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Validation of an Artificial Intelligence System Based on Raman Spectroscopy for Diagnosis of Gastric Premalignant Lesions and Early Gastric Cancer

ABSTRACT: Early detection and treatment of gastric premalignant lesion and early gastric cancer (EGC) have been proposed to improve outcomes of gastric cancer. Gastric dysplasia is a premalignant lesion and the penultimate stage in gastric carcinogenesis. On white light endoscopy (WLE), it is difficult to distinguish gastric dysplasia and EGC from benign pathology such as gastric intestinal metaplasia (GIM). Image enhanced endoscopy such as narrow-band imaging (NBI) is recommended to improve characterization of suspicious gastric lesions detected on WLE. Magnified-endoscopy with NBI (ME-NBI) have been shown to be superior to HD-WLE for diagnosis of GIM and EGC. Data on gastric dysplasia is less robust. Ultimately, biopsy is required to confirm diagnosis of gastric dysplasia/EGC. Gastric dysplasia can be classified into low-grade dysplasia (LGD) or high-grade dysplasia (HGD). Biopsy sampling may not be representative of the final histopathological grade of resected specimens and may under-stage dysplasia. Thus, endoscopic resection (ER) is recommended for gastric dysplasia and EGC on biopsy for diagnostic and therapeutic purpose. The current gap is to improve concordance of endoscopic and histologic findings of gastric dysplasia and early gastric cancer. Raman spectroscopy based artificial intelligence system (SPECTRA IMDx) was developed to provide an objective method to identify patients with gastric premalignant lesions and EGC. SPECTRA IMDx interrogate tissues at the cellular level and utilizes molecular information to provide actionable information to endoscopist during gastroscopy. Studies on diagnostic performance using Raman spectroscopy analysis devices have shown high sensitivity and specificity in detection of gastric cancer and precancerous lesions compared to WLE. However, these studies included few GIM, gastric dysplasia and gastric carcinoma. It is still unclear if Raman spectroscopy outperforms WLE in diagnosis of gastric HGD and EGC. In addition, the Raman spectroscopy algorithm is only able to characterize lesions into high risk (HGD/EGC) versus low risk (GIM/LGD/Gastritis/Normal). It is also uncertain if this technology is able to differentiate GIM and LGD. We plan to conduct a prospective trial to validate the diagnostic accuracy of SPECTRA for prediction of gastric HGD and EGC prior to gastric ER. Hypothesis: SPECTRA IMDx is able to differentiate higher risk lesions (HGD/EGC) from lower risk tissue/lesion (GIM/LGD/Gastritis/Normal)

DISEASE(S): Metaplasia,Gastric Intestinal Metaplasia,Gastric Cancer,Gastric Dysplasia,Stomach Neoplasms,Precancerous Conditions

PROVIDER: 2376206 | ecrin-mdr-crc |

REPOSITORIES: ECRIN MDR

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Project description:Inflammatory bowel diseases (IBD) are chronic inflammatory disorders that can be categorized as ulcerative colitis (UC), Crohn’s disease (CD) and indeterminate colitis (IC). Deep remission has been shown to improve disease outcome. There may be a lack of concordance between endoscopic and histologic remission. IBD patients with long standing colitis are at risk of developing dysplasia and colorectal cancer (CRC). However, it can be challenging to diagnose dysplasia in IBD patients during colonoscopy, as dysplasia frequently manifests as non-pedunculated lesions that present with only subtle visible changes or are even invisible due to the surrounding inflammation, scarring, pseudopolyps, or hyperplasia. Although white light endoscopy and chromoendoscopy are the current standard modality of imaging, there is still a gap to be bridged, in terms of improving endoscopic diagnosis of dysplasia and improving concordance of endoscopic and histologic remission. Raman spectroscopy is an inelastic light scattering technique provide specific fingerprints of molecular compositions and structures of biological tissues. It may be able to provide additional diagnostic information over standard endoscopy. A second-generation Raman endoscope system for improving in vivo tissue characterization and diagnosis during colonoscopy has been developed (SPECTRA IMDx system). Preliminary data suggested its utility in the diagnosis of colorectal neoplasia during colonoscopy. There is currently a lack of data concerning the application of this novel technology in the context of IBD. Specifically, whether the spectral signals generated can be used to better classify disease remission, and thus achieve higher concordance with histology when compared to standard endoscopy. It is also unclear whether this technology can be used to differentiate dysplastic mucosa from non-dysplastic mucosal in IBD patients. Hypotheses 1. Raman spectroscopy based artificial intelligence system has the potential to be used to differentiate disease remission from active mucosal inflammation and hence improve concordance between endoscopic and histologic remission, with the potential to decrease the need for random biopsies real-time during colonoscopy. 2. Raman spectroscopy based artificial intelligence system has the potential to differentiate dysplastic mucosa in IBD patients (low grade and high grade dysplasia; colorectal cancer) from non-dysplastic mucosa. real-time during colonoscopy.

Project description:Individuals who present with premalignant endobronchial lesions are considered at high risk of lung cancer. Nonetheless, premalignant lesions behave erratically and only a minority progresses towards lung cancer. Therefore, biomarkers need to be discovered that can aid in assessing an individual’s risk for subsequent cancer to better tailor treatment choices and avoid unnecessary follow-up procedures. We recently proposed a classifier of DNA copy number alterations (CNAs) at 3p26.3-p11.1, 3q26.2-29, and 6p25.3-24.3 as risk predictor for endobronchial cancer. The current study was set out to validate the classifier among an independent series of premalignant endobronchial lesions with various histological grades. A series of 36 endobronchial premalignant lesions (8 squamous metaplasia, and 28 various grades of dysplasia) identified during autofluorescence bronchoscopy of 12 case subjects who had carcinoma in situ or carcinoma (≥CIS) during follow-up bronchoscopy at the initial site and 24 control subjects who remained cancer-free, was subjected to array Comparative Genomic Hybridization (arrayCGH). DNA copy number profiles were related to lesion outcome. Prediction accuracy of the previously defined molecular classifier to predict endobronchial cancer in this series was determined. Unsupervised hierarchical clustering analysis revealed a significant association between cluster assignment and lesion outcome (p< 0.001), independent of histological grade, with quiescent profiles in controls (24/24) and aberrant profiles in the majority of cases (9/12). Our pre-defined classifier demonstrated 92% accuracy for predicting cancer outcome in the current sample series. Our validated classifier holds great promise for stratification of patients with premalignant endobronchial lesions for risk of subsequent cancer. Fresh frozen specimens of 36 premalignant endobronchial biopsies. Test samples were compared to an external pool of normal male/female reference DNA.

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Validation of an Artificial Intelligence System Based on Raman Spectroscopy for Diagnosis of Gastric Premalignant Lesions and Early Gastric Cancer

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