Rectal Cancer
Conditions
Keywords
Radiomics features, Artificial intelligence model, Locally advanced rectal cancer, Pathologic complete response, Neoadjuvant treatment
Brief summary
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
Detailed description
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.
Interventions
PROCEDUREartificial intelligence prediction system
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.
PROCEDUREthe radiologists
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
Sponsors
Sir Run Run Shaw Hospital
The Third Affiliated Hospital of Kunming Medical College.
Sixth Affiliated Hospital, Sun Yat-sen University
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 75 Years
Healthy volunteers
No
Inclusion criteria
* pathologically diagnosed as rectal adenocarcinoma * defined as clinical II-III staging (≥T3, and/or positive nodal status) without distant metastasis * receive neoadjuvant chemoradiotherapy or chemotherapy * pre- and post-neoadjuvant treatment MRI data obtained * receive total mesorectum excision (TME) surgery after neoadjuvant therapy and get the pathologic assessment of tumor response
Exclusion criteria
* with history of other cancer * insufficient imaging quality of MRI to delineate tumor volume or obtain measurements (e.g., lack of sequence, motion artifacts) * not completing neoadjuvant chemotherapy or chemoradiotherapy * tumor recurrence or distant metastasis during neoadjuvant treatment * not undergoing surgery resulting in lack of pathologic assessment of tumor response
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| The area under curve (AUC) of Receiver Operating Characteristic (ROC) curves of AI prediction system and expert radiologists in prediction tumor response | baseline | 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. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| The specificity of AI prediction system and expert radiologists in prediction tumor response | baseline | 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. |
| The sensitivity of AI prediction system and expert radiologists in prediction tumor response | baseline | 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. |
Countries
China
Contacts
Primary ContactXiangbo Wan, MD, PhD
Backup ContactXinjuan Fan, MD, PhD
Outcome results
None listed