 questionnaires. They underwent surgery for their pathology, as appropriate. RESULTS: The SNOT-22 score was higher in patients with retraction pocket and in patients whose retraction pockets recurred after surgery (p < 0.05). Patients with higher score at SNOT-22 questionnaire, were more likely to show recurrence of atelectasis aftersurgery. CONCLUSIONS: The SNOT-22 questionnaire, administrered before surgical procedure, can help in the identification of patients who are at risk of failure in the post-operative period, as well as ETDQ-7.

Deep Learning Whole-Gland and Zonal Prostate Segmentation on a Public MRI Dataset.

BACKGROUND: Prostate volume, as determined by magnetic resonance imaging (MRI), is a useful biomarker both for distinguishing between benign and malignant pathology and can be used either alone or combined with other parameters such as prostate-specific antigen. PURPOSE: This study compared different deep learning methods for whole-gland and zonal prostate segmentation. STUDY TYPE: Retrospective. POPULATION: A total of 204 patients (train/test = 99/105) from the PROSTATEx public dataset. FIELD STRENGTH/SEQUENCE: A 3 T, TSE T2 -weighted. ASSESSMENT: Four operators performed manual segmentation of the whole-gland, central zone + anterior stroma + transition zone (TZ), and peripheral zone (PZ). U-net, efficient neural network (ENet), and efficient residual factorized ConvNet (ERFNet) were trained and tuned on the training data through 5-fold cross-validation to segment the whole gland and TZ separately, while PZ automated masks were obtained by the subtraction of the first two. STATISTICAL TESTS: Networks were evaluated on the test set using various accuracy metrics, including the Dice similarity coefficient (DSC). Model DSC was compared in both the training and test sets using the analysis of variance test (ANOVA) and post hoc tests. Parameter number, disk size, training, and inference times determined network computational complexity and were also used to assess the model performance differences. A P < 0.05 was selected to indicate the statistical significance. RESULTS: The best DSC (P < 0.05) in the test set was achieved by ENet: 91% ± 4% for the whole gland, 87% ± 5% for the TZ, and 71% ± 8% for the PZ. U-net and ERFNet obtained, respectively, 88% ± 6% and 87% ± 6% for the whole gland, 86% ± 7% and 84% ± 7% for the TZ, and 70% ± 8% and 65 ± 8% for the PZ. Training and inference time were lowest for ENet. DATA CONCLUSION: Deep learning networks can accurately segment the prostate using T2 -weighted images. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 2.

Radiomics analysis of 18F-Choline PET/CT in the prediction of disease outcome in high-risk prostate cancer: an explorative study on machine learning feature classification in 94 patients.

OBJECTIVE: The aim of this study was (1) to investigate the application of texture analysis of choline PET/CT images in prostate cancer (PCa) patients and (2) to propose a machine-learning radiomics model able to select PET features predictive of disease progression in PCa patients with a same high-risk class at restaging. MATERIAL AND METHODS: Ninety-four high-risk PCa patients who underwent restaging Cho-PET/CT were analyzed. Follow-up data were recorded for a minimum of 13 months after the PET/CT scan. PET images were imported in LIFEx toolbox to extract 51 features from each lesion. A statistical system based on correlation matrix and point-biserial-correlation coefficient has been implemented for features reduction and selection, while Discriminant analysis (DA) was used as a method for features classification in a whole sample and sub-groups for primary tumor or local relapse (T), nodal disease (N), and metastatic disease (M). RESULTS: In the whole group, 2 feature (HISTO_Entropy_log10; HISTO_Energy_Uniformity) results were able to discriminate the occurrence of disease progression at follow-up, obtaining the best performance in DA classification (sensitivity 47.1%, specificity 76.5%, positive predictive value (PPV) 46.7%, and accuracy 67.6%). In the sub-group analysis, the best performance in DA classification for T was obtained by selecting 3 features (SUVmin; SHAPE_Sphericity; GLCM_Correlation) with a sensitivity of 91.6%, specificity 84.1%, PPV 79.1%, and accuracy 87%; for N by selecting 2 features (HISTO = _Energy Uniformity; GLZLM_SZLGE) with a sensitivity of 68.1%, specificity 91.4%, PPV 83%, and accuracy 82.6%; and for M by selecting 2 features (HISTO_Entropy_log10 - HISTO_Entropy_log2) with a sensitivity 64.4%, specificity 74.6%, PPV 40.6%, and accuracy 72.5%. CONCLUSION: This machine learning model demonstrated to be feasible and useful to select Cho-PET features for T, N, and M with valuable association with high-risk PCa patients' outcomes. KEY POINTS: • Artificial intelligence applications are feasible and useful to select Cho-PET features. • Our model demonstrated the presence of specific features for T, N, and M with valuable association with high-risk PCa patients' outcomes. • Further prospective studies are necessary to confirm our results and to develop the application of artificial intelligence in PET imaging of PCa.

Low cerebrospinal fluid Amyloid-ßeta 1-42 in patients with tuberculous meningitis.

BACKGROUND: Tuberculous meningitis (TBM) is an important disease leading to morbidity, disability and mortality that primarily affects children and immune-depressed patients. Specific neuromarkers predicting outcomes, severity and inflammatory response are still lacking. In recent years an increasing number of evidences show a possible role for infective agents in developing neurodegenerative diseases. METHODS: We retrospectively included 13 HIV-negative patients presenting with TBM and we compared them with two control groups: one of patients with a confirmed diagnosis of AD, and one of those with syphilis where lumbar punctures excluded central nervous system involvement. Lumbar punctures were performed for clinical reasons and CSF biomarkers were routinely available: we analyzed blood brain barrier permeability (CSF to serum albumin ratio, "CSAR"), intrathecal IgG synthesis, (CSF to serum IgG ratio), inflammation (neopterin), amyloid deposition (Aß1-42), neuronal damage (T-tau, P-tau, 14.3.3) and astrocytosis (S-100 ß). RESULTS: TBM patients were 83 % male and 67 % Caucasian with a median age of 51 years (24.5-63.5 IQR). Apart from altered CSAR (median value 18.4, 17.1-30.9 IQR), neopterin (14.3 ng/ml, 9.7-18.8) and IgG ratios (15.4, 7.9-24.9), patients showed very low levels of Aß1-42 in their CSF (348.5 pg/mL,125-532.2), even lower compared to AD and controls [603 pg/mL (IQR 528-797) and 978 (IQR 789-1178)]. Protein 14.3.3 tested altered in 38.5 % cases. T-tau, P-tau and S100Beta were in the range of normality. Altered low level of Aß1-42 correlated over time with classical TBM findings and altered neuromarkers. CONCLUSIONS: CSF Biomarkers from patients with TBM were compatible with inflammation, blood brain barrier damage and impairment in amyloid-beta metabolism. Amyloid-beta could be tested as a prognostic markers, backing the routine use of available neuromarkers. To our knowledge this is the first case showing such low levels of Aß1-42 in TBM; its accumulation, drove by neuroinflammation related to infections, can be central in understanding neurodegenerative diseases.

Predictive factors of severity and persistence of oropharyngeal dysphagia in sub-acute stroke.

PURPOSE: This study aims to understand the factors contributing to the severity of oropharyngeal dysphagia and its persistence in the sub-acute phase of stroke. METHODS: We retrospectively collected the data of all the patients suffering from a stroke in the last year. The severity of stroke was reported according to the NIHSS score. All the patients were evaluated with the Dysphagia Risk Score and with a FEES. We classified the Dysphagia Risk Score and FEES results using the PAS score and ASHA-NOMS levels. The data were analysed statistically with ANOVA test, Student's t test and Pearson's correlation coefficient. RESULTS: A series of 54 patients were evaluated. The ANOVA test did not find any difference in the mean score of Dysphagia Risk Score, PAS and ASHA-NOMS when compared with the brain area of stroke. An NIHSS at hospital admission (stroke unit) of more than 12 was predictive of ASHA-NOMS score 1-4 after 60 days (p < 0.05). A PAS score between 6 and 8 at first FEES evaluation was predictive of poor (1-4) ASHA-NOMS score after 60 days (p < 0.01). A moderate positive linear correlation was found between NIHSS score and both PAS (r 0.65) and Dysphagia Risk Score (r 0.50); a moderate negative linear correlation was recorded between NIHSS and ASHA-NOMS (r - 0.66) scores. CONCLUSION: In the sub-acute phase of stroke, the predictive factors of persistent dysphagia are not linked to the damaged neuroanatomical region and others factors such as NIHSS value and high PAS score seem more useful.

Anti-Inflammatory and Proliferative Properties of Luteolin-7-O-Glucoside.

Flavonoids display a broad range of structures and are responsible for the major organoleptic characteristics of plant-derived foods and beverages. Recent data showed their activity, and in particular of luteolin-7-O-glucoside (LUT-7G), in reduction of oxidative stress and inflammatory mechanisms in different physiological systems. In this paper, we tried to elucidate how LUT-7G could exert both antioxidant and anti-inflammatory effects in endothelial cells cultured in vitro. Here, we showed that LUT-7G is able to inhibit the STAT3 pathway, to have an antiproliferative action, and an important antioxidant property in HUVEC cells. These properties are exerted by the flavone in endothelial through the transcriptional repression of a number of inflammatory cytokines and their receptors, and by the inhibition of ROS generation. ROS and STAT3 activation has been correlated with the production of oxysterols and other hydroxylated fatty acids, and they have been recognized important as players of atherogenesis and cardiocirculatory system diseases. The analysis of the general production pathway of these hydroxylated species, showed a strong decrease of cholesterol hydroxylated species such as 7-alpha-hydroxicholesterol, 7-beta-hydroxicholesterol by the treatment with LUT-7G. This confirms the anti-inflammatory properties of LUT-7G also in the endothelial district, showing for the first time the molecular pathway that verify previous postulated cardiovascular benefits of this flavone.

A preliminary PET radiomics study of brain metastases using a fully automatic segmentation method.

BACKGROUND: Positron Emission Tomography (PET) is increasingly utilized in radiomics studies for treatment evaluation purposes. Nevertheless, lesion volume identification in PET images is a critical and still challenging step in the process of radiomics, due to the low spatial resolution and high noise level of PET images. Currently, the biological target volume (BTV) is manually contoured by nuclear physicians, with a time expensive and operator-dependent procedure. This study aims to obtain BTVs from cerebral metastases in patients who underwent L-[11C]methionine (11C-MET) PET, using a fully automatic procedure and to use these BTVs to extract radiomics features to stratify between patients who respond to treatment or not. For these purposes, 31 brain metastases, for predictive evaluation, and 25 ones, for follow-up evaluation after treatment, were delineated using the proposed method. Successively, 11C-MET PET studies and related volumetric segmentations were used to extract 108 features to investigate the potential application of radiomics analysis in patients with brain metastases. A novel statistical system has been implemented for feature reduction and selection, while discriminant analysis was used as a method for feature classification. RESULTS: For predictive evaluation, 3 features (asphericity, low-intensity run emphasis, and complexity) were able to discriminate between responder and non-responder patients, after feature reduction and selection. Best performance in patient discrimination was obtained using the combination of the three selected features (sensitivity 81.23%, specificity 73.97%, and accuracy 78.27%) compared to the use of all features. Secondly, for follow-up evaluation, 8 features (SUVmean, SULpeak, SUVmin, SULpeak prod-surface-area, SUVmean prod-sphericity, surface mean SUV 3, SULpeak prod-sphericity, and second angular moment) were selected with optimal performance in discriminant analysis classification (sensitivity 86.28%, specificity 87.75%, and accuracy 86.57%) outperforming the use of all features. CONCLUSIONS: The proposed system is able i) to extract 108 features for each automatically segmented lesion and ii) to select a sub-panel of 11C-MET PET features (3 and 8 in the case of predictive and follow-up evaluation), with valuable association with patient outcome. We believe that our model can be useful to improve treatment response and prognosis evaluation, potentially allowing the personalization of cancer treatment plans.

Social dynamics modeling of chrono-nutrition.

Gut microbiota and human relationships are strictly connected to each other. What we eat reflects our body-mind connection and synchronizes with people around us. However, how this impacts on gut microbiota and, conversely, how gut bacteria influence our dietary behaviors has not been explored yet. To quantify the complex dynamics of this interplay between gut and human behaviors we explore the "gut-human behavior axis" and its evolutionary dynamics in a real-world scenario represented by the social multiplex network. We consider a dual type of similarity, homophily and gut similarity, other than psychological and unconscious biases. We analyze the dynamics of social and gut microbial communities, quantifying the impact of human behaviors on diets and gut microbial composition and, backwards, through a control mechanism. Meal timing mechanisms and "chrono-nutrition" play a crucial role in feeding behaviors, along with the quality and quantity of food intake. Considering a population of shift workers, we explore the dynamic interplay between their eating behaviors and gut microbiota, modeling the social dynamics of chrono-nutrition in a multiplex network. Our findings allow us to quantify the relation between human behaviors and gut microbiota through the methodological introduction of gut metabolic modeling and statistical estimators, able to capture their dynamic interplay. Moreover, we find that the timing of gut microbial communities is slower than social interactions and shift-working, and the impact of shift-working on the dynamics of chrono-nutrition is a fluctuation of strategies with a major propensity for defection (e.g. high-fat meals). A deeper understanding of the relation between gut microbiota and the dietary behavioral patterns, by embedding also the related social aspects, allows improving the overall knowledge about metabolic models and their implications for human health, opening the possibility to design promising social therapeutic dietary interventions.

Cerebrospinal fluid biomarkers in patients with central nervous system infections: a retrospective study.

BACKGROUND: Central nervous system (CNS) may be infected by several agents, resulting in different presentations and outcomes. Analysis of cerebrospinal fluid (CSF) markers could be helpful to differentiate specific conditions and setting an appropriate therapy. METHODS: Patients presenting with signs and symptoms were enrolled if, before receiving a diagnostic lumbar puncture, signed a written informed consent. We analyzed CSF indexes of blood-brain barrier permeability (CSF to serum albumin ratio or CSAR), inflammation (CSF to serum IgG ratio, neopterin), amyloid deposition (1-42 ß-amyloid), neuronal damage (Total tau (T-tau), Phosphorylated tau (P-tau), and 14.3.3 protein) and astrocyte damage (S-100ß). RESULTS: Two hundred and eighty-one patients were included: they were mainly affected by herpesvirus encephalitis, enterovirus meningoencephalitis, bacterial meningitis (Neisseria meningitidis and Streptococcus pneumoniae), and infection by other etiological agents or unknown pathogen. Their CSF features were compared with HIV-negative patients and native HIV-positive individuals without CNS involvement. 14.3.3 protein was found in bacterial and HSV infections while T-tau and neopterin were abnormally high in the herpesvirus group. P-tau, instead, was elevated in enterovirus meningitis. S-100ß was found to be high in patients with HSV-1 and HSV-2 infections but not in those with Varicella Zoster Virus (VZV). Thirty-day mortality was unexpectedly low (2.7%): patients who died had higher levels of T-tau and, significantly, lower levels of Aß1-42. CONCLUSION: This work demonstrates that CSF biomarkers of neuronal damage or inflammation may vary during CNS infections according to different causative agents. The prognostic value of these biomarkers needs to be assessed in prospective studies.

Oropharyngeal dysphagia in elderly population suffering from mild cognitive impairment and mild dementia: Understanding the link.

PURPOSE: To evaluate the prevalence of oropharyngeal dysphagia in elderly patients suffering from minimal or mild cognitive decline. PATIENTS AND METHODS: We retrospectively collected the data of patients suffering from mild cognitive impairment or mild dementia and were undergoing management for suspected oropharyngeal dysphagia, in our department. All our patients were subjected to Mini Mental State Examination test, MD Anderson dysphagia inventory and caregiver mealtime and dysphagia questionnaire. We performed a mealtime observation study and endoscopic evaluation of swallowing in all our patients. Following evaluation, we then analysed the data statistically. RESULTS: Out of 708 patients who visited us for cognitive decline and suspected oropharyngeal dysphagia in the last two years, 52 patients were confirming to the inclusion criteria of this study. Classification of oropharyngeal dysphagia patients according to ASHA-NOMS scale showed that 32.7% of patients presented with grade 4 of dysphagia followed by another 32.7% with grade 5 and 30.8% presented with grade 6. Only 3.8% of our patients were considered normal (grade 7 of ASHA-NOMS scale). MD Anderson dysphagia inventory could collected swallowing alterations in only 23.1% of the cases. The caregiver mealtime and dysphagia questionnaire showed acceptable caregivers patient management in 53.8% of patients. CONCLUSION: Our study underscores the fact that oropharyngeal dysphagia is present in many cases of mild cognitive decline. While patients understate their swallowing problems, the caregivers are not competent enough to manage this situation in a great percentage of cases. Only a mealtime observation by a speech-language pathologist along with FEES is able to identify the true prevalence of the condition.

Predictive and Prognostic Value of Early Disease Progression by PET Evaluation in Advanced Non-Small Cell Lung Cancer.

OBJECTIVE: To assess the predictive and prognostic value of progressive metabolic disease (PMD) by the use of early 18Fluorodeoxyglucose positron emission tomography (18FDG-PET) in patients with clinical stage IV non-small cell lung cancer (NSCLC) treated with first-line chemotherapy. METHODS: An 18FDG-PET performed following the first cycle of chemotherapy (PET-1) was compared with a pretreatment 18FDG-PET (PET-0) and a computed tomography (CT) scan after the third cycle (CT-3). The primary endpoint was the positive predictive value (PPV) of PMD. Secondary endpoints included the prognostic value of PMD. RESULTS: Eleven of 38 patients (29%) had a PMD by PET-1, and 15 (39%), including all patients with a PMD, experienced a progressive disease by CT-3. The PPV of PMD was 100% according to both the European Organization for Research and Treatment of Cancer (EORTC) criteria and the PET Response Criteria In Solid Tumors (PERCIST) (p value for both, <0.0001). Patients with a PMD by PET-1 had a median overall survival of 7.0 months versus 14.0 months for those without a PMD (p = 0.04, according to the EORTC criteria). CONCLUSIONS: Early 18FDG-PET assessment deserves further investigation for the identification of NSCLC patients who do not benefit from first-line chemotherapy.

Recurrent bladder carcinoma: clinical and prognostic role of 18 F-FDG PET/CT.

AIM: A small number of studies evaluated the detection rate of lesions from bladder carcinoma (BC) of 18 F-FDG PET/CT in the restaging process. However, the prognostic role of FDG PET/CT still remains unclear. The aim of the present study was to evaluate the accuracy, the effect upon treatment decision, and the prognostic value of FDG PET/CT in patients with suspected recurrent BC. MATERIALS AND METHODS: Forty-one patients affected by BC underwent FDG PET/CT for restaging purpose. The diagnostic accuracy of visually interpreted FDG PET/CT was assessed compared to histology (n = 8), other diagnostic imaging modalities (contrast-enhanced CT in 38/41 patients and MRI in 15/41) and clinical follow-up (n = 41). Semiquantitative PET values (SUVmax, SUVmean, SUL, MTV, TLG) were calculated using a graph-based method. Progression-free survival (PFS) and overall survival (OS) were assessed by using Kaplan-Meier curves. The risk of progression (hazard ratio, HR) was computed by Cox regression analysis by considering all the available variables. RESULTS: PET was considered positive in 21 of 41 patients. Of these, recurrent BC was confirmed in 20 (95 %). Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of FDG PET/CT were 87 %, 94 %, 95 %, 85 %, 90 %. AUC was 0.9 (95 %IC 0.8-1). Bayesian positive and negative likelihood ratios were 14.5 and 0.13, respectively. FDG PET/CT findings modified the therapeutic approach in 16 patients (modified therapy in 10 PET-positive patients, watch-and-wait in six PET-negative patients). PFS was significantly longer in patients with negative scan vs. those with pathological findings (85 % vs. 24 %, p < 0.05; HR = 12.4; p = 0.001). Moreover, an unremarkable study was associated with a longer OS (88 % vs. 47 % after 2 years and 87 % vs. 25 % after 3 years, respectively, p < 0.05). Standardized uptake value (SUV)max > 6 and total lesion glycolysis (TLG) > 8.5 were recognized as the most accurate thresholds to predict PFS (2-year PFS 62 % for SUVmax < 6 vs. 15 % for SUVmax > 6, p = 0.018; 2-year PFS 66 % for TLG < 8.5 vs. 18 % for TLG > 8.5, p = 0.09). CONCLUSION: A very good diagnostic performance for FDG PET/CT was confirmed in patients with suspected recurrent BC. FDG PET/CT allowed for a change in treatment decision in about 40 % of cases and showed an important prognostic value in assessing PFS and OS.

Evaluation of erlotinib treatment response in non-small cell lung cancer using metabolic and anatomic criteria.

BACKGROUND: In this paper the clinical value of PET for early prediction of tumor response to erlotinib in patients with advanced or metastatic non-small cell lung cancer (NSCLC) after failure of at least one prior chemotherapy regimen is evaluated. The aim was to compare the early metabolic treatment response using European Organization for Research and Treatment of Cancer (EORTC) 1999 recommendations and PET Response Criteria in Solid Tumors (PERCIST), and the standard treatment response using Response Evaluation Criteria in Solid Tumors (RECIST). METHODS: Twenty patients with stage IV NSCLC were enrolled prospectively. PET/CT studies were performed before, then 48 hours, and 45 days after the initiation of erlotinib treatment. The lesion with the highest uptake in each patient was evaluated according to EORTC 1999 recommendations, PERCIST and RECIST to assess metabolic and anatomic response. Response classifications were compared statistically using Wilcoxon signed-rank test. Disease-free survival (DFS) and overall survival (OS) were calculated by the Kaplan-Meier Test. RESULTS: At 48 hours, the Kaplan-Meier analysis showed that EORTC proved to be a significant prognostic factor for predicting DFS and OS. At 45 days, there was a significant difference in response evaluation between RECIST and metabolic classifications. RECIST and PERCIST were significant prognostic factors for predicting DFS and OS. EORTC was not able to discriminate responder from non-responder patients. CONCLUSIONS: This study shows that, according to the EORTC protocol, the PET exam is able to provide early identification of patients who benefit from Erlotinib treatment. Used at the end of therapy, PERCIST could be considered an appropriate metabolic evaluation method to discriminate responders from non-responders.

Plasma and erythrocyte membrane phospholipids and fatty acids in Italian general population and hemodialysis patients.

BACKGROUND: Dyslipidemia and abnormal phospholipid metabolism are frequent in uremic patients and increase their risk of cardiovascular disease (CVD): ω-3 polyunsaturated fatty acids (PUFAs) may reduce this risk in the general population. In this study we compared the plasma and erythrocyte cell membrane composition of PUFAs in a group of Caucasian hemodialysis (HD) patients and in a control group of healthy subjects and evaluated the erythrocyte/cell membrane fatty acid ratio as a marker of the dietary intake of phospholipids. The relationship between ω-3 and ω-6 fatty acids and the possible differences in PUFAs concentrations were also investigated. METHODS AND RESULTS: After obtaining a fully informed consent, a total of ninety-nine HD patients and 160 non uremic control subjects from "Tor Vergata" University Hospital were enrolled into the study. None of them took antioxidant drugs or dietary supplements for at least 90 days prior to the observation. Blood samples were analysed by gas-chromatographic coupled to a mass spectrometric detector.The daily intake of total calories, proteins, lipids and carbohydrates is significantly lower in HD patients than in controls (p < 0.001). Most plasma and erythrocyte PUFA were also reduced significantly in HD patients (p < 0.001). CONCLUSIONS: Our results suggest that many classes of PUFAs are lacking in HD patients, due to the removal of nutrients during the dialysis and to persistent malnutrition. A dietary treatment addressed to increase plasma ω-3 PUFAs and to optimize ω-6/ω-3 ratio may exert a protective action and reduce the risk of CVD in HD patient.

Challenges and limitations in applying radiomics to PET imaging: Possible opportunities and avenues for research.

Radiomics, the high-throughput extraction of quantitative imaging features from medical images, holds immense potential for advancing precision medicine in oncology and beyond. While radiomics applied to positron emission tomography (PET) imaging offers unique insights into tumor biology and treatment response, it is imperative to elucidate the challenges and constraints inherent in this domain to facilitate their translation into clinical practice. This review examines the challenges and limitations of applying radiomics to PET imaging, synthesizing findings from the last five years (2019-2023) and highlights the significance of addressing these challenges to realize the full clinical potential of radiomics in oncology and molecular imaging. A comprehensive search was conducted across multiple electronic databases, including PubMed, Scopus, and Web of Science, using keywords relevant to radiomics issues in PET imaging. Only studies published in peer-reviewed journals were eligible for inclusion in this review. Although many studies have highlighted the potential of radiomics in predicting treatment response, assessing tumor heterogeneity, enabling risk stratification, and personalized therapy selection, various challenges regarding the practical implementation of the proposed models still need to be addressed. This review illustrates the challenges and limitations of radiomics in PET imaging across various cancer types, encompassing both phantom and clinical investigations. The analyzed studies highlight the importance of reproducible segmentation methods, standardized pre-processing and post-processing methodologies, and the need to create large multicenter studies registered in a centralized database to promote the continuous validation and clinical integration of radiomics into PET imaging.

Development and Implementation of an Innovative Framework for Automated Radiomics Analysis in Neuroimaging.

Radiomics represents an innovative approach to medical image analysis, enabling comprehensive quantitative evaluation of radiological images through advanced image processing and Machine or Deep Learning algorithms. This technique uncovers intricate data patterns beyond human visual detection. Traditionally, executing a radiomic pipeline involves multiple standardized phases across several software platforms. This could represent a limit that was overcome thanks to the development of the matRadiomics application. MatRadiomics, a freely available, IBSI-compliant tool, features its intuitive Graphical User Interface (GUI), facilitating the entire radiomics workflow from DICOM image importation to segmentation, feature selection and extraction, and Machine Learning model construction. In this project, an extension of matRadiomics was developed to support the importation of brain MRI images and segmentations in NIfTI format, thus extending its applicability to neuroimaging. This enhancement allows for the seamless execution of radiomic pipelines within matRadiomics, offering substantial advantages to the realm of neuroimaging.

Radiomics Analysis of Preprocedural CT Imaging for Outcome Prediction after Transjugular Intrahepatic Portosystemic Shunt Creation.

PURPOSE: To evaluate the role of radiomics in preoperative outcome prediction in cirrhotic patients who underwent transjugular intrahepatic portosystemic shunt (TIPS) using "controlled expansion covered stents". MATERIALS AND METHODS: This retrospective institutional review board-approved study included cirrhotic patients undergoing TIPS with controlled expansion covered stent placement. From preoperative CT images, the whole liver was segmented into Volumes of Interest (VOIs) at the unenhanced and portal venous phase. Radiomics features were extracted, collected, and analyzed. Subsequently, receiver operating characteristic (ROC) curves were drawn to assess which features could predict patients' outcomes. The endpoints studied were 6-month overall survival (OS), development of hepatic encephalopathy (HE), grade II or higher HE according to West Haven Criteria, and clinical response, defined as the absence of rebleeding or ascites. A radiomic model for outcome prediction was then designed. RESULTS: A total of 76 consecutive cirrhotic patients undergoing TIPS creation were enrolled. The highest performances in terms of the area under the receiver operating characteristic curve (AUROC) were observed for the "clinical response" and "survival at 6 months" outcome with 0.755 and 0.767, at the unenhanced and portal venous phase, respectively. Specifically, on basal scans, accuracy, specificity, and sensitivity were 66.42%, 63.93%, and 73.75%, respectively. At the portal venous phase, an accuracy of 65.34%, a specificity of 62.38%, and a sensitivity of 74.00% were demonstrated. CONCLUSIONS: A pre-interventional machine learning-based CT radiomics algorithm could be useful in predicting survival and clinical response after TIPS creation in cirrhotic patients.