BMC Medical Imaging最新文献

{"title":"Comparison of biparameter and multiparameter MRI in detection of clinically significant prostate cancer across PSA stratifications.","authors":"Pengfei Jin, Zhenwei Ding, Fawei Huang, Kai Li, Yitao Liu, Ge Song, Liqin Yang, Lei Shi, Xu Wang","doi":"10.1186/s12880-025-01884-x","DOIUrl":"https://doi.org/10.1186/s12880-025-01884-x","url":null,"abstract":"<p><strong>Background: </strong>The comparative diagnostic performance of biparametric MRI (bpMRI) versus multiparametric MRI (mpMRI) for clinically significant prostate cancer (csPCa) continues to be debated. This study aimed to compare mpMRI and bpMRI in detecting csPCa across prostate-specific antigen (PSA) strata and identify supplementary tools comparable to dynamic contrast-enhanced (DCE) imaging.</p><p><strong>Methods: </strong>Images were evaluated using mpMRI-based mp-PI-RADS and bpMRI-based bp-PI-RADS and simplified PI-RADS (S-PI-RADS) schemes. The lesion volume (LV) was manually segmented by a radiologist using ITK-SNAP software on high b-value DWI images. The diagnostic performance was assessed via receiver operating characteristic (ROC) curve analysis. The differences of T2WI-score, DCE assessment and LV between csPCa and non-csPCa in peripheral zone (PZ) with DWI category 3 were compared.</p><p><strong>Results: </strong>For overall PSA, mp-PI-RADS and bp-PI-RADS showed comparable AUCs (0.889 vs. 0.882; P > 0.05). When PSA ≤ 10 ng/ml, mp-PI-RADS exhibited the highest specificity (91.0% vs. bp-PI-RADS: 64.4%, S-PI-RADS: 75.0%) and PPV (73.0% vs. bp-PI-RADS: 47.7%, S-PI-RADS: 52.5%). When PSA > 10 ng/ml, S-PI-RADS demonstrated higher sensitivity (91.6% vs. mp-PI-RADS: 83.2%, bp-PI-RADS: 81.2%) and F1-score (0.873 [0.822-0.924] vs. mp-PI-RADS: 0.832 [0.778-0.886], bp-PI-RADS: 0.831 [0.777-0.885]). Among DWI category 3 PZ lesions, neither DCE nor T2WI significantly stratified csPCa risk (P = 0.657 and P = 0.424), whereas LV ≥ 0.5 cm³ showed markedly higher csPCa risk (83.8% vs. 45.8%; P < 0.001).</p><p><strong>Conclusions: </strong>While mpMRI and bpMRI exhibit comparable overall diagnostic performance but context-dependent strengths: mpMRI demonstrates higher specificity for avoiding unnecessary biopsies when PSA ≤ 10 ng/ml, whereas bpMRI (particularly S-PI-RADS) maximizes sensitivity for csPCa detection when PSA > 10 ng/ml. LV is anticipated to serve as a complementary radiological biomarker at the absence of DCE.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"346"},"PeriodicalIF":3.2,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12376450/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144941906","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AbVLM-Q: intelligent quality assessment for abdominal ultrasound standard planes via vision-language modeling.","authors":"Baohua Wang, Yaqian Wang, Yanhua Chu, Ke Zhang, Lei Liu, Kexin Zhang, Bowen Zhu, Dong Wang, Tianan Jiang","doi":"10.1186/s12880-025-01885-w","DOIUrl":"https://doi.org/10.1186/s12880-025-01885-w","url":null,"abstract":"<p><strong>Background: </strong>Abdominal ultrasound is non-invasive and efficient, yet acquiring standard planes remains challenging due to operator dependency and procedural complexity. We propose AbVLM-Q, a vision-language framework for automated quality assessment of abdominal ultrasound standard planes.</p><p><strong>Methods: </strong>In this study, we assembled a multi-center dataset comprising 7,766 abdominal ultrasound scans, which were randomly divided into training (70%), validation (15%), and testing (15%) subsets. The proposed method, AbVLM-Q, was developed using a three-step approach: (1) hierarchical prompting that incorporates spatially aware querying and sequential reasoning; (2) a quantifiable scoring mechanism based on multi-level clinical penalty criteria; and (3) LoRA (Low-Rank Adaptation)-based fine-tuning of a pretrained vision-language model. Performance was evaluated using mean recall, precision, label accuracy, subset accuracy, and confusion matrix analysis.</p><p><strong>Results: </strong>The system achieved key structure detection with 88.90% mean recall and 98.10% precision, showing higher precision and comparable recall to Faster R-CNN (89.77% recall, 88.64% precision at a 0.5 confidence threshold). Plane classification yielded 98.96% label accuracy and 96.28% subset accuracy, surpassing the best CNN (97.84%, 94.29%; P < 0.05). Image scoring accuracy for the clinically critical \"Excellent\" grade (scores 8-10) reached 85.11% with the best-performing backbone. Confusion matrix analysis confirmed consistent performance across different backbones, with discrepancies primarily observed at grade boundaries.</p><p><strong>Conclusions: </strong>AbVLM-Q provides a novel method for automated ultrasound quality assessment, functioning as both an evaluation tool and a training platform for standardized scanning. It bridges AI-driven imaging analysis with clinical workflows, enhancing quality control in ultrasound diagnostics.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"344"},"PeriodicalIF":3.2,"publicationDate":"2025-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12374393/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144941887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Determination of lymph node metastasis using quantitative ultrasound elastography of papillary thyroid carcinoma nodule: a systematic review and meta-analysis.","authors":"Alisa Mohebbi, Saeed Mohammadzadeh, Mohammad Ghaffari, Afshin Mohammadi, Nathalie J Bureau, Ali Abbasian Ardakani","doi":"10.1186/s12880-025-01858-z","DOIUrl":"https://doi.org/10.1186/s12880-025-01858-z","url":null,"abstract":"<p><strong>Background and purpose: </strong>papillary thyroid carcinoma (PTC) as the most common thyroid tumor, tends to invade adjacent organs, especially lymphatic system. This study aimed to evaluate the discrimination performance of ultrasound elastography (USE) in assessing PTC nodule for determination of cervical lymph node metastasis (CLNM).</p><p><strong>Methods: </strong>The protocol was pre-registered at ( https://osf.io/r5tc8 ). Using PubMed, Web of Science, Embase, and Cochrane Library, studies published up to March 10, 2025, were identified. Data extraction was conducted independently, and a random-effects bivariate model was applied to estimate pooled differentiation accuracy estimates.</p><p><strong>Results: </strong>Twenty-one studies were included involving 7559 patients; 2790 (36%) were positive for CLNM, while 4769 (63%) were negative. The pooled E_mean values for positive and negative CLNM were 51.2k Pa (95% CI: 42.6 to 59.7) and 44.8 kPa (95% CI: 35.2 to 54.4), respectively. It represents an absolute increase of 6.14 kPa (95% CI: 2.70 to 9.59) in the metastatic group compared to the benign group. Additionally, the pooled E_max value for positive and negative CLNM were 87.9 kPa (95% CI: 49.5 to 126.4) and 68.7 kPa (95% CI: 44.2 to 93.1), respectively. This corresponds to an absolute increase of + 19.57 kPa (95% CI: 2.96 to 36.18) in the metastatic group, representing a more dramatic elevation compared to E_mean values. The thyroid nodule E_max and E_mean were significantly higher for positive CLNM of + 27.5% (95% CI: 10.5-44.5%) and + 12.9% (95% CI: 5.1-20.7%) respectively. Combining USE with conventional ultrasound improved differentiation accuracy, achieving a sensitivity of 80% (95% CI: 62-90%), specificity of 79% (95% CI: 70-85%), and an AUC of 0.85 (95% CI: 0.81 to 0.88).</p><p><strong>Conclusion: </strong>USE parameters demonstrated potential as a discrimination tool for the preoperative assessment of CLNM, particularly when combined with conventional ultrasound, which enhances its performance.</p><p><strong>Clinical trial number: </strong>N/A.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"342"},"PeriodicalIF":3.2,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12369039/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144941095","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A non-invasive preoperative model for predicting sentinel lymph node metastasis in breast cancer using clinical data and MRI.","authors":"Yunqing Yang, Zhulin Wang, Haidong Wang, Yun Wang, Yang Fu","doi":"10.1186/s12880-025-01890-z","DOIUrl":"https://doi.org/10.1186/s12880-025-01890-z","url":null,"abstract":"","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"343"},"PeriodicalIF":3.2,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12369150/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144941893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Consistency and reliability of ultrasound-derived fat fraction in hepatic steatosis assessment: influence of posture and breathing variations.","authors":"Tingjing You, Shengmin Zhang, Shuai Cheng, Zhenyu Shen","doi":"10.1186/s12880-025-01883-y","DOIUrl":"https://doi.org/10.1186/s12880-025-01883-y","url":null,"abstract":"","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"340"},"PeriodicalIF":3.2,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12366230/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144941930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel methodology for measuring global diaphragm thickness by ultrasonography in patients with critical illness: an exploratory pilot study.","authors":"Chen-Liang Sun, Si-Ping Zhou, Li-Sha Hou, Meng-Jie Zhan, Yi-Ping Wang, Hong-Sheng Zhao, Feng-Mei Guo, Guang-Quan Zhou","doi":"10.1186/s12880-025-01875-y","DOIUrl":"https://doi.org/10.1186/s12880-025-01875-y","url":null,"abstract":"<p><strong>Background: </strong>A global diaphragm thickness measurement technique was developed for the zone of apposition (ZOA) using an image edge identification approach. The method was assessed in terms of its repeatability and reliability when applied in patients with critical illness.</p><p><strong>Methods: </strong>Diaphragm thickness measurements were conducted by experienced ultrasound examiners in 60 critically ill adult patients. The performance of continuous global diaphragm thickness measurements was compared to traditional localized diaphragm thickness measurements with regard to intra-observer and inter-observer consistency.</p><p><strong>Results: </strong>End-expiratory diaphragm thickness was measured to assess consistency. For the traditional local diaphragm thickness measurements, the intraclass correlation coefficients (ICC) were 0.882 for intra-observer and 0.848 for inter-observer assessments (p < 0.001). The global diaphragm thickness measurements yielded ICC values of 0.968 and 0.955 for intra-observer and inter-observer assessments, respectively (p < 0.001). These findings indicated good reliability for the conventional method and excellent reliability for the global measurement method. The maximum observed variability was 16.5% with the traditional method and 3.9% with the continuous measurement approach. When using a 10% decrease in diaphragm thickness as the threshold for clinically relevant diaphragmatic atrophy, 16.7% of measurements obtained through the traditional method exceeded this error margin, whereas all measurements acquired through the continuous method remained within the acceptable range.</p><p><strong>Conclusions: </strong>Compared to traditional localized diaphragm thickness ultrasonography, the continuous approach demonstrated superior repeatability and reliability. This newly developed methodology may enhance the precision of diaphragm thickness evaluations within the ZOA in patients with critical illness.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"341"},"PeriodicalIF":3.2,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12366372/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144941960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting symptomatic carotid artery plaques with radiomics-based carotid perivascular adipose tissue characteristics: a multicenter, multiclassifier study.","authors":"Ting Zhao, Guihan Lin, Weiyue Chen, Jianhua Wu, Weiming Hu, Lei Xu, Yongjun Chen, Yang Jing, Lin Shen, Shuiwei Xia, Chenying Lu, Minjiang Chen, Jiansong Ji, Weiqian Chen","doi":"10.1186/s12880-025-01876-x","DOIUrl":"10.1186/s12880-025-01876-x","url":null,"abstract":"<p><strong>Objective: </strong>This study aims to differentiate between symptomatic and asymptomatic plaques using a computed tomography angiography (CTA)-based radiomics model of perivascular adipose tissue (PVAT).</p><p><strong>Methods: </strong>Patients were categorized into symptomatic and asymptomatic groups based on the presence or absence of acute ischemic stroke or transient ischemic attack in the anterior cerebral circulation within two weeks prior to the CTA examination. The clinical information of all patients was collected and analyzed, and the PVAT features of CTA images were further analyzed to clarify their correlation with plaque classification. K-nearest neighbors (KNN), support vector machine (SVM), logistic regression (LR), random forest (RF), linear discriminant analysis (LDA), multinomial naive Bayes (MultinomialNB), and extreme gradient boosting (XGBoost) were trained and radiomics (Rad) score was calculated using the best classifier. A combined model was further developed based on the Rad-score and independent predictors, and the calibration, receiver operating characteristic curve, decision curve analysis, and clinical applicability were evaluated.</p><p><strong>Results: </strong>The white blood cell count and hyperlipidemia were clinically independent predictors, and ten PVAT radiomics features showed significant correlation. The XGBoost classifier showed the best performance among different classifiers, with an average AUC of 0.797 in the validation set. The combined model integrating Rad-score and clinically independent predictors was further obtained, with AUCs of 0.942, 0.797, and 0.836 in the training, external validation sets, respectively.</p><p><strong>Conclusion: </strong>The combined model performed excellently in predicting symptomatic carotid plaques. By early identification of high-risk patients and selecting appropriate clinical decisions, it holds significant clinical potential for improving stroke prevention.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"337"},"PeriodicalIF":3.2,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12363075/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144881939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Growth dynamics of splenic artery aneurysms: morphology, comorbidities, and vascular anatomical factors.","authors":"Ahmet Tanyeri, Aygün Katmerlikaya, Rıdvan Akbulut, Mehmet Burak Çildağ","doi":"10.1186/s12880-025-01879-8","DOIUrl":"10.1186/s12880-025-01879-8","url":null,"abstract":"","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"333"},"PeriodicalIF":3.2,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12363010/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144881937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 deep learning reconstruction at prone position chest scanning of early interstitial lung disease.","authors":"Ruijie Zhao, Yun Wang, Jiaru Wang, Zixing Wang, Ran Xiao, Ying Ming, Sirong Piao, Jinhua Wang, Lan Song, Yinghao Xu, Zhuangfei Ma, Peilin Fan, Xin Sui, Wei Song","doi":"10.1186/s12880-025-01871-2","DOIUrl":"10.1186/s12880-025-01871-2","url":null,"abstract":"<p><strong>Aim: </strong>Timely intervention of interstitial lung disease (ILD) was promising for attenuating the lung function decline and improving clinical outcomes. The prone position HRCT is essential for early diagnosis of ILD, but limited by its high radiation exposure. This study was aimed to explore whether deep learning reconstruction (DLR) could keep the image quality and reduce the radiation dose compared with hybrid iterative reconstruction (HIR) in prone position scanning for patients of early-stage ILD.</p><p><strong>Methods: </strong>This study prospectively enrolled 21 patients with early-stage ILD. All patients underwent high-resolution CT (HRCT) and low-dose CT (LDCT) scans. HRCT images were reconstructed with HIR using standard settings, and LDCT images were reconstructed with DLR (lung/bone kernel) in a mild, standard, or strong setting. Overall image quality, image noise, streak artifacts, and visualization of normal and abnormal ILD features were analysed.</p><p><strong>Results: </strong>The effective dose of LDCT was 1.22 ± 0.09 mSv, 63.7% less than the HRCT dose. The objective noise of the LDCT DLR images was 35.9-112.6% that of the HRCT HIR images. The LDCT DLR was comparable to the HRCT HIR in terms of overall image quality. LDCT DLR (bone, strong) visualization of bronchiectasis and/or bronchiolectasis was significantly weaker than that of HRCT HIR (p = 0.046). The LDCT DLR (all settings) did not significantly differ from the HRCT HIR in the evaluation of other abnormal features, including ground glass opacities (GGOs), architectural distortion, reticulation and honeycombing.</p><p><strong>Conclusion: </strong>With 63.7% reduction of radiation dose, the overall image quality of LDCT DLR was comparable to HRCT HIR in prone scanning for early ILD patients. This study supported that DLR was promising for maintaining image quality under a lower radiation dose in prone scanning, and it offered valuable insights for the selection of images reconstruction algorithms for the diagnosis and follow-up of early ILD.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"338"},"PeriodicalIF":3.2,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12366325/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144881935","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Non-invasive MRI-based assessment of reactive stromal grade in prostate cancer using diffusion kurtosis imaging and stretched-exponential model.","authors":"Kun-Peng Zhou, Hua-Bin Huang, Shu-Yi Li, Zhong-Xing Luo, Xian-Wen Cheng, Di-Min Liu, Jie Bian, Qing-Yu Liu","doi":"10.1186/s12880-025-01881-0","DOIUrl":"10.1186/s12880-025-01881-0","url":null,"abstract":"<p><strong>Objectives: </strong>Reactive stroma plays a pivotal role in the genesis, progression, and metastasis of prostate cancer (PCa). Higher reactive stromal grade (RSG) generally portends a poorer prognosis. The aim of the study is non-invasively evaluate RSG by preoperative mono-exponential model, stretch-exponent model (SEM) and diffusion kurtosis imaging (DKI), and isolate the independent predictor of high RSG (> 50% reactive stroma) in parameters of mono-exponential model, SEM and DKI.</p><p><strong>Methods: </strong>Totally, 54 low RSG (≤ 50% reactive stroma) patients and 26 high RSG patients were prospectively enrolled in the study. Apparent diffusion coefficient (ADC), mean kurtosis (MK), mean diffusivity (MD), distributed diffusion coefficient (DDC), and heterogeneity index (α) values of all lesions were measured on GE Workstation 4.6. Spearman's rank correlation analysis was used to analysis the correlation between RSG and parameters of SEM and DKI. Receiver-operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance of those parameters in differentiating low RSG and high RSG. DeLong's test was used to assess whether the differences of AUC for each parameter were statistically significant. Binary logistic regression analysis was performed to identify independent predictors of high RSG.</p><p><strong>Results: </strong>ADC (r = - 0.352, p = 0.001), DDC (r = - 0.579, p < 0.001) and MD (r = - 0.597, p < 0.001) values showed significant negative correlations with RSG, while MK value (r = 0.658, p < 0.001) demonstrated a significant positive correlation. MK (AUC = 0.816, p < 0.001) was superior to ADC (AUC = 0.717, p < 0.001), DDC (AUC = 0.781, p < 0.001) and MD (AUC = 0.774, p < 0.001) in differentiating low and high RSG, but the differences between these AUCs were not statistically significant (all p > 0.05). Binary logistic regression analysis demonstrated a statistically significant model (χ² =43.222, p < 0.001), and showed that MK (odds ratio = 10.185; 95% CI: 2.467 ~ 21.694; p < 0.001) and MD (odds ratio = 0.014; 95% CI: 0.003 ~ 0.367; p < 0.001) were the independent predictors of high RSG.</p><p><strong>Conclusion: </strong>Although ADC, DDC, and MD values were significantly negatively correlated with RSG, and MK was significantly positively correlated, and all three models-mono-exponential model, SEM, and DKI-demonstrated good performance in differentiating between low and high RSG, only parameters MD and MK values of DKI were identified as independent predictors of high RSG.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":"25 1","pages":"339"},"PeriodicalIF":3.2,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12366237/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144881938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}