An observational study in Kidney Cancers and Prostate Cancer, sponsored by Aristotle University Of Thessaloniki. Not yet recruiting at 6 sites in 4 countries. Per ClinicalTrials.gov, last updated 2026-10-22.
Sponsored by Aristotle University Of Thessaloniki · Observational
The COMFORT study will evaluate an artificial intelligence-based platform developed to support the clinical assessment of kidney and prostate cancer. The platform utilises imaging data to provide AI-generated outputs intended to assist clinicians in the assessment of suspected lesions.
The primary objective of the study is to assess the role of the COMFORT platform in the clinical decision making process of clinicians managing kidney or prostate cancer cases.
Secondary objectives include evaluation of the performance of AI models on anatomical segmentations, tumor characterisation (localisation, volumetric measurements, lesion classification, and scoring (e.g., Bosniak, PI-RADS, RENAL). Also the acceptability, usability, utility and trustworthiness of the AI models will be evaluated.
In addition, potential causes of privacy leakage associated with the COMFORT AI models will be evaluated as part of the platform's privacy assessment.
The COMFORT study aims to evaluate an AI-based platform developed to support the clinical assessment of patients with kidney and prostate cancer. The COMFORT platform provides AI-generated outputs intended to assist clinicians in the assessment and characterization of suspected lesions. The study evaluates not only the technical performance of the AI algorithms but also their integration into clinical practice, their fairness across different groups, and potential privacy risks.
The pipelines developed for kidney and prostate cancer provide outputs relevant to the respective diagnostic pathways. Depending on the clinical application, these outputs include lesion stratification and assessments such as PI-RADS, Bosniak, and RENAL scores. The performance of the AI algorithms will be evaluated by comparing model-generated predictions with the corresponding clinical reference standards. The evaluation will include measures of classification performance and agreement between AI-generated assessments and the reference standard. Performance will also be evaluated separately for the relevant cancer types.
An additional component of the study concerns the fairness of the COMFORT AI models. Model performance will be examined across different participant groups to identify potential differences in the behavior of the algorithms between groups. This analysis will complement the overall evaluation of model performance and contribute to the assessment of the reliability of the platform across the study population.
The study will also evaluate the integration of the COMFORT platform into clinical practice from the perspective of participating clinicians. Questionnaires and structured interviews will be used to assess clinicians' perceptions and experiences with the platform. Areas of evaluation include acceptability, usability, trustworthiness of AI-generated outputs, perceived information integration, workload management, perceived effectiveness, care management efficiency, and AI literacy. These assessments will provide information on how clinicians interact with the platform and perceive its potential contribution to clinical practice.
In addition to these subjective assessments, the effectiveness of the COMFORT platform in assisting clinicians during clinical reading will be evaluated objectively. AI-assisted clinical reading will be compared with manual clinical reading in terms of the time required to complete the reading and the quality of the resulting clinical assessment. These evaluations will be considered at individual study sites as well as across the study to examine the implementation of the platform in different clinical settings.
Potential privacy risks associated with the COMFORT AI models will also be investigated. A privacy causal analysis approach will be used to examine potential causes of privacy leakage associated with AI model outputs and to characterize their potential causal effects.
Overall, the study is designed to provide a comprehensive evaluation of the COMFORT platform across several complementary dimensions, including AI model performance, fairness, clinical usability and acceptability, effects on clinical reading, and potential privacy risks. This combined evaluation is intended to provide evidence regarding the performance of the platform and its use in the clinical assessment of kidney and prostate cancer.
973 studies on the registry are indexed under Kidney Neoplasms; 214 are open to participants now.
This study's planned enrollment of 50 is below the median of 212 across 217 observational studies indexed under Kidney Neoplasms.
Browse Kidney Neoplasms studies →Aristotle University Of Thessaloniki is the lead sponsor of 281 studies on the registry; 57 are open to participants now.
Counted across the registry records on this site, refreshed daily.
Healthcare professionals participating in the study who use the COMFORT platform during the study period. Participants will use the platform as specified in the study protocol and data related to platform assessment and study outcomes will be collected
Go through the criteria one by one — yes, no or not sure — and take the list to your doctor or the study team. Kept in this browser only.
Exclusion Criteria:
-
Group of residents and specialists in urology
Other: Medical Reading · Other: AI assisted Medical reading
Group of residents and specialists in radiology
Other: Medical Reading · Other: AI assisted Medical reading
Participants will perform medical reading of abdominal CT for renal cancer and prostate mpMRI for prostate cancer, without access to AI output using the COMFORT platform.
Participants will perform medical reading of abdominal CT for renal cancer and prostate mpMRI for prostate cancer, with access to AI output using the COMFORT platform.
Accuracy of the multimodal AI algorithms
Accuracy of the multimodal AI algorithms will be assessed by comparing model predictions with the clinician-reported reference standard for each suspected lesion. Accuracy will be calculated for each applicable cancer type, with bootstrap confidence intervals.
Time frame: Through study completion, an average of 6 months.
Precision of the multimodal AI algorithms
Precision of the multimodal AI algorithms will be assessed by comparing model predictions with the clinician-reported reference standard for each suspected lesion. Precision will be calculated for each applicable cancer type, with bootstrap confidence intervals.
Time frame: Through study completion, an average of 6 months.
F1-score of the multimodal AI algorithms
F1-score will be calculated to assess the classification performance of the multimodal AI algorithms against the clinician-reported reference standard for each suspected lesion. F1-score will be calculated for each applicable cancer type, with bootstrap confidence intervals.
Time frame: Through study completion, an average of 6 months.
Sensitivity of the multimodal AI algorithms
Sensitivity (recall) will be calculated to assess the ability of the multimodal AI algorithms to correctly identify positive cases relative to the clinician-reported reference standard. Sensitivity will be calculated for each applicable cancer type, with bootstrap confidence intervals.
Time frame: Through study completion, an average of 6 months.
Specificity of the multimodal AI algorithms
Specificity will be calculated to assess the ability of the multimodal AI algorithms to correctly identify negative cases relative to the clinician-reported reference standard. Specificity will be calculated for each applicable cancer type, with bootstrap confidence intervals.
Time frame: Through study completion, an average of 6 months.
Area under the receiver operating characteristic curve (AUC-ROC)
AUC-ROC will be calculated to characterize the classification performance of the multimodal AI algorithms relative to the clinician-reported reference standard for each applicable cancer type, with bootstrap confidence intervals.
Time frame: Through study completion, an average of 6 months.
Intersection over Union (IoU)
Intersection over Union (IoU) will be calculated for applicable object detection and segmentation tasks to assess agreement between AI-generated regions and the corresponding reference standard.
Time frame: Through study completion, an average of 6 months.
Agreement between AI-generated stratification and the clinical reference standard
Agreement between model-generated lesion stratification and the clinician-reported reference standard will be evaluated using a confusion matrix and quantified using weighted kappa.
Time frame: Through study completion, an average of 6 months.
Agreement between AI-generated PI-RADS score and the clinical reference standard
Agreement between the model-generated PI-RADS score and the clinician-reported reference standard will be evaluated using a confusion matrix and quantified using weighted kappa.
Time frame: Through study completion, an average of 6 months.
Agreement between AI-generated Bosniak score and the clinical reference standard
Agreement between the model-generated Bosniak score and the clinician-reported reference standard will be evaluated using a confusion matrix and quantified using weighted kappa.
Time frame: Through study completion, an average of 6 months.
Agreement between AI-generated RENAL score and the clinical reference standard
Agreement between the model-generated RENAL score and the clinician-reported reference standard will be evaluated using a confusion matrix and quantified using weighted kappa.
Time frame: Through study completion, an average of 6 months.
Group-stratified performance of the multimodal AI algorithms
Performance metrics of the multimodal AI algorithms will be calculated separately for prespecified participant groups to assess differences in model performance across groups. Group-stratified metrics will be reported with bootstrap confidence intervals.
Time frame: Through study completion, an average of 6 months.
Group-stratified calibration of the multimodal AI algorithms
Calibration metrics of the multimodal AI algorithms will be calculated separately for prespecified participant groups to assess model calibration across groups. Group-stratified calibration metrics will be reported with bootstrap confidence intervals.
Time frame: Through study completion, an average of 6 months.
Acceptability of the COMFORT platform
Clinician acceptability of the COMFORT platform will be assessed using the Technology Acceptance Model (TAM), applicable study questionnaires, and structured interviews. Questionnaire scores will be summarized using the median and interquartile range (IQR) for each study site and across the study. Comparisons between study sites will be performed using non-parametric hypothesis testing.
Time frame: Through study completion, an average of 6 months.
Usability of the COMFORT platform
Clinician-reported usability of the COMFORT platform will be assessed using the System Usability Scale (SUS). The SUS provides a total score ranging from 0 to 100, with higher scores indicating greater perceived usability. Scores will be summarized using the median and interquartile range (IQR) for each study site and across the study. Comparisons between study sites will be performed using non-parametric hypothesis testing.
Time frame: Through study completion, an average of 6 months.
Perceived trustworthiness of COMFORT AI outputs
Clinicians' perceived trustworthiness of AI-generated outputs will be assessed using the Trustworthiness of AI Output questionnaire and structured interviews. Questionnaire scores will be summarized using the median and interquartile range (IQR) for each study site and across the study.
Time frame: Through study completion, an average of 6 months.
Perceived information integration
Clinicians' perceptions of the integration of information provided by the COMFORT platform will be assessed using the Perceived Information Integration questionnaire and structured interviews. Questionnaire scores will be summarized using the median and interquartile range (IQR) for each study site and across the study.
Time frame: Through study completion, an average of 6 months.
Perceived effectiveness of the COMFORT platform
Clinicians' perceived effectiveness of the COMFORT platform will be assessed using the Effectiveness of COMFORT Platform questionnaire and structured interviews. Questionnaire scores will be summarized using the median and interquartile range (IQR) for each study site and across the study.
Time frame: Through study completion, an average of 6 months.
Care management efficiency
Clinician-reported care management efficiency associated with use of the COMFORT platform will be assessed using the Care Management Efficiency questionnaire and structured interviews. Questionnaire scores will be summarized using the median and interquartile range (IQR) for each study site and across the study. Comparisons between study sites will be performed using non-parametric hypothesis testing.
Time frame: Through study completion, an average of 6 months.
AI literacy
Clinician AI literacy will be assessed using the study-specified AI Literacy questionnaire and structured interviews. Questionnaire scores will be summarized using the median and interquartile range (IQR) for each study site and across the study.
Time frame: Baseline
Clinical reading time: AI-assisted versus manual reading
The effectiveness of the COMFORT platform in assisting clinicians will be assessed by comparing the time required to complete AI-assisted clinical reading with the time required for manual clinical reading. Reading time will be summarized using the mean and standard deviation for each study site and across the study. Comparisons between study sites will be performed using parametric testing.
Time frame: Through study completion, an average of 6 months.
Clinical reading quality: AI-assisted versus manual reading
The effectiveness of the COMFORT platform in assisting clinicians will be assessed by comparing the quality of AI-assisted clinical reading with that of manual clinical reading. Reading quality will be summarized for each study site and across the study and compared using a test of proportions.
Time frame: Through study completion, an average of 6 months.
Average Treatment Effect of potential privacy leakage causes
Potential causes of privacy leakage from COMFORT AI models will be evaluated using a privacy causal analysis model. The model will generate a causal graph and an Average Treatment Effect (ATE) value for each potential privacy leakage cause associated with COMFORT AI model outputs, such as Bosniak Classification.
Time frame: Through study completion, an average of 6 months.
Plan to share: Undecided — A sharing plan for IPD is not in place at this moment.
No publications or documents are linked to this record.
Start is estimated for Oct 2026; primary completion for Dec 2026, 2 months later.
Of 91 non-industry observational studies under Kidney Neoplasms, half reached primary completion within 75 months of starting (the median), counting those still running; a quarter within 34. This study's planned 2 months is below that.
Counting every sponsor, half of 109 studies reached it within 75 months; a quarter within 34.
How these figures are worked out, and which studies are left out of them.
From the registry record's own update history. This site started tracking changes on Sep 25, 2026; for anything earlier, see the record history on ClinicalTrials.gov ↗
This study is not yet recruiting, as verified in Sep 2026. You cannot join it, but the record below documents what was studied.
Get an email when the registry record changes — status, dates, results — or when someone posts here.
Sign in to followQuestions and observations about this study, from anyone following it. Not medical advice, and not a channel to the study team — their contact details are on the registry record.
Sign in to join the discussion. Reading takes no account; posting does. You choose a display name, and a pseudonym is the default.
Nothing here yet. If you are running this trial, taking part in it, or weighing whether to, this is the place to say so.
Aristotle University Of Thessaloniki →