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| ID | Type | Description | Link |
|---|---|---|---|
| 952172 | Other Grant/Funding Number | European Union's Horizon 2020 |
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| Name | Class |
|---|---|
| University of Pisa | OTHER |
| University Hospital Rijeka | OTHER |
| University of Messina | OTHER |
| Istanbul Medipol University Hospital |
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The goal of this observational study is to see how useful an experimental viewer and AI solutions are for clinicians in their daily work. The investigators want to find out if the AI helps clinicians interpret medical images for different types of cancer.
The AI solutions aim to:
The technical team want to see if the AI solutions assist clinicians and could become useful in the everyday clinical practice. Clinicians will complete a survey to share their feedback on the usability of the platform and how helpful the AI solutions are.
In order to conduct a robust clinical validation, the investigators have designed a study on the required sample size. The study is design to evaluate the role of an AI-assisted tool as a support for improving the daily clinical work. The investigators used an online website (https://statulator.com/SampleSize/ss2PP.html) for the calculation and use the "paired binary proportions" option. Using the case of prostate cancer, the investigators want to compare the probability of correct risk classification in prostate cancer by clinicians alone and/or guided by AI. The study will have a significance (α) = 0.05; power (β) = 80%; the analysis will be "two sided" and with equal group sizes.
An 10% improvement in cancer risk classification was observed when clinicians had access to an AI tool solution (Yilmaz et al.,). In addition, the authors reported that expert readers had an accuracy rate of 81% compared to 69% for novice readers when determining the Gleason score of lesions (a medical term used in pathology to classify the aggressiveness of cells in a tumour). The authors also assumed an 80% correlation between paired observations.
As a result, at least 60 new cases would be needed to evaluate the performance of the AI tool.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Group 1: Evaluation with Medical expertise only | Evaluation of different medical images of people with 5 types of cancer using their own expertise. |
| |
| Group 2: Evaluation with the support of AI solutions | Evaluation of different medical images of people with 5 types of cancer guided by the AI solutions developed. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Risk in prostate cancer | Other | the prediction involves the classification of the prostate cancer according to the level of prostatic antigen (PSA), the biopsy classification of the aggressiveness of the tumour, and also the localisation of the tumour |
| Measure | Description | Time Frame |
|---|---|---|
| Usability of experimental viewer with AI tools | Usability of the platform was assessed at the end of each of the two study phases: a standard clinical phase (without artificial intelligence assistance) and a second phase assisted by AI models. Participants evaluated their experience using a 5-point Likert scale, where 1 indicated "strongly disagree" and 5 indicated "strongly agree," in response to statements regarding ease of use, interface clarity, system efficiency, overall satisfaction, and other aspects related to user interaction with the platform. This assessment enabled a comparison of user perceptions of the viewer's usability under both conventional clinical conditions and AI-assisted conditions. Higher scores reflect a better user experience. | 5 months |
| Utility of experimental medical images viewer | The utility of the experimental viewer was assessed by comparing clinicians' diagnostic accuracy and time spent when using the system alone versus with AI assistance. Higher accuracy and reduced interpretation time were considered indicators of greater utility. The goal was to determine whether the viewer enhances clinical decision-making, streamlines workflows, and supports better patient care. Additional data such as clinician gender, specialty, and experience were collected to enable subgroup analyses. Statistical evaluations included confusion matrices to assess diagnostic performance, and Sankey flow diagrams to visualize changes in decision-making between unaided and AI-assisted phases. These tools provided a comprehensive understanding of the viewer's practical benefit in real clinical scenarios. | 5 months |
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Inclusion Criteria:
Exclusion Criteria:
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Patients with a cancer diagnosis of prostate, breast, lung and colorectal from the University and politechnic Hospital la Fe, Valencia.
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Hospital Universitario y Politécnico la Fe | Valencia | 46026 | Spain |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 36408543 | Background | Yilmaz EC, Turkbey B. The added value of a deep learning-based computer-aided detection system on prostate cancer detection among readers with varying level of multiparametric MRI expertise. Chin Clin Oncol. 2022 Dec;11(6):42. doi: 10.21037/cco-22-104. Epub 2022 Nov 15. No abstract available. |
| Label | URL |
|---|---|
| Accelerating the lab to market transition of AI tools for cancer management is an European project that contemplates the evaluation of the in silico clinical validation | View source |
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| OTHER |
| Centro Hospitalar do Porto | OTHER |
| Hospitales Universitarios Virgen del Rocío | OTHER |
| IRCCS Policlinico S. Donato | OTHER |
| National Cancer Center Affiliate of Vilnius University Hospital Santaros Klinikos | OTHER |
| Charite University, Berlin, Germany | OTHER |
| Osakidetza | OTHER |
| Le Collège des Enseignants de Radiologie de France | OTHER |
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| Life expectancy in lung cancer | Other | Clinicians will evaluate life expectancy in lung cancer using CTs, together with some clinical information. |
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| Histological subtype | Other | An assessment by pathology of the subtype of breast tumour |
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| Staging of colon cancer | Other | classify size, lymph node involvement and possibility of metastasis in medical images (computerized tomosynthesis) of thorax and pelvis region |
|
| invasion in rectum cancer | Other | assess whether vascular extramural o mesorectal fascia has been invaded in the tumour using magnetic resonance medical images taken at diagnosis in the pelvic region |
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| ID | Term |
|---|---|
| D001943 | Breast Neoplasms |
| D002289 | Carcinoma, Non-Small-Cell Lung |
| D003110 | Colonic Neoplasms |
| D012004 | Rectal Neoplasms |
| D011471 | Prostatic Neoplasms |
| ID | Term |
|---|---|
| D009371 | Neoplasms by Site |
| D009369 | Neoplasms |
| D001941 | Breast Diseases |
| D012871 | Skin Diseases |
| D017437 | Skin and Connective Tissue Diseases |
| D002283 | Carcinoma, Bronchogenic |
| D001984 | Bronchial Neoplasms |
| D008175 | Lung Neoplasms |
| D012142 | Respiratory Tract Neoplasms |
| D013899 | Thoracic Neoplasms |
| D008171 | Lung Diseases |
| D012140 | Respiratory Tract Diseases |
| D015179 | Colorectal Neoplasms |
| D007414 | Intestinal Neoplasms |
| D005770 | Gastrointestinal Neoplasms |
| D004067 | Digestive System Neoplasms |
| D004066 | Digestive System Diseases |
| D005767 | Gastrointestinal Diseases |
| D003108 | Colonic Diseases |
| D007410 | Intestinal Diseases |
| D012002 | Rectal Diseases |
| D005834 | Genital Neoplasms, Male |
| D014565 | Urogenital Neoplasms |
| D005832 | Genital Diseases, Male |
| D000091662 | Genital Diseases |
| D000091642 | Urogenital Diseases |
| D011469 | Prostatic Diseases |
| D052801 | Male Urogenital Diseases |
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| ID | Term |
|---|---|
| D012306 | Risk |
| D008017 | Life Expectancy |
| ID | Term |
|---|---|
| D011336 | Probability |
| D013223 | Statistics as Topic |
| D004812 | Epidemiologic Methods |
| D008919 | Investigative Techniques |
| D055641 | Mathematical Concepts |
| D017531 | Health Care Evaluation Mechanisms |
| D011787 | Quality of Health Care |
| D017530 | Health Care Quality, Access, and Evaluation |
| D011634 | Public Health |
| D004778 | Environment and Public Health |
| D014798 | Vital Statistics |
| D003625 | Data Collection |
| D003710 | Demography |
| D011154 | Population Characteristics |
| D015991 | Epidemiologic Measurements |
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