Application of artificial intelligence to pancreatic adenocarcinoma

Chen, Xi; Fu, Ruibiao; Shao, Qian; Chen, Yan; Ye, Qinghuang; Li, Sheng; He, Xiongxiong; Zhu, Jinhui

doi:10.3389/fonc.2022.960056

Cited by 8 publications

(7 citation statements)

References 122 publications

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“…They focused on the analysis of endoscopic ultrasound images, CT images, and magnetic resonance images using AI algorithms. Chen et al [ 47 ] provided a literature review on the application of AI for pancreatic cancer. They included articles from PubMed and Google Scholar by using the keywords AI and pancreatic cancer and focused on risk assessment, treatment, diagnosis, and prognosis of pancreatic cancer.…”

Section: Discussionmentioning

confidence: 99%

“…They included articles from PubMed and Google Scholar by using the keywords AI and pancreatic cancer and focused on risk assessment, treatment, diagnosis, and prognosis of pancreatic cancer. They did not follow the PRISMA guidelines to ensure the reliability and transparency of the work [ 47 ]. Moreover, Dumitrescu et al [ 10 ] conducted a systematic review by following PRISMA guidelines and focused on the diagnostic value of AI-based ultrasound for the diagnosis of pancreatic cancer.…”

Section: Discussionmentioning

confidence: 99%

“…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%

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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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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

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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“…Previous research highlights that the significant risk factors for pancreatic cancer include age, gender, race, a family history of inheritance, smoking, drinking, obesity, chronic pancreatitis, hepatitis, and persons living with human immunodeficiency virus (PLWH) 4–6 . Additionally, patients with diabetes mellitus (DM) have an elevated risk for pancreatic cancer 6–9 …”

Section: Introductionmentioning

confidence: 99%

“…[4][5][6] Additionally, patients with diabetes mellitus (DM) have an elevated risk for pancreatic cancer. [6][7][8][9] Regarding the correlation between diabetes and pancreatic cancer, previous publications observed that up to 85% of pancreatic cancer patients have diabetes at the time of the cancer diagnosis. 10 The increased risk of pancreatic cancer is two to three times higher for patients with diabetes than patients without diabetes.…”

Section: Introductionmentioning

confidence: 99%

A novel prediction model of the risk of pancreatic cancer among diabetes patients using multiple clinical data and machine learning

Chen,

Phuc,

Nguyen

et al. 2023

Cancer Medicine

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IntroductionPancreatic cancer is associated with poor prognosis. Considering the increased global incidence of diabetes cases and that individuals with diabetes are considered a high‐risk subpopulation for pancreatic cancer, it is critical to detect the risk of pancreatic cancer within populations of person living = with diabetes. This study aimed to develop a novel prediction model for pancreatic cancer risk among patients with diabetes, using = a real‐world database containing clinical features and employing numerous artificial intelligent approach algorithms.MethodsThis retrospective observational study analyzed data on patients with Type 2 diabetes from a multisite Taiwanese EMR database between 2009 and 2019. Predictors were selected in accordance with the literature review and clinical perspectives. The prediction models were constructed using machine learning algorithms such as logistic regression, linear discriminant analysis, gradient boosting machine, and random forest.ResultsThe cohort consisted of 66,384 patients. The Linear Discriminant Analysis (LDA) model generated the highest AUROC of 0.9073, followed by the Voting Ensemble and Gradient Boosting machine models. LDA, the best model, exhibited an accuracy of 84.03%, a sensitivity of 0.8611, and a specificity of 0.8403. The most significant predictors identified for pancreatic cancer risk were glucose, glycated hemoglobin, hyperlipidemia comorbidity, antidiabetic drug use, and lipid‐modifying drug use.ConclusionThis study successfully developed a highly accurate 4‐year risk model for pancreatic cancer in patients with diabetes using real‐world clinical data and multiple machine‐learning algorithms. Potentially, our predictors offer an opportunity to identify pancreatic cancer early and thus increase prevention and invention windows to impact survival in diabetic patients.

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Machine learning to predict completion of treatment for pancreatic cancer

Pasha,

Khalid,

Levy

et al. 2024

Journal of Surgical Oncology

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BackgroundChemotherapy enhances survival rates for pancreatic cancer (PC) patients postsurgery, yet less than 60% complete adjuvant therapy, with a smaller fraction undergoing neoadjuvant treatment. Our study aimed to predict which patients would complete pre‐ or postoperative chemotherapy through machine learning (ML).MethodsPatients with resectable PC identified in our institutional pancreas database were grouped into two categories: those who completed all intended treatments (i.e., surgery plus either neoadjuvant or adjuvant chemotherapy), and those who did not. We applied logistic regression with lasso penalization and an extreme gradient boosting model for prediction, and further examined it through bootstrapping for sensitivity.ResultsAmong 208 patients, the median age was 69, with 49.5% female and 62% white participants. Most had an Eastern Cooperative Oncology Group (ECOG) performance status of ≤2. The PC predominantly affected the pancreatic head. Neoadjuvant and adjuvant chemotherapies were received by 26% and 47.1%, respectively, but only 49% completed all treatments. Incomplete therapy was correlated with older age and lower ECOG status. Negative prognostic factors included worsening diabetes, age, congestive heart failure, high body mass index, family history of PC, initial bilirubin levels, and tumor location in the pancreatic head. The models also flagged other factors, such as jaundice and specific cancer markers, impacting treatment completion. The predictive accuracy (area under the receiver operating characteristic curve) was 0.67 for both models, with performance expected to improve with larger datasets.ConclusionsOur findings underscore the potential of ML to forecast PC treatment completion, highlighting the importance of specific preoperative factors. Increasing data volumes may enhance predictive accuracy, offering valuable insights for personalized patient strategies.

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Application of artificial intelligence to pancreatic adenocarcinoma

Cited by 8 publications

References 122 publications

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

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

A novel prediction model of the risk of pancreatic cancer among diabetes patients using multiple clinical data and machine learning

Machine learning to predict completion of treatment for pancreatic cancer

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