Raman Spectroscopy-Based Deep Learning Model for Early Pan-Cancer Early Diagnosis
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About
The goal of this observational study is to explore whether a Raman-based, deep learning-assisted approach can be used to develop an effective method for early pan-cancer screening. The study includes healthy individuals, patients at risk of cancer, and patients with diagnosed cancers. The main questions it aims to answer are:
- Evaluating the deep-learning model's accuracy and specificity in identifying cancer-specific features in Raman spectral data and determining whether this method can accurately classify patients based on risk.
- Identifying which model is more adaptable to the Raman spectrum
- Providing an interpretable analysis of the model-generated diagnosis Participants are already being diagnosed and follow-up to determine the type of cancer.
Full description
This study aims to explore the use of deep learning models for classifying patients based on Raman spectroscopy analysis of blood samples, distinguishing between individuals in physiological conditions and patients with various types of precancerous conditions or malignant tumors. The study is conducted through a multi-center collaboration, where blood samples are collected from both healthy participants and patients with histopathologically diagnosed precancerous conditions or primary malignant tumors.
All blood samples are obtained from patients' routine clinical blood tests conducted during hospital admission or other necessary medical evaluations. The spectral data undergo a rigorous preprocessing pipeline, which includes alignment resampling to standardize the data, baseline removal to eliminate unwanted variations, and normalization to ensure uniformity across all samples. The data is optimized for deep learning model training.
Various deep-learning models are then employed to analyze the processed Raman spectra and develop a classification system to distinguish between pan-cancer cases and healthy controls. The preprocessed dataset is partitioned into three subsets for model training and performance evaluation: 80% for training, 10% for validation, and 10% for testing. These datasets are used for model training to identify patterns in the spectral data that correlate with the presence of specific cancers or a healthy state, enabling accurate classification.
To enhance the interpretability of deep learning models, Grad-CAM (Gradient-weighted Class Activation Mapping) is used to visualize the models' decision-making processes. This allows the identification of the Raman spectra regions that are more influential in the model's classification decision, providing a transparent understanding of how the model differentiates between the various classes.
Ultimately, this study aims to demonstrate the potential of Raman spectroscopy combined with deep learning techniques as a non-invasive, accurate, and interpretable method for cancer detection and classification, with implications for early diagnosis and personalized treatment strategies.
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Inclusion criteria
- Histopathological diagnosis of malignant tumors, including colorectal cancer, gastric cancer, hepatic cancer, pancreatic cancer, and esophageal cancer.
- Patients in normal physiological conditions without any malignant tumors or precancerous lesions.
- Patients with malignant tumor without recieving any interventions, including chemotherapy, surgery, radiotherapy, immunotherapy or other anti-tumor treatments.
- Patients with a histopathological diagnosis of any precancerous lesions or non-malignant disease.
Exclusion criteria
- Patients with metastatic tumors or in the condition with two or more kinds of malignant tumors at the same time
- Post-cancer treatment patients.
Trial design
600 participants in 11 patient groups
Trial contacts and locations
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Central trial contact
Jiasheng Xu, MD
Data sourced from clinicaltrials.gov
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