Functional Living Skills in Patients with Major Neurocognitive Disorder Due to Degenerative or Non-Degenerative Conditions: Effectiveness of a Non-Immersive Virtual Reality Training.

Virtual reality has gained attention as an effective tool for cognitive, motor, and daily activity rehabilitation in patients with major neurocognitive disorder (M-NCD). The first objective of this study was to check for differences between M-NCD caused by degenerative and non-degenerative conditions (DC and NDC, respectively) in terms of relearning four functional living skills (FLSs), by means of a non-immersive virtual reality training (VRT). The second purpose was to verify whether spontaneous transfer from the virtual environment to the real environment occurred. Four FLS apps were developed in our institute (Information, Suitcase, Medicine, and Supermarket). A nonrandomized interventional study was carried out, comparing experimental and control groups (EG and CG, respectively). The study included three phases: in vivo test at T1; VRT at T2 only for EG; in vivo test at T3. During the in vivo test, the four FLSs were assessed in their natural environments. Both EG-DC and EG-NDC significantly improved in all of the VRT variable scores (the EG-NDC group seemed to show better outcomes than the EG-DC group). Moderate-to-high satisfaction with the VRT was reported. EG-DC and EG-NDC also enhanced their performances in the in vivo test. No statistically significant differences between them were found. CG-DC and CG-NDC improved only in the execution time of Information in the in vivo test. These findings confirm the ecological validity of VRT for FLSs.

Advanced eNose-Driven Pedestrian Tracking Pipeline for Intelligent Car Driver Assisting System: Preliminary Results.

From a biological point of view, alcohol human attentional impairment occurs before reaching a Blood Alcohol Content (BAC index) of 0.08% (0.05% under the Italian legislation), thus generating a significant impact on driving safety if the drinker subject is driving a car. Car drivers must keep a safe driving dynamic, having an unaltered physiological status while processing the surrounding information coming from the driving scenario (e.g., traffic signs, other vehicles and pedestrians). Specifically, the identification and tracking of pedestrians in the driving scene is a widely investigated problem in the scientific community. The authors propose a full, deep pipeline for the identification, monitoring and tracking of the salient pedestrians, combined with an intelligent electronic alcohol sensing system to properly assess the physiological status of the driver. More in detail, the authors propose an intelligent sensing system that makes a common air quality sensor selective to alcohol. A downstream Deep 1D Temporal Residual Convolutional Neural Network architecture will be able to learn specific embedded alcohol-dynamic features in the collected sensing data coming from the GHT25S air-quality sensor of STMicroelectronics. A parallel deep attention-augmented architecture identifies and tracks the salient pedestrians in the driving scenario. A risk assessment system evaluates the sobriety of the driver in case of the presence of salient pedestrians in the driving scene. The collected preliminary results confirmed the effectiveness of the proposed approach.

Heart rate changes associated with the different types of leg movements during sleep in children, adolescents and adults with restless legs syndrome.

The objective of this study was to describe in detail the heart rate changes accompanying short-interval leg movements during sleep, periodic leg movements during sleep, and isolated leg movements during sleep in children and adolescents with restless legs syndrome, and to compare them with the same findings in adults with restless legs syndrome. We analysed time series of R-R intervals synchronized to the onset of short-interval leg movements during sleep, periodic leg movements during sleep or isolated leg movements during sleep that entailed an arousal during non-rapid-eye-movement sleep. We assessed cardiac activation based on the heart rate changes with respect to baseline during non-rapid-eye-movement sleep without leg movements. All types of leg movements recorded during sleep were accompanied by important heart rate changes also in children, with an overall impact similar to that observed in adults. In all age groups, heart rate changes accompanying short-interval leg movements during sleep were constituted by a tachycardia, without a subsequent relative bradycardia, that was instead evident for periodic leg movements during sleep and isolated leg movements during sleep. Moreover, an age-related decline of the relative bradycardia following the heart rate increase, in association with periodic leg movements during sleep and isolated leg movements during sleep, was observed. Our findings show that important heart rate changes accompany all leg movements during sleep at all ages in restless legs syndrome, with significant age-related differences. This information represents an important contribution to the ongoing scientific debate on the possibility and opportunity to treat periodic leg movements during sleep.

Functional Living Skills: A Non-Immersive Virtual Reality Training for Individuals with Major Neurocognitive Disorders.

The loss of functional living skills (FLS) is an essential feature of major neurocognitive disorders (M-NCD); virtual reality training (VRT) offers many possibilities for improving FLS in people with M-NCD. The aim of our study was to verify the effectiveness of a non-immersive VRT on FLS for patients with M-NCD. VRT was carried out for 10 to 20 sessions, by means of four 3D apps developed in our institute and installed on a large touch screen. The experimental group (EG) and the control group (CG) included 24 and 18 patients with M-NCD, respectively. They were administered the in vivo test (in specific hospital places reproducing the natural environments) at T1 (pre-training) and T3 (post-training); at T2, only EG was administered VRT. Statistically significant differences between EG and CG in all the in vivo tests were found in the number of correct responses; during VRT, the number of correct responses increased, while the execution times and the number of clues decreased. The improvement in the in vivo tests appeared to be related to the specific VRT applied. The satisfaction of participants with the VRT was moderate to high.

Adherence to abiraterone or enzalutamide in elderly metastatic castration-resistant prostate cancer.

PURPOSE: To evaluate adherence to abiraterone or enzalutamide for the treatment of metastatic castration-resistant prostate cancer (mCRPC). METHODS: In an observational prospective cohort study, we monitored patients with mCRPC for their adherence to abiraterone or enzalutamide in the pre- or post-chemotherapy setting. RESULTS: Fifty-eight patients with median age of 76 years (range 56-94), age-adjusted Charlson comorbidity score of 10 (range, 4-15), and geriatric G8 score of 14 (range, 6-17) were enrolled. Twenty-two (38%) patients were treated with abiraterone and 36 (62%) with enzalutamide, while forty-two (72%) were in the pre-chemotherapy setting. Forty-seven patients (81%) had a caregiver. Based on the pill counting, a non-adherence rate of 4.8% and 6.2% was observed for the whole period and the first 3 months, respectively, without a statistically significant difference between abiraterone and enzalutamide cohorts. A lower non-adherence rate (1.3%) was reported by patients during the whole period, mainly due to a misperception (77%) and forgetfulness (19%). Non-adherence rate to the fulfilling of the clinical diary was 38% for the whole period. Non-adherence in the whole period was related to the radiological response (p = 0.03) and geriatric G8 score (p = 0.005). By the receiver operating characteristic (ROC) curve based on the radiological response, non-adherence cut-off was 1.87% (p = 0.04). By this non-adherence cut-off, the G8 cut-off was 14.75 (p = 0.0003). CONCLUSION: Non-adherence to abiraterone or enzalutamide for mCRPC may have an impact on disease response and be related to patients' frailty, suggesting their geriatric assessment and clinical interventions to monitor and increase their adherence.

An Advanced Bio-Inspired PhotoPlethysmoGraphy (PPG) and ECG Pattern Recognition System for Medical Assessment.

Physiological signals are widely used to perform medical assessment for monitoring an extensive range of pathologies, usually related to cardio-vascular diseases. Among these, both PhotoPlethysmoGraphy (PPG) and Electrocardiography (ECG) signals are those more employed. PPG signals are an emerging non-invasive measurement technique used to study blood volume pulsations through the detection and analysis of the back-scattered optical radiation coming from the skin. ECG is the process of recording the electrical activity of the heart over a period of time using electrodes placed on the skin. In the present paper we propose a physiological ECG/PPG "combo" pipeline using an innovative bio-inspired nonlinear system based on a reaction-diffusion mathematical model, implemented by means of the Cellular Neural Network (CNN) methodology, to filter PPG signal by assigning a recognition score to the waveforms in the time series. The resulting "clean" PPG signal exempts from distortion and artifacts is used to validate for diagnostic purpose an EGC signal simultaneously detected for a same patient. The multisite combo PPG-ECG system proposed in this work overpasses the limitations of the state of the art in this field providing a reliable system for assessing the above-mentioned physiological parameters and their monitoring over time for robust medical assessment. The proposed system has been validated and the results confirmed the robustness of the proposed approach.

Short-interval leg movements during sleep entail greater cardiac activation than periodic leg movements during sleep in restless legs syndrome patients.

Periodic leg movements during sleep (PLMS) are sequences of ≥4 motor events with intermovement intervals (IMI) of 10-90 s. PLMS are a supportive diagnostic criterion for restless legs syndrome (RLS) and entail cardiac activation, particularly when associated with arousal. RLS patients also over-express short-interval leg movements during sleep (SILMS), which have IMI <10 s and are organized mainly in sequences of two movements (doublets). We tested whether the cardiac activation associated with SILMS doublets differs from that associated with PLMS in a sample of 25 RLS patients. We analysed time-series of R-R intervals synchronized to the onset of SILMS doublets or PLMS that entailed an arousal during non-rapid eye movement (NREM) sleep. We assessed cardiac activation based on the R-R interval decrease with respect to baseline during NREM sleep without leg movements. We found that the duration of the R-R interval decrease with SILMS doublets was significantly longer than that with PLMS, whereas the maximal decrease in R-R interval was similar. Scoring SILMS in RLS patients may therefore be relevant from a cardiac autonomic perspective.

Sequence analysis of leg movements during sleep with different intervals (<10, 10-90 and >90 s) in restless legs syndrome.

The aim of this study was to define the time structure of leg movements during sleep occurring with an intermovement interval (onset-to-onset) shorter than 10 s in patients with restless legs syndrome and controls, and to compare it to the structure of movements with intervals of 10-90 s or >90 s. Polysomnographic recordings of 141 untreated patients and 68 age-matched normal controls were analysed. All movements were detected and classified into three categories, separated by intervals of <10, 10-90 or >90 s. The number of movements included in each category was significantly higher in patients than in controls. The movements with an interval of >90 s occurred steadily during the night, whereas the hourly distribution of movements with intervals of <10 or 10-90 s was decreasing or bell-shaped in patients or controls, respectively. Movements with an interval of <10 s tended to have a shorter duration and constituted shorter sequences than movements with intervals of 10-90 or >90 s. The time structure features of the three categories of movements considered in this study were found to be clearly different. This, together with previous observations on the differential effects of dopamine agonists on movements with different intervals, suggests that movements with intervals of <10 and >90 s are regulated by neurotransmitter mechanisms different from those modulating movements with an interval of 10-90 s.

Clinical validity of the Italian adaptation of the Uniform Data Set Neuropsychological Test Battery (I-UDSNB) in Mild Cognitive Impairment and Alzheimer's Disease.

BACKGROUND: The identification and staging of Alzheimer's Disease (AD) represent a challenge, especially in the prodromal stage of Mild Cognitive Impairment (MCI), when cognitive changes can be subtle. Worldwide efforts were dedicated to select and harmonize available neuropsychological instruments. In Italy, the Italian Network of Neuroscience and Neuro-Rehabilitation has promoted the adaptation of the Uniform Data Set Neuropsychological Test Battery (I-UDSNB), collecting normative data from 433 healthy controls (HC). Here, we aimed to explore the ability of I-UDSNB to differentiate between a) MCI and HC, b) AD and HC, c) MCI and AD. METHODS: One hundred thirty-seven patients (65 MCI, 72 AD) diagnosed after clinical-neuropsychological assessment, and 137 HC were included. We compared the I-UDSNB scores between a) MCI and HC, b) AD and HC, c) MCI and AD, with t-tests. To identify the test(s) most capable of differentiating between groups, significant scores were entered in binary logistic and in stepwise regressions, and then in Receiver Operating Characteristic curve analyses. RESULTS: Two episodic memory tests (Craft Story and Five Words test) differentiated MCI from HC subjects; Five Words test, Semantic Fluency (vegetables), and TMT-part B differentiated AD from, respectively, HC and MCI. CONCLUSIONS: Our findings indicate that the I-UDSNB is a suitable tool for the harmonized and concise assessment of patients with cognitive decline, showing high sensitivity and specificity for the diagnosis of MCI and AD.

Virtual Reality for the Rehabilitation of Acquired Cognitive Disorders: A Narrative Review.

This review article explores the use of Virtual Reality (VR) technology in cognitive rehabilitation for individuals with neurological conditions, such as stroke, traumatic brain injury, and neurodegenerative diseases. The introduction highlights the challenges posed by cognitive impairments and the limitations of traditional rehabilitation methods. VR is presented as a transformative tool that immerses individuals in interactive environments, offering promising opportunities for enhancing cognitive functions and improving quality of life. This article covers the foundational principles of VR, its applications across different clinical conditions and cognitive domains, and evaluates empirical evidence supporting its efficacy. It also discusses the advantages, limitations, challenges, and ethical considerations in the use of VR for cognitive rehabilitation. This review concludes by exploring future developments, including advancements in VR technology, the integration of Augmented Reality (AR) and artificial intelligence (AI), and the importance of standardized assessment tools for the objective evaluation of rehabilitation outcomes.

A new quantitative automatic method for the measurement of non-rapid eye movement sleep electroencephalographic amplitude variability.

The aim of this study was to arrange an automatic quantitative measure of the electroencephalographic (EEG) signal amplitude variability during non-rapid eye movement (NREM) sleep, correlated with the visually extracted cyclic alternating pattern (CAP) parameters. Ninety-eight polysomnographic EEG recordings of normal controls were used. A new algorithm based on the analysis of the EEG amplitude variability during NREM sleep was designed and applied to all recordings, which were also scored visually for CAP. All measurements obtained with the new algorithm correlated positively with corresponding CAP parameters. In particular, total CAP time correlated with total NREM variability time (r = 0.596; P < 1E-07), light sleep CAP time with light sleep variability time (r = 0.597; P < 1E-07) and slow wave sleep CAP time with slow wave sleep variability time (r = 0.809; P < 1E-07). Only the duration of CAP A phases showed a low correlation with the duration of variability events. Finally, the age-related modifications of CAP time and of NREM variability time were found to be very similar. The new method for the automatic analysis of NREM sleep amplitude variability presented here correlates significantly with visual CAP parameters; its application requires a minimum work time, compared to CAP analysis, and might be used in large studies involving numerous recordings in which NREM sleep EEG amplitude variability needs to be assessed.

Italian adaptation of the Uniform Data Set Neuropsychological Test Battery (I-UDSNB 1.0): development and normative data.

BACKGROUND: Neuropsychological testing plays a cardinal role in the diagnosis and monitoring of Alzheimer's disease. A major concern is represented by the heterogeneity of the neuropsychological batteries currently adopted in memory clinics and healthcare centers. The current study aimed to solve this issue. METHODS: Following the initiative of the University of Washington's National Alzheimer's Coordinating Center (NACC), we presented the Italian adaptation of the Neuropsychological Test Battery of the Uniform Data Set (I-UDSNB). We collected data from 433 healthy Italian individuals and employed regression models to evaluate the impact of demographic variables on the performance, deriving the reference norms. RESULTS: Higher education and lower age were associated with a better performance in the majority of tests, while sex affected only fluency tests and Digit Span Forward. CONCLUSIONS: The I-UDSNB offers a valuable and harmonized tool for neuropsychological testing in Italy, to be used in clinical and research settings.

A Benchmark Evaluation of Adaptive Image Compression for Multi Picture Object Stereoscopic Images.

A stereopair consists of two pictures related to the same subject taken by two different points of view. Since the two images contain a high amount of redundant information, new compression approaches and data formats are continuously proposed, which aim to reduce the space needed to store a stereoscopic image while preserving its quality. A standard for multi-picture image encoding is represented by the MPO format (Multi-Picture Object). The classic stereoscopic image compression approaches compute a disparity map between the two views, which is stored with one of the two views together with a residual image. An alternative approach, named adaptive stereoscopic image compression, encodes just the two views independently with different quality factors. Then, the redundancy between the two views is exploited to enhance the low quality image. In this paper, the problem of stereoscopic image compression is presented, with a focus on the adaptive stereoscopic compression approach, which allows us to obtain a standardized format of the compressed data. The paper presents a benchmark evaluation on large and standardized datasets including 60 stereopairs that differ by resolution and acquisition technique. The method is evaluated by varying the amount of compression, as well as the matching and optimization methods resulting in 16 different settings. The adaptive approach is also compared with other MPO-compliant methods. The paper also presents an Human Visual System (HVS)-based assessment experiment which involved 116 people in order to verify the perceived quality of the decoded images.

Deep Neuro-Vision Embedded Architecture for Safety Assessment in Perceptive Advanced Driver Assistance Systems: The Pedestrian Tracking System Use-Case.

In recent years, the automotive field has been changed by the accelerated rise of new technologies. Specifically, autonomous driving has revolutionized the car manufacturer's approach to design the advanced systems compliant to vehicle environments. As a result, there is a growing demand for the development of intelligent technology in order to make modern vehicles safer and smarter. The impact of such technologies has led to the development of the so-called Advanced Driver Assistance Systems (ADAS), suitable to maintain control of the vehicle in order to avoid potentially dangerous situations while driving. Several studies confirmed that an inadequate driver's physiological condition could compromise the ability to drive safely. For this reason, assessing the car driver's physiological status has become one of the primary targets of the automotive research and development. Although a large number of efforts has been made by researchers to design safety-assessment applications based on the detection of physiological signals, embedding them into a car environment represents a challenging task. These mentioned implications triggered the development of this study in which we proposed an innovative pipeline, that through a combined less invasive Neuro-Visual approach, is able to reconstruct the car driver's physiological status. Specifically, the proposed contribution refers to the sampling and processing of the driver PhotoPlethysmoGraphic (PPG) signal. A parallel enhanced low frame-rate motion magnification algorithm is used to reconstruct such features of the driver's PhotoPlethysmoGraphic (PPG) data when that signal is no longer available from the native embedded sensor platform. A parallel monitoring of the driver's blood pressure levels from the PPG signal as well as the driver's eyes dynamics completes the reconstruction of the driver's physiological status. The proposed pipeline has been tested in one of the major investigated automotive scenarios i.e., the detection and monitoring of pedestrians while driving (pedestrian tracking). The collected performance results confirmed the effectiveness of the proposed approach.

Three-Dimensional Deep Noninvasive Radiomics for the Prediction of Disease Control in Patients With Metastatic Urothelial Carcinoma treated With Immunotherapy.

INTRODUCTION: Immunotherapy is effective in a small percentage of patients with cancer and no reliable predictive biomarkers are currently available. Artificial Intelligence algorithms may automatically quantify radiologic characteristics associated with disease response to medical treatments. METHODS: We investigated an innovative approach based on a 3-dimensional (3D) deep radiomics pipeline to classify visual features of chest-abdomen computed tomography (CT) scans with the aim of distinguishing disease control from progressive disease to immune checkpoint inhibitors (ICIs). Forty-two consecutive patients with metastatic urothelial cancer had progressed on first-line platinum-based chemotherapy and had baseline CT scans at immunotherapy initiation. The 3D-pipeline included self-learned visual features and a deep self-attention mechanism. According to the outcome to the ICIs, a 3D deep classifier semiautomatically categorized the most discriminative region of interest on the CT scans. RESULTS: With a median follow-up of 13.3 months (95% CI, 11.1-15.6), the median overall survival was 8.5 months (95% CI, 3.1-13.8). According to disease response to immunotherapy, the median overall survival was 3.6 months (95% CI, 2.0-5.2) for patients with progressive disease; it was not yet reached for those with disease control. The predictive accuracy of the 3D-pipeline was 82.5% (sensitivity 96%; specificity, 60%). The addition of baseline clinical factors increased the accuracy to 92.5% by improving specificity to 87%; the accuracy of other architectures ranged from 72.5% to 90%. CONCLUSION: Artificial Intelligence by 3D deep radiomics is a potential noninvasive biomarker for the prediction of disease control to ICIs in metastatic urothelial cancer and deserves validation in larger series.

An explainable AI system for automated COVID-19 assessment and lesion categorization from CT-scans.

COVID-19 infection caused by SARS-CoV-2 pathogen has been a catastrophic pandemic outbreak all over the world, with exponential increasing of confirmed cases and, unfortunately, deaths. In this work we propose an AI-powered pipeline, based on the deep-learning paradigm, for automated COVID-19 detection and lesion categorization from CT scans. We first propose a new segmentation module aimed at automatically identifying lung parenchyma and lobes. Next, we combine the segmentation network with classification networks for COVID-19 identification and lesion categorization. We compare the model's classification results with those obtained by three expert radiologists on a dataset of 166 CT scans. Results showed a sensitivity of 90.3% and a specificity of 93.5% for COVID-19 detection, at least on par with those yielded by the expert radiologists, and an average lesion categorization accuracy of about 84%. Moreover, a significant role is played by prior lung and lobe segmentation, that allowed us to enhance classification performance by over 6 percent points. The interpretation of the trained AI models reveals that the most significant areas for supporting the decision on COVID-19 identification are consistent with the lesions clinically associated to the virus, i.e., crazy paving, consolidation and ground glass. This means that the artificial models are able to discriminate a positive patient from a negative one (both controls and patients with interstitial pneumonia tested negative to COVID) by evaluating the presence of those lesions into CT scans. Finally, the AI models are integrated into a user-friendly GUI to support AI explainability for radiologists, which is publicly available at http://perceivelab.com/covid-ai. The whole AI system is unique since, to the best of our knowledge, it is the first AI-based software, publicly available, that attempts to explain to radiologists what information is used by AI methods for making decisions and that proactively involves them in the decision loop to further improve the COVID-19 understanding.

MASK-RL: Multiagent Video Object Segmentation Framework Through Reinforcement Learning.

Integrating human-provided location priors into video object segmentation has been shown to be an effective strategy to enhance performance, but their application at large scale is unfeasible. Gamification can help reduce the annotation burden, but it still requires user involvement. We propose a video object segmentation framework that leverages the combined advantages of user feedback for segmentation and gamification strategy by simulating multiple game players through a reinforcement learning (RL) model that reproduces human ability to pinpoint moving objects and using the simulated feedback to drive the decisions of a fully convolutional deep segmentation network. Experimental results on the DAVIS-17 benchmark show that: 1) including user-provided prior, even if not precise, yields high performance; 2) our RL agent replicates satisfactorily the same variability of humans in identifying spatiotemporal salient objects; and 3) employing artificially generated priors in an unsupervised video object segmentation model reaches state-of-the-art performance.

3D Non-Local Neural Network: A Non-Invasive Biomarker for Immunotherapy Treatment Outcome Prediction. Case-Study: Metastatic Urothelial Carcinoma.

Immunotherapy is regarded as one of the most significant breakthroughs in cancer treatment. Unfortunately, only a small percentage of patients respond properly to the treatment. Moreover, to date, there are no efficient bio-markers able to early discriminate the patients eligible for this treatment. In order to help overcome these limitations, an innovative non-invasive deep pipeline, integrating Computed Tomography (CT) imaging, is investigated for the prediction of a response to immunotherapy treatment. We report preliminary results collected as part of a case study in which we validated the implemented method on a clinical dataset of patients affected by Metastatic Urothelial Carcinoma. The proposed pipeline aims to discriminate patients with high chances of response from those with disease progression. Specifically, the authors propose ad-hoc 3D Deep Networks integrating Self-Attention mechanisms in order to estimate the immunotherapy treatment response from CT-scan images and such hemato-chemical data of the patients. The performance evaluation (average accuracy close to 92%) confirms the effectiveness of the proposed approach as an immunotherapy treatment response biomarker.

The Promise of Digital Biopsy for the Prediction of Tumor Molecular Features and Clinical Outcomes Associated With Immunotherapy.

Immunotherapy by immune checkpoint inhibitors has emerged as an effective treatment for a slight proportion of patients with aggressive tumors. Currently, some molecular determinants, such as the expression of the programmed cell death ligand-1 (PD-L1) or the tumor mutational burden (TMB) have been used in the clinical practice as predictive biomarkers, although they fail in consistency, applicability, or reliability to precisely identify the responding patients mainly because of their spatial intratumoral heterogeneity. Therefore, new biomarkers for early prediction of patient response to immunotherapy, that could integrate several approaches, are eagerly sought. Novel methods of quantitative image analysis (such as radiomics or pathomics) might offer a comprehensive approach providing spatial and temporal information from macroscopic imaging features potentially predictive of underlying molecular drivers, tumor-immune microenvironment, tumor-related prognosis, and clinical outcome (in terms of response or toxicity) following immunotherapy. Preliminary results from radiomics and pathomics analysis have demonstrated their ability to correlate image features with PD-L1 tumor expression, high CD3 cell infiltration or CD8 cell expression, or to produce an image signature concordant with gene expression. Furthermore, the predictive power of radiomics and pathomics can be improved by combining information from other modalities, such as blood values or molecular features, leading to increase the accuracy of these models. Thus, "digital biopsy," which could be defined by non-invasive and non-consuming digital techniques provided by radiomics and pathomics, may have the potential to allow for personalized approach for cancer patients treated with immunotherapy.

[Vinflunine: still an option for patients with advanced urothelial carcinoma following immune-checkpoint inhibitors?] / Vinflunina: ancora un'opzione per i pazienti con carcinoma uroteliale avanzato trattati con inibitori dei checkpoint immunitari?

INTRODUCTION: The treatment of metastatic urothelial cancer (mUC) following first-line standard platinum-based chemotherapy and immune checkpoint inhibitors (ICIs) is not yet established. MATERIAL AND METHODS: We investigated the activity and toxicity of vinflunine at the dose, due to previous treatments, of 280 mg i.v. every 21 days until disease progression or limiting toxicity, with instrumental disease reassessment every 3 cycles, in 6 patients aged ≥18 years, with metastatic urothelial carcinoma of the upper or lower urinary tract, with performance status (PS) according to the Eastern Cooperative Oncology Group (ECOG) of 0-2, adequate hematologic function and progressive disease (PD) following first-line platinum-based chemotherapy and second-line ICI. RESULTS: The median age of the 6 patients was 67.5 years (range 63-77) and median PS 1 (range, 0-2). Four patients (67%) had a disease partial response (PR). With a median follow-up of 4.5 months (range, 3-9), 3 patients are alive (50%). The median progression-free survival following vinflunine (PFS-3) was 4 months (range, 1-8), as compared to the PFS-2 (following ICI) of 4 months (range, 2-9) and the PFS-1 (after platinum-based chemotherapy) of 6 months (range, 2-13). The PRs were not associated with the length of PFS-2 of PFS-1, the histologic subtype, primary and metastatic site of the tumour. No grade 3-4 toxicity has been observed; grade 2 asthenia occurred in 3 patients (50%), grade 1 nausea and constipation were observed in one patient (17%), respectively. CONCLUSION: Despite the low number of patients treated, the activity of vinflunine was substantial and suggests its role as chemotherapy line following previous chemotherapy and immunotherapy, deserving further retrospective or prospective investigations in this setting.