Contributions of biomechanical modeling and machine learning to the automatic registration of Multiparametric Magnetic Resonance and Transrectal Echography for prostate brachytherapy

El cáncer de próstata (CaP) es el primer cáncer por incidencia en hombres en países occidentales, y el tercero en mortalidad. Tras detectar en sangre una elevación del Antígeno Prostático Específico (PSA) o tras tacto rectal sospechoso se realiza una Resonancia Magnética (RM) de la próstata, que los radiólogos analizan para localizar las regiones sospechosas. A continuación, estas se biopsian, es decir, se toman muestras vivas que posteriormente serán analizadas histopatológicamente para confirmar la presencia de cáncer y establecer su grado de agresividad. Durante la biopsia se emplea típicamente Ultrasonidos (US) para el guiado y la localización de las lesiones. Sin embargo, estas no son directamente visibles en US, y el urólogo necesita usar software de fusión que realice un registro RM-US que transfiera la localizaciones marcadas en MR al US. Esto es fundamental para asegurar que las muestras tomadas provienen verdaderamente de la zona sospechosa. En este trabajo se compendian cinco publicaciones que emplean diversos algoritmos de Inteligencia Artificial (IA) para analizar las imágenes de próstata (RM y US) y con ello mejorar la eficiencia y precisión en el diagnóstico, biopsia y tratamiento del CaP: 1. Segmentación automática de próstata en RM y US: Segmentar la próstata consiste en delimitar o marcar la próstata en una imagen médica, separándola del resto de órganos o estructuras. Automatizar por completo esta tarea, que es previa a todo análisis posterior, permite ahorrar un tiempo significativo a radiólogos y urólogos, mejorando también la precisión y repetibilidad. 2. Mejora de la resolución de segmentación: Se presenta una metodología para mejorar la resolución de las segmentaciones anteriores. 3. Detección y clasificación automática de lesiones en RM: Se entrena un modelo basado en IA para detectar las lesiones como lo haría un radiólogo, asignándoles también una estimación del riesgo. Se logra mejorar la precisión diagnóstica, dando lugar a un sistema totalmente automático que podría implantarse para segunda opinión clínica o como criterio para priorización. 4. Simulación del comportamiento biomecánico en tiempo real: Se propone acelerar la simulación del comportamiento biomecánico de órganos blandos mediante el uso de IA. 5. Registro automático RM-US: El registro permite localizar en US las lesiones marcadas en RM. Una alta precisión en esta tarea es esencial para la corrección de la biopsia y/o del tratamiento focal del paciente (como braquiterapia de alta tasa). Se plantea el uso de la IA para resolver el problema de registro en tiempo casi real, utilizando modelos biomecánicos subyacentes.Prostate cancer (PCa) is the most common malignancy in western males, and third by mortality. After detecting elevated Prostate Specific Antigen (PSA) blood levels or after a suspicious rectal examination, a Magnetic Resonance (MR) image of the prostate is acquired and assessed by radiologists to locate suspicious regions. These are then biopsied, i.e. living tissue samples are collected and analyzed histopathologically to confirm the presence of cancer and establish its degree of aggressiveness. During the biopsy procedure, Ultrasound (US) is typically used for guidance and lesion localization. However, lesions are not directly visible in US, and the urologist needs to use fusion software to performs MR-US registration, so that the MR-marked locations can be transferred to the US image. This is essential to ensure that the collected samples truly come from the suspicious area. This work compiles five publications employing several Artificial Intelligence (AI) algorithms to analyze prostate images (MR and US) and thereby improve the efficiency and accuracy in diagnosis, biopsy and treatment of PCa: 1. Automatic prostate segmentation in MR and US: Prostate segmentation consists in delimiting or marking the prostate in a medical image, separating it from the rest of the organs or structures. Automating this task fully, which is required for any subsequent analysis, saves significant time for radiologists and urologists, while also improving accuracy and repeatability. 2. Segmentation resolution enhancement: A methodology for improving the resolution of the previously obtained segmentations is presented. 3. Automatic detection and classification of MR lesions: An AI model is trained to detect lesions as a radiologist would and to estimate their risk. The model achieves improved diagnostic accuracy, resulting in a fully automatic system that could be used as a second clinical opinion or as a criterion for patient prioritization. 4. Simulation of biomechanical behavior in real time: It is proposed to accelerate the simulation of biomechanical behavior of soft organs using AI. 5. Automatic MR-US registration: Registration allows localization of MR-marked lesions on US. High accuracy in this task is essential for the correctness of the biopsy and/or focal treatment procedures (such as high-rate brachytherapy). Here, AI is used to solve the registration problem in near-real time, while exploiting underlying biomechanically-compatible models

Mathematical Modeling for Neuropathic Pain: Bayesian Linear Regression and Self-Organizing Maps Applied to Carpal Tunnel Syndrome

A better understanding of the connection between risk factors associated with pain andfunction may assist therapists in optimizing therapeutic programs. This study applied mathematicalmodeling to analyze the relationship of psychological, psychophysical, and motor variables with pain,function, and symptom severity using Bayesian linear regressions (BLR) and self-organizing maps(SOMs) in carpal tunnel syndrome (CTS). The novelty of this work was a transfer of the symmetrymathematical background to a neuropathic pain condition, whose symptoms can be either unilateralor bilateral. Duration of symptoms, pain intensity, function, symptom severity, depressive levels,pinch tip grip force, and pressure pain thresholds (PPTs) over the ulnar, radial, and median nervetrunks, the cervical spine, the carpal tunnel, and the tibialis anterior were collected in 208 womensuffering from CTS. The first BLR model revealed that symptom severity, PPTs over the radialnerve, and function had significant correlations with pain intensity. The second BLR showed thatsymptom severity, depressive levels, pain intensity, and years with pain were associated with function.The third model demonstrated that pain intensity and function were associated with symptom severity.The SOMs visualized these correlations among variables, i.e., clinical, psychophysical, and physical,and identified a subgroup of women with CTS exhibiting worse clinical features, higher pressuresensitivity, and lower pinch tip grip force. Therefore, the application of mathematical modelingidentified some interactions among the intensity of pain, function, and symptom severity in womenwith CTS

Patient Profiling Based on Spectral Clustering for an Enhanced Classification of Patients with Tension-Type Headache

Profiling groups of patients in clusters can provide meaningful insights into the features of the population, thus helping to identify people at risk of chronification and the development of specific therapeutic strategies. Our aim was to determine if spectral clustering is able to distinguish subgroups (clusters) of tension-type headache (TTH) patients, identify the profile of each group, and argue about potential di erent therapeutic interventions. A total of 208 patients (n = 208) with TTH participated. Headache intensity, frequency, and duration were collected with a 4-week diary. Anxiety and depressive levels, headache-related burden, sleep quality, health-related quality of life, pressure pain thresholds (PPTs), dynamic pressure thresholds (DPT) and evoked-pain, and the number of trigger points (TrPs) were evaluated. Spectral clustering was used to identify clusters of patients without any previous assumption. A total of three clusters of patients based on a main difference on headache frequency were identified: one cluster including patients with chronic TTH (cluster 2) and two clusters including patients with episodic TTH (clusters 0-1). Patients in cluster 2 showed worse scores in all outcomes than those in clusters 0-1. A subgroup of patients with episodic TTH exhibited pressure pain hypersensitivity (cluster 0) similarly to those with chronic TTH (cluster 2). Spectral clustering was able to confirm subgrouping of patients with TTH by headache frequency and to identify a group of patients with episodic TTH with higher sensitization, which may need particular attention and specific therapeutic programs for avoiding potential chronification

Headache as a COVID-19 onset symptom and post-COVID-19 symptom in hospitalized COVID-19 survivors infected with the Wuhan, Alpha, or Delta SARS-CoV-2 variants

OBJECTIVE: This study looked at differences in the presence of headache as an onset symptom of coronavirus disease 2019 (COVID‐19) and as a post‐COVID‐19 symptom in individuals previously hospitalized owing to infection with the Wuhan, Alpha, or Delta variants of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2). BACKGROUND: Headache can be present in up to 50% of individuals during the acute phase of SARS‐CoV‐2 infection and in 10% of subjects during the post‐COVID‐19 phase. There are no data on differences in the occurrence of headache in the acute‐ and post‐COVID‐19 phase according to the SARS‐CoV‐2 variants. METHODS: A cross‐sectional cohort study was conducted. Unvaccinated subjects previously hospitalized for COVID‐19 caused by the Wuhan (n = 201), Alpha (n = 211), or Delta (n = 202) SARS‐CoV‐2 variants were scheduled for a telephone interview 6 months after hospital discharge. Hospitalization data were collected from hospital medical records. RESULTS: The presence of headache as a COVID‐19 onset symptom at hospitalization was higher in subjects with the Delta variant (66/202, 32.7%) than in those infected with the Wuhan (42/201, 20.9%; odds ratio [OR] 1.83, 95% confidence interval [CI] 1.17–2.88) or Alpha (25/211, 11.8%; OR 3.61, 95% CI, 2.16–6.01) variants. The prevalence of post‐COVID‐19 headache 6 months after hospital discharge was higher in individuals infected with the Delta variant (26/202, 12.9%) than in those infected with the Wuhan (11/201, 5.5%; OR 2.52, 95% CI 1.22–5.31) or Alpha (eight of 211, 3.8%; OR 3.74, 95% CI 1.65–8.49) variants. The presence of headache as a COVID‐19 onset symptom was associated with post‐COVID‐19 headache in subjects infected with the Wuhan (OR 7.75, 95% CI 2.15–27.93) and Delta variants (OR 2.78, 95% CI 1.20–6.42) but not with the Alpha variant (OR 2.60, 95% CI 0.49–13.69). CONCLUSION: Headache was a common symptom in both the acute‐ and post‐COVID‐19 phase in subjects infected with the Wuhan, Alpha, and Delta variants but mostly in those infected with the Delta variant

Spectral Clustering Reveals Different Profiles of Central Sensitization in Women with Carpal Tunnel Syndrome

Identification of subgroups of patients with chronic pain provides meaningful insights into the characteristics of a specific population, helping to identify individuals at risk of chronification and to determine appropriate therapeutic strategies. This paper proposes the use of spectral clustering (SC) to distinguish subgroups (clusters) of individuals with carpal tunnel syndrome (CTS), making use of the obtained patient profiling to argue about potential management implications. SC is a powerful algorithm that builds a similarity graph among the data points (the patients), and tries to find the subsets of points that are strongly connected among themselves, but weakly connected to others. It was chosen due to its advantages with respect to other simpler clustering techniques, such as k-means, and the fact that it has been successfully applied to similar problems. Clinical (age, duration of symptoms, pain intensity, function, and symptom severity), psycho-physical (pressure pain thresholds¿PPTs¿over the three main nerve trunks of the upper extremity, cervical spine, carpal tunnel, and tibialis anterior), psychological (depressive levels), and motor (pinch tip grip force) variables were collected in 208 women with clinical/electromyographic diagnosis of CTS, whose symptoms usually started unilaterally but eventually evolved into bilateral symmetry. SC was used to identify clusters of patients without any previous assumptions, yielding three clusters. Patients in cluster 1 exhibited worse clinical features, higher widespread pressure pain hyperalgesia, higher depressive levels, and lower pinch tip grip force than the other two. Patients in cluster 2 showed higher generalized thermal pain hyperalgesia than the other two. Cluster 0 showed less hypersensitivity to pressure and thermal pain, less severe clinical features, and more normal motor output (tip grip force). The presence of subgroups of individuals with different altered nociceptive processing (one group being more sensitive to pressure pain and another group more sensitive to thermal pain) could lead to different therapeutic programs

Trajectory of post-COVID brain fog, memory loss, and concentration loss in previously hospitalized COVID-19 survivors:the LONG-COVID-EXP multicenter study

OBJECTIVE: This study aimed to apply Sankey plots and exponential bar plots for visualizing the trajectory of post-COVID brain fog, memory loss, and concentration loss in a cohort of previously hospitalized COVID-19 survivors.METHODS: A sample of 1,266 previously hospitalized patients due to COVID-19 during the first wave of the pandemic were assessed at 8.4 (T1), 13.2 (T2), and 18.3 (T3) months after hospital discharge. They were asked about the presence of the following self-reported cognitive symptoms: brain fog (defined as self-perception of sluggish or fuzzy thinking), memory loss (defined as self-perception of unusual forgetfulness), and concentration loss (defined as self-perception of not being able to maintain attention). We asked about symptoms that individuals had not experienced previously, and they attributed them to the acute infection. Clinical and hospitalization data were collected from hospital medical records.RESULTS: The Sankey plots revealed that the prevalence of post-COVID brain fog was 8.37% (n = 106) at T1, 4.7% (n = 60) at T2, and 5.1% (n = 65) at T3, whereas the prevalence of post-COVID memory loss was 14.9% (n = 189) at T1, 11.4% (n = 145) at T2, and 12.12% (n = 154) at T3. Finally, the prevalence of post-COVID concentration loss decreased from 6.86% (n = 87) at T1, to 4.78% (n = 60) at T2, and to 2.63% (n = 33) at T3. The recovery exponential curves show a decreasing trend, indicating that these post-COVID cognitive symptoms recovered in the following years after discharge. The regression models did not reveal any medical record data associated with post-COVID brain fog, memory loss, or concentration loss in the long term.CONCLUSION: The use of Sankey plots shows a fluctuating evolution of post-COVID brain fog, memory loss, or concentration loss during the first years after the infection. In addition, exponential bar plots revealed a decrease in the prevalence of these symptoms during the first years after hospital discharge. No risk factors were identified in this cohort.</p

Robust Resolution-Enhanced Prostate Segmentation in Magnetic Resonance and Ultrasound Images through Convolutional Neural Networks

[EN] Prostate segmentations are required for an ever-increasing number of medical applications, such as image-based lesion detection, fusion-guided biopsy and focal therapies. However, obtaining accurate segmentations is laborious, requires expertise and, even then, the inter-observer variability remains high. In this paper, a robust, accurate and generalizable model for Magnetic Resonance (MR) and three-dimensional (3D) Ultrasound (US) prostate image segmentation is proposed. It uses a densenet-resnet-based Convolutional Neural Network (CNN) combined with techniques such as deep supervision, checkpoint ensembling and Neural Resolution Enhancement. The MR prostate segmentation model was trained with five challenging and heterogeneous MR prostate datasets (and two US datasets), with segmentations from many different experts with varying segmentation criteria. The model achieves a consistently strong performance in all datasets independently (mean Dice Similarity Coefficient -DSC- above 0.91 for all datasets except for one), outperforming the inter-expert variability significantly in MR (mean DSC of 0.9099 vs. 0.8794). When evaluated on the publicly available Promise12 challenge dataset, it attains a similar performance to the best entries. In summary, the model has the potential of having a significant impact on current prostate procedures, undercutting, and even eliminating, the need of manual segmentations through improvements in terms of robustness, generalizability and output resolutionThis work has been partially supported by a doctoral grant of the Spanish Ministry of Innovation and Science, with reference FPU17/01993Pellicer-Valero, OJ.; González-Pérez, V.; Casanova Ramón-Borja, JL.; Martín García, I.; Barrios Benito, M.; Pelechano Gómez, P.; Rubio-Briones, J.... (2021). 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Associated-Onset Symptoms and Post-COVID-19 Symptoms in Hospitalized COVID-19 Survivors Infected with Wuhan, Alpha or Delta SARS-CoV-2 Variant

This study compared associated-symptoms at the acute phase of infection and post-COVID-19 symptoms between individuals hospitalized with the Wuhan, Alpha or Delta SARS-CoV-2 variant. Non-vaccinated individuals hospitalized because of SARS-CoV-2 infection in one hospital during three different waves of the pandemic (Wuhan, Alpha or Delta) were scheduled for a telephone interview. The presence of post-COVID-19 symptoms was systematically assessed. Hospitalization and clinical data were collected from medical records. A total of 201 patients infected with the Wuhan variant, 211 with the Alpha variant and 202 with Delta variant were assessed six months after hospitalization. Patients infected with the Wuhan variant had a greater number of symptoms at hospital admission (higher prevalence of fever, dyspnea or gastrointestinal problems) than those infected with Alpha or Delta variant (p < 0.01). A greater proportion of patients infected with the Delta variant reported headache, anosmia or ageusia as onset symptoms (p < 0.01). The mean number of post-COVID-19 symptoms was higher (p < 0.001) in individuals infected with the Wuhan variant (mean: 2.7 ± 1.3) than in those infected with the Alpha (mean: 1.8 ± 1.1) or Delta (mean: 2.1 ± 1.5) variant. Post-COVID-19 dyspnea was more prevalent (p < 0.001) in people infected with the Wuhan variant, whereas hair loss was higher in those infected with the Delta variant (p = 0.002). No differences in post-COVID-19 fatigue by SARS-CoV-2 variant were found (p = 0.594). Differences in COVID-19 associated onset symptoms and post-COVID-19 dyspnea were observed depending on the SARS-CoV-2 variant. The presence of fatigue was a common post-COVID-19 symptom to all SARS-CoV-2 variants

Prevalence of Musculoskeletal Post-COVID Pain in Hospitalized COVID-19 Survivors Depending on Infection with the Historical, Alpha or Delta SARS-CoV-2 Variant

We compared the prevalence of musculoskeletal post-COVID pain between previously hospitalized COVID-19 survivors infected with the historical, Alpha or Delta SARS-CoV-2 variant. Data about musculoskeletal post-COVID pain were systematically collected through a telephone interview involving 201 patients who had survived the historical variant, 211 who had survived the Alpha variant and 202 who had survived the Delta variant six months after hospital discharge. Participants were recruited from non-vaccinated individuals hospitalized due to SARS-CoV-2 infection in one hospital of Madrid (Spain) during three different waves of the pandemic (historical, Alpha or Delta variant). Hospitalization and clinical data were collected from hospital medical records. In addition, anxiety/depressive levels and sleep quality were also assessed. The prevalence of musculoskeletal post-COVID pain was higher (p = 0.003) in patients infected with the historical variant (47.7%) than in those infected with the Alpha (38.3%) or Delta (41%) variants. A significantly (p = 0.002) higher proportion of individuals infected with the historical variant reported generalized pain (20.5%) when compared with those infected with the other variants. The prevalence of new-onset post-COVID musculoskeletal pain reached 80.1%, 75.2% and 79.5% of patients infected with the historical, Alpha or Delta variants, respectively. No specific risk factors for developing post-COVID pain were identified depending on the SARS-CoV-2 variant. In conclusion, this study found that musculoskeletal post-COVID pain is highly prevalent in COVID-19 survivors six months after hospital discharge, with the highest prevalence and most generalized pain symptoms in individuals infected with the historical variant. Approximately 50% developed “de novo” post-COVID musculoskeletal pain symptoms