Purpose: Because of atypical response imaging patterns in patients with metastatic non-small cell lung cancer (NSCLC) treated with immune checkpoint inhibitors (ICPIs), new biomarkers are needed for a better monitoring of treatment efficacy. The aim of this prospective study was to evaluate the prognostic value of volume-derived positron-emission tomography (PET) parameters on baseline and follow-up ¹⁸F-fluoro-deoxy-glucose PET (¹⁸F-FDG-PET) scans and compare it with the conventional PET Response Criteria in Solid Tumors (PERCIST). Methods: Patients with metastatic NSCLC were included in two different single-center prospective trials. ¹⁸F-FDG-PET studies were performed before the start of immunotherapy (PET_baseline), after 6–8 weeks (PET_interim1) and after 12–16 weeks (PET_interim2) of treatment, using PERCIST criteria for tumor response assessment. Different metabolic parameters were evaluated: absolute values of maximum standardized uptake value (SUVmax) of the most intense lesion, total metabolic tumor volume (TMTV), total lesion glycolysis (TLG), but also their percentage changes between PET studies (ΔSUVmax, ΔTMTV and ΔTLG). The median follow-up of patients was 31 (7.3–31.8) months. Prognostic values and optimal thresholds of PET parameters were estimated by ROC (Receiver Operating Characteristic) curve analysis of 12-month overall survival (12M-OS) and 6-month progression-free survival (6M-PFS). Tumor progression needed to be confirmed by a multidisciplinary tumor board, considering atypical response patterns on imaging. Results: 110 patients were prospectively included. On PET_baseline, TMTV was predictive of 12M-OS [AUC (Area Under Curve) =0.64; 95% CI: 0.61 to 0.66] whereas SUVmax and TLG were not. On PET_interim1 and PET_interim2, all metabolic parameters were predictive for 12M-OS and 6M-PFS, the residual TMTV on PET_interim1 (TMTV₁) being the strongest prognostic biomarker (AUC=0.83 and 0.82; 95% CI: 0.74 to 0.91, for 12M-OS and 6M-PFS, respectively). Using the optimal threshold by ROC curve to classify patients into three TMTV₁ subgroups (0 cm³; 0–57 cm³; >57 cm³), TMTV₁ prognostic stratification was independent of PERCIST criteria on both PFS and OS, and significantly outperformed them. Subgroup analysis demonstrated that TMTV₁ remained a strong prognostic biomarker of 12M-OS for non-responding patients (p=0.0003) according to PERCIST criteria. In the specific group of patients with PERCIST progression on PET_interim1, low residual tumor volume (<57 cm³) was still associated with a very favorable patients’ outcome (6M-PFS=73%; 24M-OS=55%). Conclusion: The absolute value of residual metabolic tumor volume, assessed 6–8 weeks after the start of ICPI, is an optimal and independent prognostic measure, exceeding and complementing conventional PERCIST criteria. Oncologists should consider it in patients with first tumor progression according to PERCIST criteria, as it helps identify patients who benefit from continued treatment. Trial registration number: 2018-A02116-49; NCT03584334.

Proprioception is a critical component for motor functions and directly affects motor learning after neurological injuries. Conventional methods for its assessment are generally ordinal in nature and hence lack sensitivity. Robotic devices designed to promote sensorimotor learning can potentially provide quantitative precise, accurate, and reliable assessments of sensory impairments. In this paper, we investigate the clinical applicability and validity of using a planar 2 degrees of freedom robot to quantitatively assess proprioceptive deficits in post-stroke participants. Nine stroke survivors and nine healthy subjects participated in the study. Participants’ hand was passively moved to the target position guided by the H-Man robot (Criterion movement) and were asked to indicate during a second passive movement towards the same target (Matching movement) when they felt that they matched the target position. The assessment was carried out on a planar surface for movements in the forward and oblique directions in the contralateral and ipsilateral sides of the tested arm. The matching performance was evaluated in terms of error magnitude (absolute and signed) and its variability. Stroke patients showed higher variability in the estimation of the target position compared to the healthy participants. Further, an effect of target was found, with lower absolute errors in the contralateral side. Pairwise comparison between individual stroke participant and control participants showed significant proprioceptive deficits in two patients. The proposed assessment of passive joint position sense was inherently simple and all participants, regardless of motor impairment level, could complete it in less than 10 minutes. Therefore, the method can potentially be carried out to detect changes in proprioceptive deficits in clinical settings.

Proprioception - the sense of body awareness - is frequently impaired after stroke. Clinical tests used to detect proprioceptive impairments require substantial amounts of time and lack sensitivity. In contrast, robotic devices demonstrated that they can objectively assess subjects' proprioceptive acuity. We here present results of a robot-aided bimanual test for wrist proprioception. Ten acute stroke patients with hemiplegic arm weakness and ten healthy subjects, participated to the experiment. Subjects actively moved the wrist of the unaffected limb to match the reference position of the affected wrist, which was passively displaced by the robot to a target across six directions. Our results are as follows. First, healthy individuals showed no effect of hand dominance. No acuity differences were found for between dominant or non-dominant hand. Second, the matching error varied significantly across movement directions. Third, subjects with stroke were less accurate than healthy individuals in matching the wrist position. Fourth, the assessment is clinically feasible. Stroke patients completed the test in about 8 minutes. We conclude that assessment of proprioception in stroke should map positions at different joint degrees of freedom to detect underlying proprioceptive abnormalities.

The selection of patients for the constitution of a cohort is a major issue for clinical research (prospective studies and retrospective studies in real life). Our objective was to validate in real life conditions the use of a Deep Learning process based on a neural network, for the classification of patients according to the pathology involved in a head and neck surgery department. 24,434 Electronic Health Records (EHR) from the first visit between 2000 and 2020 were extracted. More than 6000 EHR were manually classified in ten groups of interest according to the reason for consultation with a clinical relevance. A convolutional neural network (TensorFlow, previously reported by Hsu et al.) was then used to predict the group of patients based on their pathology, using two levels of classification based on clinically relevant criteria. On the first and second level of classification, macro-average performances were: 0.95, 0.83, 0.85, 0.97, 0.84 and 0.93, 0.76, 0.83, 0.96, 0.79 for accuracy, recall, precision, specificity and F1-score versus accuracy, recall and precision of 0.580, 580 and 0.582 for Hsu et al., respectively. We validated this model to predict the pathology involved and to constitute clinically relevant cohorts in a tertiary hospital. This model did not require a preprocessing stage, was used in French and showed equivalent or better performances than other already published techniques.

PURPOSE: Thyroid nodules are common in the general population, and assessing their malignancy risk is the initial step in care. Surgical exploration remains the sole definitive option for indeterminate nodules. Extensive database access is crucial for improving this initial assessment. Our objective was to develop an automated process using convolutional neural networks (CNNs) to extract and structure biomedical insights from electronic health reports (EHRs) in a large thyroid pathology cohort. MATERIALS AND METHODS: We randomly selected 1,500 patients with thyroid pathology from our cohort for model development and an additional 100 for testing. We then divided the cohort of 1,500 patients into training (70%) and validation (30%) sets. We used EHRs from initial surgeon visits, preanesthesia visits, ultrasound, surgery, and anatomopathology reports. We selected 42 variables of interest and had them manually annotated by a clinical expert. We developed RUBY-THYRO using six distinct CNN models from SpaCy, supplemented with keyword extraction rules and postprocessing. Evaluation against a gold standard database included calculating precision, recall, and F1 score. RESULTS: Performance remained consistent across the test and validation sets, with the majority of variables (30/42) achieving performance metrics exceeding 90% for all metrics in both sets. Results differed according to the variables; pathologic tumor stage score achieved 100% in precision, recall, and F1 score, versus 45%, 28%, and 32% for the number of nodules in the test set, respectively. Surgical and preanesthesia reports demonstrated particularly high performance. CONCLUSION: Our study successfully implemented a CNN-based natural language processing (NLP) approach for extracting and structuring data from various EHRs in thyroid pathology. This highlights the potential of artificial intelligence-driven NLP techniques for extensive and cost-effective data extraction, paving the way for creating comprehensive, hospital-wide data warehouses.