PET/CT
Henan, 450052, China
Location status: Recruiting
NCT Number: NCT06930586
Colorectal cancer (CRC), with annually increasing incidence and mortality worldwide, has become the second leading cause of cancer-related death. The development of CRC often follows the canonical normal-adenoma-carcinoma (N-A-C) sequence driven by progressive accumulation of molecular genetic events, highlighting the importance of early detection and removal of precancerous lesions. However, some patients who have had adenomas removed still have a high risk of developing new adenomas or CRC, especially for those with chronic or systemic disease, indicating that a compositive regulatory network is involved in the tumorigenesis of CRC. Additionally, despite advances in therapeutic strategies having improved the prognosis of CRC patients, tumor metastasis continues to be the predominant cause of mortality. These suggest the need to transcend limitations focusing solely on intertumoral microenvironment or single-timepoint event but adopt a more systemic perspective to elucidate the mechanisms underlying the whole sequence of CRC development and progression.
The gastrointestinal (GI) tract comprises a complex ecosystem with extensive interactions between normal or neoplastic epithelial cells with immune, neuronal, and other cell types, as well as microorganisms and metabolites within the gut lumen. Specifically, the intricate relationship between the GI tract and the central nervous system (CNS), collectively known as the brain-gut axis, plays a pivotal role in the pathogenesis of gastrointestinal disorders and neoplasm. For instance, chronic stress increased the risk of colon cancer via activating the COX-2/PEG2 system and promoted tumor cell dissemination by remodeling lymph vasculature. The bidirectional communications of the brain-gut axis are generally found to be mediated by neurotransmitters, inflammatory cytokines, metabolites, or gut microbiota. Nonetheless, the spotlight has shone primarily on the brain-gut crosstalk mechanisms in experimental cellular or animal models, with less attention paid to the structural and functional alterations on the brain networks at the patient level.
The evolution of functional neuroimaging modalities and neuroscience technologies has enabled accurate delineation of CNS activities. Specifically, nuclear medicine imaging technology using 2-18F fluoro-2-deoxy-D-glucose (18F-FDG) to adopt whole-body imaging information, is the optimal in vivo method for the investigation of regional human brain metabolism and associations with systemic disorders. We have previously identified the neuronal metabolic-ventricular dyssynchronization axis which might related to major arrhythmic events using myocardial perfusion imaging and the brain 18F-FDG positron emission tomography (PET). Given the potential dual interactions of the brain-gut axis, identification of specific brain regions associated with CRC development and progression might lead to a better understanding of the disease's neurobiological underpinnings and inform the development of targeted therapeutic strategies.
Hence, this study was structured to elucidate the role of neuro-metabolism and its potential mediator in regulating CRC tumorigenesis and metastasis. By delving into the neurometabolic-gut axis in CRC, the resulting mechanistic insights might be leveraged to identify diagnostic and prognostic biomarkers and to develop novel therapeutic interventions for CRC patients.
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Request Info18 year and older
All sexes
Observational
Henan, 450052, China
Location status: Recruiting
OBJECTIVE(S) & HYPOTHESIS(ES) Objective: Evaluate area under curve (AUC) of BRIDGE in predicting overall survival (OS) for CRC patients. Compare the AUC of BRIDGE with clinical prediction models in predicting survival status for CRC patients. Evaluate the sensitivity, specificity, positive prediction value (PPV) and negative prediction value (NPV)of BRIDGE in predicting OS for CRC patients.
Hypothesis: BRIDGE achieves an AUC over 0.80 in predicting OS for CRC patients. BRIDGE is superior to clinical prediction models in terms of AUC in predicting OS for CRC patients.
BACKGROUND & RATIONALE Colorectal cancer (CRC), with annually increasing incidence and mortality worldwide, has become the second leading cause of cancer-related death. The gastrointestinal (GI) tract comprises a complex ecosystem with extensive interactions between normal or neoplastic epithelial cells with immune, neuronal, and other cell types, as well as microorganisms and metabolites within the gut lumen. Specifically, the intricate relationship between the GI tract and the central nervous system (CNS), collectively known as the brain-gut axis, plays a pivotal role in the pathogenesis of gastrointestinal disorders and neoplasm. For instance, chronic stress increased the risk of colon cancer via activating the COX-2/PEG2 system and promoted tumor cell dissemination by remodeling lymph vasculature. The bidirectional communications of the brain-gut axis are generally found to be mediated by neurotransmitters, inflammatory cytokines, metabolites, or gut microbiota. Nonetheless, the spotlight has shone primarily on the brain-gut crosstalk mechanisms in experimental cellular or animal models, with less attention paid to the structural and functional alterations on the brain networks at the patient level.
The evolution of functional neuroimaging modalities and neuroscience technologies has enabled accurate delineation of CNS activities. Specifically, nuclear medicine imaging technology using 2-18F fluoro-2-deoxy-D-glucose (18F-FDG) to adopt whole-body imaging information, is the optimal in vivo method for the investigation of regional human brain metabolism and associations with systemic disorders. The investigators have previously identified the neuronal metabolic-ventricular dyssynchronization axis which might related to major arrhythmic events using myocardial perfusion imaging and the brain 18F-FDG positron emission tomography (PET). Given the potential dual interactions of the brain-gut axis, identification of specific brain regions associated with CRC development and progression might lead to a better understanding of the disease's neurobiological underpinnings and inform the development of targeted therapeutic strategies. Hence, this study was structured to elucidate the role of neuro-metabolism and its potential mediator in regulating CRC tumorigenesis and metastasis. By delving into the neurometabolic-gut axis in CRC, the resulting mechanistic insights might be leveraged to identify diagnostic and prognostic biomarkers and to develop novel therapeutic interventions for CRC patients.
Previously, investigators have constructed a BRIDGE based on retrospective datasets. The study is conducted to further prospectively verify the clinical applicability and generalizability of BRIDGE in predicting OS for CRC patients. The prediction performance of BRIDGE will be evaluated in a prospective dataset, and compared to conventional clinical-based prediction models in the trial, which might potentially provide important evidence for the feasibility and clinical value of integration of brain images for artificial intelligence-aided GI cancer medicine.
The primary accuracy endpoint in the study is the AUC, a significant indicator of classification performance of a binary classifier, which has been widely used to evaluate model performance in the field of machine learning.
METHODOLOGY Inclusion Criteria
Have a major neuropsychiatric abnormality, or had a life expectancy of fewer than 3 months.
Imaging Data Collection and Process PET/CT Imaging Collection and Anonymization For each subject, initial tumor imaging by PET/CT should have been performed within 1-2 weeks after enrolled. The process for imaging collection and transmission is manipulated in a uniform imaging protocol by radiologists and technicians in participating institutions. The whole series of PET/CT scans should be exported as DICOM files and completely anonymized with unique tracking number before uploaded to the designated cloud platform.
PET/CT Imaging Quality Control Images of PET/CT scans acquired in sites will be downloaded and reviewed by an independent radiologist experienced in PET/CT in the central laboratory to ensure high image quality
Healthy volunteers accepted: No
Only the study team can determine whether someone qualifies for participation.
Male/Female subjects with rectal cancer of at least 18 years of age will be enrolled in this trial.
Time frame: From March 1, 2024 to December 31, 2026-31
AUC is defined as the probability that a randomly chosen positive example is ranked higher than a randomly chosen negative example. A higher AUC indicates a better classification performance of a definite predictor. The AUC is evaluated by calculating the area under curve of receiver operating characteristics (ROC) which plots the proportion of true positive cases (sensitivity) against the proportion of false positive cases (1-specificity) based on various predictive probability threshold. The 95% confidence intervals (95%CI) of AUC are generated by bootstrapping strategy in 1000 sampling times.
Contact information is provided by the study sponsor or research team.
The First Affiliated Hospital of Zhengzhou University
Other
An Observational Study on the Prognostic Value of Neurometabolic Networks in Colorectal Cancer
Acronym: PVNM-CRC
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View the official ClinicalTrials.gov record (opens in a new tab)This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.
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