Clinical Imaging最新文献

{"title":"Breast lymphoma: Imaging features, clinical presentation, and ultrasound artificial intelligence (AI) decision support (DS)","authors":"Christine Chen , Dina Moumin , Melissa Yang , Tamar Gomolin , Genevieve Abbey , Tayler Van Denakker , Laurie Margolies","doi":"10.1016/j.clinimag.2025.110582","DOIUrl":"10.1016/j.clinimag.2025.110582","url":null,"abstract":"","PeriodicalId":50680,"journal":{"name":"Clinical Imaging","volume":"126 ","pages":"Article 110582"},"PeriodicalIF":1.5,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144827501","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":"Strengthening the evidence base for patient-centered research in radiology: Commentary on method, equity, and implementation","authors":"Deniz Esin Tekcan Sanli , Ahmet Necati Sanli","doi":"10.1016/j.clinimag.2025.110580","DOIUrl":"10.1016/j.clinimag.2025.110580","url":null,"abstract":"","PeriodicalId":50680,"journal":{"name":"Clinical Imaging","volume":"126 ","pages":"Article 110580"},"PeriodicalIF":1.5,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144831403","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":"A multimodal automated deep learning-based model for predicting biochemical recurrence of prostate cancer following prostatectomy from baseline MRI, Presurgical clinical covariates","authors":"Benjamin D. Simon ,&nbsp;Stephanie A. Harmon ,&nbsp;Katie M. Merriman ,&nbsp;Jesse Tetreault ,&nbsp;Omer T. Esengur ,&nbsp;Hunter Stecko ,&nbsp;Enis C. Yilmaz ,&nbsp;Lei Clifton ,&nbsp;Anshul Thakur ,&nbsp;Zoë Blake ,&nbsp;Maria J. Merino ,&nbsp;Julie Y. An ,&nbsp;Jamie Marko ,&nbsp;Yan Mee Law ,&nbsp;Sandeep Gurram ,&nbsp;David Clifton ,&nbsp;Bradford J. Wood ,&nbsp;Peter L. Choyke ,&nbsp;Peter A. Pinto ,&nbsp;Baris Turkbey","doi":"10.1016/j.clinimag.2025.110579","DOIUrl":"10.1016/j.clinimag.2025.110579","url":null,"abstract":"<div><h3>Purpose</h3><div>To develop a multimodal deep learning-based AI algorithm and investigate its ability to predict BCR of PCa after radical prostatectomy (RP) using MRI and clinical data.</div></div><div><h3>Methods</h3><div>PCa patients (<em>n</em> = 311) underwent prostate MRI prior to RP between January 2008 and December 2018. For each patient, CAPRA-S was calculated. Quantitative imaging features were extracted using methods developed in a previous study. Test set results were assessed independently for each model in the study, using cross-validation of the training set to tune hyperparameters and select features. DeLong's test compared AUROC curve values, and log-rank tests compared BCR-free survival curves.</div></div><div><h3>Results</h3><div>Across all patients, the AUROC of the automated multimodal model was 0.74, compared to 0.66 for CAPRA-S. This model had the highest sensitivity at 75 %, with CAPRA-S at 37 %. BCR-free survival curves for the test set were generated for each model. Log-rank tests indicated each model differentiated between patient outcomes (<em>p</em> &lt; 0.05). The automated multimodal model was the only model with <em>p</em> &lt; 0.01. Focusing on intermediate risk patients (CAPRA-S scores 3–5), this automated model was the only model which maintained the ability to differentiate between outcomes (p &lt; 0.01), while all other models and CAPRA-S failed to differentiate intermediate risk BCR outcomes (<em>p</em> &gt; 0.05).</div></div><div><h3>Conclusion</h3><div>Development of a multimodal model using quantitative imaging features and clinical covariates revealed that an automated multimodal AI approach most effectively predicts BCR in PCa patients. Based on AUROC and the ability to differentiate between BCR-free survival outcomes with statistical significance in intermediate risk patients, this model outperforms the gold standard postsurgical CAPRA-S risk scores.</div></div>","PeriodicalId":50680,"journal":{"name":"Clinical Imaging","volume":"126 ","pages":"Article 110579"},"PeriodicalIF":1.5,"publicationDate":"2025-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144831402","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":"Optimizing contrast-enhanced abdominal MRI: A comparative study of deep learning and standard VIBE techniques","authors":"Alexander Herold ,&nbsp;Nathaniel D. Mercaldo ,&nbsp;Mark A. Anderson ,&nbsp;Amirkasra Mojtahed ,&nbsp;Aoife Kilcoyne ,&nbsp;Wei-Ching Lo ,&nbsp;Robert M. Sellers ,&nbsp;Bryan Clifford ,&nbsp;Marcel D. Nickel ,&nbsp;Nabih Nakrour ,&nbsp;Susie Y. Huang ,&nbsp;Leo L. Tsai ,&nbsp;Onofrio A. Catalano ,&nbsp;Mukesh G. Harisinghani","doi":"10.1016/j.clinimag.2025.110581","DOIUrl":"10.1016/j.clinimag.2025.110581","url":null,"abstract":"<div><h3>Objective</h3><div>To validate a deep learning (DL) reconstruction technique for faster post-contrast enhanced coronal Volume Interpolated Breath-hold Examination (VIBE) sequences and assess its image quality compared to conventionally acquired coronal VIBE sequences.</div></div><div><h3>Methods</h3><div>This prospective study included 151 patients undergoing clinically indicated upper abdominal MRI acquired on 3 T scanners. Two coronal T1 fat-suppressed VIBE sequences were acquired: a DL-reconstructed sequence (VIBE_DL) and a standard sequence (VIBE_SD). Three radiologists independently evaluated six image quality parameters: overall image quality, perceived signal-to-noise ratio, severity of artifacts, liver edge sharpness, liver vessel sharpness, and lesion conspicuity, using a 4-point Likert scale. Inter-reader agreement was assessed using Gwet's AC2. Ordinal mixed-effects regression models were used to compare VIBE_DL and VIBE_SD.</div></div><div><h3>Results</h3><div>Acquisition times were 10.2 s for VIBE_DL compared to 22.3 s for VIBE_SD. VIBE_DL demonstrated superior overall image quality (OR 1.95, 95 % CI: 1.44–2.65, <em>p</em> &lt; 0.001), reduced image noise (OR 3.02, 95 % CI: 2.26–4.05, <em>p</em> &lt; 0.001), enhanced liver edge sharpness (OR 3.68, 95 % CI: 2.63–5.15, <em>p</em> &lt; 0.001), improved liver vessel sharpness (OR 4.43, 95 % CI: 3.13–6.27, p &lt; 0.001), and better lesion conspicuity (OR 9.03, 95 % CI: 6.34–12.85, <em>p</em> &lt; 0.001) compared to VIBE_SD. However, VIBE_DL showed increased severity of peripheral artifacts (OR 0.13, p &lt; 0.001). VIBE_DL detected 137/138 (99.3 %) focal liver lesions, while VIBE_SD detected 131/138 (94.9 %). Inter-reader agreement ranged from good to very good for both sequences.</div></div><div><h3>Conclusion</h3><div>The DL-reconstructed VIBE sequence significantly outperformed the standard breath-hold VIBE in image quality and lesion detection, while reducing acquisition time. This technique shows promise for enhancing the diagnostic capabilities of contrast-enhanced abdominal MRI.</div></div>","PeriodicalId":50680,"journal":{"name":"Clinical Imaging","volume":"126 ","pages":"Article 110581"},"PeriodicalIF":1.5,"publicationDate":"2025-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144809559","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":"Causal Inference Using Multimodal Propensity Score Adjustment for Assessing Gelatin Sponge Efficacy in Preventing Lung Biopsy Related Hemorrhage","authors":"Yandan Wang ,&nbsp;Jincheng Xiao ,&nbsp;Junpeng Luo","doi":"10.1016/j.clinimag.2025.110576","DOIUrl":"10.1016/j.clinimag.2025.110576","url":null,"abstract":"<div><h3>Purpose</h3><div>Pulmonary hemorrhage is a potentially serious complication of CT-guided percutaneous lung biopsy. While gelatin sponge embolization of the needle tract is widely used for pneumothorax prevention, its effectiveness against hemorrhage remains uncertain. We aimed to assess whether prophylactic gelatin sponge tract embolization reduces the incidence of radiologically significant pulmonary hemorrhage, using advanced propensity score–based techniques.</div></div><div><h3>Materials and methods</h3><div>We retrospectively analyzed 1812 patients who underwent CT-guided lung biopsy between 2018 and 2022. Clinically significant hemorrhage was defined as grade ≥ 2 according to a standardized radiologic scale. Logistic regression and three propensity score methods—matching, inverse probability of treatment weighting (IPTW), and propensity score weighting (PSW)—were used to adjust for baseline differences between the gelatin and control groups.</div></div><div><h3>Results</h3><div>Clinically significant hemorrhage occurred in 38.4 % of patients. While unadjusted regression did not show a significant effect (OR 0.88; <em>p</em> = 0.24), IPTW (OR 0.81; <em>p</em> = 0.019) and PSW (OR 0.78; <em>p</em> = 0.013) analyses revealed statistically significant reductions in hemorrhage risk with gelatin sponge use. Subgroup analyses indicated enhanced protective effects in patients with smaller lesions, multiple needle passes, or non-perpendicular angles.</div></div><div><h3>Conclusion</h3><div>Gelatin sponge embolization modestly but significantly reduces pulmonary hemorrhage during lung biopsy, especially in high-risk procedures. These findings suggest a role for individualized embolization strategies in procedural planning.</div></div>","PeriodicalId":50680,"journal":{"name":"Clinical Imaging","volume":"125 ","pages":"Article 110576"},"PeriodicalIF":1.5,"publicationDate":"2025-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144767178","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":"Vessel-specific reliability of artificial intelligence-based coronary artery calcium scoring on non-ECG-gated chest CT: a comparative study with ECG-gated cardiac CT","authors":"Jing Zhang ,&nbsp;Kefu Liu ,&nbsp;Chenyang You ,&nbsp;Jingjing Gong","doi":"10.1016/j.clinimag.2025.110575","DOIUrl":"10.1016/j.clinimag.2025.110575","url":null,"abstract":"<div><h3>Purpose</h3><div>To evaluate the performance of artificial intelligence (AI)-based coronary artery calcium scoring (CACS) on non-electrocardiogram (ECG)-gated chest CT, using manual quantification as the reference standard, while characterizing per-vessel reliability and clinical risk classification impacts.</div></div><div><h3>Methods</h3><div>Retrospective study of 290 patients (June 2023–2024) with paired non-ECG-gated chest CT and ECG-gated cardiac CT (median time was 2 days). AI-based CACS and manual CACS (CACS_man) were compared using intraclass correlation coefficient (ICC) and weighted Cohen's kappa (3,1). Error types, anatomical distributions, and CACS of the lesions of individual arteries or segments were assessed in accordance with the Society of Cardiovascular Computed Tomography (SCCT) guidelines.</div></div><div><h3>Results</h3><div>The total CACS of chest CT demonstrated excellent concordance with CACS_man (ICC = 0.87, 95 % CI 0.84–0.90). Non-ECG-gated chest showed a 7.5-fold increased risk misclassification rate compared to ECG-gated cardiac CT (41.4 % vs. 5.5 %), with 35.5 % overclassification and 5.9 % underclassification. Vessel-specific analysis revealed paradoxical reliability of the left anterior descending artery (LAD) due to stent misclassification in four cases (ICC = 0.93 on chest CT vs 0.82 on cardiac CT), while the right coronary artery (RCA) demonstrated suboptimal performance with ICCs ranging from 0.60 to 0.68. Chest CT exhibited higher false-positive (1.9 % vs 0.5 %) and false-negative rates (14.4 % vs 4.3 %). False positive mainly derived from image noise in proximal LAD/RCA (median CACS 5.97 vs 3.45) and anatomical error, while false negatives involved RCA microcalcifications (median CACS 2.64).</div></div><div><h3>Conclusions</h3><div>AI-based non-ECG-gated chest CT demonstrates utility for opportunistic screening but requires protocol optimization to address vessel-specific limitations and mitigate 41.4 % risk misclassification rates.</div></div>","PeriodicalId":50680,"journal":{"name":"Clinical Imaging","volume":"125 ","pages":"Article 110575"},"PeriodicalIF":1.5,"publicationDate":"2025-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144767179","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 role of mammography in the detection and diagnosis of pregnancy-associated breast cancer","authors":"Noam Nissan ,&nbsp;Jonathan Kuten,&nbsp;Kimberly Feigin,&nbsp;Victoria L. Mango,&nbsp;Jill Gluskin ,&nbsp;Rosa Elena Ochoa Albiztegui,&nbsp;Hila Fruchtman-Brot ,&nbsp;Yuki Arita,&nbsp;Jeffrey S. Reiner,&nbsp;Tali Amir,&nbsp;Maxine S. Jochelson ,&nbsp;Janice S. Sung","doi":"10.1016/j.clinimag.2025.110577","DOIUrl":"10.1016/j.clinimag.2025.110577","url":null,"abstract":"<div><h3>Purpose</h3><div>To evaluate the role of mammography in the diagnostic workup of pregnancy-associated breast cancer (PABC).</div></div><div><h3>Materials and methods</h3><div>This retrospective single-institution study included patients diagnosed with PABC from February 2009 to January 2024 and imaged by mammography. The additional diagnostic value of mammography as statistically compared with ultrasound (US) was evaluated, focusing on rate of initial detection, identification of additional cancer, changes in lesion size ≥1 cm, and changes in T-staging.</div></div><div><h3>Results</h3><div>A total of 167 patients with newly diagnosed PABC were included (mean age, 37.0 years ±4.4), including 30/167 (18 %) who were pregnant (mean pregnancy duration, 6.3 months ±2.7) and 137/167 (82 %) who were lactating. Almost all patient had dense breasts (163/167, 97.6 %), including 77 % with extremely dense breasts. Most PABCs (137/167, 82.0 %) were visible on mammography, including cases in which mammography was the sole detection modality (<em>n</em> = 21), had additional positive stereotactic biopsy (<em>n</em> = 17, 10.2 %), showed changes in lesion size by ≥1 cm (<em>n</em> = 35, 21.0 %) (<em>P</em> &lt; 0.001), or changed the T-staging (n = 35, 21.0 %). Excluding cases with duplicate contributions, mammography added value in 64/167 (38.3 %) patients.</div></div><div><h3>Conclusion</h3><div>Despite the high proportions of increased mammographic density, mammography successfully demonstrated most pregnancy-associated breast cancers and frequently provided valuable additional information for their evaluation. Regardless of how PABC presents clinically, mammography and US must serve as complementary tools in the diagnostic evaluation of PABC.</div></div>","PeriodicalId":50680,"journal":{"name":"Clinical Imaging","volume":"125 ","pages":"Article 110577"},"PeriodicalIF":1.5,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144767177","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":"Ultrasound-based machine learning models for predicting response to neoadjuvant chemotherapy in breast cancer: A meta-analysis","authors":"Parya Valizadeh ,&nbsp;Payam Jannatdoust ,&nbsp;Niloofar Moradi ,&nbsp;Shirin Yaghoobpoor ,&nbsp;Sajjad Toofani ,&nbsp;Nazanin Rafiei ,&nbsp;Farzan Moodi ,&nbsp;Hamed Ghorani ,&nbsp;Arvin Arian","doi":"10.1016/j.clinimag.2025.110574","DOIUrl":"10.1016/j.clinimag.2025.110574","url":null,"abstract":"<div><h3>Background and aims</h3><div>Breast cancer remains the most common cancer among women globally, with neoadjuvant chemotherapy (NAC) serving as a critical pre-surgical intervention. Ultrasound-based radiomics and machine learning (ML) models offer potential for early prediction of NAC response, aiding personalized treatment strategies. This study systematically reviews the efficacy of ultrasound-based ML models in predicting NAC response in breast cancer patients.</div></div><div><h3>Methods</h3><div>We conducted a systematic review and meta-analysis following PRISMA-DTA guidelines, searching PubMed, Scopus, Web of Science, and Embase up to August 30, 2023. Studies developing ultrasound-based radiomics or deep learning (DL) models to predict NAC response were include. Models for complete and partial response were analyzed separately.</div></div><div><h3>Results</h3><div>Twenty-two studies were included. For models predicting complete response, pooled sensitivity, specificity, and AUC were 85.1 % (95 % CI: 79.2–89.6 %), 85.8 % (95 % CI: 76.7–91.8 %), and 86 % (95 % CI: 82 %–94 %), respectively for internal validation and 82.9 % (95 % CI: 76.2 % - 88.1 %), 89.4 % (95 % CI: 84.7 %–92.9 %), and 93 % (95 % CI: 82 %–94 %), respectively for external validation. For partial response, analysis could only be performed on internal validation and the pooled sensitivity was 87.5 % (95 % CI: 85.1–89.6 %) with pooled specificity of 82.3 % (95 % CI: 75.6–87.5 %), and pooled AUC of 88 % (95 % CI: 85 %–92 %).</div></div><div><h3>Conclusion</h3><div>Ultrasound-based ML models show strong potential for predicting NAC response in breast cancer, with delta radiomics enhancing predictive accuracy. Further research is needed to develop clinically generalizable models.</div></div>","PeriodicalId":50680,"journal":{"name":"Clinical Imaging","volume":"125 ","pages":"Article 110574"},"PeriodicalIF":1.5,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144771823","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":"Cardiovascular CT angiography in systemic and pulmonary venous anomalies of the thorax","authors":"Camila Pietroski Reifegerste ,&nbsp;André Vaz (Vaz A)","doi":"10.1016/j.clinimag.2025.110569","DOIUrl":"10.1016/j.clinimag.2025.110569","url":null,"abstract":"<div><div>Thoracic venous anomalies are often asymptomatic and may go undetected by non-dedicated imaging studies. However, they have significant clinical relevance, as they can affect surgical planning, complicate interventional procedures, and predispose patients to pathological conditions such as arrhythmias, paradoxical embolism, and thrombosis. Understanding their embryologic development is critical for accurate diagnosis and differentiation from pathologic mimics. Major anomalies include left superior vena cava, retroaortic and retroesophageal left brachiocephalic veins, interruption of the inferior vena cava with azygos continuation, and various forms of anomalous pulmonary venous connections. Although many of these anomalies do not require surgical correction, their identification is essential to optimize procedural planning, particularly in patients with congenital heart disease where surgical modifications may be required. Failure to recognize these anomalies can lead to misdiagnosis, unnecessary interventions, and increased procedural risk. This review highlights the importance of identifying thoracic venous anomalies on imaging studies to ensure accurate diagnosis, prevent complications, and improve patient management.</div></div>","PeriodicalId":50680,"journal":{"name":"Clinical Imaging","volume":"125 ","pages":"Article 110569"},"PeriodicalIF":1.5,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144739645","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":"Strategies to enhance recruitment in nuclear medicine: A path forward","authors":"Shinji Rho ,&nbsp;Kevin P. Fialkowski ,&nbsp;Samantha G. Harrington ,&nbsp;Bashar Kako ,&nbsp;Merissa N. Zeman ,&nbsp;Hyewon Hyun ,&nbsp;Matthew Robertson ,&nbsp;Thomas S.C. Ng","doi":"10.1016/j.clinimag.2025.110572","DOIUrl":"10.1016/j.clinimag.2025.110572","url":null,"abstract":"<div><div>Nuclear medicine has advanced significantly since earning ACGME accreditation in 1971, with ongoing innovations in imaging and therapy. Despite these technological strides, challenges remain in developing a robust workforce to meet the growing clinical demands. Effective recruitment of interested, well-qualified, and diverse individuals is imperative to the field's continued advancement. This article reviews barriers to recruitment and explores strategies to enhance training and workforce development. The latter includes earlier exposure to nuclear medicine and radiology, improving equity in training opportunities, and establishing structured mentorship programs for trainees at various stages. Moreover, addressing systemic issues, such as the lack of clear guidance surrounding training pathways and disparities in access to advanced training opportunities, is critical to broadening participation in the field. Expanding outreach efforts alongside society-led initiatives can further diversify and strengthen the recruitment pool. By building on the progress already made and identifying opportunities for further growth, we aim to inspire greater interest in nuclear medicine among medical trainees, ensuring the field's continued innovation and ability to meet the growing clinical demands of modern medicine.</div></div>","PeriodicalId":50680,"journal":{"name":"Clinical Imaging","volume":"125 ","pages":"Article 110572"},"PeriodicalIF":1.5,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144750667","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}