Radiologia Medica最新文献

{"title":"Evaluation of sustainable diagnostic approaches in metastatic breast cancer (MBC).","authors":"Andrea Masperi, Cristiano Michele Girlando, Valerio Cubadda, Aurora Pesenti, Giuseppe Muscettola, Giuseppe Buonsanti, Gaeta Aurora, Sara Gandini, Giuseppe Petralia","doi":"10.1007/s11547-025-02082-z","DOIUrl":"10.1007/s11547-025-02082-z","url":null,"abstract":"<p><strong>Introduction: </strong>Assessing bone metastases in metastatic breast cancer is challenging. Due to rising concerns over energy use and emissions, energy-efficient imaging is essential. This study aimed to compare three diagnostic imaging approaches used in therapy monitoring of MBC patients, evaluating both their environmental impact-quantified by energy consumption and related greenhouse gas emissions-and their biological cost, defined as patient exposure to ionizing radiation and contrast media volume.</p><p><strong>Methods: </strong>We retrospectively analysed 70 patients with bone-dominant metastatic breast cancer who underwent WB-MRI (DL1) and either FDG-PET/CT (DL2) or bone scintigraphy (BS) with CT of chest, abdomen, and pelvis (CT-CAP) (DL3). We compared scan time, energy consumption, greenhouse gas emissions (kgCO2e), radiation dose, and contrast media usage across these diagnostic pathways. Energy consumption was calculated using protocol-defined active and idle phases, while biological exposure was assessed from institutional RIS-PACS records.</p><p><strong>Results: </strong>DL1 had the highest energy consumption (10.36 ± 0.11 kWh/patient) and GHG emissions (2.53 ± 0.03 kgCO2e). DL2 showed moderate energy use (4.08 ± 0.38 kWh/patient) and GHG emissions (0.99 ± 0.09 kgCO2e), which significantly increased with repeat scans. DL3 exhibited the lowest environmental impact (7.60 ± 1.07 kWh; 1.85 ± 0.26 kgCO2e), though required multiple visits and higher contrast media and radiation doses.</p><p><strong>Conclusion: </strong>WB-MRI offers a biologically safer alternative for treatment monitoring in metastatic breast cancer, yet its environmental footprint is substantial. FDG-PET/CT represents a more sustainable imaging option if repeated scans are minimized. Integrated imaging pathways and low-energy technologies should guide future diagnostic strategies.</p>","PeriodicalId":20817,"journal":{"name":"Radiologia Medica","volume":" ","pages":""},"PeriodicalIF":4.8,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144966402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Radiomics-based prediction of microsatellite instability in colorectal cancer: a non-invasive approach to treatment stratification.","authors":"Davide Mallardi, Ginevra Danti, Antonio Galluzzo, Linda Calistri, Diletta Cozzi, Daniele Lavacchi, Daniele Rossini, Lorenzo Antonuzzo, Sebastiano Paolucci, Simone Busoni, Francesca Castiglione, Luca Messerini, Fabio Cianchi, Vittorio Miele","doi":"10.1007/s11547-025-02081-0","DOIUrl":"https://doi.org/10.1007/s11547-025-02081-0","url":null,"abstract":"<p><strong>Purpose: </strong>Management of colorectal cancer (CRC) is determined by the stage of the disease and molecular features, such as microsatellite instability (MSI). MSI-high/deficient mismatch repair (MSI-H/dMMR) tumors respond better to immunotherapy but poorly to 5-FU-based treatments. With increasing use of neoadjuvant chemotherapy there is interest in developing non-invasive, radiomics models based on preoperative contrast-enhanced CT scans to predict MSI status and support personalized therapy.</p><p><strong>Material and methods: </strong>Adult patients diagnosed with CRC who underwent pre-treatment staging with contrast-enhanced CT and had known MSI status were retrospectively analyzed. Portal venous phase images were assessed. Two radiologists, blinded to MSI status, manually segmented tumor regions on CT images. Radiomic features and statistical modeling were used to develop a predictive model for identifying the MSI-H phenotype.</p><p><strong>Results: </strong>Analysis was conducted on 54 adult CRC patients who had undergone staging CT scans with known MSI status. Two different models were built considering different brands of CT machines. Twenty statistically significant radiomic features from the portal venous phase of CT images able to differentiate MSI from microsatellite stable (MSS) patients were selected for each model. LASSO regression was applied, selecting features for model construction. The best model's performance demonstrated an area under the ROC curve of 0.844 (95% CI = 0.73-0.96 DeLong, p < 0,05).</p><p><strong>Conclusion: </strong>The results demonstrate the potential of the radiomics model as a non-invasive, cost-effective tool for MSI evaluation, guiding CRC therapy. It aids in identifying patients who would benefit from immunotherapy or chemotherapy, supporting the therapeutic shift from postoperative to preoperative treatment.</p>","PeriodicalId":20817,"journal":{"name":"Radiologia Medica","volume":" ","pages":""},"PeriodicalIF":4.8,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144966422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RegGAN-based contrast-free CT enhances esophageal cancer assessment: multicenter validation of automated tumor segmentation and T-staging.","authors":"Xiaoyu Huang, Weihang Li, Yaru Wang, Qibing Wu, Ping Li, Kai Xu, Yong Huang","doi":"10.1007/s11547-025-02083-y","DOIUrl":"https://doi.org/10.1007/s11547-025-02083-y","url":null,"abstract":"<p><strong>Purpose: </strong>This study aimed to develop a deep learning (DL) framework using registration-guided generative adversarial networks (RegGAN) to synthesize contrast-enhanced CT (Syn-CECT) from non-contrast CT (NCCT), enabling iodine-free esophageal cancer (EC) T-staging.</p><p><strong>Methods: </strong>A retrospective multicenter analysis included 1,092 EC patients (2013-2024) divided into training (N = 313), internal (N = 117), and external test cohorts (N = 116 and N = 546). RegGAN synthesized Syn-CECT by integrating registration and adversarial training to address NCCT-CECT misalignment. Tumor segmentation used CSSNet with hierarchical feature fusion, while T-staging employed a dual-path DL model combining radiomic features (from NCCT/Syn-CECT) and Vision Transformer-derived deep features. Performance was validated via quantitative metrics (NMAE, PSNR, SSIM), Dice scores, AUC, and reader studies comparing six clinicians with/without model assistance.</p><p><strong>Results: </strong>RegGAN achieved Syn-CECT quality comparable to real CECT (NMAE = 0.1903, SSIM = 0.7723; visual scores: p ≥ 0.12). CSSNet produced accurate tumor segmentation (Dice = 0.89, 95% HD = 2.27 in external tests). The DL staging model outperformed machine learning (AUC = 0.7893-0.8360 vs. ≤ 0.8323), surpassing early-career clinicians (AUC = 0.641-0.757) and matching experts (AUC = 0.840). Syn-CECT-assisted clinicians improved diagnostic accuracy (AUC increase: ~ 0.1, p < 0.01), with decision curve analysis confirming clinical utility at > 35% risk threshold.</p><p><strong>Conclusions: </strong>The RegGAN-based framework eliminates contrast agents while maintaining diagnostic accuracy for EC segmentation (Dice > 0.88) and T-staging (AUC > 0.78). It offers a safe, cost-effective alternative for patients with iodine allergies or renal impairment and enhances diagnostic consistency across clinician experience levels. This approach addresses limitations of invasive staging and repeated contrast exposure, demonstrating transformative potential for resource-limited settings.</p>","PeriodicalId":20817,"journal":{"name":"Radiologia Medica","volume":" ","pages":""},"PeriodicalIF":4.8,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144966420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence in polycystic ovarian syndrome management: past, present, and future.","authors":"Jinyuan Wang, Ruxin Chen, Haojun Long, Junhui He, Masong Tang, Mingxuan Su, Renhe Deng, Yuru Chen, Rongqian Ni, Shuhua Zhao, Meng Rao, Huawei Wang, Li Tang","doi":"10.1007/s11547-025-02032-9","DOIUrl":"10.1007/s11547-025-02032-9","url":null,"abstract":"<p><strong>Background: </strong>Integrating artificial intelligence (AI) prospected in the practical clinical management of polycystic ovary syndrome (PCOS) promised significant improvement in efficiency, interpretability, and generalizability.</p><p><strong>Purpose: </strong>To delineate a comprehensive inventory of AI-driven interventions pertinent to PCOS across diverse clinical contexts.</p><p><strong>Evidence reviews: </strong>AI-based analytics profoundly transformed the management of PCOS, particularly in the domains of prediction, diagnosis, classification, and screening of potential complications.</p><p><strong>Results: </strong>Our analysis traced the principal applications of AI in PCOS management, focusing on prediction, diagnosis, classification, and screening. Furthermore, this study ventures into the potential of amalgamating and augmenting existing digital health technologies to forge an AI-augmented digital healthcare ecosystem encompassing the prevention and holistic management of PCOS. We also discuss strategic avenues that may facilitate the clinical translation of these innovative systems.</p><p><strong>Conclusion: </strong>This systematic review consolidated the latest advancements in AI-driven PCOS management encompassing prediction, diagnosis, classification, and screening of potential complications, developing a digital healthcare framework tailored to the practical clinical management of PCOS.</p>","PeriodicalId":20817,"journal":{"name":"Radiologia Medica","volume":" ","pages":"1409-1441"},"PeriodicalIF":4.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12454626/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144476455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Illuminating radiogenomic signatures in pediatric-type diffuse gliomas: insights into molecular, clinical, and imaging correlations. Part II: low-grade group.","authors":"Ryo Kurokawa, Akifumi Hagiwara, Rintaro Ito, Daiju Ueda, Tsukasa Saida, Akihiko Sakata, Kentaro Nishioka, Shunsuke Sugawara, Koji Takumi, Tadashi Watabe, Satoru Ide, Mariko Kawamura, Keitaro Sofue, Kenji Hirata, Maya Honda, Masahiro Yanagawa, Seitaro Oda, Mami Iima, Shinji Naganawa","doi":"10.1007/s11547-025-02049-0","DOIUrl":"10.1007/s11547-025-02049-0","url":null,"abstract":"<p><p>The fifth edition of the World Health Organization classification of central nervous system tumors represents a significant advancement in the molecular-genetic classification of pediatric-type diffuse gliomas. This article comprehensively summarizes the clinical, molecular, and radiological imaging features in pediatric-type low-grade gliomas (pLGGs), including MYB- or MYBL1-altered tumors, polymorphous low-grade neuroepithelial tumor of the young (PLNTY), and diffuse low-grade glioma, MAPK pathway-altered. Most pLGGs harbor alterations in the RAS/MAPK pathway, functioning as \"one pathway disease\". Specific magnetic resonance imaging features, such as the T2-fluid-attenuated inversion recovery (FLAIR) mismatch sign in MYB- or MYBL1-altered tumors and the transmantle-like sign in PLNTYs, may serve as non-invasive biomarkers for underlying molecular alterations. Recent advances in radiogenomics have enabled the differentiation of BRAF fusion from BRAF V600E mutant tumors based on magnetic resonance imaging characteristics. Machine learning approaches have further enhanced our ability to predict molecular subtypes from imaging features. These radiology-molecular correlations offer potential clinical utility in treatment planning and prognostication, especially as targeted therapies against the MAPK pathway emerge. Continued research is needed to refine our understanding of genotype-phenotype correlations in less common molecular alterations and to validate these imaging biomarkers in larger cohorts.</p>","PeriodicalId":20817,"journal":{"name":"Radiologia Medica","volume":" ","pages":"1503-1515"},"PeriodicalIF":4.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12454540/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144643260","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The impact of sarcopenia on sarcoma patients: a systematic review and meta-analysis.","authors":"Domenico Albano, Moreno Zanardo, Mariachiara Basile, Nicole Alessandra De Micheli, Alessandro Berenghi, Francesca Serpi, Salvatore Gitto, Carmelo Messina, Luca Maria Sconfienza","doi":"10.1007/s11547-025-02016-9","DOIUrl":"10.1007/s11547-025-02016-9","url":null,"abstract":"<p><strong>Purpose: </strong>Sarcopenia has been linked to poor outcomes in various cancers, but its specific effect on sarcoma patients remains unclear. This systematic review and meta-analysis investigates the impact of sarcopenia, estimated using CT, on sarcoma patients, focusing on prognostic implications and associated outcomes.</p><p><strong>Materials and methods: </strong>The PubMed, Embase, and SCOPUS databases were searched up to March 2025. Then, a meta-analysis of the data was performed. Overall survival (OS) and relapse-free survival (RFS) were the endpoints. Hazard ratios and 95% confidence intervals were assessed to evaluate the association between sarcopenia and survival of sarcoma patients.</p><p><strong>Results: </strong>Eighteen studies with a total of 1699 patients met the inclusion criteria. Liposarcoma was the most reported histotype in 67% of the studies, with extremities being the most common tumor location (50%), and chemotherapy was the primary intervention in 89% of cases, followed by radiation therapy (78%) and surgery (67%). Analyzing seven articles, a pooled HR of 1.91 (95% CI 1.09-3.34) for OS was reached, indicating that sarcopenic patients have a 91% higher risk of mortality compared to non-sarcopenic patients (p < 0.01). There is no evidence of selective publication (p = 0.137). The meta-analysis for the two studies that reported HR of RFS resulted 1.16 (95% CI 0.85-1.59), not significant (p = 0.28). The quality of the included studies demonstrated high methodological rigor.</p><p><strong>Conclusions: </strong>Worse outcomes have been observed in sarcopenic patients with sarcomas, but the impact of sarcopenia on OS and RFS still remains uncertain, highlighting the need for further research and standardized approaches. Trial Registration The protocol for this review has been registered in the International Prospective Register of Systematic Reviews (PROSPERO) database (registration unique identifying number: CRD42024578969).</p>","PeriodicalId":20817,"journal":{"name":"Radiologia Medica","volume":" ","pages":"1373-1385"},"PeriodicalIF":4.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12454457/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144209315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence-based tumor size measurement on mammography: agreement with pathology and comparison with human readers' assessments across multiple imaging modalities.","authors":"Mi-Ri Kwon, Sung Hun Kim, Ga Eun Park, Han Song Mun, Bong Joo Kang, Yun Tae Kim, Inyoung Youn","doi":"10.1007/s11547-025-02033-8","DOIUrl":"10.1007/s11547-025-02033-8","url":null,"abstract":"<p><strong>Purpose: </strong>To evaluate the agreement between artificial intelligence (AI)-based tumor size measurements of breast cancer and the final pathology and compare these results with those of other imaging modalities.</p><p><strong>Material and methods: </strong>This retrospective study included 925 women (mean age, 55.3 years ± 11.6) with 936 breast cancers, who underwent digital mammography, breast ultrasound, and magnetic resonance imaging before breast cancer surgery. AI-based tumor size measurement was performed on post-processed mammographic images, outlining areas with AI abnormality scores of 10, 50, and 90%. Absolute agreement between AI-based tumor sizes, image modalities, and histopathology was assessed using intraclass correlation coefficient (ICC) analysis. Concordant and discordant cases between AI measurements and histopathologic examinations were compared.</p><p><strong>Results: </strong>Tumor size with an abnormality score of 50% showed the highest agreement with histopathologic examination (ICC = 0.54, 95% confidential interval [CI]: 0.49-0.59), showing comparable agreement with mammography (ICC = 0.54, 95% CI: 0.48-0.60, p = 0.40). For ductal carcinoma in situ and human epidermal growth factor receptor 2-positive cancers, AI revealed a higher agreement than that of mammography (ICC = 0.76, 95% CI: 0.67-0.84 and ICC = 0.73, 95% CI: 0.52-0.85). Overall, 52.0% (487/936) of cases were discordant, with these cases more commonly observed in younger patients with dense breasts, multifocal malignancies, lower abnormality scores, and different imaging characteristics.</p><p><strong>Conclusion: </strong>AI-based tumor size measurements with abnormality scores of 50% showed moderate agreement with histopathology but demonstrated size discordance in more than half of the cases. While comparable to mammography, its limitations emphasize the need for further refinement and research.</p>","PeriodicalId":20817,"journal":{"name":"Radiologia Medica","volume":" ","pages":"1339-1351"},"PeriodicalIF":4.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144333795","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From invisible to indispensable: the radiologist's great challenge.","authors":"Nicoletta Gandolfo","doi":"10.1007/s11547-025-02070-3","DOIUrl":"10.1007/s11547-025-02070-3","url":null,"abstract":"","PeriodicalId":20817,"journal":{"name":"Radiologia Medica","volume":" ","pages":"1311-1312"},"PeriodicalIF":4.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144966431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated MRI protocoling in neuroradiology in the era of large language models.","authors":"Lara Noelle Reiner, Moudather Chelbi, Leonard Fetscher, Juliane C Stöckel, Christoph Csapó-Schmidt, Shakhnaz Guseynova, Fares Al Mohamad, Keno Kyrill Bressem, Jawed Nawabi, Eberhard Siebert, Mike P Wattjes, Michael Scheel, Aymen Meddeb","doi":"10.1007/s11547-025-02040-9","DOIUrl":"10.1007/s11547-025-02040-9","url":null,"abstract":"<p><strong>Purpose: </strong>This study investigates the automation of MRI protocoling, a routine task in radiology, using large language models (LLMs), comparing an open-source (LLama 3.1 405B) and a proprietary model (GPT-4o) with and without retrieval-augmented generation (RAG), a method for incorporating domain-specific knowledge.</p><p><strong>Material and methods: </strong>This retrospective study included MRI studies conducted between January and December 2023, along with institution-specific protocol assignment guidelines. Clinical questions were extracted, and a neuroradiologist established the gold standard protocol. LLMs were tasked with assigning MRI protocols and contrast medium administration with and without RAG. The results were compared to protocols selected by four radiologists. Token-based symmetric accuracy, the Wilcoxon signed-rank test, and the McNemar test were used for evaluation.</p><p><strong>Results: </strong>Data from 100 neuroradiology reports (mean age = 54.2 years ± 18.41, women 50%) were included. RAG integration significantly improved accuracy in sequence and contrast media prediction for LLama 3.1 (Sequences: 38% vs. 70%, P < .001, Contrast Media: 77% vs. 94%, P < .001), and GPT-4o (Sequences: 43% vs. 81%, P < .001, Contrast Media: 79% vs. 92%, P = .006). GPT-4o outperformed LLama 3.1 in MRI sequence prediction (81% vs. 70%, P < .001), with comparable accuracies to the radiologists (81% ± 0.21, P = .43). Both models equaled radiologists in predicting contrast media administration (LLama 3.1 RAG: 94% vs. 91% ± 0.2, P = .37, GPT-4o RAG: 92% vs. 91% ± 0.24, P = .48).</p><p><strong>Conclusion: </strong>Large language models show great potential as decision-support tools for MRI protocoling, with performance similar to radiologists. RAG enhances the ability of LLMs to provide accurate, institution-specific protocol recommendations.</p>","PeriodicalId":20817,"journal":{"name":"Radiologia Medica","volume":" ","pages":"1472-1482"},"PeriodicalIF":4.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12454495/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144609222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Emerging MRI-based spine scoring techniques targeting bone quality to assess osteoporosis, vertebral fracture risk, other spinal degenerative diseases, and post-surgical outcomes.","authors":"Rahman Ud Din, Haisheng Yang","doi":"10.1007/s11547-025-02029-4","DOIUrl":"10.1007/s11547-025-02029-4","url":null,"abstract":"<p><p>Osteoporosis is a metabolic skeletal disease defined by reduced bone mass and a higher risk of vertebral fractures. Due to the increasing elderly population worldwide, it is considered a major healthcare challenge for now and the future. Magnetic resonance imaging (MRI), a non-radiation modality, is emerging as an opportunistic tool for assessing osteoporosis using T1-weighted images. While extensive research has been conducted in this area, a unified and comprehensive review encompassing current knowledge, methodologies, diagnostic efficacy, and prospective research direction is still needed. Hence, this review aimed to evaluate the role of emerging MRI scoring techniques for assessing osteoporosis, predicting vertebral fracture risks, evaluating other spinal degenerative diseases, and determining spine surgical outcomes. We highlighted the fundamentals of MRI scoring techniques and their types (anatomical regions, MRI sequences, and field strengths) and also provided an overview of their diagnostic performance in the clinical applications of osteoporosis, vertebral fractures, degenerative diseases, and bone quality, both pre- and postoperatively. The prediction of new fractures and surgical outcomes, i.e., pedicle screw loosening, proximal junction kyphosis, and cage subsidence, was also presented. Considering the pathophysiology of osteoporosis, this review revealed true representation by MRI scores in defining bone quality. Finally, we discussed factors that influence threshold scores, generalizability, reliability, correlations, and lastly, suggested future directions.</p>","PeriodicalId":20817,"journal":{"name":"Radiologia Medica","volume":" ","pages":"1442-1459"},"PeriodicalIF":4.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144286420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}