Clinical Imaging最新文献

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Reply: Comment on “Limited performance of ChatGPT-4v and ChatGPT-4o in image-based core radiology cases” 回复：关于“ChatGPT-4v和chatgpt - 40在基于图像的核心放射病例中的性能有限”的评论。

IF 1.5 4区医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Clinical Imaging

Pub Date : 2025-12-09 DOI: 10.1016/j.clinimag.2025.110696

Romi Noy Achiron

引用次数: 0

AI-assisted chest radiograph interpretation enhances diagnostic confidence and standardizes diagnostic accuracy across radiologists: A multi-reader study 人工智能辅助胸片解读提高了诊断信心，并使放射科医生的诊断准确性标准化：一项多读者研究

IF 1.5 4区医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Clinical Imaging

Pub Date : 2025-12-07 DOI: 10.1016/j.clinimag.2025.110694

Hao-Yu Huang , Yu-Han Huang , Cheng-Hsun Lin , Wan-Ting Tao , Wei-Chen Liao , Shuning Yu , Huei-Chi Mo , Wenyen Feng , Yu-Ting Hsu , Jian-Chiao Wang , Kai-Hsiung Ko

Purpose

To evaluate the impact of an artificial intelligence (AI)-assisted computer-aided detection (CAD) system on the diagnostic accuracy and confidence in chest radiograph interpretation among nonthoracic radiologists and radiology residents with varying levels of experience.

Methods

In this retrospective multiple-reader, multiple-case (MRMC) study, 400 chest radiographs (100 each for pulmonary nodules, pleural effusion, pneumothorax, and controls) were independently interpreted by 12 readers (two nonthoracic radiologists, four senior residents, and six junior residents). Readings were conducted under CAD-assisted and unassisted conditions, with a 30-day washout period. Readers assigned confidence scores (0–100) to their diagnosis. Diagnostic performance was evaluated using the area under the curve (AUC), sensitivity, and specificity, while reader confidence was assessed by the proportion of high-confidence ratings among correctly interpreted cases.

Results

The AI-assisted CAD system improved diagnostic performance across all abnormalities, with significant gains for pulmonary nodules (AUC: 0.781 → 0.854; P < 0.001) and pleural effusion (0.896 → 0.948; P < 0.001). The sensitivity increased by 7.2% for effusion, while the specificity for nodules improved markedly by 15.7%. Among all the readers, junior residents showed the greatest gains, especially for nodules, where the CAD closed their baseline AUC gap (originally −7.3%, P = 0.006) relative to nonthoracic radiologists. Reader confidence also increased significantly with the CAD, particularly for nodules (+15.2 %; P < 0.001).

Conclusion

The AI-assisted CAD system significantly enhanced diagnostic accuracy and reader confidence in chest radiograph interpretation, especially for junior radiology residents. This approach may bridge experience-related diagnostic gaps and support clinical decision-making, particularly in institutions lacking thoracic radiologists.

目的评估人工智能（AI）辅助计算机辅助检测（CAD）系统对不同经验水平的非胸科放射科医师和放射科住院医师胸片解释诊断准确性和可信度的影响。方法在这项回顾性多病例（MRMC）研究中，400张胸片（肺结节、胸腔积液、气胸和对照组各100张）由12名读者（2名非胸科放射科医生、4名老年住院医生和6名初级住院医生）独立解读。在cad辅助和无辅助条件下进行读数，水洗期为30天。读者对他们的诊断给出了信心分数（0-100）。使用曲线下面积（AUC）、敏感性和特异性来评估诊断性能，而读者信心通过正确解释病例中高置信度评分的比例来评估。结果人工智能辅助CAD系统提高了对所有异常的诊断性能，对肺结节（AUC: 0.781→0.854;P < 0.001）和胸腔积液（0.896→0.948;P < 0.001）的诊断有显著提高。对积液的敏感性提高了7.2%，对结节的特异性提高了15.7%。在所有的读者中，初级住院医师的获益最大，尤其是对于结节，相对于非胸科放射科医师，他们的CAD缩小了基线AUC差距（最初为- 7.3%,P = 0.006）。读者对CAD的信心也显著增加，特别是对于结节（+ 15.2%;P < 0.001）。结论人工智能辅助CAD系统显著提高了胸片解读的诊断准确性和读者信心，特别是对初级放射科住院医师。这种方法可以弥合与经验相关的诊断差距，并支持临床决策，特别是在缺乏胸椎放射科医生的机构。

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引用次数: 0

Validation of an artificial intelligence program in the characterization of breast nodules by ultrasound 人工智能程序在超声乳腺结节表征中的验证。

IF 1.5 4区医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Clinical Imaging

Pub Date : 2025-12-06 DOI: 10.1016/j.clinimag.2025.110691

Maria Julia Gregorio Calas , Mariana Loureiro Lemos , Marcelo Adeodato Bello , Bianca Gutfilen , Anke Bergmann

Introduction

Breast ultrasound is a widely accessible imaging method but highly operator-dependent. Artificial intelligence (AI) may improve breast lesion characterization, aiding in diagnostic decisions.

Objective

To validate an AI system (Koios DS v3.1) in the BI-RADS classification of breast lesions on ultrasound.

Methods

This cross-sectional diagnostic study included 100 women with breast lesions on ultrasound (July 2022–July 2023), later submitted to histopathology. BI-RADS classifications by conventional ultrasound were compared with AI-based classifications and histopathological findings. Diagnostic agreement and validity measures were calculated.

Results

The median patient age was 58.5 years (range, 31–89). AI identified lesions in 93 % of cases. Moderate agreement (Kappa 0.41–0.60) was found between AI and conventional ultrasound BIRADS classification (Kappa = 0.405). When compared with histopathology, AI showed a Kappa of 0.626, with 95.6 % sensitivity, 68.0 % specificity, and a 2.1 % false-negative rate. Disagreement was significantly higher for lesions situated in the lower or central quadrants of the breast (OR = 4.55; p = 0.009) and in irregular heterogeneous areas (OR = 8.27; p < 0.001).

Conclusion

AI demonstrated high sensitivity and low false-negative rates in classifying breast lesions by ultrasound, showing potential as a complementary diagnostic tool. However, limitations persist, especially in irregular heterogeneous areas and for lesions situated in the lower or central quadrants of the breast.

乳房超声是一种广泛使用的成像方法，但高度依赖于操作人员。人工智能（AI）可以改善乳房病变特征，帮助诊断决策。目的：验证人工智能系统（Koios DS v3.1）在乳腺超声病变BI-RADS分类中的应用价值。方法：本横断面诊断研究纳入了100例（2022年7月- 2023年7月）超声检查发现乳腺病变的女性，随后提交组织病理学检查。将常规超声BI-RADS分类与人工智能分类及组织病理学结果进行比较。计算诊断一致性和有效性指标。结果：患者中位年龄为58.5岁（范围31-89岁）。人工智能在93%的病例中识别出病变。人工智能与常规超声BIRADS分类Kappa为0.41 ~ 0.60，Kappa为0.405。与组织病理学比较，AI的Kappa为0.626，敏感性95.6%，特异性68.0%，假阴性率2.1%。对于位于乳腺下象限或中心象限的病变（or = 4.55; p = 0.009）和不规则异质区域的病变（or = 8.27; p），差异显著较高。结论：人工智能在超声对乳腺病变进行分类时具有高灵敏度和低假阴性率，具有作为辅助诊断工具的潜力。然而，局限性仍然存在，特别是在不规则的异质区域和位于乳房下部或中央象限的病变。

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引用次数: 0

Comment on “Limited performance of ChatGPT-4v and ChatGPT-4o in image-based core radiology cases” 关于“ChatGPT-4v和chatgpt - 40在基于图像的核心放射病例中的有限表现”的评论。

IF 1.5 4区医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Clinical Imaging

Pub Date : 2025-12-06 DOI: 10.1016/j.clinimag.2025.110695

Shyam Sundar Sah , Abhishek Kumbhalwar

引用次数: 0

Comparing prostate diffusion weighted images reconstructed with a commercial deep-learning product to a deep learning phase corrected model at 1.5 T 将商业深度学习产品重建的前列腺弥散加权图像与深度学习相位校正模型在1.5 T下进行比较

IF 1.5 4区医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Clinical Imaging

Pub Date : 2025-11-23 DOI: 10.1016/j.clinimag.2025.110681

Rory L. Cochran , William R. Bradley , Ranjodh S. Dhami , Eugene Milshteyn , Maximilian Pohl , Soumyadeep Ghosh , Nabih Nakrour , Patricia Lan , Xinzeng Wang , Arnaud Guidon , Mukesh G. Harisinghani

Purpose

To determine whether a new deep learning (DL) based phase corrected (DLPC) reconstruction model can enhance image quality of diffusion weighted images of the prostate acquired at 1.5 T compared to a commercially available DL based product.

Methods and materials

A retrospective study of 30 consecutive patients undergoing conventional multiparametric MRI (mpMRI) of the prostate on a single 1.5 T scanner was performed. Diffusion image datasets reconstructed with a commercially available DL product and a new DLPC model were assessed. Qualitative image assessment was performed by three board certified radiologists using a 5-point Likert scale across four features and inter-rater agreement was estimated using Gwet's AC2 statistic. Quantitative image comparison was performed by assessing SNR of acquired intermediate b-value (b = 1000 s/mm²) diffusion images. The Wilcoxon matched-pairs signed rank test was used to assess differences between techniques. Image noise was assessed using the edge function.

Results

Median patient age was 70 years (interquartile range: 66.0–75.3). All radiologists perceived less noise and better image quality for all DLPC image sets compared to commercial DL images (p < 0.05). Significantly higher SNR was observed for the acquired intermediate b-value diffusion images reconstructed with DLPC (median SNR: 49.4 vs 27.5; p < 0.001), and mean ADC values did not significantly differ between DLPC and DL images (p = 0.63). Edge analyses demonstrated significantly reduced noise for DLPC images (p < 0.001).

Conclusions

DLPC image reconstruction of diffusion weighted prostate image datasets reduces image noise and improves SNR over a commercial DL product at 1.5 T.

目的：研究一种新的基于深度学习（DL）的相位校正（DLPC）重建模型是否能提高1.5 T时前列腺弥散加权图像的图像质量。方法与材料对30例连续在单台1.5 T扫描仪上接受前列腺常规多参数MRI （mpMRI）检查的患者进行回顾性研究。评估了用市售DL产品和新的DLPC模型重建的扩散图像数据集。定性图像评估由三名委员会认证的放射科医生进行，使用5分李克特量表跨越四个特征，并使用Gwet的AC2统计估计评分者之间的一致性。通过评估获取的中间b值（b = 1000 s/mm2）扩散图像的信噪比进行定量图像比较。使用Wilcoxon配对对符号秩检验来评估技术之间的差异。利用边缘函数评估图像噪声。结果患者年龄中位数为70岁（四分位数间66.0 ~ 75.3）。与商业DL图像相比，所有放射科医生都认为所有DLPC图像集的噪声更少，图像质量更好（p < 0.05）。DLPC重建的中间b值扩散图像的信噪比显著提高（信噪比中位数：49.4 vs 27.5; p < 0.001）， DLPC和DL图像的平均ADC值无显著差异（p = 0.63）。边缘分析表明DLPC图像的噪声显著降低（p < 0.001）。结论弥散加权前列腺图像数据集的sdlpc图像重建降低了图像噪声，提高了1.5 T时的信噪比。

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引用次数: 0

Impact of breast MRI on the surgical management of young women with breast cancer 乳腺MRI对年轻女性乳腺癌手术治疗的影响

IF 1.5 4区医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Clinical Imaging

Pub Date : 2025-11-22 DOI: 10.1016/j.clinimag.2025.110682

Tugce Erguven , Yagiz Matthew Akiska , Ayah Arafat , Rachel Brem

Objective

This study evaluates the impact of preoperative breast MRI on surgical management in young women diagnosed with breast cancer.

Methods

A retrospective chart review identified 125 women aged 40 years or younger diagnosed with breast cancer at a university academic practice between January 2015 and December 2020. Twenty-three patients were excluded due to loss of follow-up, resulting in a final sample of 102 patients. Patients were included regardless of surgical plan. Variables analyzed included demographic data, breast cancer risk factors, imaging findings, and subsequent surgical outcomes. Chi-square and Fisher's exact test were used for bivariate analyses. Univariate and multivariate logistic regression were used to assess the association between MRI findings and surgical management.

Results

Preoperative MRI detected additional findings or lesions warranting biopsy in 53.9 % of cases, with 45.5 % of these lesions confirmed as malignant. Of the confirmed malignancies, 24 were ipsilateral and 1 was contralateral. The detection of additional carcinoma was associated with a 6.34-fold increased likelihood of undergoing mastectomy (aOR: 6.34, 95 % CI: 1.85–29.82). MRI was particularly effective in detecting additional ipsilateral disease, supporting its role in preoperative planning.

Conclusion

Preoperative MRI in women aged 40 years and younger increases the detection of additional malignancies, influencing surgical decisions and supporting its integration into routine preoperative evaluation for improved patient outcomes.

目的探讨术前乳腺MRI检查对年轻乳腺癌患者手术治疗的影响。方法回顾性分析了2015年1月至2020年12月在一所大学学术实践中诊断为乳腺癌的125名年龄在40岁及以下的女性。23例患者因失去随访而被排除，最终样本为102例患者。无论手术方案如何，患者均被纳入研究。分析的变量包括人口统计数据、乳腺癌危险因素、影像学结果和随后的手术结果。双变量分析采用卡方检验和Fisher精确检验。采用单因素和多因素logistic回归来评估MRI表现与手术处理之间的关系。结果53.9%的病例术前MRI检出其他病变，其中45.5%为恶性病变。在确诊的恶性肿瘤中，同侧24例，对侧1例。发现其他癌与接受乳房切除术的可能性增加6.34倍相关（aOR: 6.34, 95% CI: 1.85-29.82）。MRI在发现其他同侧疾病方面特别有效，支持其在术前计划中的作用。结论40岁及以下女性术前MRI增加了额外恶性肿瘤的发现，影响手术决策，并支持将其纳入常规术前评估，以改善患者预后。

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引用次数: 0

Investigation of the relationship between background parenchymal enhancement and breast cancer using 3.0 Tesla magnetic resonance imaging 3.0特斯拉磁共振成像研究背景实质增强与乳腺癌的关系

IF 1.5 4区医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Clinical Imaging

Pub Date : 2025-11-21 DOI: 10.1016/j.clinimag.2025.110680

S. Sekmen , N. Hursoy , K. Panc , F. Tascı

Aim

This study aims to explore the correlation between background parenchymal enhancement (BPE) observed in breast magnetic resonance imaging (MRI) and the presence of breast cancer.

Materials and methods

A total of 761 patients (326 malignant, 435 benign) who underwent breast MRI on a 3 T MRI device between August 2019 and August 2022 were included in the study. In breast MRI, the rate of fibroglandular tissue (FGT), areas of mass/non-mass enhancement in malignant cases, and BPE in the initial post-contrast series were evaluated. In addition to the clinical and demographic data of the patients, pathology results were added to the dataset. Molecular subtyping was categorized based on immunohistochemical markers into Luminal-A, Luminal-B (HER-2 negative), Luminal-B (HER-2 positive), HER-2 positive, and triple-negative groups.

Results

In the evaluation of BPE, minimal-to-mild enhancement was more common in both groups. No significant relationship was observed between BPE and menstrual cycle timing, cancer association, lesion characteristics, and pathological results. No significant intergroup difference was observed between FGT and BPE in the malignant and benign groups. The mean age of patients with moderate-to-marked background enhancement was significantly lower (p = 0.036). When the relationships between background enhancement and other descriptive features were evaluated, no statistically significant relationship was found.

Conclusion

The findings of our study suggest that BPE in breast MRI is not a determining factor in the diagnosis of breast cancer in patients with average or low risk.

目的探讨乳腺磁共振成像（MRI）背景实质增强（BPE）与乳腺癌存在的相关性。材料与方法在2019年8月至2022年8月期间，共有761例患者（326例恶性，435例良性）在3t MRI设备上接受了乳房MRI检查。在乳腺MRI中，评估纤维腺组织（FGT）的比率，恶性病例的肿块/非肿块增强区域，以及初始对比后系列中的BPE。除了患者的临床和人口统计数据外，病理结果也被添加到数据集中。根据免疫组织化学标记物将分子分型分为Luminal-A、Luminal-B （HER-2阴性）、Luminal-B （HER-2阳性）、HER-2阳性和三阴性组。结果在BPE的评估中，两组患者均以轻度至轻度强化为主。BPE与月经周期时间、癌症相关性、病变特征和病理结果无显著关系。恶性组和良性组FGT和BPE组间差异无统计学意义。中度至显著背景增强患者的平均年龄显著降低（p = 0.036）。当评估背景增强与其他描述性特征之间的关系时，没有发现统计学上显著的关系。结论本研究结果提示，对于中等或低危患者，乳腺MRI上的BPE并不是诊断乳腺癌的决定性因素。

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Amplifying signal-to-noise: Responsible use of large language models in radiology publishing 放大信噪比：在放射学出版中负责任地使用大型语言模型

IF 1.5 4区医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

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Design and implementation of a patient management system for coordination of theranostics care at a major referral center 设计和实施一个病人管理系统，以协调一个主要的转诊中心的治疗护理。

IF 1.5 4区医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

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