Multi-Center Follow-up Study to Develop a Classification System Which Differentiates Mucinous Cystic Neoplasm of the Liver and Benign Hepatic Cyst Using Machine Learning

Hardie, Andrew D.; Chamberlin, Jordan; Boyum, James H.; Sharbidre, Kedar G; Petrocelli, Robert; Flemming, Brian P.; Zahid, M.; Venkatesh, Sudhakar K.; Mruthyunjayappa, Smitha; Hajdu, Cristina; Kovacs, Mark D.

doi:10.1016/j.acra.2021.08.025

Cited by 9 publications

(6 citation statements)

References 12 publications

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“…By machine learning, Hardie AD. et al have summarized three imaging features that distinguish between BCA and BHC: solid enhancing nodule, all septations arising from an external macro-lobulation and whether solitary or one of multiple cystic liver lesions [ 14 ].…”

Section: Discussionmentioning

confidence: 99%

Complete resection for a giant recurrent biliary cystadenoma: A surgical case report and review of literature

Hoang¹,

Nguyen²,

Thieu³

et al. 2022

Annals of Medicine &Amp; Surgery

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Section: Discussionmentioning

confidence: 99%

Complete resection for a giant recurrent biliary cystadenoma: A surgical case report and review of literature

Hoang¹,

Nguyen²,

Thieu³

et al. 2022

Annals of Medicine &Amp; Surgery

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“…The total accuracy of the proposed 4 type classification system was 93.5%, making it useful in risk-stratifying lesions for the likelihood of MCN diagnosis. 34 …”

Section: Diagnosismentioning

confidence: 99%

“…33 A recent multicenter follow-up study used machine learning to develop a classification system to differentiate MCNs from benign hepatic cysts. 34 The three imaging features that accurately differentiated MCNs from benign hepatic cysts were 1.) the presence of a solid enhancing nodule (100% specific), 2) lack of septations arising from external macrolobulations, and 3.)…”

mentioning

confidence: 99%

“…The total accuracy of the proposed 4 type classification system was 93.5%, making it useful in risk-stratifying lesions for the likelihood of MCN diagnosis. 34 An important distinction that can often be made in imaging is between MCNs and IPNBs. While the vast majority of MCNs are multilocular with septations or cyst-in-cyst appearance and rarely contain mural nodules except when malignant, IPNBs are typically multicystic with a grape-like appearance, papillary nodules, and peripheral bile duct dilation 26 (Table 1).…”

mentioning

confidence: 99%

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Mucinous Cystic Neoplasms of the Liver: Epidemiology, Diagnosis, and Management

Hutchens¹,

Lopez²,

Ceppa³

2023

HMER

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Mucinous cystic neoplasms (MCNs) are rare tumors of the liver, occasionally seen in the biliary tree. Epidemiologic data are limited by their indolence and recent changes to diagnostic criteria. They are considered premalignant lesions capable of invasive behavior. While their etiology remains unknown, their female predominance, age of onset, and hormonally responsive ovarian-type stroma suggest ectopic organogenesis during embryologic development. MCNs can typically be recognized on imaging; yet, invasiveness is often indeterminate, and percutaneous tissue biopsy has shown limited value. Therefore, complete excision is recommended for all lesions as focal malignant transformation and metastatic disease has been reported.

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“…The main application fields of deep learning in ultrasound computer-aided diagnosis system include breast disease diagnosis [15], liver disease diagnosis [16], fetal ultrasound standard plane detection [17], thyroid nodule diagnosis [18], and carotid artery ultrasound image classification [19]. In recent years, machine learning algorithms, such as decision tree (DT), support vector machine (SVM), K-nearest neighbors (KNN), and neural networks (NN), have been more and more frequently applied in medical field [20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Applied Diagnosis Method for Subcutaneous Cyst by Ultrasonography

Feng

Tang

et al. 2022

Oxidative Medicine and Cellular Longevity

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For decades, ultrasound images have been widely used in the detection of various diseases due to their high security and efficiency. However, reading ultrasound images requires years of experience and training. In order to support the diagnosis of clinicians and reduce the workload of doctors, many ultrasonic computer aided diagnostic systems have been proposed. In recent years, the success of deep learning in image classification and segmentation has made more and more scholars realize the potential performance improvement brought by the application of deep learning in ultrasonic computer-aided diagnosis systems. This study is aimed at applying several machine learning algorithms and develop a machine learning method to diagnose subcutaneous cyst. Clinical features are extracted from datasets and images of ultrasonography of 132 patients from Hunan Provincial People’s Hospital in China. All datasets are separated into 70% training and 30% testing. Four kinds of machine learning algorithms including decision tree (DT), support vector machine (SVM), K -nearest neighbors (KNN), and neural networks (NN) had been approached to determine the best performance. Compared with all the results from each feature, SVM achieved the best performance from 91.7% to 100%. Results show that SVM performed the highest accuracy in the diagnosis of subcutaneous cyst by ultrasonography, which provide a good reference in further application to clinical practice of ultrasonography of subcutaneous cyst.

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Multi-Center Follow-up Study to Develop a Classification System Which Differentiates Mucinous Cystic Neoplasm of the Liver and Benign Hepatic Cyst Using Machine Learning

Cited by 9 publications

References 12 publications

Complete resection for a giant recurrent biliary cystadenoma: A surgical case report and review of literature

Complete resection for a giant recurrent biliary cystadenoma: A surgical case report and review of literature

Mucinous Cystic Neoplasms of the Liver: Epidemiology, Diagnosis, and Management

A Machine Learning Applied Diagnosis Method for Subcutaneous Cyst by Ultrasonography

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