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restrictedcc-by-4.0Nov 2024View details →

Dataset for "Validation of SSDE calculation in a modern CT scanner and correlation with effective dose"

Open the record for dataset details and reuse information.

restrictedcc-by-4.0Jul 2022View details →

Validation dataset and reference code for Carotid Vessel Wall Segmentation and Atherosclerosis Diagnosis Challenge, MICCAI 2022.

Validation dataset and reference code for Carotid Vessel Wall Segmentation and Atherosclerosis Diagnosis Challenge, MICCAI 2022.  Please refer to our website: https://vessel-wall-segmentation-2022.grand-challenge.org/.

restrictedcc-by-4.0Mar 2024View details →

Validation dataset for StrikeLearn

Titre: Validation data for StrikeLearn. Annee: 2024 Projet: Exploration et exploitation de marqueurs de failles sismiques dans les images satellitaires par IA Auteurs: Sarah Visage, Lea Pousse, Sophie Giffard-Roisin, Sarah Perrinel. Projet finance par RT CNES

restrictedAug 2024View details →

Dataset related to article: SENSE OF OWNERSHIP INFLUENCE ON TACTILE PERCEPTION: IS THE PREDICTIVE CODING ACCOUNT VALID FOR THE SOMATIC RUBBER HAND ILLUSION?

Table including demographic data (sex, age, and education), Laterality Quotient score, Proprioceptive drift data, Body Ownership Questionnaire data, and Two-Point Discrimination score

restrictedNov 2022View details →

Dataset related to article "Nasal Polyposis Quality of Life (NPQ): Development and Validation of the First Specific Quality of Life Questionnaire for Chronic Rhinosinusitis with Nasal Polyps "

This record contains raw data related to article “Nasal Polyposis Quality of Life (NPQ): Development and Validation of the First Specific Quality of Life Questionnaire for Chronic Rhinosinusitis with Nasal Polyps" Abstract To date, no disease-specific tool has been available to assess the impact of chronic rhinosinusitis with nasal polyps (CRSwNP) on health-related quality of life (HRQoL). Therefore, the purpose of this study was to develop and validate a questionnaire specifically designed to this aim: the Nasal Polyposis Quality of Life (NPQ) questionnaire. As indicated in the current guidelines, the development and validation of the NPQ occurred in two separate steps involving different groups of patients. The questionnaire was validated by assessing internal structure, consistency, and validity. Responsiveness and sensitivity to changes were also evaluated. In the development process of NPQ an initial list of 40 items was given to 60 patients with CRSwNP; the 27 most significant items were selected and converted into questions. The validation procedure involved 107 patients (mean age 52.9 ± 12.4). NPQ revealed a five-dimensional structure and high levels of internal consistency (Cronbach's alpha 0.95). Convergent validity (Spearman' coefficient r = 0.75; p < 0.01), discriminant validity (sensitivity to VAS score), and reliability in a sample of patients with a stable health status (Interclass Coefficient 0.882) were satisfactory. Responsiveness to clinical changes was accomplished. The minimal important difference was 7. NPQ is the first questionnaire for the assessment of HRQoL in CRSwNP. Our results demonstrate that the new tool is valid, reliable, and sensitive to individual changes.

restrictedJan 2023View details →

Dataset related to article "External Validation and Comparison of Two Nomograms Predicting the Probability of Lymph Node Involvement in Patients subjected to Robot-Assisted Radical Prostatectomy and Concomitant Lymph Node Dissection: A Single Tertiary Center Experience in the MRI-Era "

This record contains raw data related to article “External Validation and Comparison of Two Nomograms Predicting the Probability of Lymph Node Involvement in Patients subjected to Robot-Assisted Radical Prostatectomy and Concomitant Lymph Node Dissection: A Single Tertiary Center Experience in the MRI-Era" Abstract Introduction: To externally validate and directly compare the performance of the Briganti 2012 and Briganti 2019 nomograms as predictors of lymph node invasion (LNI) in a cohort of patients treated with robot-assisted radical prostatectomy (RARP) and extended pelvic lymph node dissection (ePLND). Materials and methods: After the exclusion of patients with incomplete biopsy, imaging, or clinical data, 752 patients who underwent RARP and ePLND between December 2014 to August 2021 at our center, were included. Among these patients, 327 (43.5%) had undergone multi-parametric MRI (mpMRI) and mpMRI-targeted biopsy. The preoperative risk of LNI was calculated for all patients using the Briganti 2012 nomogram, while the Briganti 2019 nomogram was used only in patients who had performed mpMRI with the combination of targeted and systematic biopsy. The performances of Briganti 2012 and 2019 models were evaluated using the area under the receiver-operating characteristics curve analysis, calibrations plot, and decision curve analysis. Results: A median of 13 (IQR 9-18) nodes per patient was removed, and 78 (10.4%) patients had LNI at final pathology. The area under the curves (AUCs) for Briganti 2012 and 2019 were 0.84 and 0.82, respectively. The calibration plots showed a good correlation between the predicted probabilities and the observed proportion of LNI for both models, with a slight tendency to underestimation. The decision curve analysis (DCA) of the two models was similar, with a slightly higher net benefit for Briganti 2012 nomogram. In patients receiving both systematic- and targeted-biopsy, the Briganti 2012 accuracy was 0.85, and no significant difference was found between the AUCs of 2012 and 2019 nomograms (p = 0.296). In the sub-cohort of 518 (68.9%) intermediate-risk PCa patients, the Briganti 2012 nomogram outperforms the 2019 model in terms of accuracy (0.82 vs. 0.77), calibration curve, and net benefit at DCA. Conclusion: The direct comparison of the two nomograms showed that the most updated nomogram, which included MRI and MRI-targeted biopsy data, was not significantly more accurate than the 2012 model in the prediction of LNI, suggesting a negligible role of mpMRI in the current population.

restrictedApr 2023View details →

A Dataset for Evaluating and Validating Blood Perfusion Monitoring During an Occlusion Protocol: Multi-spectral and Plethysmography data

The dataset contains multispectral images and PPG data from 45 subjects who underwent an occlusion protocol  to induce changes in blood perfusion in the dominant hand. All the partipiants signed an informed consent. Blood perfusion Blood perfusion refers to the passage of oxygen and other nutrients through the circulatory system. We look to provide data for the evaluation of non-invasive approaches based on multi-spectral images. PPG reference data from the thumb is provided for validation purposes. Occlusion Protocol The occlusion protocol for each participant lasts around 10 minutes. The participants were seated, and their superior limbs were extended on a table.  An automatic blood pressure monitor measured their systolic and diastolic pressures. A blood cuff was placed in their dominant arm, and a PPG sensor MAX30102 (Maxim Integrated, CA, United States) was placed in their thumb finger.  A multispectral camera was positioned above to record the hand palm throughout the protocol. The camera employed is a CMS-V1-C-EVR1M-USB3 (SILIOS Technologies SA., France), which records 9 multispectral channels in the VNIR region of the spectrum. . The protocol is divided into 5 stages, each one lasts 2 minutes. <ol> <li>Start: No pressure is applied</li> <li>Vascular Occlusion: a fixed pressure of 60 mmHg is applied through the blood cuff</li> <li>Rest: The pressure is liberated</li> <li>Total Occlusion:  A pressure equal to the initial systolic pressure plus 20 mmHg is constantly applied.</li> <li>Hyperemia: The pressure is released.</li> </ol> Data available: The multispectral datasets for each subject are contained in a P#.7z file. Single-channel images are included. The name of each file specifies the participant ID, the time (minutes_seconds_milliseconds) of the acquisition with respect to the start of the occlusion protocol, the multispectral dataset, and the corresponding multispectral channel. Example: the following file name specifies a file for participant number 21. The data was captured at 9 minutes and 59.5290 seconds after the start of the occlusion protocol. The image corresponds to the first channel (the count starts at 0). P21_09_59_5290_d02459_channel0.png Files containing masks for each dataset are available for the whole hand region (hand_masks.7z) and the middle finger (finger_masks.7z). Finger masks for participants with a lot of movement are not available. The PPG data is stored in a simple csv file with txt extension for each participant. It contains the Red and Infrared channel data as well as the acquisition time of each sample. The PPG data for all the participants are contained in the compressed file PPG.7z. Reference multispectral images for calibration are also provided in the file Calibration.7z. This file contains multispectral images with the cap on (Dark_Reference) plus three folders with reference images taken during the same session as the participants.  reference_set_A:  = P1 to P10 reference_set_B:  = P11 to P30 reference_set_C:  = P31 to P45 each folder contains multispectral images of different white reference materials which can be employed for different calibration methods:  WhiteA = white tile  WhiteB = white reference bar  WhiteP = sheet of white paper WhiteT = Teflon (PTFE) sheets The participant’s data is summarized in the next table. <table> <tbody> <tr> <td> ID </td> <td> Age </td> <td> Gender </td> <td> Skin type </td> <td> Systolic Pressure </td> <td> # Multi-spectral Datasets </td> </tr> <tr> <td> P1 </td> <td> 20 </td> <td> M </td> <td> 4 </td> <td> 109 </td> <td> 2,456 </td> </tr> <tr> <td> P2 </td> <td> 20 </td> <td> M </td> <td> 3 </td> <td> 129 </td> <td> 2,458 </td> </tr> <tr> <td> P3 </td> <td> 22 </td> <td> M </td> <td> 4 </td> <td> 117 </td> <td> 2,460 </td> </tr> <tr> <td> P4 </td> <td> 19 </td> <td> F </td> <td> 3 </td> <td> 110 </td> <td> 2,454 </td> </tr> <tr> <td> P5 </td> <td> 18 </td> <td> F </td> <td> 4 </td> <td> 106 </td> <td> 2,458 </td> </tr> <tr> <td> P6 </td> <td> 18 </td> <td> F </td> <td> 4 </td> <td> 111 </td> <td> 2,471 </td> </tr> <tr> <td> P7 </td> <td> 18 </td> <td> F </td> <td> 4 </td> <td> 91 </td> <td> 2,462 </td> </tr> <tr> <td> P8 </td> <td> 18 </td> <td> F </td> <td> 4 </td> <td> 102 </td> <td> 2,474 </td> </tr> <tr> <td> P9 </td> <td> 21 </td> <td> M </td> <td> 3 </td> <td> 113 </td> <td> 2,468 </td> </tr> <tr> <td> P10 </td> <td> 19 </td> <td> F </td> <td> 3 </td> <td> 108 </td> <td> 2,461 </td> </tr> <tr> <td> P11 </td> <td> 18 </td> <td> M </td> <td> 4 </td> <td> 137 </td> <td> 2,461 </td> </tr> <tr> <td> P12 </td> <td> 18 </td> <td> F </td> <td> 3 </td> <td> 132 </td> <td> 2,457 </td> </tr> <tr> <td> P13 </td> <td> 18 </td> <td> M </td> <td> 4 </td> <td> 119 </td> <td> 2,461 </td> </tr> <tr> <td> P14 </td> <td> 18 </td> <td> F </td> <td> 4 </td> <td> 100 </td> <td> 2,460 </td> </tr> <tr> <td> P15 </td> <td> 18 </td> <td> F </td> <td> 3 </td> <td> 104 </td> <td> 2,460 </td> </tr> <tr> <td> P16 </td> <td> 18 </td> <td> M </td> <td> 3 </td> <td> 126 </td> <td> 2,462 </td> </tr> <tr> <td> P17* </td> <td> 18 </td> <td> F </td> <td> 3 </td> <td> 105 </td> <td> 2,469 </td> </tr> <tr> <td> P18 </td> <td> 21 </td> <td> F </td> <td> 4 </td> <td> 122 </td> <td> 2,461 </td> </tr> <tr> <td> P19 </td> <td> 21 </td> <td> F </td> <td> 3 </td> <td> 130 </td> <td> 2,462 </td> </tr> <tr> <td> P20 </td> <td> 21 </td> <td> F </td> <td> 3 </td> <td> 106 </td> <td> 2,457 </td> </tr> <tr> <td> P21 </td> <td> 23 </td> <td> M </td> <td> 3 </td> <td> 133 </td> <td> 2,459 </td> </tr> <tr> <td> P22 </td> <td> 23 </td> <td> M </td> <td> 3 </td> <td> 134 </td> <td> 2,471 </td> </tr> <tr> <td> P23 </td> <td> 21 </td> <td> F </td> <td> 3 </td> <td> 115 </td> <td> 2,472 </td> </tr> <tr> <td> P24 </td> <td> 21 </td> <td> M </td> <td> 3 </td> <td> 111 </td> <td> 2,445 </td> </tr> <tr> <td> P25 </td> <td> 21 </td> <td> M </td> <td> 3 </td> <td> 116 </td> <td> 2,460 </td> </tr> <tr> <td> P26 </td> <td> 21 </td> <td> M </td> <td> 4 </td> <td> 107 </td> <td> 2,462 </td> </tr> <tr> <td> P27 </td> <td> 21 </td> <td> F </td> <td> 4 </td> <td> 106 </td> <td> 2,460 </td> </tr> <tr> <td> P28 </td> <td> 24 </td> <td> F </td> <td> 4 </td> <td> 106 </td> <td> 2,460 </td> </tr> <tr> <td> P29 </td> <td> 22 </td> <td> M </td> <td> 4 </td> <td> 125 </td> <td> 2,460 </td> </tr> <tr> <td> P30 </td> <td> 21 </td> <td> M </td> <td> 4 </td> <td> 125 </td> <td> 2,461 </td> </tr> <tr> <td> P31 </td> <td> 21 </td> <td> F </td> <td> 3 </td> <td> 100 </td> <td> 2,472 </td> </tr> <tr> <td> P32 </td> <td> 22 </td> <td> M </td> <td> 3 </td> <td> 120 </td> <td> 2,460 </td> </tr> <tr> <td> P33 </td> <td> 21 </td> <td> M </td> <td> 4 </td> <td> 127 </td> <td> 2,461 </td> </tr> <tr> <td> P34 </td> <td> 19 </td> <td> F </td> <td> 3 </td> <td> 100 </td> <td> 2,457 </td> </tr> <tr> <td> P35 </td> <td> 19 </td> <td> F </td> <td> 3 </td> <td> 108 </td> <td> 2,460 </td> </tr> <tr> <td> P36 </td> <td> 20 </td> <td> F </td> <td> 3 </td> <td> 136 </td> <td> 2,471 </td> </tr> <tr> <td> P37 </td> <td> 19 </td> <td> M </td> <td> 4 </td> <td> 129 </td> <td> 2,457 </td> </tr> <tr> <td> P38* </td> <td> 19 </td> <td> M </td> <td> 3 </td> <td> 128 </td> <td> 2,463 </td> </tr> <tr> <td> P39* </td> <td> 18 </td> <td> M </td> <td> 3 </td> <td> 111 </td> <td> 2,479 </td> </tr> <tr> <td> P40 </td> <td> 23 </td> <td> M </td> <td> 4 </td> <td> 110 </td> <td> 2,462 </td> </tr> <tr> <td> P41 </td> <td> 21 </td> <td> M </td> <td> 3 </td> <td> 130 </td> <td> 2,465 </td> </tr> <tr> <td> P42 </td> <td> 20 </td> <td> F </td> <td> 3 </td> <td> 126 </td> <td> 2,461 </td> </tr> <tr> <td> P43 </td> <td> 22 </td> <td> F </td> <td> 3 </td> <td> 125 </td> <td> 2,459 </td> </tr> <tr> <td> P44 </td> <td> 22 </td> <td> M </td> <td> 3 </td> <td> 138 </td> <td> 2,467 </td> </tr> <tr> <td> P45 </td> <td> 22 </td> <td> M </td> <td> 4 </td> <td> 104 </td> <td> 2,461 </td> </tr> </tbody> </table> Table 1.- Participant's ID and relevant information.Participants with an * presented a lot of movement during the recording.

restrictedNov 2023View details →

Dataset related to article "Prospective Validation of the ROL System in Substaging pT1 High-Grade Urothelial Carcinoma: Results from a Mono-Institutional Confirmatory Analysis in BCG Treated Patients"

This record contains raw data related to article "Prospective Validation of the ROL System in Substaging pT1 High-Grade Urothelial Carcinoma: Results from a Mono-Institutional Confirmatory Analysis in BCG Treated Patients"AbstractPatients with pT1 high-grade (HG) urothelial carcinoma (UC) and a very high risk of progression might benefit from immediate radical cystectomy (RC), but this option remains controversial. Validation of a standardized method to evaluate the extent of lamina propria (LP) invasion (with recognized prognostic value) in transurethral resection (TURBT) specimens is still needed. The Rete Oncologica Lombarda (ROL) system showed a high predictive value for progression after TURBT in recent retrospective studies. The ROL system was supposed to be validated on a large prospective series of primary urothelial carcinomas from a single institution. From 2016 to 2020, we adopted ROL for all patients with pT1 HG UC on TURBT. We employed a 1.0-mm threshold to stratify tumors in ROL1 and ROL2. A total of 222 pT1 HG UC were analyzed. The median age was 74 years, with a predominance of men (73.8%). ROL was feasible in all cases: 91 cases were ROL1 (41%), and 131 were ROL2 (59%). At a median follow-up of 26.9 months (IQR 13.8-40.6), we registered 81 recurrences and 40 progressions. ROL was a significant predictor of tumor progression in both univariable (HR 3.53; CI 95% 1.56-7.99; p < 0.01) and multivariable (HR 2.88; CI 95% 1.24-6.66; p = 0.01) Cox regression analyses. At Kaplan-Meier estimates, ROL showed a correlation with both PFS (p = 0.0012) and RFS (p = 0.0167). Our results confirmed the strong predictive value of ROL for progression in a large prospective series. We encourage the application of ROL for reporting the extent of LP invasion, substaging T1 HG UC, and improving risk tables for urological decision-making.

restrictedJun 2022View details →

Dataset related to article "Is the Instability Severity Index Score a Valid Tool for Predicting Failure After Primary Arthroscopic Stabilization for Anterior Glenohumeral Instability?"

This record contains raw data related to article “Is the Instability Severity Index Score a Valid Tool for Predicting Failure After Primary Arthroscopic Stabilization for Anterior Glenohumeral Instability?”. Abstract Purpose: To assess the validity of the Instability Severity Index Score in predicting the rate of recurrence of dislocation in patients undergoing arthroscopic Bankart repair. Methods: The inclusion criteria were recurrent anterior traumatic glenohumeral instability and a minimum follow-up of 5 years. According to the preoperative Instability Severity Index Score, patients were divided into the following groups: ≤3 points (A), 4 to 6 points (B), and >6 points (C). The recurrence rate was determined by telephone interviews. The estimated overall rate of success at 5 years was defined as the estimated overall percentage of patients free of recurrence at 5 years. Results: Six hundred seventy patients (572 men and 98 women) were included. The average age was 27 years (range, 18 to 39 years) at the time of surgery. One hundred fourteen of 670 patients had a recurrence of instability, with an overall recurrence rate of 17% (95% confidence interval [CI] 14.2%-19.9%). The Instability Severity Index Score had a significant association with recurrence. Compared with patients in group A, those in group B had double the risk of recurrence (hazard ratio [HR] = 2.43, 95% CI 1.38-4.28, P = .002), and patients in group C a 9 times greater risk of recurrence (HR = 9.42, 95% CI 5.20-17.7, P < .001). The estimated overall rate of success at 5 years was 84.8% (95% CI 81.8-87.3). The rate of success with an Instability Severity Index Score ≤3 points was 93.7% (95% CI 89.6-96.2), but it dropped to 85.7% (95% CI 81.7-88.9) in those with an Instability Severity Index Score of 4 to 6 points and became 54.6% (95% CI 42.8-64.9) in those with an Instability Severity Index Score >6 points. On multivariable analysis, the Instability Severity Index Score was found to significantly affect the risk of recurrence, corrected by type of sport and glenoid bone loss. Conclusions: The Instability Severity Index Score is a validated tool with which to assess the recurrence rate of dislocation after arthroscopic surgery in patients with shoulder instability. Arthroscopic stabilization in patients with an Instability Severity Index Score ≤3 is associated with a significantly lower risk of recurrence of glenohumeral instability compared with that in patients with an Instability Severity Index Score >3 points. Level of evidence: III, case-control study.

restrictedcc-by-4.0Dec 2022View details →

Dataset for 'Quantitative investigation of the validity conditions for the Beckmann–Kirchhoff scattering model'

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restrictedFeb 2023View details →

dataset relate to article "Development and Validation of a UHPLC–MS/MS-Based Method to Quantify Cenobamate in Human Plasma Samples"

cenobamate blood concentrations measured via new method

restrictedJan 2022View details →

Dataset related to the article "Validation of a new wearable device for type 3 sleep test without flowmeter" 2021

Background: Ventilation monitoring during sleep is performed by sleep test instrumentation that is uncomfortable for the patients due to the presence of the flowmeter. The objective of this study was to evaluate if an innovative type 3 wearable system, the X10X and X10Y, is able to correctly detect events of apnea and hypopnea and to classify the severity of sleep apnea without the use of a flowmeter. Methods: 40 patients with sleep disordered breathing were analyzed by continuous and simultaneous recording of X10X and X10Y and another certified type 3 system, SOMNOtouch, used for comparison. Evaluation was performed in terms of quality of respiratory signals (scores from 1, lowest, to 5, highest), duration and classification of apneas, as well as identification and duration of hypopneas. Results: 580 periods were evaluated. Mean quality assigned score was 3.37±1.42 and 3.25±1.35 for X10X and X10Y and SOMNOtouch, respectively. The agreement between the two systems was evaluated with grades 4 and 5 in 383 out of 580 cases. A high correlation (r2 = 0.921; p<0.001) was found between the AHI indexes obtained from the two systems. X10X and X10Y devices were able to correctly classify 72.3% of the obstructive apneas, 81% of the central apneas, 61.3% of the hypopneas, and 64.6% of the mixed apneas when compared to SOMNOtouch device. Conclusion: The X10X and X10Y devices are able to provide a correct grading of sleep respiratory disorders without the need of a nasal cannula for respiratory flow measurement and can be considered as a type 3 sleep test device for screening tests.

restrictedJul 2022View details →

Dataset for manuscript "Development of a 3D cytotoxicity assay for the validation of T cell anti-tumor reactivity"

Dataset of the raw data files for the manuscript Development of a 3D cytotoxicity assay for the validation of T cell anti-tumor reactivity. Access upon request only.

restrictedApr 2024View details →

Dataset for the project "Towards precision medicine in psychiatry: clinical validation of a combinatorial pharmacogenomic approach" Italian Ministry of Health, Italy, Ricerca Finalizzata (Grant RF-2016-02361697)

Dataset for the project “Towards precision medicine in psychiatry: clinical validation of a combinatorial pharmacogenomic approach” Italian Ministry of Health, Italy, Ricerca Finalizzata (Grant RF-2016-02361697) Aim 1: Assessment of the validity and clinical utility of a pharmacogenetic test in the selection of antidepressant treatment for patients with Major Depressive Disorder. Aim 2: Pharmacokinetic analysis and identification of novel genetic variants linked with no response to treatment and/or with the occurrence of side effects. Aim 3: Implementation of the combinatorial algorithm based on possible new findings. This dataset contains : <ul> <li>RawData_Aim1</li> </ul> ·         Materials and Methods for Aim 1 (Materials and methods_RawData_Aim1) ·         An Excel table (RF16 - Genotyping - Aim1) with the following sheets: - Sheet 1: Genotyping results - Sheet 2: Metabolizer phenotype results for CYP2D6 and CYP2C19 genes <ul> <li>RawData_Aim2</li> </ul> ·         Materials and methods Sequencing_RawData_Aim2 ·         Materials and methods Pharmacokinetics_RawData_Aim2 ·         VCF data from sequencing analysis (filtered_variants - Aim2) ·         Pharmacokinetics analysis (Pharmacokinetics Data_Aim2) <ul> <li>RawData_Aim3</li> </ul> ·        An Excel table with data from clinical assessments (Clinical Data_Aim3)