The Royal College of Radiologists Open最新文献

{"title":"Leveraging Evidence from Deep Learning Studies to Develop an Improved MRI-based Model for Breast Cancer Diagnosis","authors":"Kamarul Amin Abdullah , Sara Marziali , Muzna Nanaa , Lorena Escudero Sánchez , Nicholas Payne , Fiona Gilbert","doi":"10.1016/j.rcro.2024.100198","DOIUrl":"10.1016/j.rcro.2024.100198","url":null,"abstract":"","PeriodicalId":101248,"journal":{"name":"The Royal College of Radiologists Open","volume":"3 ","pages":"Article 100198"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143135189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Demystifying the radiobiology of hypofractionation: Simple equations to determine tumour alpha beta ratio","authors":"Nuradh Joseph ,&nbsp;Ananya Choudhury ,&nbsp;Roger Dale","doi":"10.1016/j.rcro.2024.100159","DOIUrl":"10.1016/j.rcro.2024.100159","url":null,"abstract":"<div><h3>Introduction</h3><div>The radiobiological basis of hypofractionation pivots on two fundamental tumour characteristics - low α/β ratio and high repopulation factor. In our work, we present novel yet simple equations to derive the tumour α/β ratio assuming non-inferiority of two fractionation regimens.</div></div><div><h3>Methods</h3><div>A simple equation was derived to determine the α/β ratio of tumours assuming non-inferiority of shorter fractionation regimen with longer regimen, by applying the concept of biological effective dose as shown below. <span><math><mrow><mrow><mo>(</mo><mrow><mi>α</mi><mo>/</mo><mi>β</mi></mrow><mo>)</mo></mrow><mo>=</mo><mrow><mo>[</mo><mfrac><mrow><mi>H</mi><mo>.</mo><mi>h</mi><mo>−</mo><mrow><mo>{</mo><mrow><mi>c</mi><mo>.</mo><mrow><mo>(</mo><mrow><mi>C</mi><mo>−</mo><mi>R</mi></mrow><mo>)</mo></mrow></mrow><mo>}</mo></mrow></mrow><mrow><mi>C</mi><mo>−</mo><mi>H</mi><mo>−</mo><mi>R</mi></mrow></mfrac><mo>]</mo></mrow></mrow></math></span>.</div><div>Where H = total dose of the short regimen, h = dose per fraction of the short regimen, C = total dose of the long regimen and c = dose per fraction of the long regimen, R = dose lost due to repopulation.</div><div>Based on this equation, the actual α/β ratio of tumour is determined by substituting regimen of each individual clinical trial, using an iterative and non-iterative approach.</div></div><div><h3>Results</h3><div>Using this equation, in prostate cancer, the α/β ratio is in the range of 2–3 Gy. For urothelial muscle invasive bladder cancer, there is a wide range of probable values for the α/β ratio from 6 Gy to 15 Gy. Assuming the conventional value of 10 Gy for the α/β ratio for bladder cancer, the equivalence of 55 Gy in 20 fractions with 64 Gy in 32 fractions is consistent with a repopulation rate of 0.4 Gy/day.</div></div><div><h3>Conclusion</h3><div>Tumour α/β ratio can be easily derived using simple equations assuming non-inferiority of fractionation regimen.</div></div><div><h3>Advances in knowledge</h3><div>In this work we present simple equations to derive the tumour α/β ratio when a hypofractionated regimen has proven to be non-inferior to a conventional regimen.</div></div>","PeriodicalId":101248,"journal":{"name":"The Royal College of Radiologists Open","volume":"3 ","pages":"Article 100159"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143140373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing Cervical Lesion Detection through Instant Segmentation: Analysing Colposcopic Images with DeepLabv3+ Model","authors":"Yihung Sun ,&nbsp;Chien-Teng Liao ,&nbsp;John Y. Chiang ,&nbsp;Yu-En Chou","doi":"10.1016/j.rcro.2024.100205","DOIUrl":"10.1016/j.rcro.2024.100205","url":null,"abstract":"","PeriodicalId":101248,"journal":{"name":"The Royal College of Radiologists Open","volume":"3 ","pages":"Article 100205"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143176425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving the generalisation of radiographic AI using automated data curation to mitigate shortcut learning","authors":"Ian Selby ,&nbsp;Eduardo González Solares ,&nbsp;Anna Breger ,&nbsp;Michael Roberts ,&nbsp;Lorena Escudero Sánchez ,&nbsp;James Rudd ,&nbsp;Nicholas Walton ,&nbsp;Judith Babar ,&nbsp;Carola-Bibiane Schönlieb ,&nbsp;Evis Sala ,&nbsp;Jonathan Weir-McCall","doi":"10.1016/j.rcro.2024.100232","DOIUrl":"10.1016/j.rcro.2024.100232","url":null,"abstract":"","PeriodicalId":101248,"journal":{"name":"The Royal College of Radiologists Open","volume":"3 ","pages":"Article 100232"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143181481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Synthesised low-dose clinical screening mammograms: an evaluation of suitability for AI training","authors":"Anna Worthy ,&nbsp;Nadia Smith ,&nbsp;Caroline Shenton-Taylor","doi":"10.1016/j.rcro.2024.100179","DOIUrl":"10.1016/j.rcro.2024.100179","url":null,"abstract":"","PeriodicalId":101248,"journal":{"name":"The Royal College of Radiologists Open","volume":"3 ","pages":"Article 100179"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143101014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Shaping AI education in imaging and harnessing learning opportunities: initial perspectives from Radiology trainees","authors":"Alex Sawer,&nbsp;Yuen Zhang,&nbsp;Tilak Das,&nbsp;Holly Christopher","doi":"10.1016/j.rcro.2024.100177","DOIUrl":"10.1016/j.rcro.2024.100177","url":null,"abstract":"","PeriodicalId":101248,"journal":{"name":"The Royal College of Radiologists Open","volume":"3 ","pages":"Article 100177"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143101015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Revolutionising Competency-Based Medical Education with AI","authors":"David Lay","doi":"10.1016/j.rcro.2024.100186","DOIUrl":"10.1016/j.rcro.2024.100186","url":null,"abstract":"","PeriodicalId":101248,"journal":{"name":"The Royal College of Radiologists Open","volume":"3 ","pages":"Article 100186"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143092044","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Proof of Concept for an AI-Powered Pipeline in Paediatric Radiology","authors":"Louise Rigny,&nbsp;Pavithra Rajendran,&nbsp;Sebin Sabu,&nbsp;Shiren Patel,&nbsp;Susan Shelmerdine,&nbsp;Owen Arthurs","doi":"10.1016/j.rcro.2024.100178","DOIUrl":"10.1016/j.rcro.2024.100178","url":null,"abstract":"","PeriodicalId":101248,"journal":{"name":"The Royal College of Radiologists Open","volume":"3 ","pages":"Article 100178"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143181794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Creating Conditions for Success: How to Successfully Implement CXR AI","authors":"Jesus Perdomo Lampignano ,&nbsp;Sean Duncan ,&nbsp;David Lowe ,&nbsp;Mark Hall","doi":"10.1016/j.rcro.2024.100215","DOIUrl":"10.1016/j.rcro.2024.100215","url":null,"abstract":"","PeriodicalId":101248,"journal":{"name":"The Royal College of Radiologists Open","volume":"3 ","pages":"Article 100215"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143127829","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-CXR triage expedites lung cancer investigation in the NHS","authors":"Mathew Storey ,&nbsp;Jack Packer ,&nbsp;Daniel Togher ,&nbsp;M. Abubacker ,&nbsp;Anthony Chung ,&nbsp;Anne-Marie Bartsch ,&nbsp;Simon Rickaby ,&nbsp;Geraldine Dean ,&nbsp;Susan Shelmardine","doi":"10.1016/j.rcro.2024.100227","DOIUrl":"10.1016/j.rcro.2024.100227","url":null,"abstract":"","PeriodicalId":101248,"journal":{"name":"The Royal College of Radiologists Open","volume":"3 ","pages":"Article 100227"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143129140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}