Rectal Cancer
Conditions
Keywords
MRI Radiomics, Artificial intelligence, Locally advanced rectal cancer, Pathologic complete response, Neoadjuvant chemoradiotherapy
Brief summary
In this study, investigators utilize a radiomics prediction model to predict the tumor response to neoadjuvant chemoradiotherapy (nCRT) before the nCRT is administered for patients with locally advanced rectal cancer (LARC). Previously, the radiomics prediction model has been constructed based on the radiomics features extracted from pretreatment Magnetic Resonance Imaging (MRI) in the training set, and optimized in the external validation set. The predictive power of this radiomics prediction model to discriminate the pathologic complete response (pCR) patients from non-pCR individuals, will be further verified in this prospective, multicenter clinical study.
Detailed description
This is a multicenter, prospective, observational clinical study for validation of a radiomics-based artificial intelligence (AI) prediction model. Patients who have been pathologically diagnosed as rectal adenocarcinoma and defined as clinical II-III staging without distant metastasis will be enrolled from the Sixth Affiliated Hospital of Sun Yat-sen University, the Third Affiliated Hospital of Kunming Medical College and Sir Run Run Shaw Hospital Affiliated by Zhejiang University School of Medicine. All participants should follow a standard treatment protocol, including concurrent neoadjuvant chemoradiotherapy (nCRT), total mesorectum excision (TME) surgery and adjuvant chemotherapy. Enhanced Magnetic Resonance Imaging (MRI) examination should be completed before the administration of nCRT treatment. The tumor volumes at high solution T2-weighted, contrast-enhanced T1-weighted and diffusion weighted images will be manually delineated, respectively. The outlined MRI images will be captured by the radiomics prediction model to generate a predicted response (predicted pCR vs. predicted non-pCR) of each patient, whereas the true response (confirmed pCR vs. confirmed non-pCR) is derived from pathologic reports after TME surgery serving as the gold standard for evaluation. The prediction accuracy, specificity, sensitivity and Area Under Curve (AUC) of Receiver Operating Characteristic (ROC) curves will be calculated. This study is aimed to provide a reliable and accurate AI system to predict the pathologic tumor response to nCRT before its administration, which might facilitate the identification of pCR candidates for further precision therapy among patients with locally advanced rectal cancer.
Interventions
None listed
Sponsors
The Third Affiliated Hospital of Kunming Medical College.
Sir Run Run Shaw Hospital
Sixth Affiliated Hospital, Sun Yat-sen University
Study design
Observational model
OTHER
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 by enhanced Magnetic Resonance Imaging (MRI) * intending to receive or undergoing neoadjuvant concurrent chemoradiotherapy (5-fluorouracil based chemotherapy, given orally or intravenously; Intensity-Modulated Radiotherapy or Volume-Modulated Radiotherapy delivered at 50 gray (Gy) in gross tumor volume (GTV) and 45 Gy in clinical target volume (CTV) by 25 fractions) * intending to receive total mesorectum excision (TME) surgery after neoadjuvant therapy (not completed at the enrollment), and adjuvant chemotherapy * MRI (high-solution T2-weighted imaging, contrast-enhanced T1-weighted imaging, and diffusion-weighted imaging are required) examination is completed before the neoadjuvant chemoradiotherapy
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) * incomplete neoadjuvant chemoradiotherapy * no surgery after neoadjuvant chemoradiotherapy resulting in lack of pathologic assessment of tumor response * tumor recurrence or distant metastasis during neoadjuvant chemoradiotherapy
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| The prediction accuracy of the radiomics prediction model | baseline | The prediction accuracy of the MRI radiomics-based artificial intelligence prediction system for identifying pCR candidates from non-pCR individuals among nCRT treated LARC patients will be calculated. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| The specificity of the radiomics prediction model | baseline | The specificity of the MRI radiomics-based artificial intelligence prediction system for identifying pCR candidates from non-pCR individuals among nCRT treated LARC patients will be calculated. |
| The sensitivity of the radiomics prediction model | baseline | The sensitivity of the MRI radiomics-based artificial intelligence prediction system for identifying pCR candidates from non-pCR individuals among nCRT treated LARC patients will be calculated. |
| The area under curve (AUC) of Receiver Operating Characteristic (ROC) curves of the radiomics prediction model | baseline | The area under curve (AUC) of Receiver Operating Characteristic (ROC) curves of the MRI radiomics-based artificial intelligence prediction system for identifying pCR candidates from non-pCR individuals among nCRT treated LARC patients will be calculated. |
Countries
China
Contacts
Primary ContactXiangbo Wan, MD, PhD
Outcome results
None listed