Can magnetic resonance imaging radiomics of the pancreas predict postoperative pancreatic fistula?

Skawran, Stephan; Kambakamba, Patryk; Baeßler, Bettina; Spiczak, Jochen von; Kupka, M; Müller, Philip C.; Moeckli, Beat; Linecker, Michael; Petrowsky, Henrik; Reiner, Caecilia S.

doi:10.1016/j.ejrad.2021.109733

Cited by 26 publications

(20 citation statements)

References 36 publications

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“…The AI models established by Skawran et al. ( 45 ) and Kambkamba et al. ( 43 ) based on the imaging characteristics of preoperative CT and MRI respectively provided reliable predictability for the assessment of POPF.…”

Section: Discussionmentioning

confidence: 99%

Research trends of artificial intelligence in pancreatic cancer: a bibliometric analysis

Yin

Zhang²,

Yang³

et al. 2022

Front. Oncol.

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PurposeWe evaluated the related research on artificial intelligence (AI) in pancreatic cancer (PC) through bibliometrics analysis and explored the research hotspots and current status from 1997 to 2021.MethodsPublications related to AI in PC were retrieved from the Web of Science Core Collection (WoSCC) during 1997-2021. Bibliometrix package of R software 4.0.3 and VOSviewer were used to bibliometrics analysis.ResultsA total of 587 publications in this field were retrieved from WoSCC database. After 2018, the number of publications grew rapidly. The United States and Johns Hopkins University were the most influential country and institution, respectively. A total of 2805 keywords were investigated, 81 of which appeared more than 10 times. Co-occurrence analysis categorized these keywords into five types of clusters: (1) AI in biology of PC, (2) AI in pathology and radiology of PC, (3) AI in the therapy of PC, (4) AI in risk assessment of PC and (5) AI in endoscopic ultrasonography (EUS) of PC. Trend topics and thematic maps show that keywords " diagnosis ", “survival”, “classification”, and “management” are the research hotspots in this field.ConclusionThe research related to AI in pancreatic cancer is still in the initial stage. Currently, AI is widely studied in biology, diagnosis, treatment, risk assessment, and EUS of pancreatic cancer. This bibliometrics study provided an insight into AI in PC research and helped researchers identify new research orientations.

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Section: Discussionmentioning

confidence: 99%

Research trends of artificial intelligence in pancreatic cancer: a bibliometric analysis

Yin

Zhang²,

Yang³

et al. 2022

Front. Oncol.

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“…Automated image analysis, feature recognition, data extraction and deep learning models are everyday reality for the tech giants but have only partially reached precision pathology (51)(52)(53). Radiology is one step ahead and shows what is possible with the emerging field of radiomics-the extraction of data from radiograms to diagnose cancer, predict outcomes or guide therapy (54)(55)(56). Transplant pathology needs to follow this example with a concerted, multidisciplinary effort of pathologists, computational biologists and healthcare administrators.…”

Section: Ai In Transplant Pathologymentioning

confidence: 99%

Artificial Intelligence: Present and Future Potential for Solid Organ Transplantation

Peloso

Moeckli

Delaune³

et al. 2022

Transpl Int

Self Cite

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Artificial intelligence (AI) refers to computer algorithms used to complete tasks that usually require human intelligence. Typical examples include complex decision-making and- image or speech analysis. AI application in healthcare is rapidly evolving and it undoubtedly holds an enormous potential for the field of solid organ transplantation. In this review, we provide an overview of AI-based approaches in solid organ transplantation. Particularly, we identified four key areas of transplantation which could be facilitated by AI: organ allocation and donor-recipient pairing, transplant oncology, real-time immunosuppression regimes, and precision transplant pathology. The potential implementations are vast—from improved allocation algorithms, smart donor-recipient matching and dynamic adaptation of immunosuppression to automated analysis of transplant pathology. We are convinced that we are at the beginning of a new digital era in transplantation, and that AI has the potential to improve graft and patient survival. This manuscript provides a glimpse into how AI innovations could shape an exciting future for the transplantation community.

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“…AUCs of ML models in predicting the course of disease have ranged from 0.61 to 0.92, and accuracies have been reported to range between 71% and 98%. Additionally, five studies have developed ML algorithms to predict postoperative complications after pancreatic surgery [67][68][69][70][71] . For predicting postoperative complications, ML algorithms have demonstrated AUCs between 0.67 and 0.85, whereas accuracies have varied from 75% to 85%.…”

Section: Pancreatic Surgerymentioning

confidence: 99%

Artificial intelligence in hepatFIGopancreaticobiliary surgery: a systematic review

Bektaş¹,

Zonderhuis²,

Marquering³

et al. 2022

Art Int Surg

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Aim: The aim of this systematic review was to provide an overview of Machine Learning applications within hepatopancreaticobiliary surgery. The secondary aim was to evaluate the predictive performances of applied Machine Learning models. Methods: A systematic search was conducted in PubMed, EMBASE, Cochrane, and Web of Science. Studies were only eligible for inclusion when they described Machine Learning in hepatopancreaticobiliary surgery. The Cochrane and PROBAST risk of bias tools were used to evaluate the quality of studies and included Machine Learning models. Results: Out of 1821 articles, 52 studies have met the inclusion criteria. The majority of Machine Learning models were developed to predict the course of disease, and postoperative complications. The course of disease has been predicted with accuracies up to 99%, and postoperative complications with accuracies up to 89%. Most studies had a retrospective study design, in which external validation was absent for Machine Learning models. Conclusion: Machine learning models have shown promising accuracies in the prediction of short-term and long-term surgical outcomes after hepatopancreaticobiliary surgery. External validation of Machine Learning models is required to facilitate the clinical introduction of Machine Learning.

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Can magnetic resonance imaging radiomics of the pancreas predict postoperative pancreatic fistula?

Cited by 26 publications

References 36 publications

Research trends of artificial intelligence in pancreatic cancer: a bibliometric analysis

Research trends of artificial intelligence in pancreatic cancer: a bibliometric analysis

Artificial Intelligence: Present and Future Potential for Solid Organ Transplantation

Artificial intelligence in hepatFIGopancreaticobiliary surgery: a systematic review

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