Academic Radiology最新文献

{"title":"Non-invasive Prediction of Lymph Node Metastasis in NSCLC Using Clinical, Radiomics, and Deep Learning Features From 18F-FDG PET/CT Based on Interpretable Machine Learning","authors":"Furui Duan ,&nbsp;Minghui Zhang ,&nbsp;Chunyan Yang ,&nbsp;Xuewei Wang ,&nbsp;Dalong Wang","doi":"10.1016/j.acra.2024.11.037","DOIUrl":"10.1016/j.acra.2024.11.037","url":null,"abstract":"<div><h3>Purpose</h3><div>This study aimed to develop and evaluate a machine learning model combining clinical, radiomics, and deep learning features derived from PET/CT imaging to predict lymph node metastasis (LNM) in patients with non-small cell lung cancer (NSCLC). The model's interpretability was enhanced using Shapley additive explanations (SHAP).</div></div><div><h3>Methods</h3><div>A total of 248 NSCLC patients who underwent preoperative PET/CT scans were included and divided into training, test, and external validation sets. Radiomics features were extracted from segmented tumor regions on PET/CT images, and deep learning features were generated using the ResNet50 architecture. Feature selection was performed using minimum-redundancy maximum-relevance (mRMR), and the least absolute shrinkage and selection operator (LASSO) algorithm. Four models—clinical, radiomics, deep learning radiomics (DL_radiomics), and combined model—were constructed using the XGBoost algorithm and evaluated based on diagnostic performance metrics, including area under the receiver operating characteristic curve (AUC), accuracy, F1 score, sensitivity, and specificity. Shapley Additive exPlanations (SHAP) was used for model interpretability.</div></div><div><h3>Results</h3><div>The combined model achieved the highest AUC in the test set (AUC<!--> <!-->=<!--> <!-->0.853), outperforming the clinical (AUC<!--> <!-->=<!--> <!-->0.758), radiomics (AUC<!--> <!-->=<!--> <!-->0.831), and DL_radiomics (AUC<!--> <!-->=<!--> <!-->0.834) models. Decision curve analysis (DCA) demonstrated that the combined model offered greater clinical net benefits. SHAP was used for global interpretation, and the summary plot indicated that the features <em>ct_original_glrlm_LongRunHighGrayLevelEmphasis</em>, and <em>pet_gradient_glcm_lmc1</em> were the most important for the model’s predictions.</div></div><div><h3>Conclusion</h3><div>The combined model, combining clinical, radiomics, and deep learning features from PET/CT, significantly improved the accuracy of LNM prediction in NSCLC patients. SHAP-based interpretability provided valuable insights into the model's decision-making process, enhancing its potential clinical application for preoperative decision-making in NSCLC.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"32 3","pages":"Pages 1645-1655"},"PeriodicalIF":3.8,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142814980","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":"Enhancing Engagement and Communication Strategies for Remote Learning.","authors":"Mohammad Taghvaei, Omer A Awan","doi":"10.1016/j.acra.2025.02.025","DOIUrl":"https://doi.org/10.1016/j.acra.2025.02.025","url":null,"abstract":"","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143537014","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":"How to Succeed on Your Residency Interview","authors":"Talal Mourad BA ,&nbsp;Omer A. Awan MD, MPH, CIIP","doi":"10.1016/j.acra.2024.04.037","DOIUrl":"10.1016/j.acra.2024.04.037","url":null,"abstract":"","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"32 3","pages":"Pages 1768-1770"},"PeriodicalIF":3.8,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141181313","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":"Diffusion Weighted Imaging for the Assessment of Lymph Node Metastases in Women with Cervical Cancer: A Meta-analysis of the Apparent Diffusion Coefficient Values","authors":"Robyn F. Distelbrink ,&nbsp;Enise Celebi ,&nbsp;Constantijne H. Mom ,&nbsp;Jaap Stoker ,&nbsp;Shandra Bipat","doi":"10.1016/j.acra.2024.09.020","DOIUrl":"10.1016/j.acra.2024.09.020","url":null,"abstract":"<div><h3>Purpose</h3><div>To assess the diagnostic performance of Diffusion Weighted Imaging (DWI) and provide optimal apparent diffusion coefficient (ADC) cut-off values for differentiating between benign and metastatic lymph nodes in women with uterine cervical cancer.</div></div><div><h3>Method</h3><div>MEDLINE and EMBASE databases were searched. Methodological quality was assessed with QUADAS-2. Data analysis was performed for three subgroups: (1) All studies; (2) Studies with maximum b-values of 800 s/mm², and (3) Studies containing b-values of 1000 s/mm². Receiver-operating characteristics (ROC) curves were constructed and the area under the curve (AUC) was calculated. The maximum Youden index was used to determine optimal ADC cut-off values, following calculations of sensitivity and specificity.</div></div><div><h3>Results</h3><div>16 articles (1156 patients) were included. Overall, their quality was limited. For all studies combined, the optimum ADC cut-off value was 0.985<!--> <!-->×<!--> <!-->10⁻³ mm²/s at maximum Youden Index of 0.77, resulting in sensitivity and specificity of 84%, and 94%, respectively. Studies with b-values up to 800 s/mm², gave an optimum ADC cut-off value of 0.985<!--> <!-->×<!--> <!-->10⁻³ mm²/s at maximum Youden Index of 0.62, with a sensitivity and specificity of 62%, and 100%. Studies containing b-values of 1000 s/mm² gave an optimum ADC cut-off value of 0.9435<!--> <!-->×<!--> <!-->10⁻³ mm²/s at maximum Youden Index of 0.93, with a sensitivity and specificity of 100%, and 93%, respectively.</div></div><div><h3>Conclusion</h3><div>Studies using DWI including b-values of 1000 s/mm² have higher sensitivity and specificity than those with b-values up to 800 s/mm². At the cut-off value of 0.9435<!--> <!-->×<!--> <!-->10⁻³ mm²/s DWI can sufficiently discriminate between benign and metastatic lymph nodes.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"32 3","pages":"Pages 1465-1475"},"PeriodicalIF":3.8,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142331767","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":"Fully Automated and Explainable Measurement of Liver Surface Nodularity in CT: Utility for Staging Hepatic Fibrosis","authors":"Tejas Sudharshan Mathai PhD ,&nbsp;Meghan G. Lubner MD ,&nbsp;Perry J. Pickhardt MD ,&nbsp;Ronald M. Summers MD, PhD","doi":"10.1016/j.acra.2024.09.050","DOIUrl":"10.1016/j.acra.2024.09.050","url":null,"abstract":"<div><h3>Rationale and Objectives</h3><div>In the United States, cirrhosis was the 12th leading cause of death in 2016. Despite end-stage cirrhosis being irreversible, earlier stages of hepatic fibrosis can be reversed via early diagnosis and intervention. The objective is to investigate the utility of a fully automated technique to measure liver surface nodularity (LSN) for staging hepatic fibrosis (stages F0–F4).</div></div><div><h3>Materials and Methods</h3><div>In this retrospective study, a dataset consisting of patients with multiple etiologies of liver disease collected at Institution-A (METAVIR F0–F4, 2000–2016) was used. The LSN was automatically measured in contrast-enhanced CT volumes and compared against scores from a manual tool. Area under the receiver operating characteristics curve (AUC) was used to distinguish between clinically significant fibrosis (≥<!--> <!--> F2), advanced fibrosis (≥<!--> <!-->F3), and end-stage cirrhosis (F4).</div></div><div><h3>Results</h3><div>The study sample had 480 patients (304 men, 176 women, mean age, 49<!--> <!-->±<!--> <!-->9). Automatically derived LSN scores progressively increased with the fibrosis stage: F0 (1.64 [mean]<!--> <!-->±<!--> <!-->1.13 [standard deviation]), F1 (2.16<!--> <!-->±<!--> <!-->2.39), F2 (2.17<!--> <!-->±<!--> <!-->2.55), F3 (2.23<!--> <!-->±<!--> <!-->2.52), and F4 (4.21<!--> <!-->±<!--> <!-->2.94). For discriminating significant fibrosis (≥<!--> <!-->F2), advanced fibrosis (≥<!--> <!-->F3), and cirrhosis (F4), the automated tool achieved ROC AUCs of 73.9%, 82.5%, and 87.8% respectively. The sensitivity and specificity for significant fibrosis (nodularity threshold 1.51) was 85.2% and 73.3%, advanced fibrosis (nodularity threshold 1.73) was 84.2% and 79.5%, and cirrhosis (nodularity threshold 2.18) was 86.5% and 79.5%. Statistical tests revealed that the automated LSN scores distinguished patients with advanced fibrosis (<em>p</em> <!-->&lt;<!--> <!-->.001) and cirrhosis (<em>p<!--> </em>&lt;<!--> <!-->.001).</div></div><div><h3>Conclusion</h3><div>The fully automated LSN measurement retained its predictive power for distinguishing between advanced fibrosis and cirrhosis. The clinical impact is that the fully automated LSN measurement may be useful for early interventions and population-based studies. It can automatically predict the fibrosis stage in ∼45 s in comparison to the ∼2 min needed to manually measure the LSN in a CT volume.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"32 3","pages":"Pages 1398-1408"},"PeriodicalIF":3.8,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142395010","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":"FS-YOLOv9: A Frequency and Spatial Feature-Based YOLOv9 for Real-time Breast Cancer Detection","authors":"Haitian Gui ,&nbsp;Tao Su ,&nbsp;Xinhua Jiang ,&nbsp;Li Li ,&nbsp;Lang Xiong ,&nbsp;Ji Zhou ,&nbsp;Zhiyong Pang","doi":"10.1016/j.acra.2024.09.048","DOIUrl":"10.1016/j.acra.2024.09.048","url":null,"abstract":"<div><h3>Rationale and Objectives</h3><div>Breast cancer screening is critical for reducing mortality rates. YOLOv9, a new real-time object-detection model, is ideal for cancer screening. A customized YOLOv9 model with enhancements for detecting breast cancer on the basis of species and morphological diversity has potential clinical significance.</div></div><div><h3>Materials and Methods</h3><div>The internal dataset consisted of 687 cases split 3:1 for cross-validation. Additionally, 98 cases from external datasets were used for testing. We developed an FS-YOLOv9 model customized for breast cancer detection that incorporated an extra max-pooling layer before the Conv1 of the Adown to enhance high-brightness features. The Adown of the P3 in the backbone was replaced with a high-frequency Haar wavelet convolution kernel, which ignored the low-frequency components during down-sampling to enhance morphology and texture features. The reliability and robustness of our model was determined by measuring the F1 score, the area under curve of free-response receiver operating characteristic (FAUC), mean average precision (mAP), recall, and precision, and comparing them with the findings for the official YOLOv9, YOLOv8, YOLOv5 models.</div></div><div><h3>Results</h3><div>In comparison with the official YOLOv9 model, FS-YOLOv9 showed a higher average F1 score (0.700 vs. 0.669), FAUC (0.695 vs. 0.662), and mAP50 (0.713 vs. 0.679) in the internal dataset; in the external testing dataset, the FS-YOLOv9 improved the average F1 score, FAUC, and mAP50 by 4.58%, 5.78%, and 4.41% respectively.</div></div><div><h3>Conclusion</h3><div>Our FS-YOLOv9 model showed significantly improved performance in detecting breast cancer, making it more practical for high-risk breast cancer diagnosis.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"32 3","pages":"Pages 1228-1240"},"PeriodicalIF":3.8,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142480007","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 predictive performance for new vertebral compression fracture between Hounsfield units value and vertebral bone quality score following percutaneous vertebroplasty or kyphoplasty","authors":"Zhengbo Wang BS ,&nbsp;Lingzhi Li BS ,&nbsp;Tianyou Zhang BS,&nbsp;Ruya Li MS,&nbsp;Wei Ren BS,&nbsp;Helong Zhang BS,&nbsp;Zhiwen Tao BS,&nbsp;Yongxin Ren MD","doi":"10.1016/j.acra.2024.11.039","DOIUrl":"10.1016/j.acra.2024.11.039","url":null,"abstract":"<div><h3>Rationale and Objectives</h3><div>New vertebral compression fractures (NVCF) are very common in patients following percutaneous vertebroplasty (PVP) or kyphoplasty (PKP). The study aims to evaluate the comparative predictive performance of vertebral bone quality (VBQ) score and Hounsfield units (HU) value in forecasting NVCF after surgery.</div></div><div><h3>Materials and Methods</h3><div>This study retrospectively analyzed patients who underwent PVP/PKP at our institution between 2020 and 2021. The VBQ score and HU value were obtained from preoperative magnetic resonance imaging (MRI) and computed tomography (CT) scans, respectively. Subsequently, the forecasting capabilities of these two parameters were assessed by contrasting their receiver operating characteristic (ROC) curve.</div></div><div><h3>Results</h3><div>A total of 303 eligible patients (56 with NVCF and 247 without) were identified in the study. Six relevant literature factors were identified and included in the multivariate analysis revealed that lower HU value (OR = 0.967, 95% CI = 0.953–0.981, <em>P</em> &lt; 0.001) and higher VBQ score (OR = 3.964, 95% CI = 2.369–6.631, <em>P</em> &lt; 0.001) emerged as independent predictors of NCVF occurrence. Compared to the ROC curve of the HU value, demonstrating a diagnostic accuracy of 83.2% (95% CI = 77.5%−88.9%, <em>P</em> &lt; 0.001), the VBQ score was 85.8%. And, a statistically significant negative correlation was observed between the VBQ score and the T-score (r = −0.529, <em>P</em> &lt; 0.001).</div></div><div><h3>Conclusion</h3><div>In patients undergoing PVP/PKP, VBQ score, and HU value are independently associated with the occurrence of NVCF. Assessing the HU value and the VBQ score could play an effective role in planning PVP/PKP operations.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"32 3","pages":"Pages 1562-1573"},"PeriodicalIF":3.8,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142792761","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":"AI Applied to Breast Cancer: Early Detection and Explainable Predictive Models as the Basis of Precision Medicine","authors":"Carmelo Militello","doi":"10.1016/j.acra.2025.01.045","DOIUrl":"10.1016/j.acra.2025.01.045","url":null,"abstract":"","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"32 3","pages":"Pages 1226-1227"},"PeriodicalIF":3.8,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143371336","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":"Advances in spatial resolution and radiation dose reduction using super-resolution deep learning–based reconstruction for abdominal computed tomography: A phantom study","authors":"Yoshinori Funama ,&nbsp;Yasunori Nagayama ,&nbsp;Daisuke Sakabe ,&nbsp;Yuya Ito ,&nbsp;Yutaka Chiba ,&nbsp;Takeshi Nakaura ,&nbsp;Seitaro Oda ,&nbsp;Masafumi Kidoh ,&nbsp;Toshinori Hirai","doi":"10.1016/j.acra.2024.09.012","DOIUrl":"10.1016/j.acra.2024.09.012","url":null,"abstract":"<div><h3>Rationale and Objectives</h3><div>This study evaluated the performance of super-resolution deep learning-based reconstruction (SR-DLR) and compared with it that of hybrid iterative reconstruction (HIR) and normal-resolution DLR (NR-DLR) for enhancing image quality in computed tomography (CT) images across various field of view (FOV) sizes, radiation doses, and noise reduction strengths.</div></div><div><h3>Materials and Methods</h3><div>A Catphan phantom equipped with an external body ring was used. CT images were reconstructed using filtered back-projection (FBP), HIR, NR-DLR, and SR-DLR across three noise reduction strengths: mild, standard, and strong. The noise power spectrum (NPS) was obtained from the FBP, HIR, NR-DLR, and SR-DLR images at various FOVs, radiation doses, and noise reduction strengths. The noise magnitude ratio (NMR) and central frequency ratio (CFR) were calculated from the HIR, NR-DLR, and SR-DLR images relative to the FBP images using NPS. The high-contrast value was obtained from the amplitude values of the peaks and valleys of profile curve and the task-based transfer function were also analyzed.</div></div><div><h3>Results</h3><div>SR-DLR consistently demonstrated superior noise reduction capabilities, with NMR of 0.29–0.36 at reduced dose and 0.35–0.45 at standard dose, outperforming HIR and showing comparable efficiency to NR-DLR. The high-contrast values for SR-DLR were highest at mild and standard levels for both low and standard doses (0.610 and 0.726 at mild and 0.725 and 0.603 at standard levels). At the standard dose, the spatial resolution of SR-DLR was significantly improved, regardless of the noise reduction strength and FOV.</div></div><div><h3>Conclusion</h3><div>SR-DLR images achieved more substantial noise reduction than HIR and similar noise reduction as NR-DLR reconstructions while also improving spatial resolution.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"32 3","pages":"Pages 1517-1524"},"PeriodicalIF":3.8,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142300161","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":"Tumor Apparent Diffusion Coefficient is Associated with Early Recurrence of Intrahepatic Cholangiocarcinoma","authors":"Ruofan Sheng MD ,&nbsp;Beixuan Zheng MD ,&nbsp;Donglong He MB ,&nbsp;Wei Sun MM ,&nbsp;Yunfei Zhang PhD ,&nbsp;Chun Yang MD ,&nbsp;Mengsu Zeng PhD, MD","doi":"10.1016/j.acra.2024.09.035","DOIUrl":"10.1016/j.acra.2024.09.035","url":null,"abstract":"<div><h3>Rationale and Objectives</h3><div>Identifying intrahepatic cholangiocarcinoma (iCCA) patients who are at high risk for early recurrence (ER) can guide personalized treatment strategy and improve survival. This study aimed to investigate the value of preoperative MRI, especially diffusion-weighted imaging, in predicting ER, including in patients receiving neoadjuvant therapy.</div></div><div><h3>Materials and methods</h3><div>This study included 175 pathologically-confirmed iCCA patients who underwent curative resection (114 men, 61 women; mean age 59.0 ± 9.56 years). MRI features, particularly apparent diffusion coefficient (ADC), were analyzed and compared between ER and non-ER cases. Survival analyses of ER were evaluated using Cox regression and Kaplan-Meier analysis.</div></div><div><h3>Results</h3><div>ER occurred in 54.3% (95/175) of patients. Multivariate logistic regression analysis identified tumor ADC as the only independent predictor of ER (odds ratio = 0.034, <em>P</em> &lt; 0.001), with AUCs of 0.758 (95%CI 0.664, 0.836) in the testing cohort and 0.779 (95%CI 0.622, 0.893) in the validation cohort. The optimal ADC threshold was 1.273 × 10⁻³ mm²/s. Tumor ADC was comparable to the AJCC 8th staging system in predicting ER (AUC 0.758 vs 0.650 in testing cohort and 0.779 vs 0.661 in validation cohort). Multivariate Cox analysis identified high tumor burden score (HR = 1.109, <em>P</em> = 0.009), non-smooth margin (HR = 2.265, <em>P</em> = 0.008) and tumor ADC (HR = 0.111, <em>P</em> &lt; 0.001) as independent risk factors for ER. Lower ADC values were linked to shorter RFS in both testing and validation cohorts (<em>P</em> &lt; 0.001 and 0.0219), as well as in patients receiving neoadjuvant therapy (<em>P</em> = 0.003).</div></div><div><h3>Conclusion</h3><div>Preoperative MRI, particularly ADC, can help predict ER in iCCA, regardless of the application of neoadjuvant therapy, comparable to the AJCC 8th staging system.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"32 3","pages":"Pages 1409-1418"},"PeriodicalIF":3.8,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142331774","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}