BMC Medical Imaging最新文献

{"title":"The influence of fine-needle aspiration on dual-layer detector spectral CT parameters of papillary thyroid carcinoma: a propensity-match analysis.","authors":"Jiayi Yu, Liang Lv, Zuhua Song, Bi Zhou, Xiaodi Zhang, Jing Peng, Dan Zhang","doi":"10.1186/s12880-026-02393-1","DOIUrl":"https://doi.org/10.1186/s12880-026-02393-1","url":null,"abstract":"<p><strong>Background: </strong>Dual-layer detector spectral computed tomography (DLCT) and fine-needle aspiration (FNA) are commonly used in papillary thyroid carcinoma (PTC). However, whether FNA affects DLCT quantitative parameters remains uncertain. This study aimed to investigate the effect of FNA on DLCT parameters in PTC and to determine the optimal timing for DLCT examination.</p><p><strong>Methods: </strong>This retrospective study included 689 patients with PTC, who were categorized into pre-FNA (n = 222) and post-FNA (n = 467) groups according to the sequence of FNA and DLCT. Propensity score matching (PSM) was applied to balance confounding variables. Sensitivity analyses were performed using inverse probability of treatment weighting and multivariate regression. DLCT parameters, including virtual monoenergetic images (VMIs) at 40, 70, and 100 keV; spectral attenuation curve slope (λ_HU); iodine concentration (IC); normalized IC (NIC); effective atomic number (Z_eff); and normalized Zeff (NZ_eff), were compared between groups in arterial (AP) and venous phases (VP). Post-FNA time intervals were ranked and divided into deciles. For each DLCT parameter, standardized differences from the pre-FNA baseline were plotted across deciles to propose temporal cutoffs, which were validated by comparing post-FNA subgroups with the pre-FNA group.</p><p><strong>Results: </strong>Covariates were well balanced after PSM, with all standardized mean differences below 0.25. Sensitivity analyses confirmed consistent direction of effect estimates across methods. Two clinically relevant exploratory temporal landmarks were observed around 6 and 18 days. Within 6 days after FNA, significant differences were observed in AP VMIs (40, 70, 100 keV), λHU, IC, NIC, and VP VMI at 100 keV. Between 7 and 18 days, only VP-IC remained significantly altered. Beyond 18 days, no parameters differed from pre-FNA baselines.</p><p><strong>Conclusions: </strong>DLCT parameters showed a dynamic post-FNA temporal pattern, with early and later candidate recovery windows centered around approximately 6 and 18 days. From a clinical perspective, DLCT may be considered before FNA or after about 6 days, whereas a later interval around 18 days may be preferable when minimizing biopsy-related perturbation is particularly important.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2026-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147833150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning for automated diagnosis and differentiation of otitis media on temporal bone CT.","authors":"Yong Tang, Weixuan Fan, Zhitao Cheng, Yanlin Leng, Yuhang Liu, Tingfang Wu, Song Su, Jing Fei, Leiji Li","doi":"10.1186/s12880-026-02386-0","DOIUrl":"https://doi.org/10.1186/s12880-026-02386-0","url":null,"abstract":"<p><strong>Background: </strong>The accurate diagnosis of otitis media (OM) has long been a challenge for clinicians (especially for less experienced clinicians) due to the variety of types and the complex anatomical structures of the middle ear. Although deep learning (DL) based on different examination methods (mostly otoscopy) has been applied to the diagnosis of single species OM in previous studies, DL using temporal bone computed tomography (TBCT) images to diagnose OM and simultaneously differentiate between chronic otitis media (COM) and otitis media with effusion (OME) has not been investigated in depth. This study aimed to develop and evaluate a DL framework for the automated diagnosis of OM and identifying OME and COM with or without cholesteatoma using TBCT images.</p><p><strong>Methods: </strong>Our team created a unique large dataset of 2011 TBCT images from 1200 patients who were diagnosed with OM, which was determined the regions of interest (ROI) for middle ear (ME) by experienced experts. Then, a DL model was trained to detect the MEs in TBCT images and determine the OM status with this dataset of pre-processed images. Five-fold cross-validation was utilized for training and selecting the models. Finally, we evaluated the model using 406 images and verified the effectiveness of model-assisted diagnosis for different levels of clinicians in a comparative study.</p><p><strong>Results: </strong>In the detection of the ME, the DL model achieved a detection ratio of 98.53%. The model showed satisfying performance in the classification of normal middle ear (NME), OME, and COM with an accuracy of 0.9238. With the assistance of the DL, the diagnostic accuracies were significantly improved from 81.53% to 93.60% (junior clinician) and from 87.93% to 95.57% (senior clinician), respectively.</p><p><strong>Conclusions: </strong>The findings suggested that the DL model could accurately identify MEs in TBCT images and classify NME, OME, and COM with satisfying accuracy. DL could also effectively assist clinicians in TBCT interpretation for OM diagnosis.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2026-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147833174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pediatric lung ground glass nodules: a real-world, large-scale CT cohort analysis.","authors":"Ya-Ni Duan, Yue-Fei Guo, Jun-Zhe Wen, Xue Lin, Yan-Qiu Zhu, Jie Qin","doi":"10.1186/s12880-026-02405-0","DOIUrl":"https://doi.org/10.1186/s12880-026-02405-0","url":null,"abstract":"<p><strong>Background: </strong>Increasing detection of pediatric ground-glass nodules (GGNs) presents a clinical dilemma lacking robust evidence and guidelines. We aimed to evaluate the short-term natural course of incidental pediatric GGNs through real-world observation.</p><p><strong>Methods: </strong>This retrospective, single-center, real-world study screened children (0-18 years) undergoing low-dose chest CT between January 1, 2010, and December 15, 2025. Patients with GGNs were included, excluding those with malignancy, immune dysfunction, specific infections, mean diameter < 3 mm or > 30 mm, artificial intelligence recognition failure, or poor image quality. Baseline characteristics, clinical presentation, and CT imaging features were collected and analyzed, with subgroup analyses performed. For patients with follow-up CT, nodule evolution was assessed.</p><p><strong>Results: </strong>Among 14,106 children, 901 (6.4%) had GGNs. After exclusions, 602 patients were included, with a median age of 15 (14, 17) years, 58.6% were male. From these patients, 602 most suspicious GGNs were analyzed, comprising 43 (7.1%) mixed GGNs and 559 (92.9%) pure GGNs. Mixed GGNs showed significantly larger size and higher attenuation than pure GGNs (P < 0.01). Children aged > 12 years had GGNs with larger volume and lower attenuation compared to younger children (P < 0.05). Among the follow-up subgroup (n = 78), with a median follow-up period of 268.5 days, 32 GGNs regressed, 45 remained stable, and only 1 increased in size (pathologically confirmed adenocarcinoma in situ). Smaller GGNs at baseline were more likely to regress (P < 0.05).</p><p><strong>Conclusions: </strong>GGNs are not uncommon in children on chest CT. In our cohort, most GGNs remained stable or regressed over short-term follow-up. These observations suggest a relatively indolent short-term natural course and may support a conservative management strategy for incidentally detected GGNs in children. Given the limited follow-up duration, these findings should be interpreted with caution. Further studies with longer follow-up durations and larger sample sizes are warranted to elucidate the long-term natural course of pediatric GGNs.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2026-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147833203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Utilizing imaging features of preoperative gadoxetic acid-enhanced MRI for predicting lymphovascular invasion in colorectal cancer liver metastases and exploring its impact on survival.","authors":"Qian Xing, Yong Cui, Xiao-Lei Gu, Xiao-Ting Li, Ying-Shi Sun","doi":"10.1186/s12880-026-02395-z","DOIUrl":"https://doi.org/10.1186/s12880-026-02395-z","url":null,"abstract":"<p><strong>Purpose: </strong>To construct an imaging model based on preoperative gadoxetic acid- enhanced MRI for predicting lymphovascular invasion (LVI) in colorectal cancer liver metastases (CRLM) and explore its impact on survival.</p><p><strong>Method: </strong>A total of 91 patients with CRLM were retrospectively enrolled in this study. The liver lesions were categorized into two groups, with LVI and without LVI by pathological examination. The long diameter, vascular penetration sign, peritumoral hepatobiliary phase (HBP) hypointensity and other qualitative signs were evaluated. The mean value and standard deviation (SD) value of signal intensity (SI) of the liver lesion and the region with 5-mm tumor expansion were recorded. The relative enhancement rate (RER) was calculated on each phase. Univariate and multivariate logistic regression were used to construct the imaging model (LVI _model) for predicting LVI in CRLM. Each liver lesion in all the patients was assessed using the LVI _model to classify patients into predicted LVI-negative or predicted LVI-positive groups.</p><p><strong>Results: </strong>The vascular penetration sign (odds ratio [OR] = 30.052, p<0.001) and SD of tumor on HBP (SD_Tumor-HBP)(OR = 1.004, p = 0.026) were used to construct the imaging model for predicting LVI and the AUC of the model was 0.874 (95%CI:0.747-1.000). The liver recurrence-free survival (LRFS) of 22 predicted LVI-positive patients was significantly lower than that of 69 predicted LVI-negative patients (median 9.0 vs. 28.0 months, p = 0.028). The overall survival (OS) of predicted LVI-positive patients was significantly lower than that of predicted LVI-negative patients (median 21.0 vs. 63.0 months, p = 0.001).</p><p><strong>Conclusion: </strong>The imaging model based on vascular penetration sign and SD_Tumor-HBP has good efficiency in predicting lymphovascular invasion and survival in patients with CRLM.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2026-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147833230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integration of glymphatic system function and hippocampal radiomics for diagnosis and conversion prediction of Alzheimer's disease.","authors":"Xiaohan Mao, Di Zhang, Danqing Ying, Juncheng Yu, Yongqian Ge, Zhongzheng Jia","doi":"10.1186/s12880-026-02390-4","DOIUrl":"https://doi.org/10.1186/s12880-026-02390-4","url":null,"abstract":"<p><strong>Background: </strong>Glymphatic system (GS) function and hippocampal microstructural changes are promising imaging markers of Alzheimer's disease (AD). This study aims to investigate the effectiveness of combining diffusion tensor image analysis along the perivascular space (DTI-ALPS) with hippocampal radiomics for diagnosing AD, and to develop an innovative multivariable model integrating hippocampal radiomics and clinical biomarkers for predicting mild cognitive impairment (MCI) progression.</p><p><strong>Methods: </strong>We included three cohorts from two databases retrospectively, using an internal (n = 210) and an external dataset (n = 430) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The ALPS index was employed to measure GS function, and 3D-T1WI hippocampal radiomics features were extracted to construct machine learning models for classifying and diagnosing AD. Conversion of MCI to AD was assessed through integrating the hippocampal radiomics features, ALPS index, and AD-related clinical biomarkers.</p><p><strong>Results: </strong>The ALPS index was lower in patients with AD than in healthy controls (HCs) in both the internal and external cohorts (p < 0.001). The combined hippocampal radiomics features and ALPS index model demonstrated good performance in AD classification. The multivariable prediction model of MCI progression to AD achieved an area under the curve of 0.97 and 0.92 for the training and testing cohorts, respectively.</p><p><strong>Conclusions: </strong>Integrated ALPS index and hippocampal-based radiomics features can improve diagnostic performance in patients with AD, showing predictive capability for identifying the MCI conversion.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2026-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147833160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting MRI-derived total brain volume from DXA-derived head composition in middle-aged and older adults: WASEDA'S Health Study.","authors":"Toshiharu Tsutsui, Suguru Torii, Kumpei Tanisawa, Toru Takahashi, Kaori Usui, Nobuhiro Nakamura, Taishi Midorikawa, Kento Nakagawa, Reiji Ohkuma, Hiroaki Kumano, Kaori Ishii, Katsuhiko Suzuki, Shizuo Sakamoto, Mitsuru Higuchi, Koichiro Oka","doi":"10.1186/s12880-026-02341-z","DOIUrl":"https://doi.org/10.1186/s12880-026-02341-z","url":null,"abstract":"<p><strong>Background: </strong>Total brain volume (TBV) derived from brain MRI is an important marker of brain structural health in middle-aged and older adults, but MRI is resource-intensive and not always feasible in largescale or repeated assessments. We examined whether dual-energy X-ray absorptiometry (DXA)-derived head composition measures can estimate MRI-derived TBV in middle-aged and older adults.</p><p><strong>Methods: </strong>This study included 314 participants (≥ 40 years) who underwent whole-body DXA (head ROI manually defined using a sub-region tool) and 3T brain MRI within 1 year. MRI-derived TBV was defined as the sum of gray and white matter volumes. We developed multivariable linear regression models using either DXA-derived head lean-and-fat mass or head fat mass as the primary predictor. Nested models were fitted: Model 1 (predictor only), Model 2 (+ age and sex), and Model 3 (+ BMI). Apparent model performance was summarized using R² and RMSE, and internal validation was performed using 1,000 bootstrap resamples to obtain optimism-corrected performance estimates. Calibration was evaluated using calibration-in-the-large (CITL) and calibration slope. Agreement between observed and predicted TBV was assessed using Bland-Altman analysis. Sensitivity analyses additionally adjusted for the MRI-DXA measurement interval and evaluated sex-stratified performance.</p><p><strong>Results: </strong>Model 3 was treated as the prespecified primary model because it was the fully adjusted model including clinically relevant covariates. In Model 3, both head lean-and-fat mass and head fat mass were positively associated with TBV, whereas age was negatively associated and male sex was associated with larger TBV. Across the nested models, optimism-corrected bootstrap validation showed broadly similar performance, with numerically slightly higher R² values and lower RMSE values for Model 3. Calibration was favorable in both predictor-based primary models (CITL approximately 0; calibration slope approximately 1.00). Bland-Altman analyses showed small mean bias with evidence of proportional bias across the TBV range. Bootstrap validation indicated stable performance. Sensitivity analyses yielded similar results after accounting for measurement interval and across sex strata.</p><p><strong>Conclusions: </strong>DXA-derived head composition measures can provide a practical approximation of MRI-derived TBV in middle-aged and older adults, with good calibration and stable internal validation performance.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2026-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147833186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The effect of head coil configuration and channel count on the quality of double inversion recovery (DIR) MRI images.","authors":"Adnan Alahmadi, Razan A Alshehri, Rana A Gasem, Abdullah Aljuhani, Almotazbillah Bedaiwi, Afnan A Malaih, Jamaan Alghamdi, Amal Alsalamah, Shyma M Alkhateeb, Ghouth Waggass, Mohammad Khalil, Mustafa S Alhasan, Khalid M Alshamrani, Ali M Hendi, Njoud Aldusary, Walaa Alsharif, Norah Y Hakami, Ibrahem Hussain Kanbayti","doi":"10.1186/s12880-026-02377-1","DOIUrl":"https://doi.org/10.1186/s12880-026-02377-1","url":null,"abstract":"<p><strong>Background: </strong>Double inversion recovery (DIR) MRI provides high sensitivity for detecting white matter abnormalities but suffers from reduced signal-to-noise ratio (SNR) due to simultaneous suppression of multiple tissue signals. Head-coil configuration and channel count may influence the resulting image quality.</p><p><strong>Methods: </strong>Seventeen healthy subjects underwent DIR imaging on a 3-T MRI system using both 64-channel and 20-channel head/neck coils. Quantitative image quality was assessed using SNR and contrast-to-noise ratio (CNR) measurements across multiple brain regions, with comparisons performed using paired t-tests. Structural Similarity Index Measure (SSIM) was additionally computed between registered 64-channel and 20-channel DIR images to quantify inter-coil structural image similarity. Qualitative image quality was evaluated by three experienced neuroradiologists using a 5-point rating scale for contrast, spatial resolution, and noise; inter-rater agreement was assessed using Kendall's coefficient of concordance (Kendall's W).</p><p><strong>Results: </strong>Quantitative analysis demonstrated significantly higher SNR and CNR values for the 64-channel coil compared with the 20-channel coil across all assessed regions (p < 0.0001). Qualitative evaluation showed that images acquired with the 64-channel coil received marginally higher mean scores for contrast, spatial resolution, and noise from all raters; inter-rater agreement was moderate-to-strong across all domains (Kendall's W = 0.33-0.89).</p><p><strong>Conclusion: </strong>At 3 T, the use of a 64-channel head/neck coil provides significant quantitative improvements in DIR image quality compared with a 20-channel coil, with small but consistent advantages also observed in qualitative assessments. These findings support the use of higher-channel-count coils to mitigate SNR limitations inherent to DIR imaging. However, qualitative differences between coil configurations were modest and inter-rater agreement was moderate-to-strong by Kendall's W (W = 0.33-0.89). The clinical benefit of the 64-channel coil in pathological conditions such as multiple sclerosis or cortical dysplasia requires further investigation in patient-based studies.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2026-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147833148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of tumor regression grading in rectal cancer neoadjuvant chemoradiotherapy: a habitat radiomics analysis of imaging biomarker.","authors":"Xue Sha, Xue Dou, Luping Ma, Qingtao Qiu, Zhenkai Li, Tengxiang Li, Yongbin Cui, Huazhong Shu, Yong Yin","doi":"10.1186/s12880-026-02397-x","DOIUrl":"https://doi.org/10.1186/s12880-026-02397-x","url":null,"abstract":"<p><strong>Background: </strong>Tumor regression grading (TRG) is a core prognostic predictor of treatment outcomes in rectal cancer. Conventional TRG assessment methods are limited in capturing the full complexity of intratumoral heterogeneity. Advances in medical imaging, particularly radiomics and habitat-based analysis, hold promise the improve TRG prediction by quantitatively characterizing subregional tumor features. This study aimed to evaluate the performance of habitat radiomics in preoperatively predicting TRG in rectal cancer patients receiving neoadjuvant chemoradiotherapy (nCRT).</p><p><strong>Methods: </strong>Computed tomography (CT) images were analyzed to compare the predictive performance of conventional radiomics features and habitat-based analysis. Tumor regions of interest (ROIs) were segmented, extracting local imaging features. Voxel-level clustering was employed to identify distinct intratumoral subregions. Machine learning algorithms, including ExtraTrees, support vector machine (SVM), and Random Forest, were applied to predict TRG.</p><p><strong>Results: </strong>For the conventional radiomics model, the ExtraTrees algorithm yielded superior performance, with AUCs of 0.912 and 0.817 in training and testing cohorts, respectively, outperforming SVM and Random Forest. The habitat model outperformed conventional radiomics model, while the combined model integrating habitat features and clinical variables yielded the optimal efficacy (training AUC = 0.916, test AUC = 0.833). In the binary classification task of TRG0 (pathologic complete response, pCR) vs. TRG1-2, the Habitat model achieved a test AUC of 0.884, and the combined model further reached 0.929. SHAP analysis identified that features from the H1 subregion and wavelet-transformed features were the top predictive contributors.</p><p><strong>Conclusion: </strong>Habitat-based radiomics, especially when integrated with clinical data, significantly improves the preoperative prediction of TRG in rectal cancer patients undergoing nCRT, providing a powerful tool to advance personalized oncology. Further validation in large-scale, multicenter, independent cohorts is warranted to facilitate the clinical translation of this approach.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2026-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147833154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pre-treatment prediction of microsatellite instability in colon cancer: a nomogram model combining clinicopathological features and pre-treatment CT-based radiomics.","authors":"Meng Wei, Congzhen Jia, Ying Zhang, Peng You, Weizhi Chen","doi":"10.1186/s12880-026-02394-0","DOIUrl":"https://doi.org/10.1186/s12880-026-02394-0","url":null,"abstract":"<p><strong>Background: </strong>Determining microsatellite instability (MSI) status in colon cancer is crucial for selecting treatment strategies in advanced stages. Thus, accurately identifying MSI status before treatment is essential.</p><p><strong>Objective: </strong>This study aims to evaluate the utility of nomogram model that integrates clinicopathological indicators and pre-treatment CT-based radiomics features for predicting DNA mismatch repair deficiency (dMMR) /microsatellite instability (MSI) status in colon cancer prior to treatment.</p><p><strong>Methods: </strong>A total of 201 colon cancer patients who had undergone preoperative contrast-enhanced CT scans were categorized into the dMMR/MSI group or the proficient Mismatch Repair (pMMR)/Microsatellite Stable (MSS) group based on surgical pathology results. Multivariate logistic regression was applied to identify independent clinical predictors. The least absolute shrinkage and selection operator (LASSO) regression was applied for dimensionality reduction of radiomics features. Clinical, radiomics, and nomogram models were established through logistic regression analysis based on the risk clinicopathological predictors and radiomics features.</p><p><strong>Results: </strong>Multivariate logistic regression identified patient age, pericentric lymph node metastasis, and CA72-4 levels as significant (P < 0.05). Four radiomic features were selected to construct the radiomics model. In the training set, the AUC values for the clinical model, Rad score, and combined model were 0.86, 0.89, and 0.94, respectively, and in the validation set, 0.81, 0.89, and 0.91, respectively. The Delong test showed the nomogram model outperformed both the clinical model and Rad score (P < 0.05). The calibration curve confirmed good consistency between predicted and actual outcomes for dMMR/MSI colon cancer using the combined model.</p><p><strong>Conclusion: </strong>The nomogram model, which combines clinicopathological features with pre-treatment CT-based radiomics features, demonstrates greater predictive accuracy for dMMR/MSI colon cancer than the standalone clinical and radiomics models.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2026-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147833177","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison of elastosonographic changes of the tibial nerve and Achilles tendon in patients with type II diabetes mellitus.","authors":"HuiHui Yang, JianMei Huang, ShuCheng Chen, JiaYing Lin, Kuan Cai, Qing Feng, Yu He","doi":"10.1186/s12880-026-02401-4","DOIUrl":"https://doi.org/10.1186/s12880-026-02401-4","url":null,"abstract":"<p><strong>Background: </strong>To compare the elastosonographic changes of the tibial nerve (TN) and Achilles tendon (AT) in patients with type 2 diabetes mellitus (T2DM) and explore their relationship and respective relevant factors.</p><p><strong>Methods: </strong>This case-control study enrolled 165 subjects, comprising 126 patients with T2DM and 39 healthy controls matched for age and gender. The patients were further divided into those with and without diabetic peripheral neuropathy (PN-DM and NPN-DM groups). Clinical and laboratory data were collected. Conventional ultrasound and elastography were performed to assess the changes in the morphology and elasticity of the bilateral TN and AT. Sonographic features were compared across the three groups, relevant factors affecting the stiffness of TN and AT were analyzed, respectively.</p><p><strong>Results: </strong>Diabetic patients exhibited significantly higher levels of HbA1C and a higher rate of smoking than healthy controls (P < 0.01 and P = 0.02, respectively). Their levels of body mass index (BMI) and total cholesterol have a significant difference between the NPN-DM group and healthy controls (both P = 0.02). The incidence of other microvascular complications in the NPN-DM group was significantly lower among diabetic patients (P = 0.04). Compared with healthy controls, the cross-sectional area (CSA) and transverse diameter of TN in diabetic patients were significantly larger (both P < 0.01), and CSA and anteroposterior diameter of AT were notably greater (P = 0.02 and P < 0.01). Besides, the stiffness of TN in the longitudinal section was significantly higher (P < 0.01), and the stiffness of AT in the cross-section was remarkably lower (P < 0.01). There was no significant difference in the morphology or elastography of TN or AT between NPN-DM and PN-DM groups. Furthermore, the stiffness of TN was not linearly related to that of AT, but independently correlated with age, HbA1C, and other microvascular complications (P < 0.05). The stiffness of AT was only independent of age (P < 0.01).</p><p><strong>Conclusions: </strong>The size of both TN and AT in diabetic patients was significantly larger. The stiffness of TN increased, and that of AT decreased; however, these changes were independent of each other.</p><p><strong>Clinical trial number: </strong>Not applicable.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2026-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147810550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}