Japanese Journal of Radiology最新文献

{"title":"Comparative analysis of image quality and diagnostic performance among SS-EPI, MS-EPI, and rFOV DWI in bladder cancer.","authors":"Mitsuru Takeuchi, Atsushi Higaki, Yuichi Kojima, Kentaro Ono, Takuma Maruhisa, Takatoshi Yokoyama, Hiroyuki Watanabe, Akira Yamamoto, Tsutomu Tamada","doi":"10.1007/s11604-024-01694-1","DOIUrl":"10.1007/s11604-024-01694-1","url":null,"abstract":"<p><strong>Purpose: </strong>To compare image quality and diagnostic performance among SS-EPI diffusion weighted imaging (DWI), multi-shot (MS) EPI DWI, and reduced field-of-view (rFOV) DWI for muscle-invasive bladder cancer (MIBC).</p><p><strong>Materials and methods: </strong>This retrospective study included 73 patients with bladder cancer who underwent multiparametric MRI in our referral center between August 2020 and February 2023. Qualitative image assessment was performed in 73; and quantitative assessment was performed in 66 patients with maximum lesion diameter > 10 mm. The diagnostic performance of the imaging finding of muscle invasion was evaluated in 47 patients with pathological confirmation of MIBC. T2-weighted imaging, SS-EPI DWI, MS-EPI DWI, rFOV DWI, and dynamic contrast-enhanced imaging were acquired with 3 T-MRI. Qualitative image assessment was performed by three readers who rated anatomical distortion, clarity of bladder wall, and lesion conspicuity using a four-point scale. Quantitative assessment included calculation of SNR and CNR, and grading of the presence of muscle layer invasion according to the VI-RADS diagnostic criteria. Wilcoxon matched pairs signed rank test was used to compare qualitative and quantitative image quality. McNemar test and receiver-operating characteristic analysis were used to compare diagnostic performance.</p><p><strong>Results: </strong>Anatomical distortion was less in MS-EPI DWI, rFOV DWI, and SS-EPI DWI, in that order with significant difference. Clarity of bladder wall was greater for MS-EPI DWI, SS-EPI DWI, and rFOV DWI, in that order. There were significant differences between any two combinations of the three DWI types, except between SS-EPI DWI and MS-EPI in Reader 1. Lesion conspicuity, diagnostic performance, SNR and CNR were not significantly different among the three DWI types.</p><p><strong>Conclusions: </strong>Among the three DWI sequences evaluated, MS-EPI DWI showed the least anatomical distortion and superior bladder wall delineation but no improvement in diagnostic performance for MIBC. MS-EPI DWI may be considered for additional imaging if SS-EPI DWI is of poor quality.</p>","PeriodicalId":14691,"journal":{"name":"Japanese Journal of Radiology","volume":" ","pages":"666-675"},"PeriodicalIF":2.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11953214/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142638982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The added value of including thyroid nodule features into large language models for automatic ACR TI-RADS classification based on ultrasound reports.","authors":"Pilar López-Úbeda, Teodoro Martín-Noguerol, Alba Ruiz-Vinuesa, Antonio Luna","doi":"10.1007/s11604-024-01707-z","DOIUrl":"10.1007/s11604-024-01707-z","url":null,"abstract":"<p><strong>Objective: </strong>The ACR Thyroid Imaging, Reporting, and Data System (TI-RADS) uses a score based on ultrasound (US) imaging to stratify the risk of nodule malignancy and recommend appropriate follow-up. This study aims to analyze US reports and explore how Natural Language Processing (NLP) leveraging Transformers models can classify ACR TI-RADS from text reports using the description of thyroid nodule features.</p><p><strong>Materials and methods: </strong>This retrospective study evaluated 16,847 thyroid-free text reports from our institution. An automated system, followed by manual review by a radiologist, established baseline annotations by assigning ACR TI-RADS categories from 1 to 5. Two types of systems were evaluated and compared in the dataset. The first by performing a multiclass classification to detect the associated ACR TI-RADS, and the second by extracting thyroid nodule features from the textual reports and incorporating them into the classifier.</p><p><strong>Results: </strong>Our study showed that models enhanced with specific features systematically outperformed those without. Particularly, the BERTIN model, to which additional features were added, achieved the highest level of accuracy, with a score of 0.8426. Moreover, we found a correlation between the presence of punctate echogenic foci, a feature often linked to malignant thyroid lesions, and increased ACR TI-RADS scores.</p><p><strong>Conclusions: </strong>The features of the thyroid nodules described in thyroid US reports, such as composition, echogenicity, shape, margin or echogenic foci, help the NLP classifier to predict the associated ACR TI-RADS most accurately.</p>","PeriodicalId":14691,"journal":{"name":"Japanese Journal of Radiology","volume":" ","pages":"593-602"},"PeriodicalIF":2.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142710127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Be familiar with benign pediatric head and neck lesions! Image interpretation guides to overcome your weakness.","authors":"Motoo Nakagawa, Wenya Zhao, Kumiko Nozawa, Noriko Aida, Norio Shiraki, Yuki Yasuda, Akio Hiwatashi","doi":"10.1007/s11604-024-01697-y","DOIUrl":"10.1007/s11604-024-01697-y","url":null,"abstract":"<p><p>Pediatric head and neck lesions include three main categories: congenital, inflammatory, and neoplastic. It is important for management to understand the imaging features. The purpose of this pictorial review is to demonstrate the imaging features of benign head and neck lesions of pediatric patients. To get tips on overcoming anxiety about this area, this article also presents pitfalls related to each of these diseases.</p>","PeriodicalId":14691,"journal":{"name":"Japanese Journal of Radiology","volume":" ","pages":"542-550"},"PeriodicalIF":2.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11953090/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142769005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Generation of short-term follow-up chest CT images using a latent diffusion model in COVID-19.","authors":"Naoko Kawata, Yuma Iwao, Yukiko Matsuura, Takashi Higashide, Takayuki Okamoto, Yuki Sekiguchi, Masaru Nagayoshi, Yasuo Takiguchi, Takuji Suzuki, Hideaki Haneishi","doi":"10.1007/s11604-024-01699-w","DOIUrl":"10.1007/s11604-024-01699-w","url":null,"abstract":"<p><strong>Purpose: </strong>Despite a global decrease in the number of COVID-19 patients, early prediction of the clinical course for optimal patient care remains challenging. Recently, the usefulness of image generation for medical images has been investigated. This study aimed to generate short-term follow-up chest CT images using a latent diffusion model in patients with COVID-19.</p><p><strong>Materials and methods: </strong>We retrospectively enrolled 505 patients with COVID-19 for whom the clinical parameters (patient background, clinical symptoms, and blood test results) upon admission were available and chest CT imaging was performed. Subject datasets (n = 505) were allocated for training (n = 403), and the remaining (n = 102) were reserved for evaluation. The image underwent variational autoencoder (VAE) encoding, resulting in latent vectors. The information consisting of initial clinical parameters and radiomic features were formatted as a table data encoder. Initial and follow-up latent vectors and the initial table data encoders were utilized for training the diffusion model. The evaluation data were used to generate prognostic images. Then, similarity of the prognostic images (generated images) and the follow-up images (real images) was evaluated by zero-mean normalized cross-correlation (ZNCC), peak signal-to-noise ratio (PSNR), and structural similarity (SSIM). Visual assessment was also performed using a numerical rating scale.</p><p><strong>Results: </strong>Prognostic chest CT images were generated using the diffusion model. Image similarity showed reasonable values of 0.973 ± 0.028 for the ZNCC, 24.48 ± 3.46 for the PSNR, and 0.844 ± 0.075 for the SSIM. Visual evaluation of the images by two pulmonologists and one radiologist yielded a reasonable mean score.</p><p><strong>Conclusions: </strong>The similarity and validity of generated predictive images for the course of COVID-19-associated pneumonia using a diffusion model were reasonable. The generation of prognostic images may suggest potential utility for early prediction of the clinical course in COVID-19-associated pneumonia and other respiratory diseases.</p>","PeriodicalId":14691,"journal":{"name":"Japanese Journal of Radiology","volume":" ","pages":"622-633"},"PeriodicalIF":2.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11953082/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142710105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of NotebookLM, a large language model with retrieval-augmented generation, for lung cancer staging.","authors":"Ryota Tozuka, Hisashi Johno, Akitomo Amakawa, Junichi Sato, Mizuki Muto, Shoichiro Seki, Atsushi Komaba, Hiroshi Onishi","doi":"10.1007/s11604-024-01705-1","DOIUrl":"10.1007/s11604-024-01705-1","url":null,"abstract":"<p><strong>Purpose: </strong>In radiology, large language models (LLMs), including ChatGPT, have recently gained attention, and their utility is being rapidly evaluated. However, concerns have emerged regarding their reliability in clinical applications due to limitations such as hallucinations and insufficient referencing. To address these issues, we focus on the latest technology, retrieval-augmented generation (RAG), which enables LLMs to reference reliable external knowledge (REK). Specifically, this study examines the utility and reliability of a recently released RAG-equipped LLM (RAG-LLM), NotebookLM, for staging lung cancer.</p><p><strong>Materials and methods: </strong>We summarized the current lung cancer staging guideline in Japan and provided this as REK to NotebookLM. We then tasked NotebookLM with staging 100 fictional lung cancer cases based on CT findings and evaluated its accuracy. For comparison, we performed the same task using a gold-standard LLM, GPT-4 Omni (GPT-4o), both with and without the REK. For GPT-4o, the REK was provided directly within the prompt rather than through RAG.</p><p><strong>Results: </strong>NotebookLM achieved 86% diagnostic accuracy in the lung cancer staging experiment, outperforming GPT-4o, which recorded 39% accuracy with the REK and 25% without it. Moreover, NotebookLM demonstrated 95% accuracy in searching reference locations within the REK.</p><p><strong>Conclusion: </strong>NotebookLM, a RAG-LLM, successfully performed lung cancer staging by utilizing the REK, demonstrating superior performance compared to GPT-4o (without RAG). Additionally, it provided highly accurate reference locations within the REK, allowing radiologists to efficiently evaluate the reliability of NotebookLM's responses and detect possible hallucinations. Overall, this study highlights the potential of NotebookLM, a RAG-LLM, in image diagnosis.</p>","PeriodicalId":14691,"journal":{"name":"Japanese Journal of Radiology","volume":" ","pages":"706-712"},"PeriodicalIF":2.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142710098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The critical need for an open medical imaging database in Japan: implications for global health and AI development.","authors":"Daiju Ueda, Shannon Walston, Hirotaka Takita, Yasuhito Mitsuyama, Yukio Miki","doi":"10.1007/s11604-024-01716-y","DOIUrl":"10.1007/s11604-024-01716-y","url":null,"abstract":"<p><p>Japan leads OECD countries in medical imaging technology deployment but lacks open, large-scale medical imaging databases crucial for AI development. While Japan maintains extensive repositories, access restrictions limit their research utility, contrasting with open databases like the US Cancer Imaging Archive and UK Biobank. The 2018 Next Generation Medical Infrastructure Act attempted to address this through new data-sharing frameworks, but implementation has been limited by strict privacy regulations and institutional resistance. This data gap risks compromising AI system performance for Japanese patients and limits global medical AI advancement. The solution lies not in developing individual AI models, but in democratizing access to well-curated Japanese medical imaging data. By implementing privacy-preserving techniques and streamlining regulatory processes, Japan could enhance domestic healthcare outcomes while contributing to more robust global AI models, ultimately reclaiming its position as a leader in medical innovation.</p>","PeriodicalId":14691,"journal":{"name":"Japanese Journal of Radiology","volume":" ","pages":"537-541"},"PeriodicalIF":2.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11953178/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142818230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"White-matter alterations in dysthyroid optic neuropathy: a diffusion kurtosis imaging study using tract-based spatial statistics.","authors":"Jiang Zhou, Jun Liu, Jin-Ling Lu, Xiong-Ying Pu, Huan-Huan Chen, Hu Liu, Xiao-Quan Xu, Fei-Yun Wu, Hao Hu","doi":"10.1007/s11604-024-01710-4","DOIUrl":"10.1007/s11604-024-01710-4","url":null,"abstract":"<p><strong>Purpose: </strong>So far, there is no gold standard to diagnosis dysthyroid optic neuropathy (DON). Diffusion kurtosis imaging (DKI) has the potential to provide imaging biomarkers for the timely and accurate diagnosis of DON. This study aimed to explore the white matter (WM) alterations in thyroid-associated ophthalmopathy (TAO) patients with and without DON using DKI with tract-based spatial statistics method.</p><p><strong>Materials and methods: </strong>Fifty-three TAO patients (21 DON and 32 non-DON) and 30 healthy controls (HCs) were recruited in this cross-sectional study. DKI data were analyzed and compared among groups. The correlations between diffusion parameters and clinical variables were assessed. Receiver-operating characteristic curve analysis was used to evaluate the feasibility of using DKI parameters to distinguish DON and non-DON.</p><p><strong>Results: </strong>Compared with HCs, both DON and non-DON groups exhibited significantly decreased radial kurtosis (RK), mean kurtosis (MK), axial kurtosis (AK), kurtosis fractional anisotropy, and fractional anisotropy values in several WM tracts. No significant differences were observed in mean diffusivity values among groups. Meanwhile, DON patients exhibited lower RK, MK, and AK values than non-DON patients mainly in the visual system. Significant correlations were observed between RK values of posterior thalamic radiation (PTR) and best-corrected visual acuity. For distinguishing DON, the RK values of PTR exhibited decent diagnostic performance.</p><p><strong>Conclusion: </strong>Microstructural abnormalities in WM, especially in the visual system, could provide novel insights into the potential neural mechanisms of the disease, thereby contributing to the timely diagnosis of DON and the development of neuroprotective therapy.</p>","PeriodicalId":14691,"journal":{"name":"Japanese Journal of Radiology","volume":" ","pages":"603-611"},"PeriodicalIF":2.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142710051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MRI characteristics of ovarian metastasis: differentiation from stomach and colorectal cancer.","authors":"Yukiko Takai, Hiroki Kato, Masaya Kawaguchi, Kazuhiro Kobayashi, Kyoko Kikuno, Tatsuro Furui, Masanori Isobe, Yoshifumi Noda, Fuminori Hyodo, Masayuki Matsuo","doi":"10.1007/s11604-024-01700-6","DOIUrl":"10.1007/s11604-024-01700-6","url":null,"abstract":"<p><strong>Purpose: </strong>To evaluate the efficacy of MRI findings for differentiating between ovarian metastasis from stomach cancer (OMSC) and colorectal cancer (OMCC).</p><p><strong>Methods: </strong>Twenty-six patients with histopathologically proven ovarian metastasis (n = 8 with 12 OMSCs and n = 18 with 25 OMCCs) were enrolled in the study. All patients had undergone pelvic MRI before surgery. We retrospectively reviewed MRI findings and compared them between the two pathologies. The black scrunchie sign was defined as a thick (> 5 mm) and lobulated hypointense rim (> 180°) with central hyperintense areas on T2-weighted images.</p><p><strong>Results: </strong>Predominantly solid lesions (100% vs. 20%, p < 0.01), black scrunchie sign (33% vs. 0%, p < 0.01), and flow void (67% vs. 20%, p < 0.01) were frequently observed in OMSCs than in OMCCs. The signal intensity ratio of solid components on T2-weighted images (3.30 ± 0.70 vs. 2.52 ± 0.77, p < 0.01) and gadolinium-enhanced T1-weighted images (2.21 ± 0.57 vs. 1.43 ± 0.32, p < 0.01) were significantly higher in OMSCs than in OMCCs. Furthermore, hyperintense areas within cystic components on T1-weighted images (71% vs. 18%, p < 0.01) and stained-glass appearance (44% vs. 0%, p < 0.01) were frequently observed in OMCCs than in OMSCs.</p><p><strong>Conclusion: </strong>The black scrunchie sign was only observed in OMSCs. OMSCs always exhibited predominantly solid lesions and had higher signal intensity of solid components on T2- and gadolinium-enhanced T1-weighted images. OMCCs usually presented as cystic lesions, usually accompanied by hyperintense areas within the cystic components on T1-weighted images.</p>","PeriodicalId":14691,"journal":{"name":"Japanese Journal of Radiology","volume":" ","pages":"676-686"},"PeriodicalIF":2.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11953186/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142620898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"External validation of the performance of commercially available deep-learning-based lung nodule detection on low-dose CT images for lung cancer screening in Japan.","authors":"Wataru Fukumoto, Yuki Yamashita, Ikuo Kawashita, Toru Higaki, Asako Sakahara, Yuko Nakamura, Yoshikazu Awaya, Kazuo Awai","doi":"10.1007/s11604-024-01704-2","DOIUrl":"10.1007/s11604-024-01704-2","url":null,"abstract":"<p><strong>Purpose: </strong>Artificial intelligence (AI) algorithms for lung nodule detection have been developed to assist radiologists. However, external validation of its performance on low-dose CT (LDCT) images is insufficient. We examined the performance of the commercially available deep-learning-based lung nodule detection (DL-LND) using LDCT images at Japanese lung cancer screening (LCS).</p><p><strong>Materials and methods: </strong>Included were 43 patients with suspected lung cancer on LDCT images and pathologically confirmed lung cancer. The reference standard for nodules whose diameter exceeded 4 mm was set by a radiologist who referred to the reports of two other radiologists reading the LDCT images. After we applied commercially available DL-LND to the LDCT images, the radiologist reviewed all nodules detected by DL-LND. When he failed to identify an existing nodule, it was also included in the reference standard. To validate the performance of DL-LND, the sensitivity for lung nodules and lung cancer, the positive-predictive value (PPV) for lung nodules, and the mean number of false-positive (FP) nodules per CT scan were recorded.</p><p><strong>Results: </strong>The radiologist detected 97 nodules including 43 lung cancers and missed 3 solid nodules detected by DL-LND. A total of 100 nodules was included in the reference standard. DL-LND detected 396 nodules including 40 lung cancers. The sensitivity for the 100 nodules was 96.0%; the PPV was 24.2% (96/396). The mean number of FP nodules per CT scan was 7.0; sensitivity for lung cancer was 93.0% (40/43). DL-LND missed three lung cancers; 2 of these were atypical pulmonary cysts.</p><p><strong>Conclusion: </strong>We externally verified that the sensitivity for lung nodules and lung cancer by DL-LND was very high. However, its low PPV and the increased FP nodules remains a serious drawback of DL-LND.</p>","PeriodicalId":14691,"journal":{"name":"Japanese Journal of Radiology","volume":" ","pages":"634-640"},"PeriodicalIF":2.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11953200/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142755014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Risk factors of non-diagnostic percutaneous liver tumor biopsy: a single-center retrospective analysis of 938 biopsies based on cause of error.","authors":"Shintaro Kimura, Miyuki Sone, Shunsuke Sugawara, Chihiro Itou, Takumi Oshima, Mizuki Ozawa, Rakuhei Nakama, Sho Murakami, Yoshiyuki Matsui, Yasuaki Arai, Masahiko Kusumoto","doi":"10.1007/s11604-024-01703-3","DOIUrl":"10.1007/s11604-024-01703-3","url":null,"abstract":"<p><strong>Purpose: </strong>To evaluate the risk factors of non-diagnostic results based on cause of error in liver tumor biopsy.</p><p><strong>Materials and methods: </strong>This single-institution, retrospective study included 843 patients [445 men, 398 women; median age, 67 years] who underwent a total of 938 liver tumor biopsies between April 2018 and September 2022. An 18-G cutting biopsy needle with a 17-G introducer needle was used. Ultrasound was used as the first choice for image guidance, and computed tomography was alternatively or complementarily used only for tumors with poor ultrasound visibility. Non-diagnostic biopsies were divided into two groups depending on the cause of error, either technical or targeting error. Biopsies in which the biopsy needle did not hit the target tumor were classified as technical error. Biopsies in which insufficient tissue was obtained due to necrosis or degeneration despite the biopsy needle hitting the target tumor were classified as targeting error. This classification was based on pre-procedural enhanced-imaging, intro-procedural imaging, and pathological findings. Statistical analysis was performed using binary logistic regression.</p><p><strong>Results: </strong>The non-diagnostic rate was 4.6%. Twenty-six and seventeen biopsies were classified as technical and targeting errors, respectively. In the technical error group, tumor size ≤ 17 mm and computed tomography-assisted biopsy due to poor ultrasound visibility were identified as risk factors (p < 0.001 and p = 0.021, respectively), and the tumors with both factors had a significantly high risk of technical error compared to those without both factors (non-diagnostic rate: 17.2 vs 1.1%, p < 0.001). In the targeting error group, tumor size ≥ 42 mm was identified as a risk factor (p = 0.003).</p><p><strong>Conclusion: </strong>Tumor size ≤ 17 mm and computed tomography-assisted biopsy due to poor ultrasound visibility were risk factors for technical error, and tumor size ≥ 42 mm was a risk factor for targeting error in liver tumor biopsies.</p>","PeriodicalId":14691,"journal":{"name":"Japanese Journal of Radiology","volume":" ","pages":"696-705"},"PeriodicalIF":2.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11953201/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142620901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}