Radiologia Medica放射学与医学影像
Radiologia Medica(英文缩写 RADIOL MED),ISSN 0033-8362,eISSN 1826-6983,中文译名:放射学与医学影像 是一本学术期刊。本页汇总该期刊的最新影响因子、分区信息以及最新收录于 PubMed 的文献,帮助您快速了解期刊全貌。
发文量统计区间：2025-09-27 至 2026-09-27，按本站收录文献的发表日期统计。
期刊介绍
历年影响因子趋势
| JCR 数据年份 | 影响因子 | JCR 分区 |
|---|---|---|
| 2021 | 6.313 | Q1 |
| 2022 | 8.900 | Q1 |
| 2023 | 9.700 | Q1 |
| 2024 | 4.800 | Q1 |
| 2025 | 5.300 | Q1 |
Radiologia Medica 最新收录文献
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1. Methodological considerations in chest CT-based classification of normal lung function, PRISm, and COPD.
PMID:日期:2026-09-24该文献暂无摘要。
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2. Standardizing MR defecography referral and reporting: a multidisciplinary Delphi consensus.
PMID:日期:2026-09-21Dynamic magnetic resonance defecography (MRD) is a key imaging modality for comprehensive evaluation of pelvic floor dysfunction, enabling detailed multiplanar anatomical and functional assessment without ionizing radiation. However, despite its established clinical role, MRD reporting is heterogeneous among centers, hindering standardized interpretation, multidisciplinary communication, and comparison across studies. This modified Delphi consensus study aimed to develop a standardized referral dataset and structured reporting framework for MRD. A multidisciplinary panel of 20 Italian experts (radiologists, colorectal surgeons, gynecologists, urologists, gastroenterologists, physiatrists, and a methodologist) conducted a three-round Delphi process to develop consensus-based templates for MRD referral and reporting. Consensus was set at ≥75% agreement. A total of 36 items were evaluated, covering technical parameters, anatomical/functional findings, and reporting format. Consensus was achieved for 21 items (58%), including technical parameters, standardized measurements (anorectal angle, pubococcygeal line), and clinically relevant descriptors (prolapse, dyssynergia, rectocele, rectal inertia). A structured referral template was also developed to improve information transfer from clinicians to radiologists. Both tools were unanimously endorsed. This modified Delphi consensus study provides a multidisciplinary framework for standardized MRD referral and reporting. The proposed reporting template may facilitate greater consistency in image interpretation, reporting terminology, and multidisciplinary communication. These recommendations may serve as a foundation for the harmonization of MRD practice and future validation studies in pelvic floor imaging.
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3. Outcomes and adverse events of postoperative stereotactic interventional radiotherapy/brachytherapy for brain metastases: a systematic review.
PMID:日期:2026-09-18Postoperative radiotherapy is commonly used after resection of brain metastases, but durable local control with a low rate of adverse events (AEs) remains challenging. This systematic review summarizes outcomes and AEs of postoperative stereotactic interventional radiotherapy (S-IRT; brachytherapy) for resected brain metastases. PubMed, Embase, Scopus, and Web of Science were searched through June 30, 2024. No review protocol was registered. Eligible studies reported postoperative S-IRT after metastasis resection. Data were extracted independently by two reviewers, with disagreements resolved by consensus. Risk of bias and methodological quality were assessed using the Newcastle-Ottawa Scale and MINORS. Because of clinical and methodological heterogeneity, results were synthesized narratively, and no meta-analysis was performed. Eighteen studies (9 prospective, 8 retrospective, 1 case series) including 542 patients were eligible. Eleven studies used Cs-131, six used I-125, and one used both. Prescribed doses ranged from 60 to 80 Gy, usually at a depth of 5 mm. One-year local control ranged from 83 to 100%. Symptomatic radionecrosis occurred in 4.6% of Cs-131 and 9% of I-125 implants; leptomeningeal disease rates were 1.6% and 12.5%, respectively. Postoperative S-IRT appears promising, but most studies were non-comparative and heterogeneous, so findings should be considered hypothesis-generating.
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4. Early post-transplant ventilation and perfusion assessment using phase-resolved functional lung MRI.
PMID:日期:2026-09-18Post-transplant functional lung surveillance relies on global ventilatory assessment using spirometry, lacking regional ventilation and perfusion measures. We aimed to assess early post-transplant lung function in clinically stable recipients using phase-resolved functional lung (PREFUL) MRI. In this prospective, single-center study (March 2022-December 2024), clinically stable bilateral lung transplant recipients and 1:1 age- (± 5 years) sex-matched participants (n = 100) underwent PREFUL MRI and spirometry. Ventilation and perfusion MRI was voxel-based quantified: Regional ventilation (RVent, [mL/mL]), flow-volume loop ventilation (FVL, [au]) and perfusion (Q, [mL/min/100 mL]). Whole lung defect maps (VDP, QDP [%]), V/Q ratios (%) based on RVent, FVL and Q were generated. Median MRI parameters were compared with controls, spirometry and perioperative data (Bonferroni correction P<.0045 considered significant). 84 participants (42 transplant recipients and 42 healthy participants) were evaluated at median 16 days post-transplantation. In lung transplant recipients RVent, FVL and Q did not differ from controls (all P > 0.01). Transplant recipients demonstrated increased RVent VDP (10% vs 3.7%, P < 0.001), VDP (14% vs 7.1%, P < 0.001) and QDP (7.9% vs 2.4%, P < 0.001). In transplant recipients FVL, FVL VDP, QDP and V/Q matched defects correlated with spirometry. QDP was associated with prolonged cold ischemic times of first lung allografts (ρ = 0.43, P = 0.004), V/Q mismatch with cold ischemic times of first and second implanted lungs (ρ = 0.48, P = 0.001; ρ = 0.46, P = 0.002). Free-breathing phase-resolved functional lung MRI-derived parameters revealed increased regional ventilation and perfusion heterogeneity in stable lung transplant recipients two weeks post-transplantation and correlated with spirometry and prolonged cold ischemic times of allografts.
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5. Clarifying the interpretation of chest CT radiomics for pre-bronchodilator spirometric patterns.
PMID:日期:2026-09-18该文献暂无摘要。
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6. Radiomics-based prediction of pituitary adenoma consistency: a systematic review and meta-analysis.
PMID:日期:2026-09-18Pituitary adenoma consistency significantly influences surgical strategy and outcomes, yet it cannot be reliably predicted using conventional imaging. By converting images into quantitative data, radiomics can identify imaging patterns that may not be distinguishable by standard visual interpretation. This study aims to evaluate the diagnostic performance of radiomics-based MRI models for predicting pituitary adenoma consistency, to determine its potential for presurgical planning. A systematic search of PubMed, EMBASE, and Scopus was performed on 12/03/2025 following PRISMA-DTA guidelines and a pre-registered protocol (PROSPERO/CRD420251246028). Eligible studies applied radiomics and machine learning/deep learning to predict pituitary adenoma consistency. We performed random-effects meta-analyses to evaluate the area under the receiver operating characteristic (ROC) curve (AUC). A hierarchical summary ROC (HSROC) model estimated sensitivity and specificity. Risk of bias and study quality were assessed with QUADAS-2 and METRICS. Fourteen studies were included. The pooled discriminative performance was good (AUC = 0.86,95% C.I. [0.76;0.92], I = 92.45%, k = 14). The HSROC model showed a sensitivity = 0.74,95% C.I. [0.56;0.87] and specificity = 0.79, 95% C.I. [0.73;0.84]. No significant performance differences were observed across algorithms, dimensionality, sequence, although T2w and combined sequence models showed higher AUC trends. Risk of bias was low to moderate, and study quality was good overall. Heterogeneity resulted high and small-study effect emerged in the main analysis. Radiomics-based MRI models demonstrate good diagnostic performance for predicting pituitary adenoma consistency and may support presurgical assessment and planning. However, substantial heterogeneity, evidence of small-study effects, and limited external validation warrant cautious interpretation of the pooled estimates and currently restrict clinical generalizability. Future studies should prioritize multicenter external validation and standardized radiomics workflows.
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7. IgeaNet: a deep learning model for opportunistic osteoporosis screening from chest X-ray images.
PMID:日期:2026-09-17To develop and validate a multi-modal deep learning model for opportunistic screening of osteoporosis by classifying bone mineral density (BMD) from chest radiographs integrated with clinical metadata. This single-centre retrospective study included 833 adult patients who underwent chest radiography and dual-energy X-ray absorptiometry (DEXA) within 12 months (January 2018-December 2024). A convolutional neural network (CNN) model was developed using a convolutional branch to process chest radiographs, while a dense branch processed patient demographics (age, sex, height, and weight). Clinical data available in the DEXA report were also included. The two representations were merged into a fully connected classifier optimized end-to-end. The dataset comprised training (n = 589), validation (n = 124), and testing (n = 120) sets. On the internal test set, the model achieved 85.8% accuracy (95% CI: 78.5-90.9%), F1-macro 0.86, and AUC-macro 0.95. Class-specific performance showed sensitivity/specificity of 84.2%/91.4% for normal, 90.2%/77.1% for osteopenia, and 82.3%/91.9% for osteoporosis, with 14.2% misclassification rate. Analysis of DEXA anatomical site distribution revealed wrist as the determining site in 302 patients (36.3%), hip in 384 patients (46.1%), and lumbar spine in 141 patients (16.9%). Inclusion of wrist measurements changed diagnostic classification in 115 patients (13.8%), with 34 patients reclassified from normal to osteopenia, 14 from normal to osteoporosis, and 67 from osteopenia to osteoporosis. This differential diagnostic yield reflects distinct bone composition differences, as wrist cortical bone may reveal mineral density reductions undetected at predominantly trabecular sites. Integration of chest radiographs and clinical data by residual network with attention represents a feasible approach for the preliminary opportunistic classification of BMD without additional radiation exposure. However, these findings are hypothesis-generating and derive from internal validation; external prospective studies are required before clinical implementation.
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8. xsCT-based radiomics for adrenal incidentaloma characterization.
PMID:日期:2026-09-17The aim of this retrospective monocentric study is to examine the role of radiomics in distinguishing benign and malignant adrenal incidentalomas (AIs) analyzing computed tomography (CT) images. We retrospectively selected patients with clinical or histologic diagnosis of adrenal pathology studied in the period 2011-2025 with pre-operative or pre-diagnostic CT in our center. Two radiology residents and a radiologist with 10 years of experience delineated the volume of interest of the AI using 3D-slicer software. All 135 patients included in the analysis underwent segmentation of the non-contrast acquisition; among these, 115 were also segmented on the portal phase acquisition. Statistical analysis was performed by dividing patients into two groups: group 0, benign adrenal pathology (adenoma, myelolipoma, ganglioneuroma, pseudocyst); group 1, malignant adrenal pathology (pheochromocytoma, adrenal cortical carcinoma, metastasis, lymphoma). 135 patients were included. On non-contrast acquisition, the logistic regression model LASSO selected three first-order features and two second-order features able to distinguish patients belonging to Group 0 or Group 1. The features selected were used to construct a radiomic model that showed excellent AUC/discrimination (AUC: 0.88-95% CI 0.83-0.94). For portal phase, the logistic regression model LASSO selected three first-order features and two second-order features able to distinguish Group 0 and Group 1. The features selected were used to construct a radiomic model that showed excellent AUC/discrimination (AUC: 0.84- 95% CI 0.77-0.91). The radiomic models developed from both non-contrast and portal CT phases were effective in differentiating benign from malignant AIs.
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10. Artificial intelligence-assisted triage of screening mammograms following breast-conserving therapy: a comparative simulation study.
PMID:日期:2026-09-11To evaluate the diagnostic performance and potential of artificial intelligence (AI)-based triage for screening mammograms following breast-conserving therapy (BCT). In this retrospective study, consecutive post-BCT mammograms obtained between January and May 2021 were analyzed and divided into ipsilateral and contralateral breasts. Triage was simulated using three models with a commercial AI-based computer-aided detection (CAD), and outcomes were categorized as recall or no recall: (1) original report-based triage, (2) standalone AI, and (3) decision referral AI-triage. Cancer detection rate (CDR), recall rates, and diagnostic performance were evaluated. A total of 1190 women were enrolled. For the ipsilateral breast, 10 mammography-visible recurrences were identified. All three models-original report, standalone AI, and decision referral AI-achieved equivalent CDR (6.6 per 1000) and sensitivity (80%), with recall rates of 3.4%, 23.0%, and 2.8%, respectively. AI-CAD classified 77% of examinations as negative without a reduction in CDR or sensitivity. For the contralateral breast, three recurrences were identified. The original report yielded a CDR of 1.8 per 1000, a recall rate of 1.9%, and 66.7% sensitivity. While AI-CAD triaged 90% of examinations as negative, standalone AI achieved a CDR of 2.6 per 1000, recall rate of 9.8%, and 100% sensitivity. Decision referral AI maintained CDR and sensitivity with a lower recall rate (2.0%). Our simulation suggests that AI-based triage can exclude a substantial portion of negative mammograms following BCT without a reduction in CDR or sensitivity. Nonetheless, radiologist expertise remains crucial, particularly in interpreting the ipsilateral breast, given the higher false positive rate of AI-CAD.