Fully end-to-end deep-learning-based diagnosis of pancreatic tumors

Si, Ke; Xue, Ying; Yu, Xiazhen; Zhu, Xinpei; Gong, Wei; Liang, Tingbo; Duan, Shumin

doi:10.7150/thno.52508

Cited by 63 publications

(37 citation statements)

References 18 publications

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“…CT imaging can collect information about tumor location, size, and morphology. The pancreas is considerably different in size, shape, and location among the individuals and possesses only a very small part of the entire CT image, or about 1.3% of each CT image in a CT dataset[ 42 ]. Furthermore, a tumor shows high similarity to the surrounding tissues.…”

Section: Detection Of Early Pdac By Radiomicsmentioning

confidence: 99%

Recent advances in artificial intelligence for pancreatic ductal adenocarcinoma

Hayashi¹,

Uemura²,

Matsumura³

et al. 2021

WJG

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Pancreatic ductal adenocarcinoma (PDAC) remains the most lethal type of cancer. The 5-year survival rate for patients with early-stage diagnosis can be as high as 20%, suggesting that early diagnosis plays a pivotal role in the prognostic improvement of PDAC cases. In the medical field, the broad availability of biomedical data has led to the advent of the “big data” era. To overcome this deadly disease, how to fully exploit big data is a new challenge in the era of precision medicine. Artificial intelligence (AI) is the ability of a machine to learn and display intelligence to solve problems. AI can help to transform big data into clinically actionable insights more efficiently, reduce inevitable errors to improve diagnostic accuracy, and make real-time predictions. AI-based omics analyses will become the next alterative approach to overcome this poor-prognostic disease by discovering biomarkers for early detection, providing molecular/genomic subtyping, offering treatment guidance, and predicting recurrence and survival. Advances in AI may therefore improve PDAC survival outcomes in the near future. The present review mainly focuses on recent advances of AI in PDAC for clinicians. We believe that breakthroughs will soon emerge to fight this deadly disease using AI-navigated precision medicine.

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Section: Detection Of Early Pdac By Radiomicsmentioning

confidence: 99%

Recent advances in artificial intelligence for pancreatic ductal adenocarcinoma

Hayashi¹,

Uemura²,

Matsumura³

et al. 2021

WJG

View full text Add to dashboard Cite

show abstract

“…Recently, AI has become a hot spot in medical research. AI plays an essential role in the diagnosis, treatment, and management of cancer ( 85 ). Bile duct stricture is a common clinical condition, which may be divided into benign and malignant bile duct stricture ( 86 ).…”

Section: Application Of Ms In the Diagnosis And Prognosis Of Pcmentioning

confidence: 99%

Application of Mass Spectrometry in Pancreatic Cancer Translational Research

Luo

Chen

et al. 2021

Front. Oncol.

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Pancreatic cancer (PC) is one of the most common malignant tumors in the digestive tract worldwide, with increased morbidity and mortality. In recent years, with the development of surgery, chemotherapy, radiotherapy, targeted therapy, and immunotherapy, and the change of the medical thinking model, remarkable progress has been made in researching comprehensive diagnosis and treatment of PC. However, the present situation of diagnostic and treatment of PC is still unsatisfactory. There is an urgent need for academia to fully integrate the basic research and clinical data from PC to form a research model conducive to clinical translation and promote the proper treatment of PC. This paper summarized the translation progress of mass spectrometry (MS) in the pathogenesis, diagnosis, prognosis, and PC treatment to promote the basic research results of PC into clinical diagnosis and treatment.

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“…To bolster the clinical impact of deep learning models in the medical field, inclusion of an end-to-end (E2E) process that proceeds a fully automatic process from input to output is becoming increasingly important 18 to obtaining simpler and more explainable models. To increase high performance of deep learning model, published models was used complex model such as ensemble approach with several deep learning models, 19 20 two or three phases approach, 21 22 23 and various pre- or post-processing steps. 24 These increase in the number of steps added to the evaluation model that classification, detection, and segmentation for specific disease is difficult because it is necessary to consider not only the optimization of each step but also harmony with other steps.…”

Section: Introductionmentioning

confidence: 99%

Effective End-to-End Deep Learning Process in Medical Imaging Using Independent Task Learning: Application for Diagnosis of Maxillary Sinusitis

Kim

Lee

et al. 2021

Yonsei Med J

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Purpose This study aimed to propose an effective end-to-end process in medical imaging using an independent task learning (ITL) algorithm and to evaluate its performance in maxillary sinusitis applications. Materials and Methods For the internal dataset, 2122 Waters’ view X-ray images, which included 1376 normal and 746 sinusitis images, were divided into training (n=1824) and test (n=298) datasets. For external validation, 700 images, including 379 normal and 321 sinusitis images, from three different institutions were evaluated. To develop the automatic diagnosis system algorithm, four processing steps were performed: 1) preprocessing for ITL, 2) facial patch detection, 3) maxillary sinusitis detection, and 4) a localization report with the sinusitis detector. Results The accuracy of facial patch detection, which was the first step in the end-to-end algorithm, was 100%, 100%, 99.5%, and 97.5% for the internal set and external validation sets #1, #2, and #3, respectively. The accuracy and area under the receiver operating characteristic curve (AUC) of maxillary sinusitis detection were 88.93% (0.89), 91.67% (0.90), 90.45% (0.86), and 85.13% (0.85) for the internal set and external validation sets #1, #2, and #3, respectively. The accuracy and AUC of the fully automatic sinusitis diagnosis system, including site localization, were 79.87% (0.80), 84.67% (0.82), 83.92% (0.82), and 73.85% (0.74) for the internal set and external validation sets #1, #2, and #3, respectively. Conclusion ITL application for maxillary sinusitis showed reasonable performance in internal and external validation tests, compared with applications used in previous studies.

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Fully end-to-end deep-learning-based diagnosis of pancreatic tumors

Cited by 63 publications

References 18 publications

Recent advances in artificial intelligence for pancreatic ductal adenocarcinoma

Recent advances in artificial intelligence for pancreatic ductal adenocarcinoma

Application of Mass Spectrometry in Pancreatic Cancer Translational Research

Effective End-to-End Deep Learning Process in Medical Imaging Using Independent Task Learning: Application for Diagnosis of Maxillary Sinusitis

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