Robotic revision surgery after failed Nissen anti-reflux surgery: a single center experience and a literature review

Ceccarelli, Graziano; Valeri, Manuel; Amato, Lavinia; Rosa, Michele De; Rondelli, Fabio; Cappuccio, Micaela; Gambale, Francesca Elvira; Fantozzi, Mariarita; Sciaudone, Guido; Avella, Pasquale; Rocca, Aldo

doi:10.1007/s11701-023-01546-6

Cited by 5 publications

(2 citation statements)

References 48 publications

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“…This surgical technique requires an important learning curve because it is a prerogative of HB surgeons trained in oncological resections or liver transplantations [ 114 , 115 , 116 , 117 , 118 , 119 ]. To reduce healthcare migration and enhance the patient survival rate, it could organize the Hub&Spoke learning program, similarly to other general surgery procedures [ 120 , 121 , 122 , 123 , 124 , 125 , 126 , 127 , 128 , 129 ].…”

Section: Discussionmentioning

confidence: 99%

Future Perspectives on Radiomics in Acute Liver Injury and Liver Trauma

Brunese,

Avella,

Cappuccio

et al. 2024

JPM

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Background: Acute liver injury occurs most frequently due to trauma, but it can also occur because of sepsis or drug-induced injury. This review aims to analyze artificial intelligence (AI)’s ability to detect and quantify liver injured areas in adults and pediatric patients. Methods: A literature analysis was performed on the PubMed Dataset. We selected original articles published from 2018 to 2023 and cohorts with ≥10 adults or pediatric patients. Results: Six studies counting 564 patients were collected, including 170 (30%) children and 394 adults. Four (66%) articles reported AI application after liver trauma, one (17%) after sepsis, and one (17%) due to chemotherapy. In five (83%) studies, Computed Tomography was performed, while in one (17%), FAST-UltraSound was performed. The studies reported a high diagnostic performance; in particular, three studies reported a specificity rate > 80%. Conclusions: Radiomics models seem reliable and applicable to clinical practice in patients affected by acute liver injury. Further studies are required to achieve larger validation cohorts.

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Section: Discussionmentioning

confidence: 99%

Future Perspectives on Radiomics in Acute Liver Injury and Liver Trauma

Brunese,

Avella,

Cappuccio

et al. 2024

JPM

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“…Minimally invasive surgery is now common in clinical practice not only in general surgery but also in oncological pathologies [28,[194][195][196][197][198][199][200][201][202][203].…”

Section: Post-operative Pancreatic Fistulamentioning

confidence: 99%

Pancreatic Ductal Adenocarcinoma: Update of CT-Based Radiomics Applications in the Pre-Surgical Prediction of the Risk of Post-Operative Fistula, Resectability Status and Prognosis

Pacella,

Brunese,

D’Imperio

et al. 2023

JCM

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Background: Pancreatic ductal adenocarcinoma (PDAC) is the seventh leading cause of cancer-related deaths worldwide. Surgical resection is the main driver to improving survival in resectable tumors, while neoadjuvant treatment based on chemotherapy (and radiotherapy) is the best option-treatment for a non-primally resectable disease. CT-based imaging has a central role in detecting, staging, and managing PDAC. As several authors have proposed radiomics for risk stratification in patients undergoing surgery for PADC, in this narrative review, we have explored the actual fields of interest of radiomics tools in PDAC built on pre-surgical imaging and clinical variables, to obtain more objective and reliable predictors. Methods: The PubMed database was searched for papers published in the English language no earlier than January 2018. Results: We found 301 studies, and 11 satisfied our research criteria. Of those included, four were on resectability status prediction, three on preoperative pancreatic fistula (POPF) prediction, and four on survival prediction. Most of the studies were retrospective. Conclusions: It is possible to conclude that many performing models have been developed to get predictive information in pre-surgical evaluation. However, all the studies were retrospective, lacking further external validation in prospective and multicentric cohorts. Furthermore, the radiomics models and the expression of results should be standardized and automatized to be applicable in clinical practice.

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