Academic Radiology最新文献

{"title":"Predicting the Prognosis of HIFU Ablation of Uterine Fibroids Using a Deep Learning-Based 3D Super-Resolution DWI Radiomics Model: A Multicenter Study","authors":"Chengwei Li ,&nbsp;Zhimin He ,&nbsp;Fajin Lv ,&nbsp;Hongjian Liao ,&nbsp;Zhibo Xiao","doi":"10.1016/j.acra.2024.06.027","DOIUrl":"10.1016/j.acra.2024.06.027","url":null,"abstract":"<div><h3>Rationale and Objectives</h3><div>To assess the feasibility and efficacy of a deep learning-based three-dimensional (3D) super-resolution diffusion-weighted imaging (DWI) radiomics model in predicting the prognosis of high-intensity focused ultrasound (HIFU) ablation of uterine fibroids.</div></div><div><h3>Methods</h3><div>This retrospective study included 360 patients with uterine fibroids who received HIFU treatment, including Center A (training set: <em>N</em> = 240; internal testing set: <em>N</em> = 60) and Center B (external testing set: <em>N</em> = 60) and were classified as having a favorable or unfavorable prognosis based on the postoperative non-perfusion volume ratio. A deep transfer learning approach was used to construct super-resolution DWI (SR-DWI) based on conventional high-resolution DWI (HR-DWI), and 1198 radiomics features were extracted from manually segmented regions of interest in both image types. Following data preprocessing and feature selection, radiomics models were constructed for HR-DWI and SR-DWI using Support Vector Machine (SVM), Random Forest (RF), and Light Gradient Boosting Machine (LightGBM) algorithms, with performance evaluated using area under the curve (AUC) and decision curves.</div></div><div><h3>Result</h3><div>All DWI radiomics models demonstrated superior AUC in predicting HIFU ablated uterine fibroids prognosis compared to expert radiologists (AUC: 0.706, 95% CI: 0.647–0.748). When utilizing different machine learning algorithms, the HR-DWI model achieved AUC values of 0.805 (95% CI: 0.679–0.931) with SVM, 0.797 (95% CI: 0.672–0.921) with RF, and 0.770 (95% CI: 0.631–0.908) with LightGBM. Meanwhile, the SR-DWI model outperformed the HR-DWI model (P &lt; 0.05) across all algorithms, with AUC values of 0.868 (95% CI: 0.775–0.960) with SVM, 0.824 (95% CI: 0.715–0.934) with RF, and 0.821 (95% CI: 0.709–0.933) with LightGBM. And decision curve analysis further confirmed the good clinical value of the models.</div></div><div><h3>Conclusion</h3><div>Deep learning-based 3D SR-DWI radiomics model demonstrated favorable feasibility and effectiveness in predicting the prognosis of HIFU ablated uterine fibroids, which was superior to HR-DWI model and assessment by expert radiologists.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"31 12","pages":"Pages 4996-5007"},"PeriodicalIF":3.8,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141538892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multiparametric MRI-based Radiomics Analysis for Prediction of Lymph Node Metastasis and Survival Outcome in Gastric Cancer: A Dual-center Study","authors":"Ruirui Song MD ,&nbsp;Wujie Chen MD ,&nbsp;Junjie Zhang MD ,&nbsp;Jianxin Zhang MD, PhD ,&nbsp;Yan Du MD ,&nbsp;Jialiang Ren MD ,&nbsp;Lei Shi MD, PhD ,&nbsp;Yanfen Cui MD, PhD ,&nbsp;Xiaotang Yang MD, PhD","doi":"10.1016/j.acra.2024.05.032","DOIUrl":"10.1016/j.acra.2024.05.032","url":null,"abstract":"<div><h3>Rationale and Objectives</h3><div>Gastric cancer (GC) is highly heterogeneous, and accurate preoperative assessment<span> of lymph node status remains challenging. We aimed to develop a multiparametric MRI-based model for predicting lymph node metastasis (LNM) in GC and to explore its prognostic implications.</span></div></div><div><h3>Materials and Methods</h3><div><span><span>In this dual-center retrospective study, 479 GC patients undergoing preoperative multiparametric MRI before radical </span>gastrectomy<span><span><span> were enrolled. 1595 imaging features were extracted from T2-weighted imaging, apparent diffusion coefficient maps, and contrast-enhanced T1-weighted imaging (cT1WI), respectively. Feature selection steps, including the Boruta and Simulated Annealing algorithms, were conducted to identify key features. Different </span>radiomics models (RMs) based on the single- and multiple-sequence were constructed. The performance of various RMs in predicting LNM was assessed in terms of discrimination, calibration, and clinical usefulness. Additionally, Kaplan-Meier </span>survival curves were employed to estimate differences in disease-free survival (DFS) and </span></span>overall survival (OS).</div></div><div><h3>Results</h3><div>The multi-sequence radiomics model (MRM) achieved area under the curves (AUCs) of 0.774 [95 % confidence interval (CI), 0.703–0.845], 0.721 (95 % CI, 0.593–0.850), and 0.720 (95 % CI, 0.639–0.801) in the training and two validation cohorts, respectively, outperforming the single-sequence RMs. Notably, the RM derived from cT1WI demonstrated superior performance compared to the other two single-sequence models. Furthermore, the proposed MRM exhibited a significant association with DFS and OS in GC patients (both P &lt; 0.05).</div></div><div><h3>Conclusion</h3><div>The multiparametric MRI-based radiomics model, derived from primary lesions, demonstrated moderate performance in predicting LNM and survival outcomes in patients with GC, which could provide valuable insights for personalized treatment strategies.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"31 12","pages":"Pages 4900-4911"},"PeriodicalIF":3.8,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141288866","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":"Evaluation of the Contrast-Enhanced Ultrasound Nonradiation Treatment Response Assessment LI-RADS v2024 Using Data From a Multi-Center Transarterial Chemoembolization Study","authors":"Cristina M. Kuon Yeng Escalante MD ,&nbsp;Tania Siu Xiao MD ,&nbsp;Rohit U. Nagaraj BS ,&nbsp;Esika Savsani BS ,&nbsp;Amr Mohammed MD ,&nbsp;Joy Li MD ,&nbsp;Andrej Lyshchik MD, PhD ,&nbsp;Ji-Bin Liu MD ,&nbsp;Corinne E. Wessner MS, RDMS, RVT ,&nbsp;Aylin Tahmasebi MD ,&nbsp;Michael C. Soulen MD ,&nbsp;Yuko Kono MD, PhD ,&nbsp;John R. Eisenbrey PhD","doi":"10.1016/j.acra.2024.06.005","DOIUrl":"10.1016/j.acra.2024.06.005","url":null,"abstract":"<div><h3>Rationale and Objective</h3><div><span>Hepatocellular carcinoma<span> (HCC) locoregional treatment response is commonly evaluated using the Modified Response Evaluation Criteria in Solid Tumors<span> and the American College of Radiology (ACR) Liver Reporting and Data System (LI-RADS) Treatment Response Assessment (TRA) for MRI/CT</span></span></span><strong>.</strong><span><span> This study aims to evaluate the diagnostic performance of the new ACR contrast-enhanced ultrasound (CEUS) Nonradiation TRA LI-RADS v2024 in HCC treated with </span>transarterial chemoembolization (TACE).</span></div></div><div><h3>Materials and Methods</h3><div>This retrospective observational study included 87 patients treated with TACE from a previously reported cohort. At 15- and 30-days post-treatment, 68 and 72 HCC lesions were evaluated. Three blinded radiologists with different levels of CEUS experience interpreted the images independently. According to CEUS Nonradiation TRA LI-RADSv2024, both intralesional and perilesional tumor viability were evaluated and final TRA categories were as follows: TR-Nonviable, TR-Equivocal, and TR-Viable. The reference standard used was a composite of histology and imaging.</div></div><div><h3>Results</h3><div>140 HCC lesions were analyzed. At 15 days post-treatment, the sensitivity (SN), specificity (SP), positive predictive value (PPV), negative predictive value (NPV), and accuracy of TR-Viable classification ranged from 72.5–94.3%, 72.2–86.4%, 86.8–91.4%, 65.6–86.7%, 76.9–86.8%, respectively. At 30 days post-treatment, the SN, PPV, and NPV of TR-Viable classification decreased, ranging from 65.9–84.2%, 85.7–90.6%, and 59.5–73.9%, respectively, while the SP increased, ranging from 80.0–88.0%. Kappa values ranged from 0.557–0.730, indicating moderate to substantial agreement.</div></div><div><h3>Conclusion</h3><div>CEUS Nonradiation TRA LI-RADS is a reliable tool for the detection of viable tumors in lesions treated with TACE and demonstrates reproducibility across readers.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"31 12","pages":"Pages 5078-5086"},"PeriodicalIF":3.8,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141441079","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":"Use of Intravoxel Incoherent Motion Diffusion-Weighted Imaging to Assess Mesenchymal Stromal Cells Promoting Liver Regeneration in a Rat Model","authors":"Xuyang Wang MD ,&nbsp;Shuangshuang Xie MD ,&nbsp;Caixin Qiu MD ,&nbsp;Xinzhe Du MD ,&nbsp;Jiaming Qin MD ,&nbsp;Zhandong Hu MD ,&nbsp;Robert Grimm PhD ,&nbsp;Jinxia Zhu PhD ,&nbsp;Wen Shen MD","doi":"10.1016/j.acra.2024.05.018","DOIUrl":"10.1016/j.acra.2024.05.018","url":null,"abstract":"<div><h3>Rationale and Objectives</h3><div>Mesenchymal stem cells<span><span> (MSCs) have the potential to promote liver regeneration<span>, but the process is unclear. This study aims to explore the therapeutic effects and dynamic processes of MSCs in liver regeneration through intravoxel incoherent </span></span>motion (IVIM) imaging.</span></div></div><div><h3>Animal model</h3><div>70 adult Sprague–Dawley rats were randomly divided into either the control or MSC group (<em>n</em><span> = 35/group). All rats received a partial hepatectomy (PH) with the left lateral and middle lobes removed. Each group was divided into seven subgroups: pre-PH and 1, 2, 3, 5, 7, and 14 days post-PH (</span><em>n</em><span><span><span> = 5 rats/subgroup). Magnetic resonance imaging (MRI) was performed before obtaining pathological specimens at each time point on postoperative days 1, 2, 3, 5, 7, and 14. The MRI parameters for the pure diffusion coefficient (D), pseudodiffusion coefficient (D*), and perfusion fraction (PF) were calculated. Correlation analysis was conducted for the biochemical markers (alanine transaminase [ALT], </span>aspartate transaminase [AST], and total </span>bilirubin<span> [TBIL]), histopathological findings (hepatocyte size and Ki-67 proliferation index), liver volume (LV) and liver regeneration rate (LLR).</span></span></div></div><div><h3>Results</h3><div>Liver D, D* , and PF differed significantly between the control and MSC groups at all time points (all <em>P</em> &lt; 0.05). After PH, the D increased, then decreased, and the D* and PF decreased, then increased in both groups. The hepatocyte Ki-67 proliferation index of the MSC group was lower on day 2 post-PH, but higher on days 3 and 5 post-PH than that of the control group. Starting from day 3 post-PH, both the LV and LLR in the MSC group were greater than those in the control group (all <em>P</em><span> &lt; 0.05). Hepatocytes were larger in the MSC group than in the control group on days 2 and 7 post-PH. In the MSC group, the D, D* , and PF were correlated with the AST levels, Ki-67 index and hepatocyte size (|r|</span> <!-->=<!--> <!-->0.35–0.71; <em>P</em><span> &lt; 0.05). In the control group, the D and D* were correlated with ALT levels<span>, AST levels, Ki-67 index, LLR, LV, and hepatocyte size (|r|</span></span> <!-->=<!--> <!-->0.34–0.95; <em>P</em> &lt; 0.05).</div></div><div><h3>Conclusion</h3><div>Bone marrow MSC therapy can promote hepatocyte hypertrophy and prolong liver proliferation post-PH. IVIM parameters allow non-invasively evaluating the efficacy of MSCs in promoting LR.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"31 12","pages":"Pages 4955-4964"},"PeriodicalIF":3.8,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141441081","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":"Diagnostic Accuracy of Dual-Energy CT-Derived Metrics for the Prediction of Osteoporosis-Associated Fractures","authors":"Philipp Reschke MD,&nbsp;Vitali Koch MD,&nbsp;Scherwin Mahmoudi MD,&nbsp;Jennifer Gotta MD,&nbsp;Elena Höhne MD,&nbsp;Christian Booz MD,&nbsp;Ibrahim Yel MD,&nbsp;Jan-Erik Scholtz MD,&nbsp;Simon S. Martin MD,&nbsp;Tatjana Gruber-Rouh MD,&nbsp;Katrin Eichler MD,&nbsp;Thomas J. Vogl MD,&nbsp;Leon D. Gruenewald MD","doi":"10.1016/j.acra.2024.07.010","DOIUrl":"10.1016/j.acra.2024.07.010","url":null,"abstract":"<div><h3>Rationale and Objectives</h3><div>This study aimed to compare the diagnostic value of dual-energy CT (DECT)-based volumetric material decomposition with that of Hounsfield units (HU)-based values and cortical thickness ratio for predicting the 2-year risk of osteoporosis-associated fractures.</div></div><div><h3>Methods</h3><div>The L1 vertebrae of 111 patients (55 men, 56 women; median age, 62 years) who underwent DECT between 01/2015 and 12/2018 were retrospectively analyzed. For phantomless bone mineral density (BMD) assessment, a specialized DECT postprocessing software employing material decomposition was utilized. The digital records of all patients were monitored for two years after the DECT scans to track the incidence of osteoporotic fractures. Diagnostic accuracy parameters were calculated for all metrics using receiver-operating characteristic (ROC) and precision-recall (PR) curves. Logistic regression models were used to determine associations of various predictive metrics with the occurrence of osteoporotic fractures.</div></div><div><h3>Results</h3><div>Patients who sustained one or more osteoporosis-associated fractures in a 2-year interval were significantly older (median age 74.5 years [IQR 57–83 years]) compared those without such fractures (median age 50.5 years [IQR 38.5–69.5 years]). According to logistic regression models, DECT-derived BMD was the sole predictive parameter significantly associated with osteoporotic fracture occurrence across all age groups. ROC and PR curve analyses confirmed the highest diagnostic accuracy for DECT-based BMD, with an area under the curve (AUC) of 0.95 [95% CI: 0.89–0.98] for the ROC curve and an AUC of 0.96 [95% CI: 0.85–0.99] for the PR curve.</div></div><div><h3>Conclusion</h3><div>The diagnostic performance of DECT-based BMD in predicting the 2-year risk of osteoporotic fractures is greater than that of HU-based metrics and the cortical thickness ratio. DECT-based BMD values are highly valuable in identifying patients at risk for osteoporotic fractures.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"31 12","pages":"Pages 5108-5117"},"PeriodicalIF":3.8,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141908268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spaced Repetition in Medical Education: Its Importance and Applications","authors":"Alexander Upegui MD (MS-IV, Medical Student),&nbsp;Omer A. Awan MD, MPH, CIIP (Associate Vice Chair of Education)","doi":"10.1016/j.acra.2024.01.025","DOIUrl":"10.1016/j.acra.2024.01.025","url":null,"abstract":"","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"31 12","pages":"Pages 5339-5340"},"PeriodicalIF":3.8,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142746159","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":"Photon-Counting Versus Dual-Source CT for Transcatheter Aortic Valve Implantation Planning","authors":"Timm Dirrichs MD ,&nbsp;Jörg Schröder MD ,&nbsp;Michael Frick MD ,&nbsp;Marc Huppertz MD ,&nbsp;Roman Iwa ,&nbsp;Thomas Allmendinger ,&nbsp;Ines Mecking ,&nbsp;Christiane K. Kuhl MD","doi":"10.1016/j.acra.2024.06.014","DOIUrl":"10.1016/j.acra.2024.06.014","url":null,"abstract":"<div><h3>Background</h3><div>Cardiovascular CT is required for planning transcatheter aortic valve implantation (TAVI).</div></div><div><h3>Purpose</h3><div>To compare image quality, suitability for TAVI planning, and radiation dose of photon-counting CT (PCCT) with that of dual-source CT (DSCT).</div></div><div><h3>Material and Methods</h3><div>Retrospective study on consecutive TAVI candidates with aortic valve stenosis who underwent contrast-enhanced aorto-ilio-femoral PCCT and/or DSCT between 01/2022 and 07/2023. Signal-to-noise (SNR) and contrast-to-noise ratio (CNR) were calculated by standardized ROI analysis. Image quality and suitability for TAVI planning were assessed by four independent expert readers (two cardiac radiologists, two cardiologists) on a 5-point-scale. CT dose index (CTDI) and dose-length-product (DLP) were used to calculate effective radiation dose (eRD).</div></div><div><h3>Results</h3><div>300 patients (136 female, median age: 81 years, IQR: 76–84) underwent 302 CT examinations, with PCCT in 202, DSCT in 100; two patients underwent both. Although SNR and CNR were significantly lower in PCCT vs. DSCT images (33.0 ± 10.5 vs. 47.3 ± 16.4 and 47.3 ± 14.8 vs. 59.3 ± 21.9, P &lt; .001, respectively), visual image quality was higher in PCCT vs. DSCT (4.8 vs. 3.3, P &lt; .001), with moderate overall interreader agreement among radiologists and among cardiologists (κ = 0.60, respectively). Image quality was rated as “excellent” in 160/202 (79.2%) of PCCT vs. 5/100 (5%) of DSCT cases. Readers found images suitable to depict the aortic valve hinge points and to map the femoral access path in 99% of PCCT vs. 85% of DSCT (P &lt; 0.01), with suitability ranked significantly higher in PCCT vs. DSCT (4.8 vs. 3.3, P &lt; .001). Mean CTDI and DLP, and thus eRD, were significantly lower for PCCT vs. DSCT (22.4 vs. 62.9; 519.4 vs. 895.5, and 8.8 ± 4.5 mSv vs. 15.3 ± 5.8 mSv; all P &lt; .001).</div></div><div><h3>Conclusion</h3><div>PCCT improves image quality, effectively avoids non-diagnostic CT imaging for TAVI planning, and is associated with a lower radiation dose compared to state-of-the-art DSCT. Radiologists and cardiologists found PCCT images more suitable for TAVI planning.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"31 12","pages":"Pages 4780-4789"},"PeriodicalIF":3.8,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141437808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Magnetic Resonance Imaging Radiomics Predicts Histological Response to Neoadjuvant Chemotherapy in Localized High-grade Osteosarcoma of the Extremities","authors":"Yun Zhang MD ,&nbsp;Lanlan Zhi MB ,&nbsp;Jiao Li MD ,&nbsp;Murong Wang PhD ,&nbsp;Guoquan Chen MB ,&nbsp;Shaohan Yin MD","doi":"10.1016/j.acra.2024.07.015","DOIUrl":"10.1016/j.acra.2024.07.015","url":null,"abstract":"<div><h3>Rationale and Objectives</h3><div>Research involving radiomics models based on magnetic resonance imaging (MRI) has mainly used radiomics features derived from a single MRI sequence at a single time point to develop predictive models. This study aimed to construct radiomics models based on before and after neoadjuvant chemotherapy (NAC) MRI for predicting the histological response to NAC in patients with high-grade osteosarcoma.</div></div><div><h3>Materials and Methods</h3><div>We included 109 patients with localized high-grade osteosarcomas of the extremities, who underwent pre- and post-NAC MRI examinations, from which radiomics features were extracted. According to the tumor necrosis rate, all patients were classified as good responders (GRs) or poor responders (PRs) and were randomly allocated into training and test sets at a 7:3 ratio. Radiomics features were extracted from T2-weighted (T2WI) and contrast-enhanced T1-weighted imaging (T1CE) of the two MRI scans to construct three models: pre-NAC, post-NAC, and combined pre-NAC and post-NAC (combined model).</div></div><div><h3>Results</h3><div>In total, 1175 radiomics features were extracted from each sequence. Following feature selection, nine radiomics features were selected for each model to construct radiomics signatures. The radiomics signatures of the pre-NAC, post-NAC, and combined models demonstrated good predictive performance for chemotherapy response in osteosarcoma. The combined model achieved the highest areas under the receiver operating curve (AUC) values of 0.999 and 0.915 in the training and test sets, respectively. The AUCs of the post-NAC model were higher than those of the pre-NAC model.</div></div><div><h3>Conclusion</h3><div>MRI-based radiomics models demonstrate excellent performance in predicting the histological response to NAC in patients with high-grade osteosarcoma.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"31 12","pages":"Pages 5100-5107"},"PeriodicalIF":3.8,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141857049","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":"Progress in Noninvasive Assessment of Esophageal Varices","authors":"Yuki Arita MD, PhD","doi":"10.1016/j.acra.2024.10.034","DOIUrl":"10.1016/j.acra.2024.10.034","url":null,"abstract":"","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"31 12","pages":"Pages 4875-4877"},"PeriodicalIF":3.8,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142548785","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":"Guest Editorial: A Case for Quantitative Image Formalized Standardization","authors":"David Raunig","doi":"10.1016/j.acra.2024.11.005","DOIUrl":"10.1016/j.acra.2024.11.005","url":null,"abstract":"","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"31 12","pages":"Pages 4821-4822"},"PeriodicalIF":3.8,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142631718","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}