Academic Radiology最新文献

{"title":"Diffusion Tensor Imaging and Evaluation of Cardiac Ischemic Disorders: A Systematic Review","authors":"Hossein Iezi ,&nbsp;Maryam Zamanian ,&nbsp;Kasra Talebi ,&nbsp;Amir Dareini ,&nbsp;Iraj Abedi","doi":"10.1016/j.acra.2024.11.022","DOIUrl":"10.1016/j.acra.2024.11.022","url":null,"abstract":"<div><h3>Background</h3><div>A suitable diagnostic method can be beneficial owing to the high prevalence of myocardial infarction (MI) and structural changes that affect systolic and diastolic performance. In this systematic review, we focused on the possibility of using DTI instead of current methods such as cardiac biopsy, an invasive procedure.</div></div><div><h3>Methods</h3><div>Articles published in PubMed, Scopus, Embase, and Scholar electronic databases from 2010 to 2023 were reviewed using the determined keywords. The articles included evaluating DTI in patients with MI, compared to standard myocardial structure. Studies that did not examine the association between DTI and cardiac ischemic disorders only considered original articles. Methodological risk was evaluated using QUADAS-2 tool.</div></div><div><h3>Results</h3><div>Sixteen articles were selected from 16855 searched articles and divided into two subgroups: human-based (six studies) and animal-based (ten studies). Among the results obtained from both animal and human-based evaluations: (a) the values of the FA, RD (λ⊥), and AD (λ∥) indices in the infarcted region were lower than those in the remote or adjacent areas, and the value of the MD index increased. (b) This trend was also present in both acute and chronic conditions and in the follow-up of patients for the assessment of microstructure remodeling. (c) Confirming the process of change evaluated in preclinical human studies. More studies should be conducted on the effects of factors such as the B-value index and environmental conditions.</div></div><div><h3>Conclusion</h3><div>Clarifying the trend of changes in DTI indices for AMI and CMI makes them suitable diagnostic tools in this regard. Meanwhile, the study faced limitations such as low sample size and the risk of bias about “patient selection” index.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"32 4","pages":"Pages 1874-1887"},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142774369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating CT Dose Variation Across Scanner Technologies: Implications for Compliance with New CMS CT Radiation Dose Measure.","authors":"Madan M Rehani, Maria T Mataac, Xinhua Li","doi":"10.1016/j.acra.2025.03.026","DOIUrl":"https://doi.org/10.1016/j.acra.2025.03.026","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>In 2025, the Centers for Medicare and Medicaid Services introduced a computed tomography (CT) dose measure for pay-for-performance programs. Hospitals employ diverse scanner fleets, but the impact of scanner technologies on dose benchmarking remains unclear. This study evaluates dose variation across scanner models and its benchmarking implications.</p><p><strong>Materials and methods: </strong>A retrospective analysis examined CT exams from January to December 2023 at a quaternary-care hospital, focusing on median-sized adults (water-equivalent diameter: 16-19cm head, 18-22cm neck, 29-33cm torso). Dose indices from seven scanner models across eight adult exams were evaluated. The 50^th and 75^th percentile doses were calculated per exam and scanner model combination, compared to American College of Radiology achievable doses and diagnostic reference levels.</p><p><strong>Results: </strong>Analyzing 34,166 studies, significant dose variations with scanner models emerged. Head without contrast (N=21,654) had median volume CT-dose-index (CTDI_vol) of 36.1-68.3mGy and dose-length-product (DLP) 704-1307.8mGy·cm; 75^th percentiles were 43.1-69.1mGy and 838.2-1378.1mGy·cm. Chest with contrast (N=3065) showed median CTDI_vol of 6.7-16.1mGy and DLP 263.8-579.7mGy·cm; 75^th percentiles were 8.2-19.5mGy and 329-713.7mGy·cm. Abdomen/pelvis with contrast (N=2740) had median CTDI_vol of 8.8-15.2mGy and DLP 429.3-782.1mGy·cm; 75^th percentiles were 10-18.5mGy and 533.4-941.5mGy·cm. While the number of studies was smaller, five other exams also showed large dose variations across scanner models.</p><p><strong>Conclusion: </strong>Single-value dose benchmarks ignoring scanner technology may be inadequate, even for similar-sized patients, potentially requiring scanner removal. Incorporating benchmarks with diverse technologies could prevent increased healthcare costs and patient care disruptions.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143774781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multiparametric MRI-based Interpretable Machine Learning Radiomics Model for Distinguishing Between Luminal and Non-luminal Tumors in Breast Cancer: A Multicenter Study.","authors":"Yi Zhou, Guihan Lin, Weiyue Chen, Yongjun Chen, Changsheng Shi, Zhiyi Peng, Ling Chen, Shibin Cai, Ying Pan, Minjiang Chen, Chenying Lu, Jiansong Ji, Shuzheng Chen","doi":"10.1016/j.acra.2025.03.010","DOIUrl":"https://doi.org/10.1016/j.acra.2025.03.010","url":null,"abstract":"<p><strong>Rationale and objectives: </strong>To construct and validate an interpretable machine learning (ML) radiomics model derived from multiparametric magnetic resonance imaging (MRI) images to differentiate between luminal and non-luminal breast cancer (BC) subtypes.</p><p><strong>Methods: </strong>This study enrolled 1098 BC participants from four medical centers, categorized into a training cohort (n = 580) and validation cohorts 1-3 (n = 252, 89, and 177, respectively). Multiparametric MRI-based radiomics features, including T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC), and dynamic contrast-enhanced (DCE) imaging, were extracted. Five ML algorithms were applied to develop various radiomics models, from which the best performing model was identified. A ML-based combined model including optimal radiomics features and clinical predictors was constructed, with performance assessed through receiver operating characteristic (ROC) analysis. The Shapley additive explanation (SHAP) method was utilized to assess model interpretability.</p><p><strong>Results: </strong>Tumor size and MR-reported lymph node status were chosen as significant clinical variables. Thirteen radiomics features were identified from multiparametric MRI images. The extreme gradient boosting (XGBoost) radiomics model performed the best, achieving area under the curves (AUCs) of 0.941, 0.903, 0.862, and 0.894 across training and validation cohorts 1-3, respectively. The XGBoost combined model showed favorable discriminative power, with AUCs of 0.956, 0.912, 0.894, and 0.906 in training and validation cohorts 1-3, respectively. The SHAP visualization facilitated global interpretation, identifying \"ADC_wavelet-HLH_glszm_ZoneEntropy\" and \"DCE_wavelet-HLL_gldm_DependenceVariance\" as the most significant features for the model's predictions.</p><p><strong>Conclusion: </strong>The XGBoost combined model derived from multiparametric MRI may proficiently differentiate between luminal and non-luminal BC and aid in treatment decision-making.</p><p><strong>Critical relevance statement: </strong>An interpretable machine learning radiomics model can preoperatively predict luminal and non-luminal subtypes in breast cancer, thereby aiding therapeutic decision-making.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143774786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison of Peer Reviewer Instructions of Radiology Journals to Recommended Peer Review Checklists","authors":"Omar Msto Hussain Nasser MD ,&nbsp;Paul Cronin MD, MS ,&nbsp;James V. Rawson MD","doi":"10.1016/j.acra.2025.01.018","DOIUrl":"10.1016/j.acra.2025.01.018","url":null,"abstract":"<div><h3>Rationale and Objectives</h3><div>The objective of this study was to identify differences in peer review guidance provided to reviewer by journals, and to compare radiology journal instructions to recommended peer review checklists.</div></div><div><h3>Methods</h3><div>Peer review instructions from four prominent radiology journals (Radiology, JACR, Academic Radiology, AJR) were obtained from journal websites and instructions to reviewers in the journal. Two recommended checklists from radiology literature published by Provenzale and Stanley in 2005 with 30 items, and another by Duchesne and Jannin with 69 items published in 2008 were utilized. Journal-based instructions were compared to both recommended checklists using Excel.</div></div><div><h3>Results</h3><div>Variability was observed in the online available instructions for reviewers of the four radiology journals. Radiology journals’ instructions for reviewers were more likely to address certain parts of the manuscript. Items that were consistently emphasized included rationale, reproducibility, results of statistical test, whether results justify the conclusion, whether the research question was addressed, and the clinical and practical applicability. Other items that were more likely to be mentioned in the instruction checklists include; if the abstract stands alone, a sufficient and concise background, logical flow of results that follows from the methods, appropriate tables and figures, and appropriate references. Items least likely to be addressed included the title, keywords, justification of study design and study methodology, unexpected results, generalizability of findings, and ethical considerations.</div></div><div><h3>Conclusion</h3><div>Variability was observed in journals’ guidelines for reviewers. This could be attributed to differences in journal aims, scopes, and article types. Radiology journals’ instructions for reviewers are more likely to address certain parts of the manuscript.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"32 4","pages":"Pages 1793-1797"},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143460516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evidence-Based Radiology: The Importance of Reviewers","authors":"N. Reed Dunnick MD","doi":"10.1016/j.acra.2025.02.027","DOIUrl":"10.1016/j.acra.2025.02.027","url":null,"abstract":"","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"32 4","pages":"Pages 1781-1782"},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143574574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Learning Algorithm‑Based MRI Radiomics and Pathomics for Predicting Microsatellite Instability Status in Rectal Cancer: A Multicenter Study","authors":"Xiuzhen Yao ,&nbsp;Shuitang Deng ,&nbsp;Xiaoyu Han ,&nbsp;Danjiang Huang ,&nbsp;Zhengyu Cao ,&nbsp;Xiaoxiang Ning ,&nbsp;Weiqun Ao","doi":"10.1016/j.acra.2024.09.008","DOIUrl":"10.1016/j.acra.2024.09.008","url":null,"abstract":"<div><h3>Rationale and Objectives</h3><div>To develop and validate multimodal deep-learning models based on clinical variables, multiparametric MRI (mp-MRI) and hematoxylin and eosin (HE) stained pathology slides for predicting microsatellite instability (MSI) status in rectal cancer patients.</div></div><div><h3>Materials and Methods</h3><div>A total of 467 surgically confirmed rectal cancer patients from three centers were included in this study. Patients from center 1 were randomly divided into a training set (242 patients) and an internal validation (invad) set (105 patients) in a 7:3 ratio. Patients from centers 2 and 3 (120 patients) were included in an external validation (exvad) set. HE and immunohistochemistry (IHC) staining were analyzed, and MSI status was confirmed by IHC staining. Independent predictive factors were identified through univariate and multivariate analyses based on clinical evaluations and were used to construct a clinical model. Deep learning with ResNet-101 was applied to preoperative MRI (T2WI, DWI, and contrast-enhanced T1WI sequences) and postoperative HE-stained images to calculate deep-learning radiomics score (DLRS) and deep-learning pathomics score (DLPS), respectively, and to DLRS and DLPS models. Receiver operating characteristic (ROC) curves were plotted, and the area under the curve (AUC) was used to evaluate and compare the predictive performance of each model.</div></div><div><h3>Results</h3><div>Among all rectal cancer patients, 82 (17.6%) had MSI. Long diameter (LD) and pathological T stage (pT) were identified as independent predictors and were used to construct the clinical model. After undergoing deep learning and feature selection, a final set of 30 radiomics features and 30 pathomics features were selected to construct the DLRS and DLPS models. A nomogram combining the clinical model, DLRS, and DLPS was created through weighted linear combination. The AUC values of the clinical model for predicting MSI were 0.714, 0.639, and 0.697 in the training, invad, and exvad sets, respectively. The AUCs of DLPS and DLRS ranged from 0.896 to 0.961 across the training, invad, and exvad sets. The nomogram achieved AUC values of 0.987, 0.987, and 0.974, with sensitivities of 1.0, 0.963, and 1.0 and specificities of 0.919, 0.949, and 0.867 in the training, invad, and exvad sets, respectively. The nomogram outperformed the other three models in all sets, with DeLong test results indicating superior predictive performance in the training set.</div></div><div><h3>Conclusion</h3><div>The nomogram, incorporating clinical data, mp-MRI, and HE staining, effectively reflects tumor heterogeneity by integrating multimodal data. This model demonstrates high predictive accuracy and generalizability in predicting MSI status in rectal cancer patients.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"32 4","pages":"Pages 1934-1945"},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142261937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving Prediction Accuracy of Residual Axillary Lymph Node Metastases in Node-Positive Triple-Negative Breast Cancer: A Radiomics Analysis of Ultrasound-Guided Clip Locations Using the SHAP Method","authors":"Qing Yao ,&nbsp;Yu Du ,&nbsp;Wei Liu ,&nbsp;Xinpei Liu ,&nbsp;Manqi Zhang ,&nbsp;Hailing Zha ,&nbsp;Liwen Du ,&nbsp;Xiaoming Zha ,&nbsp;Jue Wang ,&nbsp;Cuiying Li","doi":"10.1016/j.acra.2024.10.039","DOIUrl":"10.1016/j.acra.2024.10.039","url":null,"abstract":"<div><h3>Rationale and Objectives</h3><div>To construct a radiomics nomogram derived from multiparametric ultrasound (US) imaging using the SHapley Additive exPlanations (SHAP) method for the accurate identification of residual axillary lymph node metastases post-neoadjuvant chemotherapy (NAC) among patients with triple-negative breast cancer (TNBC).</div></div><div><h3>Methods</h3><div>A total of 405 consecutive patients with pathologically confirmed TNBC between 2016 and 2023 were recruited in the study and were divided into training (n = 284) and validation cohorts (n = 121). Radiomics features capturing detailed tumor characteristics were extracted from pre-NAC gray-scale US images at the locations of US-guided clip placement. The least absolute shrinkage and selection operator and the maximum relevance minimum redundancy algorithm were employed to identify key features and formulate the radiomics signature (RS). A nomogram based on US radiomics was then constructed using multivariable logistic regression analysis. The predictive efficacy of this model was evaluated through receiver operating characteristic curve analysis, calibration assessment, and decision curve analysis. SHAP summary plots were used to visualize the distribution of SHAP values across all features.</div></div><div><h3>Results</h3><div>The nomogram integrates clinical and US characteristics with RS, yielded optimal AUC of 0.922 (95% CI, 0.890–0.954) in the training cohort, 0.904 (95% CI, 0.853–0.955) in the validation cohort. The calibration and decision curves confirmed favorable calibration and clinical value of the nomogram. SHAP provided further insight into the contributions of each feature to the model’s outcomes.</div></div><div><h3>Conclusion</h3><div>The combined multiparametric US based radiomics nomogram plays a potential role in predicting residual axillary lymph node metastases after NAC in TNBCs.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"32 4","pages":"Pages 1827-1837"},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142631731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Novel Approach Based on Integrating Radiomics, Bone Morphometry and Hounsfield Unit-Derived From Routine Chest CT for Bone Mineral Density Assessment","authors":"Mahmoud Mohammadi-Sadr ,&nbsp;Mohsen Cheki ,&nbsp;Masoud Moslehi ,&nbsp;Marziyeh Zarasvandnia ,&nbsp;Mohammad Reza Salamat","doi":"10.1016/j.acra.2024.10.049","DOIUrl":"10.1016/j.acra.2024.10.049","url":null,"abstract":"<div><h3>Rationale and Objectives</h3><div>The purpose of this study is the feasibility of using radiomics features, bone morphometry features (BM), and Hounsfield unit (HU) values obtained from routine chest computed tomography (CT) for assessing bone mineral density (BMD) status.</div></div><div><h3>Materials and Methods</h3><div>This retrospective study analyzed 120 patients who underwent routine chest CT and dual-energy X-ray absorptiometry examinations within a month. Whole thoracic vertebral bodies from routine chest CT images were segmented using the GrowCut semi-automatic segmentation method, and radiomics features, BM features, and HU values were extracted. To assess the intra- and inter-observer variability of segmentation, the Dice similarity coefficient (DSC) was utilized. Feature selection was carried out using the intra-class correlation coefficient and the Boruta algorithm. Six machine learning classification models were employed for classification in a three-class manner. The models’ performance was evaluated using the area under the receiver operator characteristics curve (AUC). Other evaluation parameters of the models were calculated, including overall accuracy, precision, and sensitivity.</div></div><div><h3>Results</h3><div>The DSC values showed high similarity by achieving 0.907 ± 0.034 and 0.887 ± 0.048 for intra- and inter-observer segmentation agreement, respectively. After a two-stepwise feature selection, 21 radiomics features were selected. Different combinations of these radiomics features with five BM features and HU values were applied to six classification models for evaluating BMD. The multilayer perceptron (MLP) model based on integration of radiomics features and BM features in a three-class classification approach achieved higher performance compared to other models with an AUC of 0.981 (95% confidence interval (CI): 0.937–0.997) in normal BMD class, an AUC of 0.896 (95% CI: 0.826–0.944) in osteopenia class, and an AUC of 0.927 (95% CI: 0.866–0.967) in osteoporosis class.</div></div><div><h3>Conclusion</h3><div>Using the MLP classification model based on a combination of radiomics features and BM features in a three-class classification approach can effectively distinguish different BMD conditions.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"32 4","pages":"Pages 2284-2296"},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142677769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative Evaluation of Clinical-MRI, Radiomics, and Integrated Nomogram Models for Preoperative Prediction of Placenta Accreta Spectrum","authors":"Zhiwei Wang ,&nbsp;Xinyao Jiao ,&nbsp;Weiwu Liu ,&nbsp;Han Song ,&nbsp;Jiapeng Li ,&nbsp;Jing Hu ,&nbsp;Yuanbo Huang ,&nbsp;Yang Liu ,&nbsp;Sa Huang","doi":"10.1016/j.acra.2024.10.021","DOIUrl":"10.1016/j.acra.2024.10.021","url":null,"abstract":"<div><h3>Rationale and Objectives</h3><div>The escalating incidence of placental accreta spectrum (PAS), a pregnancy complication, underscores the need for accurate prenatal diagnosis to guide optimal management strategies. This study aims to develop, validate, and compare various prenatal PAS prediction models integrating clinical data, MRI signs, and radiomics signatures.</div></div><div><h3>Materials and Methods</h3><div>A cohort comprising 111 patients (72 with PAS and 39 without, denoted as N-PAS) served as the training set, while another 47 patients (33 PAS and 14 N-PAS) constituted the validation set. Clinical features and MRI signs were subjected to univariate and multivariate analyses to construct the Clinical-MRI model. Radiomic features were extracted from MRI images and refined through the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm, thereby establishing the Radiomics model. An optimal set of radiomic features was utilized to derive the Radscore, which was then integrated with clinical features and MRI signs to formulate the Nomogram model. The performance of these models was comprehensively evaluated and compared.</div></div><div><h3>Results</h3><div>In the validation set evaluation, the Nomogram model, which integrated Radscore, a pivotal clinical indicator, and two MRI signs, demonstrated superior performance. With an area under the curve (AUC) of 0.861 (95% CI: 0.745, 0.978), this model significantly outperformed both the clinical-MRI model (AUC = 0.796, 95% CI: 0.649, 0.943) and the radiomics model (AUC = 0.704, 95% CI: 0.531, 0.877). Specifically, the Nomogram model achieved a high sensitivity of 81.8% and a specificity of 78.6% in the prenatal diagnosis of placenta accreta spectrum (PAS), thereby offering clinicians a precise and efficient diagnostic aid.</div></div><div><h3>Conclusion</h3><div>The radiomics-derived Radscore serves as an independent predictor for prenatal PAS. Combining Radscore with clinical features and MRI signs into a Nomogram model provides a non-invasive tool with high sensitivity or specificity for PAS diagnosis, enhancing prenatal assessment and management.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"32 4","pages":"Pages 2041-2052"},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142711809","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and Validation of a Machine Learning Radiomics Model based on Multiparametric MRI for Predicting Progesterone Receptor Expression in Meningioma: A Multicenter Study","authors":"Guihan Lin ,&nbsp;Weiyue Chen ,&nbsp;Yongjun Chen ,&nbsp;Changsheng Shi ,&nbsp;Qianqian Cao ,&nbsp;Yang Jing ,&nbsp;Weiming Hu ,&nbsp;Ting Zhao ,&nbsp;Pengjun Chen ,&nbsp;Zhihan Yan ,&nbsp;Minjiang Chen ,&nbsp;Chenying Lu ,&nbsp;Shuiwei Xia ,&nbsp;Jiansong Ji","doi":"10.1016/j.acra.2024.11.019","DOIUrl":"10.1016/j.acra.2024.11.019","url":null,"abstract":"<div><h3>Rationale and Objectives</h3><div>This study aimed to develop and validate a machine learning-based prediction model for preoperatively predicting progesterone receptor (PR) expression in meningioma patients using multiparametric magnetic resonance imaging (MRI).</div></div><div><h3>Materials and Methods</h3><div>The study retrospectively enrolled 739 patients with pathologically confirmed meningioma from three medical centers, dividing them into four cohorts: training (n = 294), internal test (n = 126), external test 1 (n = 217), and external test 2 (n = 102). Radiomics characteristics were derived from T2-weighted and contrast-enhanced T1-weighted MRI images, followed by feature selection. A machine learning-based combined model was developed by incorporating radiomics scores (rad-scores) from the optimal radiomics model along with clinical predictors. The Shapley additive explanation (SHAP) method was employed to visually represent the process of making predictions. The prognostic value of the model was evaluated using Kaplan-Meier analysis.</div></div><div><h3>Results</h3><div>Among the 739 patients, 299 (40.5%) had negative PR expression confirmed by pathology. Twelve radiomics features derived from multiparametric MRI were selected to build the radiomics model. Tumor location and enhancement pattern were identified as key clinical predictors and were combined with rad-scores to create a combined model utilizing the extreme gradient boosting (XGBoost) algorithm. The combined model demonstrated strong accuracy and robustness, with area under the curve values of 0.907, 0.827, 0.846, and 0.807 across training, internal test, external test 1, and external test 2 cohorts, respectively. The recurrence-free survival analysis indicated that the combined model was able to effectively categorize patients based on recurrence outcomes.</div></div><div><h3>Conclusion</h3><div>The XGBoost combined model, utilizing multiparametric MRI, shows promise for predicting PR expression in meningioma patients.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"32 4","pages":"Pages 2182-2196"},"PeriodicalIF":3.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142755630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}