Journal of imaging informatics in medicine最新文献

{"title":"Do you Really Want to Know-Patient and Physician Attitudes of Physicians and English-Proficient Asian Patients toward Direct Release of Radiology Reports in Singapore.","authors":"David Meng-Guang Chian, Lishya Liauw, Sin Lee Chua, Lai Peng Chan, Tessa Sundaram Cook, Charles Xian-Yang Goh, Winnie Wing Chuen Lam, Wei Ming Chua, Kheng Choon Lim","doi":"10.1007/s10278-025-01655-8","DOIUrl":"10.1007/s10278-025-01655-8","url":null,"abstract":"<p><p>In Singapore, there are plans to release radiological reports to patients directly, potentially before their physician clinic visits. While several studies have researched this policy in Caucasian-majority populations, there is scarce data for Asian-majority populations. This study aims to understand the perceptions of releasing radiological reports directly to patients before their clinic visit, by surveying physicians and patients at a major tertiary hospital in Singapore. Voluntary surveys were fielded to English-proficient patients who presented for select cross-sectional imaging, as well as physicians working at the hospital between March and July 2024. Statistical analysis was performed using Pearson's χ² test and multivariate linear regression with a two-tailed statistical significance value of 0.05. An institutional review board waiver of consent was received. Analyzing 280 physician and 137 patient responses showed significant differences in agreement across all questions (p < 0.0001). Patients favored, and physicians opposed, the direct release of results. Subgroup analysis revealed significant differences (p < 0.05) between tertiary and pre-tertiary educated patients. Tertiary-educated patients highlighted presumed better understanding of radiological reports (53.6% vs. 25%), less confusion (16.5% vs. 37.5%), felt that the report would better prepare them for the clinic visit (79.4% vs. 47.5%), and increased understanding of their medical condition (79.4% vs. 50%). Wide differences in opinion on direct release of radiological reports, especially as education levels rise, need to be addressed. A \"one-size-fits-all\" approach does not suit our population, and physicians play a vital role by advocating for patients, helping them navigate complex medical terminology, and the increasingly complex medical landscape.</p>","PeriodicalId":516858,"journal":{"name":"Journal of imaging informatics in medicine","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144985198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fusion of Deep Transfer Learning and Radiomics in MRI-Based Prediction of Post-Surgical Recurrence in Soft Tissue Sarcoma.","authors":"Yujian Wang, Tongyu Wang, Fei Zheng, Wenhan Hao, Qi Hao, Wenjia Zhang, Ping Yin, Nan Hong","doi":"10.1007/s10278-025-01653-w","DOIUrl":"https://doi.org/10.1007/s10278-025-01653-w","url":null,"abstract":"<p><p>Soft  tissue sarcomas (STS) are heterogeneous malignancies with high recurrence rates (33-39%) post-surgery, necessitating improved prognostic tools. This study proposes a fusion model integrating deep transfer learning and radiomics from MRI to predict postoperative STS recurrence. Axial T2-weighted fat-suppressed imaging (T₂WI) of 803 STS patients from two institutions was retrospectively collected and divided into training (n = 527), internal validation (n = 132), and external validation (n = 144) cohorts. Tumor segmentation was performed using the SegResNet model within the Auto3DSeg framework. Radiomic features and deep learning features were extracted. Feature selection employed LASSO regression, and the deep learning radiomic (DLR) model combined radiomic and deep learning signatures. Using the features, nine models were constructed based on three classifiers. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, negative predictive value, and positive predictive value were calculated for performance evaluation. The SegResNet model achieved Dice coefficients of 0.728 after refinement. Recurrence rates were 22.8% (120/527) in the training, 25.0% (33/132) in the internal validation, and 32.6% (47/144) in the external validation cohorts. The DLR model (ExtraTrees) demonstrated superior performance, achieving an AUC of 0.818 in internal validation and 0.809 in external validation, better than the radiomic model (0.710, 0.612) and the deep learning model (0.751, 0.667). Sensitivity and specificity ranged from 0.702 to 0.976 and 0.732 to 0.830, respectively. Decision curve analysis confirmed superior clinical utility. The DLR model provides a robust, non-invasive tool for preoperative STS recurrence prediction, enabling personalized treatment decisions and postoperative management.</p>","PeriodicalId":516858,"journal":{"name":"Journal of imaging informatics in medicine","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144985143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Pilot Study on Quantitative Accuracy and Radiomic Feature Stability of Deep Progressive Learning Reconstruction in NSCLC PET.","authors":"Takuro Shiiba, Takeru Abe, Masanori Watanabe","doi":"10.1007/s10278-025-01654-9","DOIUrl":"https://doi.org/10.1007/s10278-025-01654-9","url":null,"abstract":"<p><p>Deep progressive learning reconstruction (DPR) is a novel deep learning-based algorithm for PET imaging, yet its impact on quantitative metrics and radiomic feature stability is not fully characterized. This preliminary study systematically evaluated DPR against conventional ordered-subset expectation maximization (OSEM) in non-small cell lung cancer (NSCLC) PET imaging. In this retrospective study of 24 NSCLC patients, PET data were reconstructed using OSEM and three DPR strength levels. We compared standardized uptake values (SUV), contrast-to-noise ratio (CNR), and background noise. As a secondary objective, the stability of 93 radiomic features was quantified using an intra-patient coefficient of variation (COV_RF) across all four reconstruction methods. DPR significantly increased SUV, particularly in smaller tumors, but this came at the expense of image quality, with only the lowest DPR strength improving CNR. The stability analysis revealed a stark stratification of radiomic features. While 31 features (33.3%) were robust against algorithmic changes (median COV_RF ≤ 10%), a larger group of 38 features (40.9%), including the commonly used glcm_Contrast, proved highly unstable. In conclusion, DPR presents a critical trade-off between enhanced SUV quantification and image quality, requiring careful parameter optimization. Furthermore, our findings demonstrate that the stability of radiomic features is highly algorithm-dependent. The reliable application of advanced reconstruction techniques like DPR in quantitative and radiomic pipelines is therefore contingent upon a rigorous, evidence-based selection of features verified to be robust.</p>","PeriodicalId":516858,"journal":{"name":"Journal of imaging informatics in medicine","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144985183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Super-Resolution MR Spectroscopic Imaging via Diffusion Models for Tumor Metabolism Mapping.","authors":"Mohammed Alsubaie, Sirani M Perera, Linxia Gu, Sean B Subasi, Ovidiu C Andronesi, Xianqi Li","doi":"10.1007/s10278-025-01652-x","DOIUrl":"https://doi.org/10.1007/s10278-025-01652-x","url":null,"abstract":"<p><p>High-resolution magnetic resonance spectroscopic imaging (MRSI) plays a crucial role in characterizing tumor metabolism and guiding clinical decisions for glioma patients. However, due to inherently low metabolite concentrations and signal-to-noise ratio (SNR) limitations, MRSI data are often acquired at low spatial resolution, hindering accurate visualization of tumor heterogeneity and margins. In this study, we propose a novel deep learning framework based on conditional denoising diffusion probabilistic models for super-resolution reconstruction of MRSI, with a particular focus on mutant isocitrate dehydrogenase (IDH) gliomas. The model progressively transforms noise into high-fidelity metabolite maps through a learned reverse diffusion process, conditioned on low-resolution inputs. Leveraging a Self-Attention UNet backbone, the proposed approach integrates global contextual features and achieves superior detail preservation. On simulated patient data, the proposed method achieved Structural Similarity Index Measure (SSIM) values of 0.956, 0.939, and 0.893; Peak Signal-to-Noise Ratio (PSNR) values of 29.73, 27.84, and 26.39 dB; and Learned Perceptual Image Patch Similarity (LPIPS) values of 0.025, 0.036, and 0.045 for upsampling factors of 2, 4, and 8, respectively, with LPIPS improvements statistically significant compared to all baselines ( <math><mrow><mi>p</mi> <mo><</mo> <mn>0.01</mn></mrow> </math> ). We validated the framework on in vivo MRSI from healthy volunteers and glioma patients, where it accurately reconstructed small lesions, preserved critical textural and structural information, and enhanced tumor boundary delineation in metabolic ratio maps, revealing heterogeneity not visible in other approaches. These results highlight the promise of diffusion-based deep learning models as clinically relevant tools for noninvasive, high-resolution metabolic imaging in glioma and potentially other neurological disorders.</p>","PeriodicalId":516858,"journal":{"name":"Journal of imaging informatics in medicine","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144985258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Selected Abstracts from the SIIM 2025 Annual Meeting of the Society for Imaging Informatics in Medicine (SIIM).","authors":"","doi":"10.1007/s10278-025-01501-x","DOIUrl":"10.1007/s10278-025-01501-x","url":null,"abstract":"","PeriodicalId":516858,"journal":{"name":"Journal of imaging informatics in medicine","volume":" ","pages":"1-49"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12443643/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144487652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-regional Multiparametric Deep Learning Radiomics for Diagnosis of Clinically Significant Prostate Cancer.","authors":"Xijun Liu, Rongzong Liu, Haihao He, Yifei Yan, Limin Zhang, Qi Zhang","doi":"10.1007/s10278-025-01551-1","DOIUrl":"https://doi.org/10.1007/s10278-025-01551-1","url":null,"abstract":"<p><p>Non-invasive and precise identification of clinically significant prostate cancer (csPCa) is essential for the management of prostatic diseases. Our study introduces a novel and interpretable diagnostic method for csPCa, leveraging multi-regional, multiparametric deep learning radiomics based on magnetic resonance imaging (MRI). The prostate regions, including the peripheral zone (PZ) and transition zone (TZ), are automatically segmented using a deep learning framework that combines convolutional neural networks and transformers to generate region-specific masks. Radiomics features are then extracted and selected from multiparametric MRI at the PZ, TZ, and their combined area to develop a multi-regional multiparametric radiomics diagnostic model. Feature contributions are quantified to enhance the model's interpretability and assess the importance of different imaging parameters across various regions. The multi-regional model substantially outperforms single-region models, achieving an optimal area under the curve (AUC) of 0.903 on the internal test set, and an AUC of 0.881 on the external test set. Comparison with other methods demonstrates that our proposed approach exhibits superior performance. Features from diffusion-weighted imaging and apparent diffusion coefficient play a crucial role in csPCa diagnosis, with contribution degrees of 53.28% and 39.52%, respectively. We introduce an interpretable, multi-regional, multiparametric diagnostic model for csPCa using deep learning radiomics. By integrating features from various zones, our model improves diagnostic accuracy and provides clear insights into the key imaging parameters, offering strong potential for clinical applications in csPCa management.</p>","PeriodicalId":516858,"journal":{"name":"Journal of imaging informatics in medicine","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144985280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detection of Jaw Lesions on Panoramic Radiographs Using Deep Learning Method.","authors":"Dilek Çoban, Yasin Yaşa, Abdulsamet Aktaş, Hamza Osman İlhan","doi":"10.1007/s10278-025-01642-z","DOIUrl":"https://doi.org/10.1007/s10278-025-01642-z","url":null,"abstract":"<p><p>This study aimed to evaluate and compare the performance of state-of-the-art deep learning models for detecting and segmenting both radiolucent and radiopaque jaw lesions on panoramic radiographs. A total of 2371 anonymized panoramic radiographs containing jaw lesions were retrospectively collected and categorized into radiolucent and radiopaque datasets. Expert annotation was performed to delineate lesion boundaries and assign anatomical localization (anterior/posterior maxilla and mandible). Four deep learning architectures-YOLOv8, YOLOv11, Mask R-CNN, and RT-DETR-were trained and evaluated under three experimental scenarios: (I) training without spatial labels, (II) data augmentation with unlabeled background images, and (III) inclusion of spatial localization annotations. Performance metrics included precision, recall, F1-score, and mean average precision (mAP@0.5 and mAP@0.5-0.95), with paired t-tests used for statistical comparison. In Scenario I, YOLOv11x-seg and YOLOv8x-seg achieved the highest segmentation performance for radiolucent and radiopaque lesions, respectively. For detection, YOLOv8x performed best on radiolucent lesions, while RT-DETR-L outperformed others on radiopaque lesions. In Scenario II, while YOLOv8x-seg achieved the best segmentation results across both lesion types, RT-DETR-L demonstrated superior detection performance, particularly for radiolucent lesions. In Scenario III, RT-DETR-L consistently outperformed all models across both lesion types. This study demonstrates the potential of state-of-the-art deep learning models for effective detection of lesions in panoramic radiographs. The developed models may offer valuable support to clinicians in lesion evaluation; however, it is recommended that they be employed primarily as decision support tools within clinical workflows, rather than as standalone diagnostic systems.</p>","PeriodicalId":516858,"journal":{"name":"Journal of imaging informatics in medicine","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144985130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An AI-Based Solution for Denoising Fast-Acquisition [¹⁸F]FDG PET: Clinical Feasibility and Quantitative Assessment.","authors":"Luísa C Silva, Cláudia S Constantino, Ricardo Teixeira, Joana C Castanheira, Francisco P M Oliveira, Durval C Costa","doi":"10.1007/s10278-025-01638-9","DOIUrl":"https://doi.org/10.1007/s10278-025-01638-9","url":null,"abstract":"<p><p>Benefits in patient comfort, efficiency, and sustainability can come from reducing positron emission tomography (PET) scan's acquisition duration. This study assesses the clinical adequacy of restoring fast-acquisition ¹⁸F-fluorodeoxyglucose ([¹⁸F]FDG) PET to its standard-of-care image quality through deep-learning-based (DL) methods. Fast and standard whole-body [¹⁸F]FDG PET acquisitions of 117 oncological patients were included in the training and testing of three convolutional neural networks. The best-performing network during training was chosen for clinical evaluation on the test set (N = 25). Visual assessment and lesion detectability of the fast acquisitions, of 20 and 30 seconds per axial field of view (s/AFOV), with and without DL-based denoising, and of the local standard of care, of 70 s/AFOV, were performed by three experienced nuclear medicine physicians. Quantification was conducted globally (voxel-wise), in healthy organs and the reported lesions. Optimised Gaussian and non-local means filters served as benchmarks. Visual assessment revealed 20 and 30 s/AFOV with DL-based denoising to have similar image quality to the standard of care. Average lesion-based sensitivity and positive predictive value were 74% and 72%, respectively, for 20 s/AFOV + DL and 72% and 80% for 30 s/AFOV + DL. DL-based denoising displayed the highest voxel-wise agreement with the standard-of-care (p < 0.001). Liver and lungs in the DL-denoised images exhibited a higher signal-to-noise ratio than the standard of care. The median absolute maximum standardised uptake value deviation in the lesions was as low as 0.39 for 20 s/AFOV + DL and 0.30 for 30 s/AFOV + DL. The proposed DL-based method proved to be suitable for the restoration of fast-acquisition whole-body [¹⁸F]FDG PET, having resulted in images similar to the standard-of-care acquisitions. DL-based denoising outperformed standard benchmark methods.</p>","PeriodicalId":516858,"journal":{"name":"Journal of imaging informatics in medicine","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144985211","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction: Ensemble of Handcrafted and Learned Features for Colorectal Cancer Classification.","authors":"Larissa F Rodrigues Moreira, André Ricardo Backes","doi":"10.1007/s10278-025-01647-8","DOIUrl":"https://doi.org/10.1007/s10278-025-01647-8","url":null,"abstract":"","PeriodicalId":516858,"journal":{"name":"Journal of imaging informatics in medicine","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144985206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust Quantification of Affected Brain Volume from Computed Tomography Perfusion: A Hybrid Approach Combining Deep Learning and Singular Value Decomposition.","authors":"Gi-Youn Kim, Hyeon Sik Yang, Jundong Hwang, Kijeong Lee, Jin Wook Choi, Woo Sang Jung, Regina Eun Young Kim, Donghyeon Kim, Minho Lee","doi":"10.1007/s10278-025-01612-5","DOIUrl":"https://doi.org/10.1007/s10278-025-01612-5","url":null,"abstract":"<p><p>Volumetric estimation of affected brain volumes using computed tomography perfusion (CTP) is crucial in the management of acute ischemic stroke (AIS) and relies on commercial software, which has limitations such as variations in results due to image quality. To predict affected brain volume accurately and robustly, we propose a hybrid approach that integrates singular value decomposition (SVD), deep learning (DL), and machine learning (ML) techniques. We included 449 CTP images of patients with AIS with manually annotated vessel landmarks provided by expert radiologists, collected between 2021 and 2023. We developed a CNN-based approach for predicting eight vascular landmarks from CTP images, integrating ML components. We then used SVD-related methods to generate perfusion maps and compared the results with those of the RapidAI software (RapidAI, Menlo Park, California). The proposed CNN model achieved an average Euclidean distance error of 4.63 <math><mo>±</mo></math> 2.00 mm on the vessel localization. Without the ML components, compared to RapidAI, our method yielded concordance correlation coefficient (CCC) scores of 0.898 for estimating volumes with cerebral blood flow (CBF) < 30% and 0.715 for Tmax > 6 s. Using the ML method, it achieved CCC scores of 0.905 for CBF < 30% and 0.879 for Tmax > 6 s. For the data assessment, it achieved 0.8 accuracy. We developed a robust hybrid model combining DL and ML techniques for volumetric estimation of affected brain volumes using CTP in patients with AIS, demonstrating improved accuracy and robustness compared to existing commercial solutions.</p>","PeriodicalId":516858,"journal":{"name":"Journal of imaging informatics in medicine","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144985287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}