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| Name | Class |
|---|---|
| Sir Run Run Shaw Hospital | OTHER |
| The Third Affiliated Hospital of Kunming Medical College. | OTHER |
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In this study, investigators seek for a better way to identify the potential pathologic complete response (pCR) patients form non-pCR patients with locally advanced rectal cancer (LARC), based on their post-neoadjuvant treatment Magnetic Resonance Imaging (MRI) data.
Previously, a post neoadjuvant treatment MRI based radiomics AI model had been constructed and trained. Here, the predictive power of this artificial intelligence system and expert radiologist to identify pCR patients from non-pCR LARC patients will be compared in this prospective, multicenter, back-to-back clinical study
This is a multicenter, prospective, observational clinical study for seeking out a better way to predict the pathologic complete response (pCR) in patients with locally advanced rectal cancer (LARC) based on the post-neoadjuvant treatment Magnetic Resonance Imaging (MRI) data. Patients who have been pathologically diagnosed as rectal adenocarcinoma and defined as clinical II-III stage will be enrolled from the Sixth Affiliated Hospital of Sun Yat-sen University, Sir Run Run Shaw Hospital and the Third Affiliated Hospital of Kunming Medical College. All participants should follow a standard treatment protocol, including neoadjuvant treatment, total mesorectum excision (TME) surgery. Patients with LARC who received neoadjuvant treatment will be enrolled and their post-neoadjuvant treatment MRI images will be used to predict their pathologic response (pCR vs. non-pCR). The artificial intelligence prediction system and the expert radiologist will define the pathologic response as pCR or non-pCR, respectively. The pathologist will provide the final pathology report of TME surgery specimen (pCR or non-pCR) as a standard. The predictive efficacy of these two back-to-back approaches generated will be compared in this multicenter, prospective clinical study.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| patients will be evaluated by artificial intelligence system and expert radiologist | the patients with locally advanced rectal cancer (LARC) finished the neoadjuvant treatment, and not yet receive total mesorectum excision (TME) surgery will be enrolled. The post-neoadjuvant treatment MRI images features of each enrolled patients will be captured by the artificial intelligence system, and evaluated by experienced radiologists as well. Blind to the pathologic report of TME specimen, both approaches further respectively yield a predicted pathologic response to neoadjuvant treatment for each enrolled patient, shown as pCR or non-pCR. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| artificial intelligence prediction system | Procedure | The tumor ROI in the post- neoadjuvant treatment MRI images will be manually delineated, and further subjected to the AI prediction system arm to verify the predictive accuracy of this AI prediction system in identifying the pCR individuals from non-pCR patients with LARC. |
| Measure | Description | Time Frame |
|---|---|---|
| The area under curve (AUC) of Receiver Operating Characteristic (ROC) curves of AI prediction system and expert radiologists in prediction tumor response | The area under curve (AUC) of Receiver Operating Characteristic (ROC) curves of AI prediction system and expert radiologists in identifying the pCR candidates from non-pCR individuals among neoadjuvant chemotherapy or chemoradiotherapy treated LARC patients will be calculated respectively. | baseline |
| Measure | Description | Time Frame |
|---|---|---|
| The specificity of AI prediction system and expert radiologists in prediction tumor response | The specificity of AI prediction system and expert radiologists in identifying the pCR candidates from non-pCR individuals among neoadjuvant chemotherapy or chemoradiotherapy treated LARC patients will be calculated respectively. | baseline |
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Inclusion Criteria:
Exclusion Criteria:
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In the study, the population are the patients with LARC, who receive neoadjuvant chemoradiotherapy or chemotherapy and TME surgery. The response of neoadjuvant treatment is unknown.
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Xiangbo Wan, MD, PhD | Contact | +86 13826017157 | wanxbo@mail.sysu.edu.cn | |
| Xinjuan Fan, MD, PhD | Contact | 020-38254037 | fanxjuan@mail.sysu.edu.cn |
| Name | Affiliation | Role |
|---|---|---|
| Xiangbo Wan, MD, PhD | Sixth Affiliated Hospital, Sun Yat-sen University | Study Chair |
| Weidong Han, MD, PhD | Sir Run Run Shaw Hospital | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| the Sixth Affiliated Hospital of Sun Yat-sen University | Recruiting | Guangzhou | Guangdong | 510655 | China |
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| ID | Term |
|---|---|
| D012004 | Rectal Neoplasms |
| D000095384 | Pathologic Complete Response |
| ID | Term |
|---|---|
| D015179 | Colorectal Neoplasms |
| D007414 | Intestinal Neoplasms |
| D005770 | Gastrointestinal Neoplasms |
| D004067 | Digestive System Neoplasms |
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| the radiologists | Procedure | The enrolled patients will be assigned to the trained experienced radiologists to evaluate their predictive accuracy in identifying the pCR individuals from non-pCR patients |
|
| The sensitivity of AI prediction system and expert radiologists in prediction tumor response |
The sensitivity of AI prediction system and expert radiologists in identifying the pCR candidates from non-pCR individuals among neoadjuvant chemotherapy or chemoradiotherapy treated LARC patients will be calculated respectively. |
| baseline |
| Zhenhui Li, MD |
| The Third Affiliated Hospital of Kunming Medical College. |
| Principal Investigator |
| The Third Affiliated Hospital of Kunming Medical College | Recruiting | Kunming | Yunnan | 650000 | China |
|
| Sir Run Run Shaw Hospital | Recruiting | Hangzhou | Zhejiang | 310000 | China |
|
| D009371 | Neoplasms by Site |
| D009369 | Neoplasms |
| D004066 | Digestive System Diseases |
| D005767 | Gastrointestinal Diseases |
| D007410 | Intestinal Diseases |
| D012002 | Rectal Diseases |
| D018450 | Disease Progression |
| D020969 | Disease Attributes |
| D010335 | Pathologic Processes |
| D013568 | Pathological Conditions, Signs and Symptoms |