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A scoping review of radiomic techniques used to investigate cardiovascular disease
Pub Date : 2025-01-01 DOI: 10.1016/j.rcro.2024.100168
Arshpreet Singh Badesha , Russell Frood , Marc Bailey , Patrick Coughlin , Andrew Scarsbrook
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引用次数: 0
Artificial Intelligence for diagnostics in radiology practice: A rapid systematic scoping review
Pub Date : 2025-01-01 DOI: 10.1016/j.rcro.2024.100218
Rachel Lawrence , Emma Dodsworth , Efthalia Massou , Chris Sherlaw-Johnson , Angus Ramsay , Holly Walton , Tracy O'Regan , Fergus Gleeson , Nadia Crellin , Kevin Herbert , Pei Li Ng , Holly Elphinstone , Raj Mehta , Joanne Lloyd , Amanda Halliday , Stephen Morris , Naomi Fulop
{"title":"Artificial Intelligence for diagnostics in radiology practice: A rapid systematic scoping review","authors":"Rachel Lawrence , Emma Dodsworth , Efthalia Massou , Chris Sherlaw-Johnson , Angus Ramsay , Holly Walton , Tracy O'Regan , Fergus Gleeson , Nadia Crellin , Kevin Herbert , Pei Li Ng , Holly Elphinstone , Raj Mehta , Joanne Lloyd , Amanda Halliday , Stephen Morris , Naomi Fulop","doi":"10.1016/j.rcro.2024.100218","DOIUrl":"10.1016/j.rcro.2024.100218","url":null,"abstract":"","PeriodicalId":101248,"journal":{"name":"The Royal College of Radiologists Open","volume":"3 ","pages":"Article 100218"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143181731","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}
引用次数: 0
An Audit of the experience of Aidence lung nodule detection software users 对 Aidence 肺结节检测软件用户体验的审计
Pub Date : 2025-01-01 DOI: 10.1016/j.rcro.2024.100162
George Sylvester , Dan Kim , James Ricketts , Huub Wit
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引用次数: 0
Does AI consider children as little adults? Performance of adult radiology AI tools applied to a paediatric population – a scoping review
Pub Date : 2025-01-01 DOI: 10.1016/j.rcro.2025.100259
Lene Laborie , Jennifer Lee , Edward Antram , Susan Shelmerdine
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引用次数: 0
Evaluation of Large Language Models with clinical guidance for vetting outpatient MRI lumbar spine referrals
Pub Date : 2025-01-01 DOI: 10.1016/j.rcro.2024.100231
William Clackett , Hatim Alsusa , Hannah Watson , Antanas Kascenas , David Scott , Avinash K. Kanodia , T. Barry Oliver , Alison Q. O'Neil
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引用次数: 0
How Is AI Transforming Radiography Reporting Workflows? An Analysis of Its Effectiveness and Practical Benefits
Pub Date : 2025-01-01 DOI: 10.1016/j.rcro.2024.100200
M'Saraaz Khalil
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引用次数: 0
Does AI-Assisted Chest X-ray Interpretation Influence Clinical Management of Frontline Clinicians?
Pub Date : 2025-01-01 DOI: 10.1016/j.rcro.2024.100228
Ruchir Shah , Howell Fu , Louise Wing , Marusa Kotnik , Indrajeet Das , Farhaan Khan , Abdala Espinosa , Edwin Vanbeek , John Murchison , Fergus Gleeson , Alex Novak , Sarim Ather
{"title":"Does AI-Assisted Chest X-ray Interpretation Influence Clinical Management of Frontline Clinicians?","authors":"Ruchir Shah , Howell Fu , Louise Wing , Marusa Kotnik , Indrajeet Das , Farhaan Khan , Abdala Espinosa , Edwin Vanbeek , John Murchison , Fergus Gleeson , Alex Novak , Sarim Ather","doi":"10.1016/j.rcro.2024.100228","DOIUrl":"10.1016/j.rcro.2024.100228","url":null,"abstract":"","PeriodicalId":101248,"journal":{"name":"The Royal College of Radiologists Open","volume":"3 ","pages":"Article 100228"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143134403","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}
引用次数: 0
Leveraging Evidence from Deep Learning Studies to Develop an Improved MRI-based Model for Breast Cancer Diagnosis
Pub Date : 2025-01-01 DOI: 10.1016/j.rcro.2024.100198
Kamarul Amin Abdullah , Sara Marziali , Muzna Nanaa , Lorena Escudero Sánchez , Nicholas Payne , Fiona Gilbert
{"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}
引用次数: 0
Demystifying the radiobiology of hypofractionation: Simple equations to determine tumour alpha beta ratio
Pub Date : 2025-01-01 DOI: 10.1016/j.rcro.2024.100159
Nuradh Joseph , Ananya Choudhury , Roger Dale

Introduction

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.

Methods

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. (α/β)=[H.h{c.(CR)}CHR].
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.
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.

Results

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.

Conclusion

Tumour α/β ratio can be easily derived using simple equations assuming non-inferiority of fractionation regimen.

Advances in knowledge

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.
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引用次数: 0
Enhancing Cervical Lesion Detection through Instant Segmentation: Analysing Colposcopic Images with DeepLabv3+ Model
Pub Date : 2025-01-01 DOI: 10.1016/j.rcro.2024.100205
Yihung Sun , Chien-Teng Liao , John Y. Chiang , Yu-En Chou
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引用次数: 0
期刊
The Royal College of Radiologists Open
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