Computer-aided diagnosis of gallbladder polyps based on high resolution ultrasonography

Chen, Tao; Tu, S; Wang, Haolu; Liu, Xuesong; Li, Fenghua; Wang, Jin; Liang, Xiaowen; Zhang, Xiaoqun; Wang, Jian

doi:10.1016/j.cmpb.2019.105118

Cited by 22 publications

(5 citation statements)

References 27 publications

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“…The most widely used diagnostic tool as a first step in day practice is abdominal US, [25,[46][47][48][49] followed by CT [50][51][52] and MRI, [3,10,20,53,54] to evaluate GB pathology, particularly for differentiating benign from malignant lesions. CT or better MRI can overcome any misdiagnosis of conventional US incidental findings alone that may lead to potentially unnecessary operations.…”

Section: Diagnosismentioning

confidence: 99%

Contemporary diagnosis and management of gallbladder polyps

Pavlidis,

Galanis,

Pavlidis

2023

Gastroenterol Funct Med

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Gallbladder (GB) polyps are present in 5%–10% of the general population and consist of true neoplastic polyps (adenomas) and pseudopolyps (predominantly cholesterol, inflammatory, hyperplastic, focal adenomyomatosis). True polyps, although relatively rare neoplastic lesions (0.5%) are considered an important factor in malignant transformation and cancer development (5%) when their size is ≥ 1 cm. Given that it is essential to diagnose GB adenocarcinoma at an early stage to optimize therapeutic management, controversy exists about whether cholecystectomy is always necessary. Their imaging characteristics, size ≥ 1 cm, age > 50 years and genetic predisposition determine the indications for immediate cholecystectomy. In younger patients with polyps < 1 cm in size and without a familial history of GB carcinoma, imaging follow-up by ultrasound (US) seems to be a reasonable recommended policy. A scoring system by multivariate analysis (cross-sectional area > 123 mm2, positive blood flow signal, age > 55.5 years, alanine aminotransferase (ALT) levels > 50 U/L and an ALT/AST (aspartate aminotransferase) ratio > 0.77) can accurately predict true polyps. The widely accepted size threshold for US follow-up is 7 mm, and for intervention, it is 10 mm. Computed tomography or better magnetic resonance imaging can overcome any misdiagnosis of conventional US incidental findings alone that may lead to potentially unnecessary operations. In challenging cases, high-resolution US, novel three-dimensional US, endoscopic US or contrast-enhanced endoscopic US could be helpful. Novel microflow imaging can safely predict polyps. Risk factors for malignancy include age > 60 years, large gallstones, primary sclerosing cholangitis, Asian ethnicity and sessile polyps accompanied by focal gallbladder wall thickening > 4 mm. For polyps sized 6–9 mm, the absence of growth at recommended follow-up (6 months, one year, and two years) indicates treatment discontinuation; however, it is not required for size < 5 mm without risk factors. In addition to laparoscopic cholecystectomy, the standard management, novel interventional modalities preserving the GB in selected cases include per-oral transmural endoscopic resection of GB polyps after a bridge of endoscopic US-guided cholecystostomy or laparoscopic gallbladder-preserving polypectomy. Generally, there are still no precise and strong evidence-based guidelines; thus, the management policy of GB polyps should be individualized in ambiguous cases.

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Section: Diagnosismentioning

confidence: 99%

Contemporary diagnosis and management of gallbladder polyps

Pavlidis,

Galanis,

Pavlidis

2023

Gastroenterol Funct Med

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“…For instance, [35] applied YOLOv3 to identify the GB and stones in CT images. [11] focused on GB segmentation and employed an AdaBoost classifier for polyp diagnosis. Meanwhile, [30] concentrated on classifying neoplastic polyps in cropped gallbladder ultrasound (USG) images, utilizing an InceptionV3 model.…”

Section: Related Workmentioning

confidence: 99%

Surpassing the Human Accuracy: Detecting Gallbladder Cancer from USG Images with Curriculum Learning

Basu

Gupta

Rana

et al. 2022

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

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In recent years, automated Gallbladder Cancer (GBC) detection has gained the attention of researchers. Current state-of-the-art (SOTA) methodologies relying on ultrasound sonography (US) images exhibit limited generalization, emphasizing the need for transformative approaches. We observe that individual US frames may lack sufficient information to capture disease manifestation. This study advocates for a paradigm shift towards video-based GBC detection, leveraging the inherent advantages of spatiotemporal representations. Employing the Masked Autoencoder (MAE) for representation learning, we address shortcomings in conventional image-based methods. We propose a novel design called FocusMAE to systematically bias the selection of masking tokens from high-information regions, fostering a more refined representation of malignancy. Additionally, we contribute the most extensive US video dataset for GBC detection. We also note that, this is the first study on US video-based GBC detection. We validate the proposed methods on the curated dataset, and report a new SOTA accuracy of 96.4% for the GBC detection problem, against an accuracy of 84% by current Image-based SOTA -GBCNet and RadFormer, and 94.7% by Video-based SOTA -AdaMAE. We further demonstrate the generality of the proposed FocusMAE on a public CTbased Covid detection dataset, reporting an improvement in accuracy by 3.3% over current baselines. Project page with source code, trained models, and data is available at: https://gbc-iitd.github.io/focusmae.

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“…Nine studies developed models that assisted with detection or classification of hepatobiliary neoplastic lesion, six of which involved DL-based CNN[ 193 - 202 ]. Schmauch et al [ 197 ] constructed an internally validated CNN-based DL model using ultrasonographic images of the liver to detect and classify focal liver lesions, achieving an overall AUC of 0.891.…”

Section: Hepatobiliary Systemmentioning

confidence: 99%

Artificial intelligence in gastroenterology: A state-of-the-art review

Kröner¹,

Engels²,

Glicksberg³

et al. 2021

WJG

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The development of artificial intelligence (AI) has increased dramatically in the last 20 years, with clinical applications progressively being explored for most of the medical specialties. The field of gastroenterology and hepatology, substantially reliant on vast amounts of imaging studies, is not an exception. The clinical applications of AI systems in this field include the identification of premalignant or malignant lesions ( e.g. , identification of dysplasia or esophageal adenocarcinoma in Barrett’s esophagus, pancreatic malignancies), detection of lesions ( e.g. , polyp identification and classification, small-bowel bleeding lesion on capsule endoscopy, pancreatic cystic lesions), development of objective scoring systems for risk stratification, predicting disease prognosis or treatment response [ e.g. , determining survival in patients post-resection of hepatocellular carcinoma), determining which patients with inflammatory bowel disease (IBD) will benefit from biologic therapy], or evaluation of metrics such as bowel preparation score or quality of endoscopic examination. The objective of this comprehensive review is to analyze the available AI-related studies pertaining to the entirety of the gastrointestinal tract, including the upper, middle and lower tracts; IBD; the hepatobiliary system; and the pancreas, discussing the findings and clinical applications, as well as outlining the current limitations and future directions in this field.

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Computer-aided diagnosis of gallbladder polyps based on high resolution ultrasonography

Cited by 22 publications

References 27 publications

Contemporary diagnosis and management of gallbladder polyps

Contemporary diagnosis and management of gallbladder polyps

Surpassing the Human Accuracy: Detecting Gallbladder Cancer from USG Images with Curriculum Learning

Artificial intelligence in gastroenterology: A state-of-the-art review

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