Segmentation of pancreatic ductal adenocarcinoma (PDAC) and surrounding vessels in CT images using deep convolutional neural networks and texture descriptors

Mahmoudi, Tahereh; Kouzehkanan, Zahra Mousavi; Radmard, Amir Reza; Kafieh, Raheleh; Salehnia, Aneseh; Davarpanah, Amir H.; Arabalibeik, Hossein; Ahmadian, Alireza

doi:10.1038/s41598-022-07111-9

Cited by 35 publications

(26 citation statements)

References 26 publications

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“…Whereas major progress has been made for automatic segmentation of the whole pancreas, 24 fully automated and volumetric segmentation of pancreatic tumors is still challenging with insufficient similarity indexes or Dice scores. 25…”

Section: Image and Data Acquisitionmentioning

confidence: 99%

Imaging of Pancreatic Ductal Adenocarcinoma: An Update on Recent Advances

et al. 2022

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Pancreatic ductal carcinoma (PDAC) is one of the leading causes of cancer-related death worldwide. Computed tomography (CT) remains the primary imaging modality for diagnosis of PDAC. However, CT has limitations for early pancreatic tumor detection and tumor characterization so that it is currently challenged by magnetic resonance imaging. More recently, a particular attention has been given to radiomics for the characterization of pancreatic lesions using extraction and analysis of quantitative imaging features. In addition, radiomics has currently many applications that are developed in conjunction with artificial intelligence (AI) with the aim of better characterizing pancreatic lesions and providing a more precise assessment of tumor burden. This review article sums up recent advances in imaging of PDAC in the field of image/data acquisition, tumor detection, tumor characterization, treatment response evaluation, and preoperative planning. In addition, current applications of radiomics and AI in the field of PDAC are discussed.

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Section: Image and Data Acquisitionmentioning

confidence: 99%

Imaging of Pancreatic Ductal Adenocarcinoma: An Update on Recent Advances

et al. 2022

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“…Data type a [21,[24][25][26][27][28][29][30][31]34,36,40,42,44] 14 (47) Radiology images [18,19,22,23,32,33,35,[37][38][39]41,43] 12 (40) Clinical data [16,17,20,23,32,35,39,41…”

Section: References Studies N (%) Featuresmentioning

confidence: 99%

“…Validation approach a [18,[20][21][22]28,31,33,37,38,40] 10 (33) External validation [16,17,19,[23][24][25]28,34,35,37,39] 10 (33) K-fold cross-validation [19,22,39,41,43] 5 (17) Hold-out cross-validation [31,35] 2 (7) Leave-one-out cross-validation [29] 1 (3) Shuffle-split cross-validation [26,32,36,42] 4 (13) Not reported…”

Section: References Studies N (%) Validation and Statisticsmentioning

confidence: 99%

“…Confusion matrix [18,20,35,40,45] 5 (17) Reported [16,17,19,21,[23][24][25][26][27][28][29][30][31][32][33][34][36][37][38]41,42,44]…”

Section: References Studies N (%) Validation and Statisticsmentioning

confidence: 99%

“…Performance measures b [16][17][18][19][20]27,29,33,40,[43][44][45] 12 (40) Sensitivity [16][17][18][19][20]22,27,29,40,[43][44][45] 12 ( 40) Specificity [16,17,20,28,33,34,37,40,44] 9 (30) Accuracy [16,19,25,28,29,31,32,34,40,41,43] 11 (37) Area under the curve [21,22,24] 3 (10) Precision…”

Section: (83) Not Reportedmentioning

confidence: 99%

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Artificial Intelligence for the Prediction and Early Diagnosis of Pancreatic Cancer: Scoping Review

Jan¹,

Assadi²,

Abd‐Alrazaq³

et al. 2023

J Med Internet Res

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Background Pancreatic cancer is the 12th most common cancer worldwide, with an overall survival rate of 4.9%. Early diagnosis of pancreatic cancer is essential for timely treatment and survival. Artificial intelligence (AI) provides advanced models and algorithms for better diagnosis of pancreatic cancer. Objective This study aims to explore AI models used for the prediction and early diagnosis of pancreatic cancers as reported in the literature. Methods A scoping review was conducted and reported in line with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. PubMed, Google Scholar, Science Direct, BioRXiv, and MedRxiv were explored to identify relevant articles. Study selection and data extraction were independently conducted by 2 reviewers. Data extracted from the included studies were synthesized narratively. Results Of the 1185 publications, 30 studies were included in the scoping review. The included articles reported the use of AI for 6 different purposes. Of these included articles, AI techniques were mostly used for the diagnosis of pancreatic cancer (14/30, 47%). Radiological images (14/30, 47%) were the most frequently used data in the included articles. Most of the included articles used data sets with a size of <1000 samples (11/30, 37%). Deep learning models were the most prominent branch of AI used for pancreatic cancer diagnosis in the studies, and the convolutional neural network was the most used algorithm (18/30, 60%). Six validation approaches were used in the included studies, of which the most frequently used approaches were k-fold cross-validation (10/30, 33%) and external validation (10/30, 33%). A higher level of accuracy (99%) was found in studies that used support vector machine, decision trees, and k-means clustering algorithms. Conclusions This review presents an overview of studies based on AI models and algorithms used to predict and diagnose pancreatic cancer patients. AI is expected to play a vital role in advancing pancreatic cancer prediction and diagnosis. Further research is required to provide data that support clinical decisions in health care.

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Segmentation of pancreatic tumors based on multi‐scale convolution and channel attention mechanism in the encoder‐decoder scheme

Zuo

Liu

et al. 2023

Medical Physics

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BackgroundComputer‐aided diagnosis is of great significance to improve the diagnostic accuracy of pancreatic cancer that has an insidious course and does not have obvious symptoms at first. However, segmentation of pancreatic cancer is challenging because the tumors vary in size with the smallest tumor having a size of about 0.5 in diameter, and most of them have irregular shapes and unclear boundaries.PurposeIn this study, we developed a deep learning architecture Multi‐Scale Channel Attention Unet (MSCA‐Unet) for pancreatic tumor segmentation and collected CT images of 419 patients from The Affiliated Hospital of Qingdao University and a public dataset. We embedded the multi‐scale network into the encoder to extract semantic information at different scales and the decoder to provide supplemental information to overcome the loss of information in the upsampling and the drift of the localized tumor due to the upsampling and skip connections.MethodsWe adopted the channel attention unit after the multi‐scale convolution to emphasize the informative channels, which was observed to have the effects of accelerating the positioning process, reducing false positives, and improving the accuracy of outlining very small, irregular pancreatic tumors.ResultsOur results show that our network outperformed the other current mainstream segmentation networks and achieved a Dice index of 68.03%, a Jaccard of 59.31%, and an FPR of 1.36% on the private dataset Task‐01 without data pre‐processing. Compared with the other pancreatic tumor segmentation networks on the public dataset Task‐02, our network produced the best Dice index, 80.12%, with the assistance of the data pre‐processing scheme.ConclusionsThis study strategically utilizes the multi‐scale convolution and channel attention mechanism of the architecture to provide a dedicated network for segmentation of small and irregular pancreatic tumors.

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Segmentation of pancreatic ductal adenocarcinoma (PDAC) and surrounding vessels in CT images using deep convolutional neural networks and texture descriptors

Cited by 35 publications

References 26 publications

Imaging of Pancreatic Ductal Adenocarcinoma: An Update on Recent Advances

Imaging of Pancreatic Ductal Adenocarcinoma: An Update on Recent Advances

Artificial Intelligence for the Prediction and Early Diagnosis of Pancreatic Cancer: Scoping Review

Segmentation of pancreatic tumors based on multi‐scale convolution and channel attention mechanism in the encoder‐decoder scheme

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