A systematic scoping review exploring variation in practice in specimen mammography for Intraoperative Margin Analysis in Breast Conserving Surgery and the role of artificial intelligence in optimising diagnostic accuracy

IF 3.2 3区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

European Journal of Radiology Pub Date : 2024-10-09 DOI:10.1016/j.ejrad.2024.111777

Thomas J.E. Hubbard , Ola Shams , Benjamin Gardner , Finley Gibson , Sareh Rowlands , Tim Harries , Nick Stone

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引用次数: 0

Abstract

Purpose

Specimen Mammography (SM) is commonly used in Breast Conserving Surgery (BCS) for intraoperative margin analysis. A systematic scoping review was conducted to identify sources of methodological variation in Specimen Mammography Interpretation (SMI) and assess the role of Artificial Intelligence (AI) techniques to optimise Diagnostic Accuracy (DA).

Methods

Embase, Pubmed, Cochrane and web of science databases were searched. Studies were included if SM was used for margin analysis for BCS with reported DA compared with pathological margin status and data extracted.

Results

1242 unique studies were identified, of which 40 were included. 39/40 studies did not utilise AI for SMI, with 4 studies comparing 2 relevant techniques, giving 43 non-AI study arms for analysis. There was wide variation in SM techniques, including number of views and location of SM. Specialist performing SMI in usual clinical practice was surgeon (13/39 studies;33 %), radiologist(s) (16/39;41 %), surgeon and radiologist (3/39;8 %) or not stated (7/39;18 %) which differed from the study specialist in 15/39 (38 %) of studies. Diagnostic accuracy in studies ranged from sensitivity 19–91.7 % and specificity 25–100 %.

Conclusions

There is marked variation in current techniques used for SM for intraoperative margin analysis with correspondingly disparate DA. Only 1 study applied AI to SMI, and we identify how AI could optimise SMI and a template for future work to apply AI techniques to SMI, reduce unwarranted variation and optimise DA.

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探索保乳手术术中边缘分析标本乳腺摄影实践中的差异以及人工智能在优化诊断准确性中的作用的系统性范围界定综述。

目的：保乳手术（BCS）中通常使用乳腺标本成像（SM）进行术中边缘分析。我们进行了一项系统性的范围界定综述，以确定标本乳腺 X 线造影术（SMI）方法差异的来源，并评估人工智能（AI）技术在优化诊断准确性（DA）方面的作用：方法：检索了 Embase、Pubmed、Cochrane 和 web of science 数据库。如果BCS的边缘分析使用了SM，且报告的DA与病理边缘状态进行了比较并提取了数据，则纳入研究：结果：共发现 1242 项独特的研究，其中 40 项被纳入。39/40项研究未使用AI进行SMI，其中4项研究比较了两种相关技术，因此有43项非AI研究臂可供分析。SM技术差异很大，包括视图的数量和SM的位置。在通常临床实践中执行 SMI 的专家是外科医生（13/39 项研究；33%）、放射科医生（16/39；41%）、外科医生和放射科医生（3/39；8%）或未说明（7/39；18%），其中 15/39 项研究（38%）的专家与研究专家不同。各项研究的诊断准确性介于敏感性19%-91.7%和特异性25%-100%之间：结论：目前用于术中边缘分析的 SM 技术存在明显差异，相应的诊断结果也不尽相同。只有 1 项研究将人工智能应用于 SMI，我们确定了人工智能如何优化 SMI，以及未来工作的模板，以便将人工智能技术应用于 SMI，减少不必要的差异并优化 DA。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

European Journal of Radiology 医学-核医学

CiteScore

6.70

自引率

3.00%

发文量

398

审稿时长

42 days

期刊介绍： European Journal of Radiology is an international journal which aims to communicate to its readers, state-of-the-art information on imaging developments in the form of high quality original research articles and timely reviews on current developments in the field. Its audience includes clinicians at all levels of training including radiology trainees, newly qualified imaging specialists and the experienced radiologist. Its aim is to inform efficient, appropriate and evidence-based imaging practice to the benefit of patients worldwide.