Kidney CancersCOMFORTNCT07888322
Assessment of Intelligent Algorithms for Detection, Diagnosis and Risk Stratification of Tumors of the Kidney and Prostate
An observational study of Kidney Cancers and Prostate Cancer, run by Aristotle University Of Thessaloniki at 6 sites in 4 countries.
Trial summary
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. Read more about this study
- Who is it for?
- Any ageHealthy volunteers can take part
- Conditions the study is for
- Kidney Cancers, Prostate Cancer
- What is being tested?
- Medical Reading and AI assisted Medical reading
- How many people
- 50 participants
- Where
- 6 sitesin 4 countries
- Study dates
- 15 Oct 2026to 28 Feb 2027
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- Healthy volunteers accepted
- Child, Adult, Older adult
Study population: 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
Who may be able to join
- Healthcare professional resident or specialised in Urology
- Healthcare professional resident or specialised in Radiology
- Proficient in English
Who cannot join
- -
Trial locations and contacts
6 sites in 4 countries. Contact the study team or a site near you to ask about taking part.
6 sites in 4 countries
Germany2 sites
Department of Neuroradiology, Charite
Berlin, State of Berlin, 13353
Jawed Nawabi, MD, MHBA, MSc, +49 30 450 557 192, jawed.nawabi@charite.de
Jawed Nawabi, MD, MHBA, MSc
Klinikum rechts der Isar, TUM
München, Bavaria, 81675
Lisa C Adams, Prof., +49 89 4140-1084, lisa.adams@tum.de
Lisa C Adams, Prof.
Greece2 sites
Department of Urology, University Hospital of Patras
Pátrai, Achaia, 26504
Evangelos Liatsikos, MD, +302613603386, liatsikos@yahoo.com
Vasilis Tatanis, MD, tatanisbas@gmail.com
Laboratory of Medical Physics and Digital Innovation, School of Medicine
Thessaloniki, Central Macedonia
Antonis Billis, PhD, +302310999237, ampillis@auth.gr
Panagiotis-Emmanouil Kartsidis, MSc, +302310999237, pkartsidis@auth.gr
Italy1 site
Department of Advanced Biomedical Sciences
Naples, Campania, 80131
Arnaldo Stanzione, MD, PhD, +393426927819, arnaldo.stanzione@unina.it
Alberto Cuocolo, MD, PhD
Spain1 site
Hospital Universitario 12 de Octubre
Madrid, Madrid, 28041
Guillermo de Velasco, MD, PhD, +34 91 390 87 83 - 83 49, unidadc@h12o.es
Guillermo de Velasco, MD, PhD
About this study
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.
Read more
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.
Read the detailed description
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.
- Conditions
- Kidney Cancers, Prostate Cancer
- Being tested
- Medical Reading, AI assisted Medical reading
- Keywords
- Artificial Intelligence, Decision support, Reader study, Kidney cancer, Prostate cancer
- Medical subject headings
- Kidney Neoplasms, Prostatic Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Medical ReadingOther
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.
AI assisted Medical readingOther
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.
| Study group | Role | What they receive |
|---|---|---|
| Urologists | Group of residents and specialists in urology Receives: Other: Medical Reading; Other: AI assisted Medical reading | |
| Radiologists | Group of residents and specialists in radiology Receives: Other: Medical Reading; Other: AI assisted Medical reading |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: Through study completion, an average of 6 months.
Secondary outcomes (10)
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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: Through study completion, an average of 6 months.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted22 Oct 2026
- Study start15 Oct 2026 (estimated)
- Primary completion31 Dec 2026 (estimated)
- Study completion28 Feb 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Kidney CancersStudies of the same condition, open and under way.
- More trials run by Aristotle University Of ThessalonikiThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 50 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Aristotle University Of Thessaloniki
- Sponsor type
- University, hospital or other
- Collaborators
- Charite University, Berlin, Germany, Technical University of Munich, University of Patras, Radboud University Medical Center, Fraunhofer Institute for Digital Medicine MEVIS, University of Salerno, Hospital Universitario 12 de Octubre, Federico II University
- Responsible party
- Sponsor
- Investigators
- Keno Bressem, MD, Principal investigator, Technical University of MunichAntonis Billis, PhD, Study director, Aristotle University Of ThessalonikiAlessa Hering, PhD, Study director, Radboud Universiteit
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07888322
- Sponsor's protocol ID
- COMFORT-2
- Acronym
- COMFORT
- Status last verified
- Oct 2026
- First submitted
- 30 Sep 2026
- Last updated
- 2 Nov 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided: A sharing plan for IPD is not in place at this moment.
Study at a glance
From the registry record, last updated by the sponsor on 2 Nov 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Assessment of Intelligent Algorithms for Detection, Diagnosis and Risk Stratification of Tumors of the Kidney and Prostate
- ClinicalTrials.gov ID
- NCT07888322
- Acronym / sponsor ID
- COMFORT / COMFORT-2
- Conditions
- Kidney Cancers, Prostate Cancer
- Keywords
- Artificial Intelligence, Decision support, Reader study, Kidney cancer, Prostate cancer
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 50 participants (estimated)
- Lead sponsor
- Aristotle University Of ThessalonikiUniversity, hospital or other
- Collaborators
- Charite University, Berlin, Germany, Technical University of Munich, University of Patras, Radboud University Medical Center, Fraunhofer Institute for Digital Medicine MEVIS, University of Salerno, Hospital Universitario 12 de Octubre, Federico II University
- Study start
- 15 Oct 2026 (estimated)
- Primary completion
- 31 Dec 2026 (estimated)
- Study completion
- 28 Feb 2027 (estimated)
- First posted
- 22 Oct 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
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.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Urologists | – | Other: Medical Reading, Other: AI assisted Medical readingGroup of residents and specialists in urology |
| Radiologists | – | Other: Medical Reading, Other: AI assisted Medical readingGroup of residents and specialists in radiology |
Who may qualify
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
- Study population
- 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
Key inclusion criteria (3 listed)
- Healthcare professional resident or specialised in Urology
- Healthcare professional resident or specialised in Radiology
- Proficient in English
Key exclusion criteria (1 listed)
- -
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Medical ReadingParticipants 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. | Other | Radiologists, Urologists |
| AI assisted Medical readingParticipants 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. | Other | Radiologists, Urologists |
Primary outcome
Accuracy of the multimodal AI algorithms
Time frame: Through study completion, an average of 6 months.
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.
Precision of the multimodal AI algorithms
Time frame: Through study completion, an average of 6 months.
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.
F1-score of the multimodal AI algorithms
Time frame: Through study completion, an average of 6 months.
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.
Sensitivity of the multimodal AI algorithms
Time frame: Through study completion, an average of 6 months.
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.
Specificity of the multimodal AI algorithms
Time frame: Through study completion, an average of 6 months.
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.
Area under the receiver operating characteristic curve (AUC-ROC)
Time frame: Through study completion, an average of 6 months.
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.
Intersection over Union (IoU)
Time frame: Through study completion, an average of 6 months.
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.
Agreement between AI-generated stratification and the clinical reference standard
Time frame: Through study completion, an average of 6 months.
Agreement between model-generated lesion stratification and the clinician-reported reference standard will be evaluated using a confusion matrix and quantified using weighted kappa.
Agreement between AI-generated PI-RADS score and the clinical reference standard
Time frame: Through study completion, an average of 6 months.
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.
Agreement between AI-generated Bosniak score and the clinical reference standard
Time frame: Through study completion, an average of 6 months.
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.
Agreement between AI-generated RENAL score and the clinical reference standard
Time frame: Through study completion, an average of 6 months.
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.
Group-stratified performance of the multimodal AI algorithms
Time frame: Through study completion, an average of 6 months.
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.
Group-stratified calibration of the multimodal AI algorithms
Time frame: Through study completion, an average of 6 months.
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.
10 secondary outcomes
Acceptability of the COMFORT platform
Time frame: Through study completion, an average of 6 months.
Usability of the COMFORT platform
Time frame: Through study completion, an average of 6 months.
Perceived trustworthiness of COMFORT AI outputs
Time frame: Through study completion, an average of 6 months.
Perceived information integration
Time frame: Through study completion, an average of 6 months.
Perceived effectiveness of the COMFORT platform
Time frame: Through study completion, an average of 6 months.
Care management efficiency
Time frame: Through study completion, an average of 6 months.
AI literacy
Time frame: Baseline
Clinical reading time: AI-assisted versus manual reading
Time frame: Through study completion, an average of 6 months.
Clinical reading quality: AI-assisted versus manual reading
Time frame: Through study completion, an average of 6 months.
Average Treatment Effect of potential privacy leakage causes
Time frame: Through study completion, an average of 6 months.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 2 Nov 2026.
- Individual participant dataSharing planned: Undecided. A sharing plan for IPD is not in place at this moment.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Keno Bressem, MD principal investigator
Technical University of Munich
Antonis Billis, PhD study director
Aristotle University Of Thessaloniki
Alessa Hering, PhD study director
Radboud Universiteit
6 sites in 4 countries: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Kidney Cancers and Prostate Cancer, run by Aristotle University Of Thessaloniki at 6 sites in 4 countries.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 2 Nov 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 6 sites in Germany, Greece, Italy, Spain. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Keno Bressem, MD (+498941402621, keno.bressem@tum.de), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Accuracy of the multimodal AI algorithms (time frame: Through study completion, an average of 6 months.). The study lists 13 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 2 Nov 2026.