Identification of lesion images from gastrointestinal endoscope based on feature extraction of combinational methods with and without learning process

Liu, Dingyun; Gan, Tao; Rao, Nini; Xing, Yao-Wen; Zheng, Jie; Li, Sang; Luo, Chengsi; Zhou, Zhongjun; Wan, Yongli

doi:10.1016/j.media.2016.04.007

Cited by 74 publications

(53 citation statements)

References 15 publications

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“…Gastroscopy [20] and computed tomography (PET/CT) [21] are the commonly used tools for the diagnosis of GC. Carcinoembryonic antigen (CEA), CA19-9 [22], and CA-724 [23] are classic tumor markers commonly used in the management of GC [24].…”

Section: Discussionmentioning

confidence: 99%

The combination of circulating long noncoding RNAs AK001058, INHBA-AS1, MIR4435-2HG, and CEBPA-AS1 fragments in plasma serve as diagnostic markers for gastric cancer

Dong

Li²,

Zhang

et al. 2017

Oncotarget

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BackgroundSuitable diagnostic markers for cancers are urgently required in clinical practice. Long non-coding RNAs, which have been reported in many cancer types, are a potential new class of biomarkers for tumor diagnosis.ResultsFive lncRNAs, including AK001058, INHBA-AS1, MIR4435-2HG, UCA1 and CEBPA-AS1 were validated to be increased in gastric cancer tissues. Furthermore, we found that plasma level of these five lncRNAs were significantly higher in gastric cancer patients compared with normal controls. By receiver operating characteristic analysis, we found that the combination of plasma lncRNAs with the area under the curve up to 0.921, including AK001058, INHBA-AS1, MIR4435-2HG, and CEBPA-AS1, is a better indicator of gastric cancer than their individual levels or other lncRNA combinations. Simultaneously, we found that the expression levels of a series of MIR4435-2HG fragments are different in gastric cancer plasma samples, but most of them higher than that in healthy control plasma samples.Materials and MethodsLncRNA gene expression profiles were analyzed in two pairs of human gastric cancer and adjacent non-tumor tissues by microarray analysis. Nine gastric cancer-associated lncRNAs were selected and assessed by quantitative real-time polymerase chain reaction in gastric tissues, and 5 of them were further analyzed in gastric cancer patients’ plasma.ConclusionsOur results demonstrate that certain lncRNAs, such as AK001058, INHBA-AS1, MIR4435-2HG, and CEBPA-AS1, are enriched in human gastric cancer tissues and significantly elevated in the plasma of patients with gastric cancer. These findings indicate that the combination of these four lncRNAs might be used as diagnostic or prognostic markers for gastric cancer patients.

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Section: Discussionmentioning

confidence: 99%

The combination of circulating long noncoding RNAs AK001058, INHBA-AS1, MIR4435-2HG, and CEBPA-AS1 fragments in plasma serve as diagnostic markers for gastric cancer

Dong

Li²,

Zhang

et al. 2017

Oncotarget

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“…Twenty-seven studies were dedicated to improving diagnostic accuracy in case of colorectal polyps or cancer. Nineteen studies focused on the diagnosis of premalignant or malignant lesions of the upper gastrointestinal tract, [39][40][41][42][43][44][45][46][47][48][49][50][51][52][53][54][55][56][57] only 4 studies were limited to the small bowel, [58][59][60][61] and 3 studies assessed the entire digestive tract. [62][63][64] Twenty-four studies used specific validation techniques-mainly k-fold cross- validation.…”

Section: Analysis Of Malignant and Premalignant Lesionsmentioning

confidence: 99%

“…Twelve studies have assessed the use of AI in detection of small bowel bleeding based on images collected during wireless capsule endoscopy (Table 3). 55,[102][103][104][105][106][107][108][109][110][111][112] Eight of the 12 studies used specific validation techniques, mainly k-fold cross-validation. Among these studies, 9 identified patients with small bowel bleeding with an accuracy of >90%.…”

Section: Gastrointestinal Bleedingmentioning

confidence: 99%

Application of Artificial Intelligence to Gastroenterology and Hepatology

et al. 2020

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Since 2010, substantial progress has been made in artificial intelligence (AI) and its application to medicine. AI is explored in gastroenterology for endoscopic analysis of lesions, in detection of cancer, and to facilitate the analysis of inflammatory lesions or gastrointestinal bleeding during wireless capsule endoscopy. AI is also tested to assess liver fibrosis and to differentiate patients with pancreatic cancer from those with pancreatitis. AI might also be used to establish prognoses of patients or predict their response to treatments, based on multiple factors. We review the ways in which AI may help physicians make a diagnosis or establish a prognosis and discuss its limitations, knowing that further randomized controlled studies will be required before the approval of AI techniques by the health authorities.

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“…With this good classification results could be www.nature.com/scientificreports www.nature.com/scientificreports/ reached. Second, Liu et al 31 used the complete endoscopic image and the classifier just decided if there is a carcinoma somewhere present in the image. However, they could not show where it is.…”

Section: Resultsmentioning

confidence: 99%

Factors influencing the accuracy for tissue classification in multi spectral in-vivo endoscopy for the upper gastro-internal tract

Hohmann

Albrecht

Lengenfelder

et al. 2020

Sci Rep

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Hyper spectral imaging is a possible way for disease detection. However, for carcinoma detection most of the results are ex-vivo. However, in-vivo results of endoscopic studies still show fairly low accuracies in contrast to the good results of many ex-vivo studies. To overcome this problem and to provide a reasonable explanation, Monte-Carlo simulations of photon trajectories are proposed as a tool to generate multi spectral images including inter patient variations to simulate 40 patients. Furthermore, these simulations have the huge advantage that the position of the carcinoma is known. Due to this, the effect of mislabelled data can be studied. As shown in this study, a percentage of 30–35% of mislabelled data might lead to significant decrease of the accuracy from around 90% to around 70–75%. Therefore, the main focus of hyper spectral imaging has to be the exact characterization of the training data in the future.

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Identification of lesion images from gastrointestinal endoscope based on feature extraction of combinational methods with and without learning process

Cited by 74 publications

References 15 publications

The combination of circulating long noncoding RNAs AK001058, INHBA-AS1, MIR4435-2HG, and CEBPA-AS1 fragments in plasma serve as diagnostic markers for gastric cancer

The combination of circulating long noncoding RNAs AK001058, INHBA-AS1, MIR4435-2HG, and CEBPA-AS1 fragments in plasma serve as diagnostic markers for gastric cancer

Application of Artificial Intelligence to Gastroenterology and Hepatology

Factors influencing the accuracy for tissue classification in multi spectral in-vivo endoscopy for the upper gastro-internal tract

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