Diagnostic and Interventional Imaging最新文献_第6页

Endovascular management of pelvic congestion syndrome: An expert consensus statement from the French Society of Cardiovascular Imaging (SFICV), Interventional Radiology Federation (FRI), College of French Radiology Teachers (CERF), and French Society of Women's Imaging (SIFEM) 盆腔充血综合征的血管内治疗：来自法国心血管影像学学会（SFICV）、介入放射学联合会（FRI）、法国放射学教师学院（CERF）和法国妇女影像学学会（SIFEM）的专家共识声明。

IF 8.1 2区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Diagnostic and Interventional Imaging

Pub Date : 2025-10-01 DOI: 10.1016/j.diii.2025.04.004

Vincent Le Pennec , Frédéric Douane , Jean Luc Brun , Francine Thouveny , Thomas Martinelli , Marine Bravetti , Charles Mastier , Yan Le Bras , André Rogopoulos , Pierre Antoine Barral , Henri Marret , Pascal Chabrot , Alexis Jacquier , Quentin Senechal , Gary Doppelt , Julien Frandon

Purpose

Pelvic congestion syndrome (PCS) is a major cause of chronic pelvic pain in women of reproductive age. It is often associated with pelvic venous insufficiency and venous dilatation of the ovarian and uterine veins, resulting in a variety of symptoms exacerbated by venous hypertension. Despite its prevalence, PCS lacks standardized diagnostic and management protocols, making effective treatment challenging. The purpose of this expert consensus statement was to summarize the opinions of French radiologists and gynecologists regarding the diagnosis, imaging, treatment, and management of PCS.

Materials and methods

A working group of 14 expert radiologists and gynecologists from various French medical centers used a Delphi panel approach with several rounds of remote and face-to-face meetings to formulate and refine expert opinions based on the current literature and clinical expertise. These opinions were categorized according to diagnostic criteria, imaging techniques, therapeutic options, and follow-up protocols.

Results

The group formulated 72 initial opinions, and 65 were retained after rigorous evaluation for consensus. Key diagnostic tools include Doppler ultrasound for detection of venous reflux and magnetic resonance imaging for detailed assessment of venous anatomy. Endovascular embolization was highlighted as the primary treatment approach and recommended after thorough imaging evaluation. Noninvasive treatments and multidisciplinary care were also emphasized for comprehensive management. The expert opinion also included post-treatment follow-up to assess quality of life and symptom resolution.

Conclusion

This structured consensus approach helped develop standardized expert opinions on management of, providing clear guidelines for diagnosis, treatment, and follow-up. These guidelines should improve clinical practice and patient care in the management of PCS.

目的：盆腔充血综合征（PCS）是育龄妇女慢性盆腔疼痛的主要原因。它常伴有盆腔静脉功能不全和卵巢、子宫静脉扩张，导致多种症状因静脉高压而加重。尽管流行，但PCS缺乏标准化的诊断和管理方案，使有效治疗具有挑战性。本专家共识声明的目的是总结法国放射科医生和妇科医生对PCS的诊断、成像、治疗和管理的意见。材料和方法：由来自法国各医疗中心的14名放射科专家和妇科专家组成的工作组采用德尔菲小组方法，通过几轮远程和面对面会议，根据当前文献和临床专业知识制定和完善专家意见。这些意见根据诊断标准、成像技术、治疗方案和随访方案进行分类。结果：小组形成了72条初步意见，其中65条经过严格的评估一致保留。主要的诊断工具包括用于检测静脉回流的多普勒超声和用于详细评估静脉解剖的磁共振成像。血管内栓塞被强调为主要的治疗方法，并在充分的影像学评估后推荐。强调无创治疗和多学科综合护理。专家意见还包括治疗后随访，以评估生活质量和症状缓解。结论：这种结构化的共识方法有助于形成标准化的专家意见，为诊断、治疗和随访提供明确的指导。这些指南应改善临床实践和患者护理的管理PCS。

{"title":"Endovascular management of pelvic congestion syndrome: An expert consensus statement from the French Society of Cardiovascular Imaging (SFICV), Interventional Radiology Federation (FRI), College of French Radiology Teachers (CERF), and French Society of Women's Imaging (SIFEM)","authors":"Vincent Le Pennec , Frédéric Douane , Jean Luc Brun , Francine Thouveny , Thomas Martinelli , Marine Bravetti , Charles Mastier , Yan Le Bras , André Rogopoulos , Pierre Antoine Barral , Henri Marret , Pascal Chabrot , Alexis Jacquier , Quentin Senechal , Gary Doppelt , Julien Frandon","doi":"10.1016/j.diii.2025.04.004","DOIUrl":"10.1016/j.diii.2025.04.004","url":null,"abstract":"<div><h3>Purpose</h3><div>Pelvic congestion syndrome (PCS) is a major cause of chronic pelvic pain in women of reproductive age. It is often associated with pelvic venous insufficiency and venous dilatation of the ovarian and uterine veins, resulting in a variety of symptoms exacerbated by venous hypertension. Despite its prevalence, PCS lacks standardized diagnostic and management protocols, making effective treatment challenging. The purpose of this expert consensus statement was to summarize the opinions of French radiologists and gynecologists regarding the diagnosis, imaging, treatment, and management of PCS.</div></div><div><h3>Materials and methods</h3><div>A working group of 14 expert radiologists and gynecologists from various French medical centers used a Delphi panel approach with several rounds of remote and face-to-face meetings to formulate and refine expert opinions based on the current literature and clinical expertise. These opinions were categorized according to diagnostic criteria, imaging techniques, therapeutic options, and follow-up protocols.</div></div><div><h3>Results</h3><div>The group formulated 72 initial opinions, and 65 were retained after rigorous evaluation for consensus. Key diagnostic tools include Doppler ultrasound for detection of venous reflux and magnetic resonance imaging for detailed assessment of venous anatomy. Endovascular embolization was highlighted as the primary treatment approach and recommended after thorough imaging evaluation. Noninvasive treatments and multidisciplinary care were also emphasized for comprehensive management. The expert opinion also included post-treatment follow-up to assess quality of life and symptom resolution.</div></div><div><h3>Conclusion</h3><div>This structured consensus approach helped develop standardized expert opinions on management of, providing clear guidelines for diagnosis, treatment, and follow-up. These guidelines should improve clinical practice and patient care in the management of PCS.</div></div>","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":"106 10","pages":"Pages 356-366"},"PeriodicalIF":8.1,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144051346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Unveiling an under-recognized strain: New guidelines for pelvic congestion syndrome 揭示一个未被认识的压力：骨盆充血综合征的新指南。

IF 8.1 2区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Diagnostic and Interventional Imaging

Pub Date : 2025-10-01 DOI: 10.1016/j.diii.2025.06.001

Maxime Barat , Philippe Soyer , Anthony Dohan

引用次数: 0

Imaging features of focal nodular hyperplasia on gadoxetic acid-enhanced MRI gadoxetic酸增强MRI的局灶性结节增生影像特征。

IF 8.1 2区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Diagnostic and Interventional Imaging

Pub Date : 2025-10-01 DOI: 10.1016/j.diii.2025.07.007

Francesco Matteini , Valérie Vilgrain , Maxime Ronot

引用次数: 0

Comparison between multimodal foundation models and radiologists for the diagnosis of challenging neuroradiology cases with text and images 多模态基础模型与放射科医师对具有挑战性的神经放射学病例的文本和图像诊断的比较。

IF 8.1 2区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Diagnostic and Interventional Imaging

Pub Date : 2025-10-01 DOI: 10.1016/j.diii.2025.04.006

Bastien Le Guellec , Cyril Bruge , Najib Chalhoub , Victor Chaton , Edouard De Sousa , Yann Gaillandre , Riyad Hanafi , Matthieu Masy , Quentin Vannod-Michel , Aghiles Hamroun , Grégory Kuchcinski , ARIANES investigators

Purpose

The purpose of this study was to compare the ability of two multimodal models (GPT-4o and Gemini 1.5 Pro) with that of radiologists to generate differential diagnoses from textual context alone, key images alone, or a combination of both using complex neuroradiology cases.

Materials and methods

This retrospective study included neuroradiology cases from the "Diagnosis Please" series published in the Radiology journal between January 2008 and September 2024. The two multimodal models were asked to provide three differential diagnoses from textual context alone, key images alone, or the complete case. Six board-certified neuroradiologists solved the cases in the same setting, randomly assigned to two groups: context alone first and images alone first. Three radiologists solved the cases without, and then with the assistance of Gemini 1.5 Pro. An independent radiologist evaluated the quality of the image descriptions provided by GPT-4o and Gemini for each case. Differences in correct answers between multimodal models and radiologists were analyzed using McNemar test.

Results

GPT-4o and Gemini 1.5 Pro outperformed radiologists using clinical context alone (mean accuracy, 34.0 % [18/53] and 44.7 % [23.7/53] vs. 16.4 % [8.7/53]; both P < 0.01). Radiologists outperformed GPT-4o and Gemini 1.5 Pro using images alone (mean accuracy, 42.0 % [22.3/53] vs. 3.8 % [2/53], and 7.5 % [4/53]; both P < 0.01) and the complete cases (48.0 % [25.6/53] vs. 34.0 % [18/53], and 38.7 % [20.3/53]; both P < 0.001). While radiologists improved their accuracy when combining multimodal information (from 42.1 % [22.3/53] for images alone to 50.3 % [26.7/53] for complete cases; P < 0.01), GPT-4o and Gemini 1.5 Pro did not benefit from the multimodal context (from 34.0 % [18/53] for text alone to 35.2 % [18.7/53] for complete cases for GPT-4o; P = 0.48, and from 44.7 % [23.7/53] to 42.8 % [22.7/53] for Gemini 1.5 Pro; P = 0.54). Radiologists benefited significantly from the suggestion of Gemini 1.5 Pro, increasing their accuracy from 47.2 % [25/53] to 56.0 % [27/53] (P < 0.01). Both GPT-4o and Gemini 1.5 Pro correctly identified the imaging modality in 53/53 (100 %) and 51/53 (96.2 %) cases, respectively, but frequently failed to identify key imaging findings (43/53 cases [81.1 %] with incorrect identification of key imaging findings for GPT-4o and 50/53 [94.3 %] for Gemini 1.5).

Conclusion

Radiologists show a specific ability to benefit from the integration of textual and visual information, whereas multimodal models mostly rely on the clinical context to suggest diagnoses.

目的：本研究的目的是比较两种多模式模型（gpt - 40和Gemini 1.5 Pro）与放射科医生在使用复杂的神经放射学病例时，单独从文本背景、单独的关键图像或两者结合产生鉴别诊断的能力。材料和方法：本回顾性研究包括2008年1月至2024年9月在放射学杂志上发表的“请诊断”系列神经放射学病例。这两种多模态模型被要求仅从文本上下文、关键图像或完整病例中提供三种鉴别诊断。六名获得委员会认证的神经放射学家在相同的环境下解决了这些病例，他们被随机分为两组：首先单独处理环境，首先单独处理图像。三位放射科医生在没有使用Gemini 1.5 Pro的情况下解决了这些病例。一位独立的放射科医生评估了gpt - 40和Gemini为每个病例提供的图像描述的质量。采用McNemar检验分析多模态模型与放射科医师正确答案的差异。结果：gpt - 40和Gemini 1.5 Pro优于单独使用临床背景的放射科医生(平均准确率分别为34.0%[18/53]和44.7% [23.7/53]vs. 16.4% [8.7/53]；P < 0.01)。放射科医生单独使用图像的表现优于gpt - 40和Gemini 1.5 Pro(平均准确率为42.0%[22.3/53]，3.8%[2/53]和7.5% [4/53]；P < 0.01)和完全病例（48.0% [25.6/53]vs. 34.0%[18/53]和38.7% [20.3/53]）；P均< 0.001)。而放射科医生在结合多模态信息时提高了准确率(从单独图像的42.1%[22.3/53]提高到完整病例的50.3% [26.7/53]；P < 0.01)， gpt - 40和Gemini 1.5 Pro没有从多模态环境中获益(从单纯文本的34.0%[18/53]到完整病例的35.2% [18.7/53]；P = 0.48, Gemini 1.5 Pro从44.7%[23.7/53]降至42.8% [22.7/53]；P = 0.54)。使用Gemini 1.5 Pro后，放射科医生的准确率从47.2%[25/53]提高到56.0% [27/53]（P < 0.01）。gpt - 40和Gemini 1.5 Pro分别在53/53（100%）和51/53（96.2%）的病例中正确识别成像方式，但经常不能识别关键影像学表现（43/53例[81.1%]，gpt - 40和Gemini 1.5的50/53[94.3%]不能正确识别关键影像学表现）。结论：放射科医生表现出从文本和视觉信息的整合中获益的特殊能力，而多模态模型主要依赖于临床背景来建议诊断。

{"title":"Comparison between multimodal foundation models and radiologists for the diagnosis of challenging neuroradiology cases with text and images","authors":"Bastien Le Guellec , Cyril Bruge , Najib Chalhoub , Victor Chaton , Edouard De Sousa , Yann Gaillandre , Riyad Hanafi , Matthieu Masy , Quentin Vannod-Michel , Aghiles Hamroun , Grégory Kuchcinski , ARIANES investigators","doi":"10.1016/j.diii.2025.04.006","DOIUrl":"10.1016/j.diii.2025.04.006","url":null,"abstract":"<div><h3>Purpose</h3><div>The purpose of this study was to compare the ability of two multimodal models (GPT-4o and Gemini 1.5 Pro) with that of radiologists to generate differential diagnoses from textual context alone, key images alone, or a combination of both using complex neuroradiology cases.</div></div><div><h3>Materials and methods</h3><div>This retrospective study included neuroradiology cases from the \"<em>Diagnosis Please</em>\" series published in the <em>Radiology</em> journal between January 2008 and September 2024. The two multimodal models were asked to provide three differential diagnoses from textual context alone, key images alone, or the complete case. Six board-certified neuroradiologists solved the cases in the same setting, randomly assigned to two groups: context alone first and images alone first. Three radiologists solved the cases without, and then with the assistance of Gemini 1.5 Pro. An independent radiologist evaluated the quality of the image descriptions provided by GPT-4o and Gemini for each case. Differences in correct answers between multimodal models and radiologists were analyzed using McNemar test.</div></div><div><h3>Results</h3><div>GPT-4o and Gemini 1.5 Pro outperformed radiologists using clinical context alone (mean accuracy, 34.0 % [18/53] and 44.7 % [23.7/53] <em>vs</em>. 16.4 % [8.7/53]; both <em>P</em> < 0.01). Radiologists outperformed GPT-4o and Gemini 1.5 Pro using images alone (mean accuracy, 42.0 % [22.3/53] <em>vs</em>. 3.8 % [2/53], and 7.5 % [4/53]; both <em>P</em> < 0.01) and the complete cases (48.0 % [25.6/53] <em>vs</em>. 34.0 % [18/53], and 38.7 % [20.3/53]; both <em>P</em> < 0.001). While radiologists improved their accuracy when combining multimodal information (from 42.1 % [22.3/53] for images alone to 50.3 % [26.7/53] for complete cases; <em>P</em> < 0.01), GPT-4o and Gemini 1.5 Pro did not benefit from the multimodal context (from 34.0 % [18/53] for text alone to 35.2 % [18.7/53] for complete cases for GPT-4o; <em>P</em> = 0.48, and from 44.7 % [23.7/53] to 42.8 % [22.7/53] for Gemini 1.5 Pro; <em>P</em> = 0.54). Radiologists benefited significantly from the suggestion of Gemini 1.5 Pro, increasing their accuracy from 47.2 % [25/53] to 56.0 % [27/53] (<em>P</em> < 0.01). Both GPT-4o and Gemini 1.5 Pro correctly identified the imaging modality in 53/53 (100 %) and 51/53 (96.2 %) cases, respectively, but frequently failed to identify key imaging findings (43/53 cases [81.1 %] with incorrect identification of key imaging findings for GPT-4o and 50/53 [94.3 %] for Gemini 1.5).</div></div><div><h3>Conclusion</h3><div>Radiologists show a specific ability to benefit from the integration of textual and visual information, whereas multimodal models mostly rely on the clinical context to suggest diagnoses.</div></div>","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":"106 10","pages":"Pages 345-352"},"PeriodicalIF":8.1,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144057816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Identification of structural predictors of lung function improvement in adults with cystic fibrosis treated with elexacaftor-tezacaftor-ivacaftor using deep-learning 利用深度学习技术鉴定成人囊性纤维化患者肺功能改善的结构预测因子。

IF 8.1 2区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Diagnostic and Interventional Imaging

Pub Date : 2025-09-30 DOI: 10.1016/j.diii.2025.09.003

Guillaume Chassagnon , Rafael Marini , Valentin Ong , Jennifer Da Silva , Denis Habip Gatenyo , Isabelle Honore , Reem Kanaan , Nicolas Carlier , Johanna Fesenbeckh , Espérie Burnet , Marie-Pierre Revel , Clémence Martin , Pierre-Régis Burgel

Purpose

The purpose of this study was to evaluate the relationship between structural abnormalities on CT and lung function prior to and after initiation of elexacaftor-tezacaftor-ivacaftor (ETI) in adults with cystic fibrosis (CF) using a deep learning model.

Materials and methods

A deep learning quantification model was developed using 100 chest computed tomography (CT) examinations of patients with CF and 150 chest CT examinations of patients with various other bronchial diseases to quantify seven types of abnormalities. This model was then applied to an independent dataset of CT examinations of 218 adults with CF who were treated with ETI. The relationship between structural abnormalities and percent predicted forced expiratory volume in one second (ppFEV₁) was examined using general linear regression models.

Results

The deep learning model performed as well as radiologists for the quantification of the seven types of abnormalities. Chest CT examinations obtained before to and one year after the initiation of ETI were analyzed. The independent structural predictors of ppFEV₁ prior to ETI were bronchial wall thickening (P = 0.011), mucus plugging (P < 0.001), consolidation/atelectasis (P < 0.001), and mosaic perfusion (P < 0.001). An increase in ppFEV₁ after initiation of ETI independently correlated with a decrease in bronchial wall thicknening (-49 %; P = 0.004), mucus plugging (-92 %; P < 0.001), centrilobular nodules (-78 %; P = 0.009) and mosaic perfusion (-14 %; P < 0.001). Younger age (P < 0.001), greater mucus plugging extent (P = 0.016), and centrilobular nodules (P < 0.001) prior to ETI initiation were independent predictors of ppFEV₁ improvement.

Conclusion

A deep learning model can quantify CT lung abnormalities in adults with CF. Lung function impairment in adults with CF is associated with muco-inflammatory lesions on CT, which are largely reversible with ETI, and with mosaic perfusion, which appear less reversible and is presumably related to irreversible damage. Predictors of lung function improvement are a younger age and a greater extent of muco-inflammatory lesions obstructing the airways.

目的：本研究的目的是利用深度学习模型评估成人囊性纤维化（CF）患者进行elexacaftor-tezacaftor-ivacaftor （ETI）治疗前后CT结构异常与肺功能的关系。材料与方法：利用100例CF患者胸部CT检查和150例其他支气管疾病患者胸部CT检查，建立深度学习量化模型，量化7种异常类型。然后将该模型应用于218名接受ETI治疗的CF成人CT检查的独立数据集。使用一般线性回归模型检验结构异常与预测一秒钟用力呼气量百分比（ppFEV1）之间的关系。结果：深度学习模型在量化七种异常类型方面的表现与放射科医生一样好。分析ETI开始前和开始后一年的胸部CT检查结果。ETI前ppFEV1的独立结构预测因子为支气管壁增厚（P = 0.011）、粘液堵塞（P < 0.001）、固变/肺不张（P < 0.001）和花叶灌注（P < 0.001）。ETI开始后ppFEV1的增加与支气管壁增厚（- 49%,P = 0.004）、粘液堵塞（- 92%,P < 0.001）、小叶中心结节（- 78%,P = 0.009）和花叶灌注（- 14%,P < 0.001）的减少独立相关。ETI开始前的年龄较小（P < 0.001）、粘液堵塞程度较大（P = 0.016）和小叶中心结节（P < 0.001）是ppFEV1改善的独立预测因子。结论：深度学习模型可以量化CF成人CT肺异常，CF成人肺功能损害与CT黏膜炎性病变相关，其与ETI的可逆性较大，与马赛克灌注的可逆性较小，可能与不可逆损伤有关。肺功能改善的预测因素是年龄越小，粘膜炎性病变阻塞气道的程度越高。

{"title":"Identification of structural predictors of lung function improvement in adults with cystic fibrosis treated with elexacaftor-tezacaftor-ivacaftor using deep-learning","authors":"Guillaume Chassagnon , Rafael Marini , Valentin Ong , Jennifer Da Silva , Denis Habip Gatenyo , Isabelle Honore , Reem Kanaan , Nicolas Carlier , Johanna Fesenbeckh , Espérie Burnet , Marie-Pierre Revel , Clémence Martin , Pierre-Régis Burgel","doi":"10.1016/j.diii.2025.09.003","DOIUrl":"10.1016/j.diii.2025.09.003","url":null,"abstract":"<div><h3>Purpose</h3><div>The purpose of this study was to evaluate the relationship between structural abnormalities on CT and lung function prior to and after initiation of elexacaftor-tezacaftor-ivacaftor (ETI) in adults with cystic fibrosis (CF) using a deep learning model.</div></div><div><h3>Materials and methods</h3><div>A deep learning quantification model was developed using 100 chest computed tomography (CT) examinations of patients with CF and 150 chest CT examinations of patients with various other bronchial diseases to quantify seven types of abnormalities. This model was then applied to an independent dataset of CT examinations of 218 adults with CF who were treated with ETI. The relationship between structural abnormalities and percent predicted forced expiratory volume in one second (ppFEV<sub>1</sub>) was examined using general linear regression models.</div></div><div><h3>Results</h3><div>The deep learning model performed as well as radiologists for the quantification of the seven types of abnormalities. Chest CT examinations obtained before to and one year after the initiation of ETI were analyzed. The independent structural predictors of ppFEV<sub>1</sub> prior to ETI were bronchial wall thickening (<em>P</em> = 0.011), mucus plugging (<em>P</em> < 0.001), consolidation/atelectasis (<em>P</em> < 0.001), and mosaic perfusion (<em>P</em> < 0.001). An increase in ppFEV<sub>1</sub> after initiation of ETI independently correlated with a decrease in bronchial wall thicknening (-49 %; <em>P</em> = 0.004), mucus plugging (-92 %; <em>P</em> < 0.001), centrilobular nodules (-78 %; <em>P</em> = 0.009) and mosaic perfusion (-14 %; <em>P</em> < 0.001). Younger age (<em>P</em> < 0.001), greater mucus plugging extent (<em>P</em> = 0.016), and centrilobular nodules (<em>P</em> < 0.001) prior to ETI initiation were independent predictors of ppFEV<sub>1</sub> improvement.</div></div><div><h3>Conclusion</h3><div>A deep learning model can quantify CT lung abnormalities in adults with CF. Lung function impairment in adults with CF is associated with muco-inflammatory lesions on CT, which are largely reversible with ETI, and with mosaic perfusion, which appear less reversible and is presumably related to irreversible damage. Predictors of lung function improvement are a younger age and a greater extent of muco-inflammatory lesions obstructing the airways.</div></div>","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":"107 2","pages":"Pages 62-72"},"PeriodicalIF":8.1,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145208079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Diagnostic performance of pelvic CT, T1-weighted MRI and mineralized-tissue MRI for the assessment of structural lesions in sacroiliitis in patients with axial spondyloarthritis 盆腔CT、t1加权MRI和矿化组织MRI对轴性脊柱炎患者骶髂炎结构性病变的诊断价值

IF 8.1 2区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Diagnostic and Interventional Imaging

Pub Date : 2025-09-30 DOI: 10.1016/j.diii.2025.09.004

Romain Gillet , Samy Obeid , Maxime Clara , Gabriela Hossu , Khalid Ambarki , Fatma Boubaker , Pedro Augusto Gondim Teixeira , Alain Blum

Purpose

The purpose of this study was to evaluate the diagnostic capability of mineralized tissue-magnetic resonance imaging (MT-MRI) to detect erosions, sclerosis, and ankylosis of the sacroiliac joint (SIJ) in patients with axial spondyloarthritis, by comparison with T1-weighted MRI and pelvic computed tomography (CT), using SIJ CT as the standard of reference.

Material and methods

This retrospective study included 100 patients (62 women, 38 men; mean age, 39.9 ± 14.3 [standard deviation] years; age range: 19–79) with suspected axial spondyloarthritis, who underwent SIJ MRI and CT on the same day between August 2023 and March 2025. Two musculoskeletal radiologists independently scored the amount of structural lesions of SIJs. The diagnostic confidence in each imaging modality was also evaluated.

Results

The erosion scores showed no significant difference between SIJ CT and MT-MRI for both readers (P ≥ 0.07) but were lower than those of SIJ CT when using T1-weighted MRI for both readers (P ≤ 0.011). The performance of pelvic CT for depicting erosion was intermediate but closer to that of MT-MRI. The sclerosis scores were 16–20 % lower with T1-weighted MRI than with SIJ CT (P ≤ 0.012) and 3 % lower with MT-MRI (P ≤ 0.04), but 8 % overestimated using pelvic CT (P ≤ 0.001). For both groups, SIJ CT obtained the highest confidence score, which was superior to pelvic CT (P ≤ 0.001), itself superior to T1-weighted MRI and MT-MRI (P ≤ 0.001), with the latter two not differing significantly (P ≥ 0.109).

Conclusion

T1-weighted MRI alone is insufficient for reliably evaluating structural lesions of SIJs due to sacroiliitis in patients with axial spondyloarthritis. MT-MRI emerges as the closest alternative to SIJ CT, demonstrating excellent diagnostic performance while eliminating radiation exposure.

目的：本研究的目的是评价矿化组织磁共振成像（MT-MRI）对轴型脊柱性关节炎患者骶髂关节（SIJ）糜烂、硬化、强直的诊断能力，并与t1加权MRI和骨盆计算机断层扫描（CT）进行比较，以SIJ CT为参考标准。材料与方法：本回顾性研究纳入100例疑似轴型脊柱炎患者（女性62例，男性38例，平均年龄39.9±14.3岁，年龄范围19-79岁），于2023年8月至2025年3月同期行SIJ MRI和CT检查。两名肌肉骨骼放射科医生独立对sij的结构性病变数量进行评分。对每种成像方式的诊断可信度也进行了评估。结果：SIJ CT与MT-MRI在两种读卡器上的糜烂评分差异无统计学意义（P≥0.07），但在t1加权MRI上的糜烂评分低于SIJ CT （P≤0.011）。盆腔CT对糜烂的描述是中等的，但更接近于MT-MRI。与SIJ CT相比，t1加权MRI的硬化评分低16- 20% (P≤0.012)，MT-MRI低3% (P≤0.04)，但盆腔CT高估了8% （P≤0.001）。两组SIJ CT置信度评分最高，均优于盆腔CT (P≤0.001)，本身优于t1加权MRI和MT-MRI (P≤0.001)，后两者无显著差异（P≥0.109）。结论：单纯的t1加权MRI不足以可靠地评估骶髂炎引起的轴型脊柱炎患者sij的结构性病变。MT-MRI是SIJ CT最接近的替代品，在消除辐射暴露的同时表现出优异的诊断性能。

{"title":"Diagnostic performance of pelvic CT, T1-weighted MRI and mineralized-tissue MRI for the assessment of structural lesions in sacroiliitis in patients with axial spondyloarthritis","authors":"Romain Gillet , Samy Obeid , Maxime Clara , Gabriela Hossu , Khalid Ambarki , Fatma Boubaker , Pedro Augusto Gondim Teixeira , Alain Blum","doi":"10.1016/j.diii.2025.09.004","DOIUrl":"10.1016/j.diii.2025.09.004","url":null,"abstract":"<div><h3>Purpose</h3><div>The purpose of this study was to evaluate the diagnostic capability of mineralized tissue-magnetic resonance imaging (MT-MRI) to detect erosions, sclerosis, and ankylosis of the sacroiliac joint (SIJ) in patients with axial spondyloarthritis, by comparison with T1-weighted MRI and pelvic computed tomography (CT), using SIJ CT as the standard of reference.</div></div><div><h3>Material and methods</h3><div>This retrospective study included 100 patients (62 women, 38 men; mean age, 39.9 ± 14.3 [standard deviation] years; age range: 19–79) with suspected axial spondyloarthritis, who underwent SIJ MRI and CT on the same day between August 2023 and March 2025. Two musculoskeletal radiologists independently scored the amount of structural lesions of SIJs. The diagnostic confidence in each imaging modality was also evaluated.</div></div><div><h3>Results</h3><div>The erosion scores showed no significant difference between SIJ CT and MT-MRI for both readers (<em>P</em> ≥ 0.07) but were lower than those of SIJ CT when using T1-weighted MRI for both readers (<em>P</em> ≤ 0.011). The performance of pelvic CT for depicting erosion was intermediate but closer to that of MT-MRI. The sclerosis scores were 16–20 % lower with T1-weighted MRI than with SIJ CT (<em>P</em> ≤ 0.012) and 3 % lower with MT-MRI (<em>P</em> ≤ 0.04), but 8 % overestimated using pelvic CT (<em>P</em> ≤ 0.001). For both groups, SIJ CT obtained the highest confidence score, which was superior to pelvic CT (<em>P</em> ≤ 0.001), itself superior to T1-weighted MRI and MT-MRI (<em>P</em> ≤ 0.001), with the latter two not differing significantly (<em>P</em> ≥ 0.109).</div></div><div><h3>Conclusion</h3><div>T1-weighted MRI alone is insufficient for reliably evaluating structural lesions of SIJs due to sacroiliitis in patients with axial spondyloarthritis. MT-MRI emerges as the closest alternative to SIJ CT, demonstrating excellent diagnostic performance while eliminating radiation exposure.</div></div>","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":"107 2","pages":"Pages 73-82"},"PeriodicalIF":8.1,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145208023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Image quality and dose reduction with photon counting detector CT: Comparison between ultra-high resolution mode and standard mode using a phantom study 光子计数检测器CT的图像质量和剂量减少：超高分辨率模式和标准模式的比较。

IF 8.1 2区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Diagnostic and Interventional Imaging

Pub Date : 2025-09-01 DOI: 10.1016/j.diii.2025.03.009

Joël Greffier , Claire Van Ngoc Ty , Skander Sammoud , Cédric Croisille , Jean-Paul Beregi , Djamel Dabli , Isabelle Fitton

Purpose

The purpose of this study was to assess the image quality and dose reduction potential of ultra-high resolution (UHR) mode compared with standard mode, both available on a commercial photon-counting detector computed tomography (PCCT) scanner.

Materials and methods

Images were acquired on a PCCT with a phantom using UHR and standard modes at three dose levels (3/6/12 mGy). Raw data were reconstructed using soft tissue (Br36) and bone (Br68) reconstruction kernels and 0.4-mm slice thickness. Noise power spectrum (NPS) and task-based transfer function (TTF) were calculated to assess noise magnitude, noise texture (f_av), and spatial resolution (f₅₀), respectively. Detectability indexes (d’) were calculated to model the detection of two abdominal lesions for a Br36 soft tissue reconstruction kernel and three bone lesions for a Br68 bone reconstruction kernel.

Results

At all dose levels, noise magnitude values were lower with UHR than with standard mode (mean difference, -18.0 ± 2.6 [standard deviation (SD)] % for Br36 and -33.9 ± 2.3 [SD] % for Br68). Noise texture was lower with UHR than with standard mode (mean difference, -4.2 ± 0.9 [SD] % for Br36 and -16.0 ± 1.8 [SD] % for Br68). For the solid water insert and Br36, f₅₀ values were similar for both UHR (0.34 ± [SD] 0.04 mm^-1) and standard (0.33 ± [SD] 0.04 mm^-1) modes. For Br68, f₅₀ values were greater with UHR than with standard for iodine (mean difference, 18.5 ± 1.9 [SD] %) and bone (11.7 ± 5.7 [SD] %) inserts. For all simulated lesions, d’ values were greater with UHR than with standard and, compared to standard, the dose reduction potential with UHR was -32.9 ± 0.0 (SD) % for abdominal lesions and -68.7 ± 3.2 (SD) % for bone lesions.

Conclusion

Compared to the standard mode, the UHR mode offers lower noise levels and better detectability of abdominal and bone lesions, paving the way for potential dose reduction with PCCT in clinical applications.

目的：本研究的目的是评估超高分辨率（UHR）模式与标准模式的图像质量和剂量减少潜力，两者都可以在商用光子计数检测器计算机断层扫描（PCCT）扫描仪上获得。材料和方法：使用UHR和标准模式，在三个剂量水平（3/6/12 mGy）下，在带假体的PCCT上获取图像。原始数据采用软组织（Br36）和骨（Br68）重建核，切片厚度为0.4 mm。计算噪声功率谱（NPS）和基于任务的传递函数（TTF），分别评估噪声强度、噪声纹理（fav）和空间分辨率（f50）。计算可检测性指数（d'）来模拟Br36软组织重建核的2个腹部病变和Br68骨重建核的3个骨病变的检测。结果：在所有剂量水平下，UHR的噪声值均低于标准模式（Br36的平均差值为-18.0±2.6[标准差（SD）] %， Br68的平均差值为-33.9±2.3 [SD] %）。与标准模式相比，UHR模式下的噪声纹理较低（Br36的平均差值为-4.2±0.9 [SD] %， Br68的平均差值为-16.0±1.8 [SD] %）。固体水插入剂和Br36在UHR（0.34±[SD] 0.04 mm-1）和标准（0.33±[SD] 0.04 mm-1）模式下的f50值相似。对于Br68， UHR的f50值高于标准碘（平均差值为18.5±1.9 [SD] %）和骨（11.7±5.7 [SD] %）插入物。对于所有模拟病变，UHR的d'值大于标准，与标准相比，UHR对腹部病变的剂量减少潜力为-32.9±0.0 (SD) %，对骨骼病变的剂量减少潜力为-68.7±3.2 (SD) %。结论：与标准模式相比，UHR模式噪音水平更低，对腹部和骨骼病变的可检出性更好，为PCCT在临床应用中的潜在减剂量铺平了道路。

{"title":"Image quality and dose reduction with photon counting detector CT: Comparison between ultra-high resolution mode and standard mode using a phantom study","authors":"Joël Greffier , Claire Van Ngoc Ty , Skander Sammoud , Cédric Croisille , Jean-Paul Beregi , Djamel Dabli , Isabelle Fitton","doi":"10.1016/j.diii.2025.03.009","DOIUrl":"10.1016/j.diii.2025.03.009","url":null,"abstract":"<div><h3>Purpose</h3><div>The purpose of this study was to assess the image quality and dose reduction potential of ultra-high resolution (UHR) mode compared with standard mode, both available on a commercial photon-counting detector computed tomography (PCCT) scanner.</div></div><div><h3>Materials and methods</h3><div>Images were acquired on a PCCT with a phantom using UHR and standard modes at three dose levels (3/6/12 mGy). Raw data were reconstructed using soft tissue (Br36) and bone (Br68) reconstruction kernels and 0.4-mm slice thickness. Noise power spectrum (NPS) and task-based transfer function (TTF) were calculated to assess noise magnitude, noise texture (f<sub>av</sub>), and spatial resolution (f<sub>50</sub>), respectively. Detectability indexes (d’) were calculated to model the detection of two abdominal lesions for a Br36 soft tissue reconstruction kernel and three bone lesions for a Br68 bone reconstruction kernel.</div></div><div><h3>Results</h3><div>At all dose levels, noise magnitude values were lower with UHR than with standard mode (mean difference, -18.0 ± 2.6 [standard deviation (SD)] % for Br36 and -33.9 ± 2.3 [SD] % for Br68). Noise texture was lower with UHR than with standard mode (mean difference, -4.2 ± 0.9 [SD] % for Br36 and -16.0 ± 1.8 [SD] % for Br68). For the solid water insert and Br36, f<sub>50</sub> values were similar for both UHR (0.34 ± [SD] 0.04 mm<sup>-1</sup>) and standard (0.33 ± [SD] 0.04 mm<sup>-1</sup>) modes. For Br68, f<sub>50</sub> values were greater with UHR than with standard for iodine (mean difference, 18.5 ± 1.9 [SD] %) and bone (11.7 ± 5.7 [SD] %) inserts. For all simulated lesions, d’ values were greater with UHR than with standard and, compared to standard, the dose reduction potential with UHR was -32.9 ± 0.0 (SD) % for abdominal lesions and -68.7 ± 3.2 (SD) % for bone lesions.</div></div><div><h3>Conclusion</h3><div>Compared to the standard mode, the UHR mode offers lower noise levels and better detectability of abdominal and bone lesions, paving the way for potential dose reduction with PCCT in clinical applications.</div></div>","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":"106 9","pages":"Pages 320-326"},"PeriodicalIF":8.1,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143812728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficacy of a deep learning-based software for chest X-ray analysis in an emergency department 基于深度学习的胸部 X 光分析软件在急诊科的应用效果。

IF 8.1 2区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Diagnostic and Interventional Imaging

Pub Date : 2025-09-01 DOI: 10.1016/j.diii.2025.03.007

Sathiyamurthy Selvam , Olivier Peyrony , Arben Elezi , Adelia Braganca , Anne-Marie Zagdanski , Lucie Biard , Jessica Assouline , Guillaume Chassagnon , Guillaume Mulier , Constance de Margerie-Mellon

Purpose

The purpose of this study was to evaluate the efficacy of a deep learning (DL)-based computer-aided detection (CAD) system for the detection of abnormalities on chest X-rays performed in an emergency department setting, where readers have access to relevant clinical information.

Materials and methods

Four hundred and four consecutive chest X-rays performed over a two-month period in patients presenting to an emergency department with respiratory symptoms were retrospectively collected. Five readers (two radiologists, three emergency physicians) with access to clinical information were asked to identify five abnormalities (i.e., consolidation, lung nodule, pleural effusion, pneumothorax, mediastinal/hilar mass) in the dataset without assistance, and then after a 2-week period, with the assistance of a DL-based CAD system. The reference standard was a chest X-ray consensus review by two experienced radiologists. Reader performances were compared between the reading sessions, and interobserver agreement was assessed using Fleiss’ kappa test.

Results

The dataset included 118 occurrences of the five abnormalities in 103 chest X-rays. The CAD system improved sensitivity for consolidation, pleural effusion, and nodule, with respective absolute differences of 8.3 % (95 % CI: 3.8–12.7; P < 0.001), 7.9 % (95 % CI: 1.7–14.1; P = 0.012), and 29.5 % (95 % CI: 19.8–38.2; P < 0.001), respectively. Specificity was greater than 89 % for all abnormalities and showed a minimal but significant decrease with DL for nodules and mediastinal/hilar masses (-1.8 % [95 % CI: -2.7 – -0.9]; P < 0.001 and -0.8 % [95 % CI: -1.5 – -0.2]; P = 0.005). Inter-observer agreement improved with DL, with kappa values ranging from 0.40 [95 % CI: 0.37–0.43] for mediastinal/hilar mass to 0.84 [95 % CI: 0.81–0.87] for pneumothorax.

Conclusion

Our results suggest that DL-assisted reading increases the sensitivity for detecting important chest X-ray abnormalities in the emergency department, even when clinical information is available to the radiologist.

目的：本研究的目的是评估基于深度学习（DL）的计算机辅助检测（CAD）系统在急诊科环境中检测胸片异常的有效性，读者可以获得相关的临床信息。材料和方法：回顾性收集急诊出现呼吸道症状的患者在两个月内连续进行的144次胸部x光片。五名读者（两名放射科医生，三名急诊医生）被要求在没有帮助的情况下识别数据集中的五种异常（即实变、肺结节、胸腔积液、气胸、纵隔/肺门肿块），然后在2周后，在基于dl的CAD系统的帮助下。参考标准是由两名经验丰富的放射科医生进行的胸部x线一致审查。比较阅读会话之间的读者表现，并使用Fleiss' kappa测试评估观察者之间的一致性。结果：该数据集包括103张胸片中5种异常的118例。CAD系统提高了对实变、胸腔积液和结节的敏感性，绝对差异分别为8.3% (95% CI: 3.8-12.7；P < 0.001), 7.9% (95% ci: 1.7-14.1；P = 0.012), 29.5% (95% CI: 19.8-38.2；P < 0.001)。所有异常的特异性均大于89%，结节和纵隔/肺门肿块的DL有微小但显著的降低(- 1.8% [95% CI: -2.7 - -0.9]；P < 0.001和- 0.8% [95% CI: -1.5 - -0.2]；P = 0.005)。DL改善了观察者间的一致性，kappa值从纵隔/肺门肿块的0.40 [95% CI: 0.37-0.43]到气胸的0.84 [95% CI: 0.81-0.87]不等。结论：我们的研究结果表明，即使在放射科医生可以获得临床信息的情况下，dl辅助阅读也可以提高发现急诊科重要胸部x线异常的灵敏度。

{"title":"Efficacy of a deep learning-based software for chest X-ray analysis in an emergency department","authors":"Sathiyamurthy Selvam , Olivier Peyrony , Arben Elezi , Adelia Braganca , Anne-Marie Zagdanski , Lucie Biard , Jessica Assouline , Guillaume Chassagnon , Guillaume Mulier , Constance de Margerie-Mellon","doi":"10.1016/j.diii.2025.03.007","DOIUrl":"10.1016/j.diii.2025.03.007","url":null,"abstract":"<div><h3>Purpose</h3><div>The purpose of this study was to evaluate the efficacy of a deep learning (DL)-based computer-aided detection (CAD) system for the detection of abnormalities on chest X-rays performed in an emergency department setting, where readers have access to relevant clinical information.</div></div><div><h3>Materials and methods</h3><div>Four hundred and four consecutive chest X-rays performed over a two-month period in patients presenting to an emergency department with respiratory symptoms were retrospectively collected. Five readers (two radiologists, three emergency physicians) with access to clinical information were asked to identify five abnormalities (<em>i.e</em><span>., consolidation, lung nodule, pleural effusion, pneumothorax, mediastinal/hilar mass) in the dataset without assistance, and then after a 2-week period, with the assistance of a DL-based CAD system. The reference standard was a chest X-ray consensus review by two experienced radiologists. Reader performances were compared between the reading sessions, and interobserver agreement was assessed using Fleiss’ kappa test.</span></div></div><div><h3>Results</h3><div>The dataset included 118 occurrences of the five abnormalities in 103 chest X-rays. The CAD system improved sensitivity for consolidation, pleural effusion, and nodule, with respective absolute differences of 8.3 % (95 % CI: 3.8–12.7; <em>P</em> < 0.001), 7.9 % (95 % CI: 1.7–14.1; <em>P</em> = 0.012), and 29.5 % (95 % CI: 19.8–38.2; <em>P</em> < 0.001), respectively. Specificity was greater than 89 % for all abnormalities and showed a minimal but significant decrease with DL for nodules and mediastinal/hilar masses (-1.8 % [95 % CI: -2.7 – -0.9]; <em>P</em> < 0.001 and -0.8 % [95 % CI: -1.5 – -0.2]; <em>P</em> = 0.005). Inter-observer agreement improved with DL, with kappa values ranging from 0.40 [95 % CI: 0.37–0.43] for mediastinal/hilar mass to 0.84 [95 % CI: 0.81–0.87] for pneumothorax.</div></div><div><h3>Conclusion</h3><div>Our results suggest that DL-assisted reading increases the sensitivity for detecting important chest X-ray abnormalities in the emergency department, even when clinical information is available to the radiologist.</div></div>","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":"106 9","pages":"Pages 299-311"},"PeriodicalIF":8.1,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143781705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Relaxing the PI-RADS dominant sequence rule improves the characterization of high-grade prostate cancer on multiparametric MRI 放宽PI-RADS显性序列规则可改善多参数MRI对高级别前列腺癌的表征。

IF 8.1 2区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Diagnostic and Interventional Imaging

Pub Date : 2025-09-01 DOI: 10.1016/j.diii.2025.04.003

Pierre Baseilhac , Nicolas Romain-Scelle , Amna Klich , Sébastien Crouzet , Marc Colombel , Alain Ruffion , Muriel Rabilloud , Olivier Rouvière

Purpose

The Prostate Imaging-Reporting and Data System 2.0 (PI-RADSv2.0) and 2.1 (PI-RADSv2.1) scores are deduced from the pulse sequence categories using the "dominant sequence" scoring rule. The purpose of this study was to develop and evaluate a new scoring rule that makes better use of non-dominant pulse sequence findings.

Material and methods

The new scoring rule was developed using a single-center database of 1627 patients who underwent prostate multiparametric MRI and prostate biopsy. The combinations of PI-RADSv2.0 pulse sequence categories observed at sextant level were ranked based on their rate of high-grade (grade group ≥ 2) prostate cancer and assigned to one of the five levels of the new score. Then, a hidden evaluation dataset of 240 MRI lesions to which 21 readers of varying experience had assigned PI-RADSv2.1 pulse sequence categories was used. For each reader, the PI-RADSv2.1 score of the lesions (PI-RADSv2.1 dominant sequence rule) and the new score (scoring rule defined in the development cohort) were computed. The scores were compared using areas under the curve (AUC), sensitivities, specificities, reproducibility, and clinical utility.

Results

Across all readers, the mean AUC of the new score (0.78; 95 % confidence interval [CI]: 0.73–0.83) was significantly greater than that of the PI-RADSv2.1 score (0.76; 95 % CI: 0.71–0.81; P < 0.01). The new score showed lower sensitivity, higher specificity and better inter-reader agreement in all reader experience subgroups. Across all readers, for a ≥ 3 dichotomization, it provided a higher net benefit than the PIRADSv2.1 score for risk thresholds > 0.15.

Conclusion

The new scoring rule outperformed the dominant sequence rule in characterizing high-grade prostate cancer regardless of reader experience.

目的：前列腺成像报告和数据系统2.0 （PI-RADSv2.0）和2.1 （PI-RADSv2.1）评分是使用“优势序列”评分规则从脉冲序列类别中推断出来的。本研究的目的是开发和评估一种新的评分规则，以更好地利用非显性脉冲序列的发现。材料和方法：新的评分规则是使用1627例接受前列腺多参数MRI和前列腺活检的患者的单中心数据库制定的。在六分仪水平观察到的PI-RADSv2.0脉冲序列类别组合根据其高级别（分级组≥2）前列腺癌的发生率进行排名，并分配到新评分的五个级别之一。然后，使用一个包含240个MRI病变的隐藏评估数据集，其中21个不同经验的读者已经分配了PI-RADSv2.1脉冲序列类别。对于每个读者，计算病变的PI-RADSv2.1评分（PI-RADSv2.1显性序列规则）和新评分（发展队列中定义的评分规则）。使用曲线下面积（AUC）、敏感性、特异性、可重复性和临床实用性对评分进行比较。结果：在所有读者中，新评分的平均AUC (0.78；95%置信区间[CI]: 0.73-0.83)显著大于PI-RADSv2.1评分(0.76；95% ci: 0.71-0.81；P < 0.01)。在所有读者体验亚组中，新评分显示出较低的敏感性，较高的特异性和更好的读者间一致性。在所有读者中，对于≥3的二分类，它提供了高于风险阈值PIRADSv2.1评分> 0.15的净收益。结论：无论读者经验如何，新的评分规则在评价高级别前列腺癌方面优于显性序列规则。

{"title":"Relaxing the PI-RADS dominant sequence rule improves the characterization of high-grade prostate cancer on multiparametric MRI","authors":"Pierre Baseilhac , Nicolas Romain-Scelle , Amna Klich , Sébastien Crouzet , Marc Colombel , Alain Ruffion , Muriel Rabilloud , Olivier Rouvière","doi":"10.1016/j.diii.2025.04.003","DOIUrl":"10.1016/j.diii.2025.04.003","url":null,"abstract":"<div><h3>Purpose</h3><div>The Prostate Imaging-Reporting and Data System 2.0 (PI-RADSv2.0) and 2.1 (PI-RADSv2.1) scores are deduced from the pulse sequence categories using the \"dominant sequence\" scoring rule. The purpose of this study was to develop and evaluate a new scoring rule that makes better use of non-dominant pulse sequence findings.</div></div><div><h3>Material and methods</h3><div>The new scoring rule was developed using a single-center database of 1627 patients who underwent prostate multiparametric MRI and prostate biopsy. The combinations of PI-RADSv2.0 pulse sequence categories observed at sextant level were ranked based on their rate of high-grade (grade group ≥ 2) prostate cancer and assigned to one of the five levels of the new score. Then, a hidden evaluation dataset of 240 MRI lesions to which 21 readers of varying experience had assigned PI-RADSv2.1 pulse sequence categories was used. For each reader, the PI-RADSv2.1 score of the lesions (PI-RADSv2.1 dominant sequence rule) and the new score (scoring rule defined in the development cohort) were computed. The scores were compared using areas under the curve (AUC), sensitivities, specificities, reproducibility, and clinical utility.</div></div><div><h3>Results</h3><div>Across all readers, the mean AUC of the new score (0.78; 95 % confidence interval [CI]: 0.73–0.83) was significantly greater than that of the PI-RADSv2.1 score (0.76; 95 % CI: 0.71–0.81; <em>P</em> < 0.01). The new score showed lower sensitivity, higher specificity and better inter-reader agreement in all reader experience subgroups. Across all readers, for a ≥ 3 dichotomization, it provided a higher net benefit than the PIRADSv2.1 score for risk thresholds > 0.15.</div></div><div><h3>Conclusion</h3><div>The new scoring rule outperformed the dominant sequence rule in characterizing high-grade prostate cancer regardless of reader experience.</div></div>","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":"106 9","pages":"Pages 312-319"},"PeriodicalIF":8.1,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144017392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Real-world validation of a deep learning algorithm for chest radiography in the emergency department: A tale of two specialties 急诊胸部x线摄影的深度学习算法的真实验证：两个专业的故事。

IF 8.1 2区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Diagnostic and Interventional Imaging

Pub Date : 2025-09-01 DOI: 10.1016/j.diii.2025.03.008

Bo Gong

引用次数: 0