The Role of Artificial Intelligence in Interventional Oncology: A Primer

Letzen, Brian; Wang, Clinton J.; Chapiro, Julius

doi:10.1016/j.jvir.2018.08.032

Cited by 33 publications

(25 citation statements)

References 9 publications

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“…140 Other machine learning applications in interventional oncology include intraprocedural support, such as catheter guidance, and postprocedural assessment, such as posttreatment MRI-based texture analysis of tumors. 141,142…”

Section: Future Therapiesmentioning

confidence: 99%

Embolotherapy for Hepatic Oncology: Current Perspectives and Future Directions

Bajwa

Madoff

Kishore

2020

Digestive Disease Interventions

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Primary and secondary liver cancers are a major cause of mortality worldwide. Transarterial liver-directed therapy, or embolotherapy, represents an important locoregional treatment strategy for primary and secondary liver tumors. Embolotherapeutic modalities include bland embolization (transarterial embolization), chemoembolization (transarterial chemoembolization), and radioembolization or selective internal radiotherapy. A brief technical overview of embolotherapeutic modalities as well as supportive evidence for the treatment of most common primary and secondary liver tumors will be discussed in this review. Several potential future applications, including synergy with systemic therapy, interventional theranostics, and artificial intelligence will also be reviewed briefly.

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Section: Future Therapiesmentioning

confidence: 99%

Embolotherapy for Hepatic Oncology: Current Perspectives and Future Directions

Bajwa

Madoff

Kishore

2020

Digestive Disease Interventions

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“…One of the biggest challenges of interventional radiology is to estimate/forecast the outcomes and/or the benefits of a treatment before actually performing it [18]. The identification of an accurate method to predict the success rate of a specific treatment in a specific patient could reduce unnecessary and useless procedures and interventions, reducing healthcare costs and dramatically decreasing the risk for the patient.…”

Section: Ai and Interventional Radiologymentioning

confidence: 99%

“…The field of interventional oncology could greatly benefit from AI, given the great variety of data on which the prediction for daily clinical practice can be made, even though there is the need for more data to help implement ML in the best way [18]. A robust and trustworthy perspective on procedural outcomes could give interventional radiologist more and more solid data upon which to recommend a particular and specific treatment to each patient.…”

Section: Ai and Interventional Radiologymentioning

confidence: 99%

“…As in the above-mentioned case, questions regarding the correct size of a device or on the time-consuming task of assess for the availability of a particular device or instrument according to the hospital stocks could be directly and instantaneously answered by the smart computer. Questions to the smart assistant could also imply a cost analysis, allowing the operator to choose between two devices not only assessing their dimensions but also their expensiveness in relation to outcome data, providing to all angio-suite staff the perception of the real global cost of a procedure, which must not be taken lightly, minimizing the waste and the inappropriate utilization of guidewires, catheters, coils, and other devices [18].…”

Section: Ai and Interventional Radiologymentioning

confidence: 99%

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Artificial Intelligence in Interventional Radiology: A Literature Review and Future Perspectives

Iezzi

Goldberg

Merlino

et al. 2019

Journal of Oncology

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The term “artificial intelligence” (AI) includes computational algorithms that can perform tasks considered typical of human intelligence, with partial to complete autonomy, to produce new beneficial outputs from specific inputs. The development of AI is largely based on the introduction of artificial neural networks (ANN) that allowed the introduction of the concepts of “computational learning models,” machine learning (ML) and deep learning (DL). AI applications appear promising for radiology scenarios potentially improving lesion detection, segmentation, and interpretation with a recent application also for interventional radiology (IR) practice, including the ability of AI to offer prognostic information to both patients and physicians about interventional oncology procedures. This article integrates evidence-reported literature and experience-based perceptions to assist not only residents and fellows who are training in interventional radiology but also practicing colleagues who are approaching to locoregional mini-invasive treatments.

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“…A variety of fields in medicine, including Anesthesiology, Urology, Oncology, and Emergency Medicine, have used artificial intelligence (AI) to assist decision-making. [1][2][3][4] Echocardiography generates a dataset that would benefit from AI-guided processing. Automated software has been developed for complex threedimensional valve analysis and chamber quantification, for example.…”

mentioning

confidence: 99%