The goal of this observational study is to explore whether ctDNA dynamic monitoring plus AI-based pathology can more effectively predict the therapeutic effect of neoadjuvant chemoimmunotherapy for resectable lung squamous cell carcinoma, so as to accurately guide clinical diagnosis and treatment.
This study is a single-center, observational, non-interventional, prospective study. 50 patients diagnosed with lung squamous cell carcinoma receiving neoadjuvant chemoimmunotherapy (ranging 2 to 4 cycles) will be planned to be enrolled in the study. Pre-treatment biopsy tissues of enrolled patients will be collected for whole exon sequencing (WES) testing, and personalized detection panel will be customized based on WES testing results. Peripheral blood will be collected 1 day before each cycle of neoadjuvant therapy, 1 day before surgery, 3 days after surgery, and 3 weeks after surgery for ctDNA testing. In addition, the prediction model of AI-based pathology will be constructed by AI deep learning based on pathological sections of pre-treatment biopsy tissues. All inclued patients will be regularly followed up for at least 5 years.
Study Type
OBSERVATIONAL
Enrollment
50
WES and ctDNA detection
Department of Thoracic Surgery, Second Xiangya Hospital of Central South University, China
Changsha, Hunan, China
RECRUITINGPathologic completet response (pCR) rate
pCR rate is defined as the percentage of participants having an absence of residual tumor cells in resected lung specimens and lymph nodes following completion of neoadjuvant therapy.
Time frame: Up to 1 year
ctDNA resolution
ctDNA resolution is defined as the change or resolution in tumor-derived DNA found in the bloodstream from diagnosis to after neoadjuvant therapy and after surgery, correlated with pathologic complete response (pCR)
Time frame: Up to 2 years
Development of computer algorithm to identify pCR features
Development of computer algorithm to identify pCR features
Time frame: From retrospective data collection to algorithm development (6 month)
Validation of computer algorithm to identify pCR features
Validation of computer algorithm to identify pCR features
Time frame: From prospective data collection to algorithm validation (6 months)
Major pathological response (MPR) rate
MPR rate is defined as the percentage of participants having ≤10% viable tumor cells in the resected primary tumor and resected lymph nodes following completion of neoadjuvant therapy.
Time frame: Up to 1 year
Objective response rate(ORR)
The proportion of patients achieved complete or patial remission(Imageological) according to RECIST 1.1 prior to definitive surgery.
Time frame: Up to 1 year
Adverse events (AEs)
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Number of participants experiencing AEs will be recorded. An AE is defined as any untoward medical occurrence in a clinical study participant, temporally associated with the use of study intervention, whether or not considered related to the study intervention.
Time frame: Up to 5 years
Perioperative complications rate
Number of participants experiencing perioperative complications will be recorded.
Time frame: Up to 3 years
Health-related Quality of Life
Health-related Quality of Life will be assessed by the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ-C30) version 3.0, the EORTC Quality of Life Questionnaire in Lung Cancer (EORTC QLQ-LC13), and the European Quality of Life 5 Dimensions (EQ-5D) questionnaire.
Time frame: Up to 5 years
Perioperative pain evaluation
Perioperative pain evaluation assessed by a numeric rating scale (NRS)
Time frame: Up to 3 years
Disease free survival (DFS)
From the date of surgery to any of the following events: disease progression, disease recurrence or death from any cause.
Time frame: Up to 5 years
Overall survival (OS)
From the date of participated in study to the date of death.
Time frame: Up to 5 years