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Image enhancement technology in bronchoscopy: a prospective multicentre study in lung cancer.

ABSTRACT: Patients with lung cancer may present with additional lesions in the central airways. Earlier studies have shown a relationship between vessel diameter, pattern and grade of malignancy. High-definition (HD+) bronchoscopy with image enhancement techniques (i-scan) detected more vascular abnormalities but correlation with pathology has not yet been established.In this investigator-initiated, randomised, controlled, crossover, multicentre study in patients with suspected lung cancer, a HD+ bronchoscopy was performed with i-scan1 and i-scan2 settings in random order. Biopsies, visual grade and vascular pattern classification were obtained by endoscopists and blinded evaluation.In 107 patients, vascular patterns were classified in 48 tumours. Abrupt-ending vessels were predominantly found in squamous cell carcinoma but overall correlation between vessel pattern and histology was not significant (p=0.339). Additional lesions were detected in 35 patients (33%) with a correlation between vessel pattern and high-grade (pre-)invasive lesions (p<0.001). In 8.4% of the patients, relevant second lesions were detected which determined treatment and staging in 3% of all patients. Interobserver agreement was excellent for visual grading of the airway epithelium, but low for classifying vascular patterns. No significant detection rate difference was found by blinded and unblinded evaluation.HD+ bronchoscopy with i-scan image enhancement readily detects additional lesions. In one-third of all the patients, additional lesions were detected. Their vascular pattern correlates to pathology outcome, but the interobserver correlation for vascular pattern classification is low. These lesions were relevant in 8.4% and affected treatment and work-up in 3% of the cases.NCT02285426; Results.

SUBMITTER: van der Heijden EHFM

PROVIDER: S-EPMC5976136 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Image enhancement technology in bronchoscopy: a prospective multicentre study in lung cancer.

van der Heijden Erik H F M EHFM Candoli Piero P Vasilev Igor I Messi Alessandro A Pérez Pallarés Javier J Yablonskii Piotr P van der Vorm Anna A Schuurbiers Olga C J OCJ Hoefsloot Wouter W

BMJ open respiratory research 20180518 1

<h4>Introduction</h4>Patients with lung cancer may present with additional lesions in the central airways. Earlier studies have shown a relationship between vessel diameter, pattern and grade of malignancy. High-definition (HD+) bronchoscopy with image enhancement techniques (i-scan) detected more vascular abnormalities but correlation with pathology has not yet been established.<h4>Methods</h4>In this investigator-initiated, randomised, controlled, crossover, multicentre study in patients with ...[more]

PMID: 29862031

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Project description:BackgroundThe gene expression profile of cytologically-normal bronchial airway epithelial cells has previously been shown to be altered in patients with lung cancer. Although bronchoscopy is often used for the diagnosis of lung cancer, its sensitivity is imperfect, especially for small and peripheral suspicious lesions. In this study, we derived a gene expression classifier from airway epithelial cells that detects the presence of cancer in current and former smokers undergoing bronchoscopy for suspect lung cancer and evaluated its sensitivity to detect lung cancer among patients from an independent cohort.MethodsWe collected bronchial epithelial cells (BECs) from the mainstem bronchus of 299 current or former smokers (223 cancer-positive and 76 cancer-free subjects) undergoing bronchoscopy for suspected lung cancer in a prospective, multi-center study. RNA from these samples was run on gene expression microarrays for training a gene-expression classifier. A logistic regression model was built to predict cancer status, and the finalized classifier was validated in an independent cohort from a previous study.ResultsWe found 232 genes whose expression levels in the bronchial airway are associated with lung cancer. We then built a classifier based on the combination of 17 cancer genes, gene expression predictors of smoking status, smoking history, and gender, plus patient age. This classifier had a ROC curve AUC of 0.78 (95% CI, 0.70-0.86) in patients whose bronchoscopy did not lead to a diagnosis of lung cancer (n = 134). In the validation cohort, the classifier had a similar AUC of 0.81 (95% CI, 0.73-0.88) in this same subgroup (n = 118). The classifier performed similarly across a range of mass sizes, cancer histologies and stages. The negative predictive value was 94% (95% CI, 83-99%) in subjects with a non-diagnostic bronchoscopy.ConclusionWe developed a gene expression classifier measured in bronchial airway epithelial cells that is able to detect lung cancer in current and former smokers who have undergone bronchoscopy for suspicion of lung cancer. Due to the high NPV of the classifier, it could potentially inform clinical decisions regarding the need for further invasive testing in patients whose bronchoscopy is non diagnostic.

Project description:BackgroundTongue images (the colour, size and shape of the tongue and the colour, thickness and moisture content of the tongue coating), reflecting the health state of the whole body according to the theory of traditional Chinese medicine (TCM), have been widely used in China for thousands of years. Herein, we investigated the value of tongue images and the tongue coating microbiome in the diagnosis of gastric cancer (GC).MethodsFrom May 2020 to January 2021, we simultaneously collected tongue images and tongue coating samples from 328 patients with GC (all newly diagnosed with GC) and 304 non-gastric cancer (NGC) participants in China, and 16 S rDNA was used to characterize the microbiome of the tongue coating samples. Then, artificial intelligence (AI) deep learning models were established to evaluate the value of tongue images and the tongue coating microbiome in the diagnosis of GC. Considering that tongue imaging is more convenient and economical as a diagnostic tool, we further conducted a prospective multicentre clinical study from May 2020 to March 2022 in China and recruited 937 patients with GC and 1911 participants with NGC from 10 centres across China to further evaluate the role of tongue images in the diagnosis of GC. Moreover, we verified this approach in another independent external validation cohort that included 294 patients with GC and 521 participants with NGC from 7 centres. This study is registered at ClinicalTrials.gov, NCT01090362.FindingsFor the first time, we found that both tongue images and the tongue coating microbiome can be used as tools for the diagnosis of GC, and the area under the curve (AUC) value of the tongue image-based diagnostic model was 0.89. The AUC values of the tongue coating microbiome-based model reached 0.94 using genus data and 0.95 using species data. The results of the prospective multicentre clinical study showed that the AUC values of the three tongue image-based models for GCs reached 0.88-0.92 in the internal verification and 0.83-0.88 in the independent external verification, which were significantly superior to the combination of eight blood biomarkers.InterpretationOur results suggest that tongue images can be used as a stable method for GC diagnosis and are significantly superior to conventional blood biomarkers. The three kinds of tongue image-based AI deep learning diagnostic models that we developed can be used to adequately distinguish patients with GC from participants with NGC, even early GC and precancerous lesions, such as atrophic gastritis (AG).FundingThe National Key R&D Program of China (2021YFA0910100), Program of Zhejiang Provincial TCM Sci-tech Plan (2018ZY006), Medical Science and Technology Project of Zhejiang Province (2022KY114, WKJ-ZJ-2104), Zhejiang Provincial Research Center for Upper Gastrointestinal Tract Cancer (JBZX-202006), Natural Science Foundation of Zhejiang Province (HDMY22H160008), Science and Technology Projects of Zhejiang Province (2019C03049), National Natural Science Foundation of China (82074245, 81973634, 82204828), and Chinese Postdoctoral Science Foundation (2022M713203).

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Image enhancement technology in bronchoscopy: a prospective multicentre study in lung cancer.

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Image enhancement technology in bronchoscopy: a prospective multicentre study in lung cancer.

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