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Artificial intelligence for renal cancer:From imaging to histology and beyond

Asian Journal of Urology 2022年第3期9卷 243-252页

作者： Karl-Friedrich Kowalewski Luisa egen chanel e.fischetti Stefano Puliatti Gomez Rivas Juan Mark Taratkin Rivero Belenchon Ines Marie Angela Sidoti Abate Julia Muhlbauer Frederik Wessels enrico Checcucci Giovanni Cacciamani Department of Urology and Urological Surgery University Medical Centre MannheimMannheimGermany Department of emergency Medicine Brigham and Women’s HospitalHarvard Medical SchoolBostonMAUSA Urology Department University of Modena&Reggio EmiliaModenaItaly ORSI Academy MelleBelgium Department of Urology Hospital Clinico San CarlosMadridSpain Institute for Urology and Reproductive Health Sechenov UniversityMoscowRussia Urology and Nephrology Department Virgen del Rocı´o University HospitalManuel Siurot s/nSevilleSpain Department of Surgery Candiolo Cancer InstituteFPO-IRCCSCandioloTurinItaly USC Institute of Urology University of Southern CaliforniaLos AngelesCAUSA

Artificial intelligence(AI)has made considerable progress within the last decade and is the subject of contemporary literature.This trend is driven by improved computational abilities and increasing amounts of complex data that allow for new approaches in analysis and interpretation.Renal cell carcinoma(RCC)has a rising incidence since most tumors are now detected at an earlier stage due to improved imaging.This creates considerable challenges as approximately 10%e17%of kidney tumors are designated as benign in histopathological evaluation;however,certain co-morbid populations(the obese and elderly)have an increased peri-interventional risk.AI offers an alternative solution by helping to optimize precision and guidance for diagnostic and therapeutic decisions.The narrative review introduced basic principles and provide a comprehensive overview of current AI techniques for RCC.Currently,AI applications can be found in any aspect of RCC management including diagnostics,perioperative care,pathology,and follow-up.Most commonly applied models include neural networks,random forest,support vector machines,and regression.However,for implementation in daily practice,health care providers need to develop a basic understanding and establish interdisciplinary collaborations in order to standardize datasets,define meaningful endpoints,and unify interpretation.

关键词： Kidney cancer Imaging Technology Artificial intelligence Machine learning

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Point-of-care ultrasound versus radiology department pelvic ultrasound on emergency department length of stay

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World Journal of emergency Medicine 2016年第3期7卷 178-182页

作者： Scan P. Wilson Kiah Connolly Shadi Lahham Mohammad Subeh chanel fischetti Alan Chiem Ariel Aspen Craig Anderson John C. Fox Department of emergency Medicine University of California Irvine Irvine California 92697 USA Department of emergency Medicine University of California Los Angeles Los Angeles California 90095 USA

BACKGROUND: The study aimed to compare the time to overall length of stay(LOS) for patients who underwent point-of-care ultrasound(POCUS) versus radiology department ultrasound(RDUS).MeTHODS: This was a prospective study on a convenience sample of patients who required pelvic ultrasound imaging as part of their emergency department(eD) assessment.ReSULTS: We enrolled a total of 194 patients who were on average 32 years-old. Ninety-eight(51%) patients were pregnant(eeks). Time to completion of RDUS was 66 minutes longer than POCUS(95%CI 60–73, Pents randomized to the RDUS arm experienced a 120 minute longer eD length of stay(LOS)(95%CI 66–173, Pents who require pelvic ultrasound as part of their diagnostic evaluation, POCUS resulted in a signifi cant decrease in time to ultrasound and eD LOS.

关键词： Point-of-care ultrasound Pelvic ultrasound Length of stay Intrauterine pregnancy

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This pain drives me crazy:Psychiatric symptoms in women with interstitial cystitis/bladder pain syndrome

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World Journal of Psychiatry 2024年第6期14卷 954-984页

作者： Marianna Mazza Stella Margoni Giuseppe Mandracchia Guglielmo Donofrio Alessia fischetti Georgios D Kotzalidis Giuseppe Marano Alessio Simonetti Delfina Janiri Lorenzo Moccia Ilaria Marcelli Greta Sfratta Domenico De Berardis Ottavia Ferrara evelina Bernardi Antonio Restaino Francesco Maria Lisci Antonio Maria D'Onofrio Caterina Brisi Flavia Grisoni Claudia Calderoni Michele Ciliberto Andrea Brugnami Sara Rossi Maria Chiara Spera Valeria De Masi ester Maria Marzo Francesca Abate Gianluca Boggio Maria Benedetta Anesini Cecilia Falsini Anna Quintano Alberto Torresi Miriam Militenda Giovanni Bartolucci Marco Biscosi Sara Ruggiero Luca Lo Giudice Giulia Mastroeni elisabetta Benini Luca Di Benedetto Romina Caso Francesco Pesaresi Francesco Traccis Luca Onori Luca Chisari Leonardo Monacelli Mariateresa Acanfora eleonora Gaetani Monia Marturano Sara Barbonetti elettra Specogna Francesca Bardi emanuela De Chiara Gianmarco Stella Andrea Zanzarri Flavio Tavoletta Arianna Crupi Giulia Battisti Laura Monti Giovanni Camardese Daniela Chieffo Antonio Gasbarrini Giovanni Scambia Gabriele Sani Department of Neurosciences Fondazione Policlinico Universitario A Gemelli IRCCSUniversita Cattolica del Sacro CuoreRome 00168Italy Department of Psychiatry UniversitàCattolica del Sacro CuoreRome 00168Italy Department of Neurosciences Section of PsychiatryFondazione Policlinico Universitario Agostino Gemelli IRCCSUniversitàCattolica del Sacro CuoreRome 00168Italy Mental Health NHSASL 4 TeramoTeramo 64100Italy Medical and Surgical Sciences UniversitàCattolica del Sacro CuoreRome 00168Italy Division of Gynecologic Oncology UniversitàCattolica del Sacro CuoreRome 00168Italy UOS Psicologia Clinica Governo ClinicoFondazione Policlinico Universitario Agostino Gemelli IRCCSRome 00168Italy CeMAD Digestive Disease Center Fondazione Policlinico Universitario A.Gemelli-IRCCSRome 00168Italy Department of Woman and Child Health Catholic UniversityRome 00168Italy UOC Psichiatria Clinica e d’Urgenza Dipartimento di Scienze Dell’InvecchiamentoNeurologicheOrtopediche e Della Testa-colloFondazione Policlinico Universitario Agostino Gemelli IRCCSRome 00168Italy

BACKGROUND Interstitial cystitis/bladder pain syndrome(IC/BPS)is an at least 6-mo noninfectious bladder inflammation of unknown origin characterized by chronic suprapubic,abdominal,and/or pelvic pain.Although the term cystitis suggests an inflammatory or infectious origin,no definite cause has been identified.It occurs in both sexes,but women are twice as much affected.AIM To systematically review evidence of psychiatric/psychological changes in persons with IC/BPS.MeTHODS Hypothesizing that particular psychological characteristics could underpin IC/BPS,we investigated in three databases the presence of psychiatric symptoms and/or disorders and/or psychological characteristics in patients with IC/BPS using the following strategy:("interstitial cystitis"OR"bladder pain syndrome")AND("mood disorder"OR depressive OR antidepressant OR depression OR depressed OR hyperthymic OR mania OR manic OR rapid cyclasterisk OR dysthymiasterisk OR dysphoriasterisk).ReSULTS On September 27,2023,the PubMed search produced 223 articles,CINAHL 62,and the combined PsycLIT/PsycARTICLeS/PsycINFO/Psychology and Behavioral Sciences Collection search 36.Search on ClinicalTrials.gov produced 14 studies,of which none had available data.eligible were peer-reviewed articles reporting psychiatric/psychological symptoms in patients with IC/BPS,i.e.63 articles spanning from 2000 to October 2023.These studies identified depression and anxiety problems in the IC/BPS population,along with sleep problems and the tendency to catastrophizing.CONCLUSION Psychotherapies targeting catastrophizing and life stress emotional awareness and expression reduced perceived pain in women with IC/BPS.Such concepts should be considered when implementing treatments aimed at reducing IC/BPS-related pain.