Automated Detection of Pancreatic Cystic Lesions on CT Using Deep Learning

Abel, Lorraine; Wasserthal, Jakob; Weikert, Thomas; Sauter, Alexander; Nešić, Ivan; Obradovic, Marko; Yang, Shan; Manneck, Sebastian; Glessgen, Carl G; Ospel, Johanna M.; Stieltjes, Bram; Boll, Daniel T.; Friebe, Björn

doi:10.3390/diagnostics11050901

Cited by 20 publications

(10 citation statements)

References 44 publications

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“…To multiphase CT images for detecting elementary hepatic malig-nancies for hepatocellular carcinoma (HCC), a deep learning-based model can see automatically [165]. On the other hand, the outbreak of incidental pancreatic cysts in abdominal CTs is increasing gradually along with ages [166,167], deep learning-based model two-step nnU-Net architecture can detect cystic pancreatic lesions on CTs [168] automatically. Patients who suffer thyroid cancer can use PET/CT for examination [169], which also provides excellent significance for the diagnosis of recurrence or metastasis in patients; there is much research based on PET/CT whole-body imaging [170,170,171,172,173] that has been using different types of works [174].…”

Section: Ct Scanmentioning

confidence: 99%

Advances of AI in Image-Based Computer-Aided Diagnosis: A Review

Yeasmin

2023

Preprint

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Over the past two decades, computer-aided detection or diagnosis has emerged as a highly promising field of research. Its primary goal is to enhance the diagnostic and treatment procedures for radiologists and clinicians in medical image analysis. With the help of big data and advanced Artificial Intelligence (AI) technologies, i.e., machine learning and deep learning algorithms, assist in making our healthcare system more convenient, active, efficient, and personalized. The primary goal of the literature survey study is to present a thorough overview of the most important developments related to Computer-Aided Diagnosis (CAD) systems in medical imaging. This survey holds considerable importance for researchers and professionals in both medical and computer science. Several reviews regarding specific facets of CAD in medical imaging already exist. Nevertheless, the main emphasis of this paper is on covering the complete range of CAD systems’ capabilities in medical imaging. This review article introduces the background concept used for typical CAD systems in medical imaging by outlining and comparing several methods frequently employed in recent studies. The article also offers a comprehensive and well-structured survey of CAD in medicine, drawing from a meticulous selection of relevant publications. Moreover, it describes the process of handling medical images and introduces state-of-the-art AI-based CAD technologies in medical imaging, along with the future directions of CAD. This study indicates that deep learning algorithms are the most effective way to diagnose or detect diseases.

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Section: Ct Scanmentioning

confidence: 99%

Advances of AI in Image-Based Computer-Aided Diagnosis: A Review

Yeasmin

2023

Preprint

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“…For other applications of AI in CT, Abel et al developed and evaluated an algorithm based on a two-step nnU-Net architecture for automated detection of pancreatic cystic lesions (PCL) in CT 137 . Lyu et al used high strength levels of the DL image reconstruction (DLIR-H) algorithm to predict the resectability of PC 138 .…”

Section: Ai In Tumor Diagnosis Processmentioning

confidence: 99%

Artificial intelligence in pancreatic cancer

Huang¹,

Huang²,

Zhang³

et al. 2022

Theranostics

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Pancreatic cancer is the deadliest disease, with a five-year overall survival rate of just 11%. The pancreatic cancer patients diagnosed with early screening have a median overall survival of nearly ten years, compared with 1.5 years for those not diagnosed with early screening. Therefore, early diagnosis and early treatment of pancreatic cancer are particularly critical. However, as a rare disease, the general screening cost of pancreatic cancer is high, the accuracy of existing tumor markers is not enough, and the efficacy of treatment methods is not exact. In terms of early diagnosis, artificial intelligence technology can quickly locate high-risk groups through medical images, pathological examination, biomarkers, and other aspects, then screening pancreatic cancer lesions early. At the same time, the artificial intelligence algorithm can also be used to predict the survival time, recurrence risk, metastasis, and therapy response which could affect the prognosis. In addition, artificial intelligence is widely used in pancreatic cancer health records, estimating medical imaging parameters, developing computer-aided diagnosis systems, etc. Advances in AI applications for pancreatic cancer will require a concerted effort among clinicians, basic scientists, statisticians, and engineers. Although it has some limitations, it will play an essential role in overcoming pancreatic cancer in the foreseeable future due to its mighty computing power.

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“…In the field of gastroenterology, AI has many applications such as liver fibrosis assessment, endoscopic lesion detection and imaging detection of inflammatory lesions [ 79 ]. With respect to PCLs, AI has been applied to cross-sectional imaging diagnosis [ 80 ], EUS [ 81 ] and fluid analysis [ 82 ]. The primary objective of AI in evaluating pancreatic cystic lesions is to differentiate between malignant and benign lesions.…”

Section: Personalized Approachmentioning

confidence: 99%

Advances in the Diagnosis of Pancreatic Cystic Lesions

et al. 2022

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Pancreatic cystic lesions (PCLs) are a heterogenous group of lesions ranging from benign to malignant. There has been an increase in PCLs prevalence in recent years, mostly due to advances in imaging techniques, increased awareness of their existence and population aging. Reliable discrimination between neoplastic and non-neoplastic cystic lesions is paramount to ensuring adequate treatment and follow-up. Although conventional diagnostic techniques such as ultrasound (US), magnetic resonance imaging (MRI) and computer tomography (CT) can easily identify these lesions, assessing the risk of malignancy is limited. Endoscopic ultrasound (EUS) is superior to cross-sectional imaging in identifying potentially malignant lesions due to its high resolution and better imaging characteristics, and the advantage of allowing for cyst fluid sampling via fine-needle aspiration (FNA). More complex testing, such as cytological and histopathological analysis and biochemical and molecular testing of the aspirated fluid, can ensure an accurate diagnosis.

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Automated Detection of Pancreatic Cystic Lesions on CT Using Deep Learning

Cited by 20 publications

References 44 publications

Advances of AI in Image-Based Computer-Aided Diagnosis: A Review

Advances of AI in Image-Based Computer-Aided Diagnosis: A Review

Artificial intelligence in pancreatic cancer

Advances in the Diagnosis of Pancreatic Cystic Lesions

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