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      Robotics in Interventional Radiology: Review of Current and Future Applications

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          Abstract

          This review is a brief overview of the current status and the potential role of robotics in interventional radiology. Literature published in the last decades, with an emphasis on the last 5 years, was reviewed and the technical developments in robotics and navigational systems using CT-, MR- and US-image guidance were analyzed. Potential benefits and disadvantages of their current and future use were evaluated. The role of fusion imaging modalities and artificial intelligence was analyzed in both percutaneous and endovascular procedures. A few hundred articles describing results of single or several systems were included in our analysis.

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          A survey of robot learning from demonstration

          Brenna Argall, Sonia Chernova, Manuela Veloso … (2009)
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            Renal Cancer

            (2016)
            Adel M. R. Youssef, Saleh O. El Mohalhel, Ahmed H. Moabed Urology Department, Riyadh Central Hospital, Riyadh, Saudi Arabia Over the last four years, 52 renal tumours were diagnosed and treated at our hospital. Of these, 40 were tumours kidney proper and twelve collecting system tumours, all of these last type were transitional cell malignancies. The others were 2 Wilm's, 3 papillary carcinoma, one sarcoma, one oncocytoma, and one hamartoma, one bilharzial mass, and the remaining 31 were clear cell carcinomas. Our main dependency for definitive diagnosis has been on U/S and CT, and recently we depend rarely on angiogram in selective cases. The treatment of choice has been radical nephrectomy for the proven kidney tumours, and nephroureterectomy for collecting system ones. But we have also done embolization for 3 cases and surgical ligation for one case, but no enucleation or partial nephrectomy for any case despite the recent publications advocating these methods in special situations, that is except in one case due to an error in judgement. And our results covering these cases justifies our commitment. Presented at the: 5th Saudi Urological Conference King Fahd Military Medical Complex 22-23 March 1989
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              CT-based peritumoral radiomics signatures to predict early recurrence in hepatocellular carcinoma after curative tumor resection or ablation

              Quan-yuan Shan, Hang-tong Hu, Shi-ting Feng … (2019)
              Objective To construct a prediction model based on peritumoral radiomics signatures from CT images and investigate its efficiency in predicting early recurrence (ER) of hepatocellular carcinoma (HCC) after curative treatment. Materials and methods In total, 156 patients with primary HCC were randomly divided into the training cohort (109 patients) and the validation cohort (47 patients). From the pretreatment CT images, we extracted 3-phase two-dimensional images from the largest cross-sectional area of the tumor. A region of interest (ROI) was manually delineated around the lesion for tumoral radiomics (T-RO) feature extraction, and another ROI was outlined with an additional 2 cm peritumoral area for peritumoral radiomics (PT-RO) feature extraction. The least absolute shrinkage and selection operator (LASSO) logistic regression model was applied for feature selection and model construction. The T-RO and PT-RO models were constructed. In the validation cohort, the prediction efficiencies of the two models and peritumoral enhancement (PT-E) were evaluated qualitatively by receiver operating characteristic (ROC) curves, calibration curves and decision curves and quantitatively by area under the curve (AUC), the category-free net reclassification index (cfNRI) and integrated discrimination improvement values (IDI). Results By comparing AUC values, the prediction accuracy in the validation cohort was good for the PT-RO model (0.80 vs. 0.79, P = 0.47) but poor for the T-RO model (0.82 vs. 0.62, P < 0.01), which was significantly overfitted. In the validation cohort, the ROC curves, calibration curves and decision curves indicated that the PT-RO model had better calibration efficiency and provided greater clinical benefits. CfNRI indicated that the PT-RO model correctly reclassified 47% of ER patients and 32% of non-ER patients compared to the T-RO model (P < 0.01); additionally, the PT-RO model correctly reclassified 24% of ER patients and 41% of non-ER patients compared to PT-E (P = 0.02). IDI indicated that the PT-RO model could improve prediction accuracy by 0.22 (P < 0.01) compared to the T-RO model and by 0.20 (P = 0.01) compared to PT-E. Conclusion The CT-based PT-RO model can effectively predict the ER of HCC and is more efficient than the T-RO model and the conventional imaging feature PT-E. Electronic supplementary material The online version of this article (10.1186/s40644-019-0197-5) contains supplementary material, which is available to authorized users.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Technol Cancer Res Treat
                Journal ID (iso-abbrev): Technol Cancer Res Treat
                Journal ID (publisher-id): TCT
                Journal ID (hwp): sptct
                Title: Technology in Cancer Research & Treatment
                Publisher: SAGE Publications (Sage CA: Los Angeles, CA )
                ISSN (Print): 1533-0346
                ISSN (Electronic): 1533-0338
                Publication date (Electronic): 27 April 2023
                Publication date Collection: 2023
                Volume: 22
                Electronic Location Identifier: 15330338231152084
                Affiliations
                [1 ]Postgraduate School in Radiodiagnostics, Ringgold 9304, universityUniversità degli Studi di Milano; , Milan, Italy
                [2 ]Foundation IRCCS Cà Granda-Ospedale Maggiore Policlinico, Milan, Italy
                [3 ]Ringgold 9304, universityUniversità degli Studi di Milano; , Milan, Italy
                [4 ]Ringgold 18602, universityOspedale Luigi Sacco; , Milan, Italy
                Author notes
                [*]Elvira Francisca Maria Buijs, Postgraduate School in Radiodiagnostics, Università degli Studi di Milano, 20122 Milan, Italy. Email: efmbuijs@ 123456gmail.com
                Author information
                https://orcid.org/0000-0002-8286-1562
                https://orcid.org/0000-0003-1774-0729
                Article
                Publisher ID: 10.1177_15330338231152084
                DOI: 10.1177/15330338231152084
                PMC ID: 10150437
                PubMed ID: 37113061
                SO-VID: 250f7b18-245c-44b1-8537-745db23e10c8
                Copyright © © The Author(s) 2023
                License:

                This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License ( https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page ( https://us.sagepub.com/en-us/nam/open-access-at-sage).

                History
                Date received : 10 August 2022
                Date revision received : 22 December 2022
                Date accepted : 2 January 2023
                Related
                Categories
                Subject: New Tools in Loco-Regional Treatments: State of Art and Future Directions
                Subject: Review
                Custom metadata
                typesetter ts19
                cover-date January-December 2023

                Keywords: endovascular,interventional radiology,navigation oncology,percutaneous,robotics,robotic systems
                Data availability:
                Keywords: endovascular, interventional radiology, navigation oncology, percutaneous, robotics, robotic systems

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