Clinical trial NCT04273451: RadioPathomics Artificial Intelligence Model to Predict Tumor Regression Grading in Locally Advanced Rectal Cancer
Official title: A RadioPathomics Integrated Artificial Intelligence System to Predict Tumor Regression Grading of Neoadjuvant Treatment in Locally Advanced Rectal Cancer: A Multicenter, Prospective and Observational Clinical Study
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Rectal Cancer
Keywords: Radiopathomics features
Artificial intelligence
Locally advanced rectal cancer
Tumor regression grading
Neoadjuvant chemoradiotherapy
Lead sponsor: Sixth Affiliated Hospital, Sun Yat-sen University
Sponsor class: OTHER
Collaborators: name: The Third Affiliated Hospital of Kunming Medical College.
name: Sir Run Run Shaw Hospital
First posted: 2020-02-18
Enrollment: 100
Sex: ALL
Minimum age (years): 18
Maximum age (years): 75
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: OTHER
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: The population in the study are the patients with LARC, who are intended to receive or undergoing standard, neoadjuvant concurrent chemoradiotherapy with tumor pathologic response unknown.

Brief summary: In this study, investigators apply a radiopathomics artificial intelligence (AI) supportive model to predict neoadjuvant chemoradiotherapy (nCRT) response before the nCRT is delivered for the patients with locally advanced rectal cancer (LARC). The radiopathomics AI system predicts individual tumor regression grading (TRG) category based on each patient's radiopathomics features extracted from the Magnetic Resonance Imaging (MRI) and biopsy images. The predictive power to classify each patient into particular TRG category will be validated in this multicenter, prospective clinical study.

Detailed description: This is a multicenter, prospective, observational clinical study for validation of a radiopathomics integrated artificial intelligence (AI) system. 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 neoadjuvant concurrent chemoradiotherapy (nCRT), total mesorectum excision (TME) surgery and adjuvant chemotherapy. Images of Magnetic Resonance Imaging (MRI) and biopsy hematoxylin \& eosin (H\&E) stained slides of each patient should be available before nCRT treatment. The tumor region within these images would be delineated manually by experienced radiologists and pathologists. Further, the outlined images will be presented to the radiopathomics AI system to classify each participant into particular tumor regression grading (TRG) category. Here, the American Joint Committee on Cancer and College of American Pathologist (AJCC/CAP) 4-category TRG system is served as the standard. The actual TRG category of each participant will be confirmed based on pathologic assessment after TME surgery. Through comparisons of the predicted TRG and actual TRG category, investigators calculate the prediction accuracy, specificity and sensitivity as well as the F1 score. This study is aimed to develop a reliable and robust AI system to predict pathologic TRG prior to nCRT administration, facilitating response-guided precision therapy for patients with locally advanced rectal cancer.

Eligibility criteria: 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
* biopsy H\&E stained slides are available and scanned with high resolution 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)
* insufficient imaging quality of biopsy slides imaging to delineate tumor volume or obtain measurements (e.g., tissue dissection, color anomaly)
* 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

Primary outcomes:
measure: The prediction accuracy of the radiopathomics artificial intelligence model
timeFrame: baseline
Secondary outcomes:
measure: The specificity of the radiopathomics artificial intelligence model
timeFrame: baseline
measure: The sensitivity of the radiopathomics artificial intelligence model
timeFrame: baseline
measure: The F1 score of the radiopathomics artificial intelligence model
timeFrame: baseline
Other outcomes:
Locations:
city: Guangzhou
country: China
facility: the Sixth Affiliated Hospital of Sun Yat-sen University
state: Guangdong
city: Kunming
country: China
facility: The Third Affiliated Hospital of Kunming Medical College
state: Yunnan
city: Hangzhou
country: China
facility: Sir Run Run Shaw Hospital
state: Zhejiang
Study officials:
affiliation: Sixth Affiliated Hospital, Sun Yat-sen University
name: Xiangbo Wan, MD, PhD
role: PRINCIPAL_INVESTIGATOR
affiliation: Sixth Affiliated Hospital, Sun Yat-sen University
name: Xinjuan Fan, MD, PhD
role: PRINCIPAL_INVESTIGATOR
References: