Biomarkers and Functional Imaging in Predicting Response of Esophageal Cancer

Air Force Military Medical University of People's Liberation Army

Status: Unknown

Conditions: Esophageal Cancer

Treatments: Other: Functional Imaging analysis; Other: Biomarkers Testing

Study type: Observational

Funder types: Other

Identifiers

NCT03029793

XJFL-2016-ESO

Details and patient eligibility

About

The purpose of this study is to determine whether combination of molecular and biomarkers with functional imaging can predict pathologic response and clinical outcomes in squamous esophageal cancer patients who undergo trimodality therapy which includes neoadjuvant chemoradiotherapy and surgery

Full description

The recommended care for the management of locally advanced esophageal cancers is neoadjuvant chemoradiotherapy (nCRT) followed by esophagectomy based on the NCCN guideline. The rate of pathologic complete response (pCR) after nCRT was around 25-30%, reflecting potential cure of unresectable esophageal cancer receiving CRT. Since esophagectomy might have substantial morbidity and mortality rate, establishing the model for predicting nCRT response and survival will guide neoadjuvant treatment decisions and personalized treatment. Thus surgery might be spared in some patients with complete responses after nCRT, and surgery could be selectively reserved for those with only partial or no response to CRT. However, reported models yield unsatisfactory results due to small sample size and low accuracy, and most cases were adenocarcinoma and few models were used to predict survival. This study will prospectively collect the tissue and blood samples of locally advanced esophageal cancer patients, detect CRT resistance biomarkers, assess the SUV value and texture parameters of 18F-FDG PET-CT as well as ADC values DWI-MRI. The investigators will use advanced statistical tools to establish the model and further validate the model in another group of patients.The investigators will also establish a model for survival prediction. This model might possibly guide the personalized treatment for esophageal squamous cell carcinoma patients in China.

Enrollment: 200 estimated patients

Sex: All

Ages: 18 to 70 years old

Volunteers: No Healthy Volunteers

Inclusion criteria

1. Patient must have histologic documentation of squamous carcinoma of the esophagus at Xijing Hospital.
2. Newly diagnosed Patients with localized carcinoma of the esophagus who have undergone standard testing and been evaluated by the multidisciplinary team at Xijing Hospital and are considered appropriate candidates for and are willing to undergo chemoradiotherapy followed by surgery at Xijing Hospital.
3. Patient must have undergone a baseline esophageal-gastroduodenoscopy (EGD) with biopsy and endoscopic ultrasonography at Xijing, with tissue available for biomarker analysis.
4. Patient must be willing to undergo this research and must provide written informed consent.

Exclusion criteria

1. Patient is unable or unwilling to comply with the requirements of the protocol.
2. History of prior malignancy within the past 3 years.
3. Patients with second primary tumors.
4. Patients who received endoscopical therapy or induction chemotherapy prior to chemoradiation therapy are excluded.
5. Patient is not a candidate for chemoradiation followed by surgery.

Trial design

200 participants in 1 patient group

Biomarkers and Imaging analysis

Description:

This study will prospectively collect the tissue and blood samples of locally advanced esophageal cancer patients, perform CRT resistance biomarkers testing and functional imaging analysis including SUV value and texture parameters of 18F-FDG PET-CT as well as ADC values DWI-MRI before treatment, 2-3 weeks after the initiation of CRT, and 4 weeks post-nCRT. The investigators will use advanced statistical tools to establish the model and further validate the model in another group of patients. The investigators will also establish a model for survival prediction.

Treatment:

Other: Biomarkers Testing

Other: Functional Imaging analysis