Clinical trial NCT04278274: Post-Neoadjuvant Treatment MRI Based AI System to Predict pCR for Rectal Cancer
Official title: A Post-Neoadjuvant Treatment MRI Based AI System to Predict Pathologic Complete Response for Patients With Rectal Cancer: A Multicenter, Prospective Clinical Study
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Rectal Cancer
Keywords: Radiomics features
Artificial intelligence model
Locally advanced rectal cancer
Pathologic complete response
Neoadjuvant treatment
Lead sponsor: Sixth Affiliated Hospital, Sun Yat-sen University
Sponsor class: OTHER
Collaborators: name: Sir Run Run Shaw Hospital
name: The Third Affiliated Hospital of Kunming Medical College.
First posted: 2020-02-20
Enrollment: 205
Sex: ALL
Minimum age (years): 18
Maximum age (years): 75
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: 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.

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.

Eligibility criteria: 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

Primary outcomes:
measure: The area under curve (AUC) of Receiver Operating Characteristic (ROC) curves of AI prediction system and expert radiologists in prediction tumor response
timeFrame: baseline
Secondary outcomes:
measure: The specificity of AI prediction system and expert radiologists in prediction tumor response
timeFrame: baseline
measure: The sensitivity of AI prediction system and expert radiologists in prediction tumor response
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: STUDY_CHAIR
affiliation: Sir Run Run Shaw Hospital
name: Weidong Han, MD, PhD
role: PRINCIPAL_INVESTIGATOR
affiliation: The Third Affiliated Hospital of Kunming Medical College.
name: Zhenhui Li, MD
role: PRINCIPAL_INVESTIGATOR
References: