Academic Radiology最新文献

{"title":"Radiomics Combined with ACR TI-RADS for Thyroid Nodules: Diagnostic Performance, Unnecessary Biopsy Rate, and Nomogram Construction","authors":"Yan-Jing Zhang,&nbsp;Tian Xue,&nbsp;Chang Liu,&nbsp;Yan-Hong Hao,&nbsp;Xiao-Hui Yan,&nbsp;Li-Ping Liu","doi":"10.1016/j.acra.2024.07.053","DOIUrl":"10.1016/j.acra.2024.07.053","url":null,"abstract":"<div><h3>Rationale and Objectives</h3><div>To develop a radiomics model with enhanced diagnostic performance, reduced unnecessary fine needle aspiration biopsy (FNA) rate, and improved clinical net benefit for thyroid nodules.</div></div><div><h3>Methods</h3><div>We conducted a retrospective study of 217 thyroid nodules. Lesions were divided into training (<em>n</em> = 152) and verification (<em>n</em> = 65) cohorts. Three radiomics scores were derived from B-mode ultrasound (B-US) and strain elastography (SE) images, alone and in combination. A radiomics nomogram was constructed by combining high-frequency ultrasonic features and the best-performing radiomics score. The area under the receiver operating characteristic curve (AUC), unnecessary FNA rate, and decision curve analysis (DCA) results for the nomogram were compared to those obtained with the American College of Radiology Thyroid Imaging, Reporting and Data System (ACR TI-RADS) score and the combined TI-RADS<!--> <!-->+<!--> <!-->SE<!--> <!-->+ contrast-enhanced ultrasound (CEUS) advanced clinical score.</div></div><div><h3>Results</h3><div>The three radiomics scores (B-US, SE, B-US<!--> <!-->+<!--> <!-->SE) achieved training AUCs of 0.753 (0.668–0.825), 0.761 (0.674–0.838), and 0.795 (0.715–0.871), and validation AUCs of 0.732 (0.579–0.867), 0.753 (0.609–0.892), and 0.752 (0.592–0.899) respectively. The AUC of the nomogram for the entire patient cohort was 0.909 (0.864–0.954), which was higher than that of the ACR TI-RADS score (<em>P</em> &lt; 0.001) and equivalent to the TI-RADS+SE+CEUS score (<em>P</em> = 0.753). Similarly, the unnecessary FNA rate of the radiomics nomogram was significantly lower than that of the ACR TI-RADS score (<em>P</em> = 0.007) and equivalent to the TI-RADS+SE+CEUS score (<em>P</em> = 0.457). DCA also showed that the radiomics nomogram brought more net clinical benefit than the ACR TI-RADS score but was similar to that of the TI-RADS<!--> <!-->+<!--> <!-->SE<!--> <!-->+<!--> <!-->CEUS score.</div></div><div><h3>Conclusion</h3><div>The radiomics nomogram developed in this study can be used as an objective, accurate, cost-effective, and noninvasive method for the characterization of thyroid nodules.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"31 12","pages":"Pages 4856-4865"},"PeriodicalIF":3.8,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142376285","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":"Super-resolution Deep Learning Reconstruction Enhances Cranial Nerve Depiction and Interobserver Agreement in Neurovascular Conflict Imaging","authors":"Yajie Li ,&nbsp;Shiman Wu MD, PhD","doi":"10.1016/j.acra.2024.10.054","DOIUrl":"10.1016/j.acra.2024.10.054","url":null,"abstract":"","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"31 12","pages":"Pages 5128-5129"},"PeriodicalIF":3.8,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142640167","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":"Role of Natural Language Processing in Automatic Detection of Unexpected Findings in Radiology Reports: A Comparative Study of RoBERTa, CNN, and ChatGPT","authors":"Pilar López-Úbeda PhD ,&nbsp;Teodoro Martín-Noguerol MD ,&nbsp;Jorge Escartín MD ,&nbsp;Antonio Luna MD, PhD","doi":"10.1016/j.acra.2024.07.057","DOIUrl":"10.1016/j.acra.2024.07.057","url":null,"abstract":"<div><h3>Rationale and Objectives</h3><div>Large Language Models can capture the context of radiological reports, offering high accuracy in detecting unexpected findings. We aim to fine-tune a Robustly Optimized BERT Pretraining Approach (RoBERTa) model for the automatic detection of unexpected findings in radiology reports to assist radiologists in this relevant task. Second, we compared the performance of RoBERTa with classical convolutional neural network (CNN) and with GPT4 for this goal.</div></div><div><h3>Materials and Methods</h3><div>For this study, a dataset consisting of 44,631 radiological reports for training and 5293 for the initial test set was used. A smaller subset comprising 100 reports was utilized for the comparative test set. The complete dataset was obtained from our institution's Radiology Information System, including reports from various dates, examinations, genders, ages, etc. For the study's methodology, we evaluated two Large Language Models, specifically performing fine-tuning on RoBERTa and developing a prompt for ChatGPT. Furthermore, extending previous studies, we included a CNN in our comparison.</div></div><div><h3>Results</h3><div>The results indicate an accuracy of 86.15% in the initial test set using the RoBERTa model. Regarding the comparative test set, RoBERTa achieves an accuracy of 79%, ChatGPT 64%, and the CNN 49%. Notably, RoBERTa outperforms the other systems by 30% and 15%, respectively.</div></div><div><h3>Conclusion</h3><div>Fine-tuned RoBERTa model can accurately detect unexpected findings in radiology reports outperforming the capability of CNN and ChatGPT for this task.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"31 12","pages":"Pages 4833-4842"},"PeriodicalIF":3.8,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141914484","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":"Practical Evaluation of ChatGPT Performance for Radiology Report Generation","authors":"Mohsen Soleimani ,&nbsp;Navisa Seyyedi ,&nbsp;Seyed Mohammad Ayyoubzadeh ,&nbsp;Sharareh Rostam Niakan Kalhori ,&nbsp;Hamidreza Keshavarz","doi":"10.1016/j.acra.2024.07.020","DOIUrl":"10.1016/j.acra.2024.07.020","url":null,"abstract":"<div><h3>Rationale and Objectives</h3><div>The process of generating radiology reports is often time-consuming and labor-intensive, prone to incompleteness, heterogeneity, and errors. By employing natural language processing (NLP)-based techniques, this study explores the potential for enhancing the efficiency of radiology report generation through the remarkable capabilities of ChatGPT (Generative Pre-training Transformer), a prominent large language model (LLM).</div></div><div><h3>Materials and Methods</h3><div>Using a sample of 1000 records from the Medical Information Mart for Intensive Care (MIMIC) Chest X-ray Database, this investigation employed Claude.ai to extract initial radiological report keywords. ChatGPT then generated radiology reports using a consistent 3-step prompt template outline. Various lexical and sentence similarity techniques were employed to evaluate the correspondence between the AI assistant-generated reports and reference reports authored by medical professionals.</div></div><div><h3>Results</h3><div>Results showed varying performance among NLP models, with Bart (Bidirectional and Auto-Regressive Transformers) and XLM (Cross-lingual Language Model) displaying high proficiency (mean similarity scores up to 99.3%), closely mirroring physician reports. Conversely, DeBERTa (Decoding-enhanced BERT with disentangled attention) and sequence-matching models scored lower, indicating less alignment with medical language. In the Impression section, the Word-Embedding model excelled with a mean similarity of 84.4%, while others like the Jaccard index showed lower performance.</div></div><div><h3>Conclusion</h3><div>Overall, the study highlights significant variations across NLP models in their ability to generate radiology reports consistent with medical professionals' language. Pairwise comparisons and Kruskal–Wallis tests confirmed these differences, emphasizing the need for careful selection and evaluation of NLP models in radiology report generation. This research underscores the potential of ChatGPT to streamline and improve the radiology reporting process, with implications for enhancing efficiency and accuracy in clinical practice.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"31 12","pages":"Pages 4823-4832"},"PeriodicalIF":3.8,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141983796","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":"Sub-regional CT Radiomics for the Prediction of Ki-67 Proliferation Index in Gastrointestinal Stromal Tumors: A Multi-center Study","authors":"Wemin Cai ,&nbsp;Kun Guo ,&nbsp;Yongxian Chen ,&nbsp;Yubo Shi ,&nbsp;Junkai Chen","doi":"10.1016/j.acra.2024.06.036","DOIUrl":"10.1016/j.acra.2024.06.036","url":null,"abstract":"<div><h3>Rationale and Objectives</h3><div><span>The objective was to assess and examine radiomics models derived from contrast-enhanced </span>CT<span> for their predictive capacity using the sub-regional radiomics<span> regarding the Ki-67 proliferation index (PI) in patients with pathologically confirmed gastrointestinal stromal tumors (GIST).</span></span></div></div><div><h3>Methods</h3><div><span>In this retrospective study, a total of 412 GIST patients across three institutions (223 from center 1, 106 from center 2, and 83 from center 3) was enrolled. Radiomic features were derived from various sub-regions of the tumor region of interest employing the K-means approach. The Least Absolute Shrinkage and Selection Operator (LASSO) regression was employed to identify features correlated with Ki-67 PI level in GIST patients. A support vector machine (SVM) model was then constructed to predict the high level of Ki-67 (Ki-67 index &gt;</span> <!-->8%), drawing on the radiomics features from each sub-region within the training cohort.</div></div><div><h3>Results</h3><div>After features selection process, 6, 9, 9, 7 features were obtained to construct SVM models based on sub-region 1, 2, 3 and the entire tumor, respectively. Among different models, the model developed by the sub-region 1 achieved an area under the receiver operating characteristic curve (AUC) of 0.880 (95% confidence interval [CI]: 0.830 to 0.919), 0.852 (95% CI: 0.770–0.914), 0.799 (95% CI: 0.697–0.879) in the training, external test set 1, and 2, respectively.</div></div><div><h3>Conclusion</h3><div>The results of the present study suggested that SVM model based on the sub-regional radiomics features had the potential of predicting Ki-67 PI level in patients with GIST.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"31 12","pages":"Pages 4974-4984"},"PeriodicalIF":3.8,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141735570","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":"Efficacy of Radiofrequency Ablation in Autonomous Functioning Thyroid Nodules: A Comprehensive Systematic Review and Meta-analysis","authors":"Mona Javid ,&nbsp;Arian Mirdamadi ,&nbsp;Fateme Sheida ,&nbsp;Sandeep Samethadka Nayak ,&nbsp;Rachana Borkar ,&nbsp;Rahul Hegde ,&nbsp;Mohammadreza Javid ,&nbsp;Bita Amirian ,&nbsp;Mohammad-Hossein Keivanlou ,&nbsp;Ehsan Amini-Salehi ,&nbsp;Soheil Hassanipour","doi":"10.1016/j.acra.2024.06.023","DOIUrl":"10.1016/j.acra.2024.06.023","url":null,"abstract":"<div><h3>Background</h3><div>Radiofrequency ablation (RFA) is a minimally invasive outpatient procedure that has recently emerged as a treatment option for autonomous functioning thyroid nodules (AFTNs), offering a less invasive alternative to surgery.</div><div>The objective of this systematic review and meta-analysis is to evaluate the efficacy of RFA for AFTNs.</div></div><div><h3>Method</h3><div>Global databases of PubMed, Scopus, Embase, Web of Science, and Cochrane Library were systematically searched from 1990 until January 5, 2024, for studies on AFTNs undergoing RFA that presented volume reduction ratio (VRR) for at least one of 1, 3, 6 or 12 months post-operative follow-up with the results presented as means. The primary outcomes were VRR and TSH normalization rate, and the secondary outcomes were the cosmetic score, symptom score, and post-procedure complications. Heterogeneity was assessed by Cochrane and I² statistics, and a random-effects model was used for meta-analysis. The study protocol was registered on PROSPERO (CRD42024499932).</div></div><div><h3>Results</h3><div>A total of 10 eligible studies with a total sample size of 254 were included. The pooled VRR after 1, 3, 6, and 12 months of follow-up post-treatment with RFA was 46.6% (95% CI: 40.3–52.9%), 62% (95% CI: 57.6–66.4%), 67.4% (95% CI:62.3–72.6%), and 77.2% (95% CI: 79.2–81.5%), respectively. The overall rate of TSH normalization was 76.4% (95% CI: 58.1–88.4%). Based on included studies the overall rate of subclinical hypothyroidism as one of the most important side effects of this method was 4% (95% CI: 1.9%−8.1%).</div></div><div><h3>Conclusion</h3><div>RFA emerges as a promising non-surgical treatment for AFTNs, showing high rates of TSH normalization, tumor size reduction, and improved cosmetic and symptom scores. However, further research is needed to compare RFA with surgical methods and assess long-term outcomes.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"31 12","pages":"Pages 4843-4855"},"PeriodicalIF":3.8,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141789786","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 Nomogram Based on DCE-MRI Radiomics for Predicting Hypoxia-Inducible Factor 1α Expression in Locally Advanced Rectal Cancer","authors":"Zhiheng Li ,&nbsp;Huizhen Huang ,&nbsp;Zhenhua Zhao ,&nbsp;Weili Ma ,&nbsp;Haijia Mao ,&nbsp;Fang Liu ,&nbsp;Ye Yang ,&nbsp;Dandan Wang ,&nbsp;Zengxin Lu","doi":"10.1016/j.acra.2024.05.015","DOIUrl":"10.1016/j.acra.2024.05.015","url":null,"abstract":"<div><h3>Rationale and Objectives</h3><div>The expression levels of hypoxia-inducible factor 1 alpha (HIF-1α) have been identified as a pivotal marker, correlating with treatment response in patients with locally advanced rectal cancer<span><span> (LARC). This study aimed to develop and validate a nomogram based on dynamic contrast-enhanced MRI (DCE-MRI) </span>radiomics<span> and clinical features for predicting the expression of HIF-1α in patients with LARC.</span></span></div></div><div><h3>Materials and Methods</h3><div>A total of 102 patients diagnosed with locally advanced rectal cancer<span><span> were divided into training (n = 71) and validation (n = 31) cohorts. The expression statuses of HIF-1α were histopathologically classified, categorizing patients into high and low expression groups. The intraclass correlation coefficient (ICC), minimum redundancy maximum relevance (mRMR), and the least absolute shrinkage and selection operator (LASSO) were employed for feature selection to construct a radiomics signature and calculate the radiomics score (Rad-score). Univariate and </span>multivariate analyses of clinical features and Rad-score were applied, and the clinical model and the nomogram were constructed. The predictive performance of the nomogram incorporating clinical features and Rad-score was assessed using Receiver Operating Characteristics (ROC) curves, decision curve analysis (DCA), and calibration curves.</span></div></div><div><h3>Results</h3><div>Seven radiomics features from DCE-MRI were used to build the radiomics signature. The nomogram incorporating CEA, Ki-67 and Rad-score had the highest AUC values in the training cohort and in the validation cohort (AUC: 0.918 and 0.920). Decision curve analysis showed that the nomogram outperformed the clinical model and radiomics signature in terms of clinical utility. In addition, the calibration curve for the nomogram demonstrated good agreement between prediction and actual observation.</div></div><div><h3>Conclusion</h3><div>The nomogram based on DCE-MRI radiomics and clinical features showed favorable predictive efficacy and might be useful for preoperatively discriminating the expression of HIF-1α.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"31 12","pages":"Pages 4923-4933"},"PeriodicalIF":3.8,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141181300","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":"Predicting Lymphovascular Invasion in Non-small Cell Lung Cancer Using Deep Convolutional Neural Networks on Preoperative Chest CT","authors":"Jian Wang ,&nbsp;Yang Yang ,&nbsp;Zongyu Xie ,&nbsp;Guoqun Mao ,&nbsp;Chen Gao ,&nbsp;Zhongfeng Niu ,&nbsp;Hongli Ji ,&nbsp;Linyang He ,&nbsp;Xiandi Zhu ,&nbsp;Hengfeng Shi ,&nbsp;Maosheng Xu","doi":"10.1016/j.acra.2024.05.010","DOIUrl":"10.1016/j.acra.2024.05.010","url":null,"abstract":"<div><h3>Rationale and Objectives</h3><div>Lymphovascular invasion (LVI) plays a significant role in precise treatments of non-small cell lung cancer (NSCLC). This study aims to build a non-invasive LVI prediction diagnosis model by combining preoperative CT images with deep learning technology.</div></div><div><h3>Materials and Methods</h3><div>This retrospective observational study included a series of consecutive patients who underwent surgical resection for non-small cell lung cancer (NSCLC) and received pathologically confirmed diagnoses. The cohort was randomly divided into a training group comprising 70 % of the patients and a validation group comprising the remaining 30 %. Four distinct deep convolutional neural network (DCNN) prediction models were developed, incorporating different combination of two-dimensional (2D) and three-dimensional (3D) CT imaging features as well as clinical-radiological data. The predictive capabilities of the models were evaluated by receiver operating characteristic curves (AUC) values and confusion matrices. The Delong test was utilized to compare the predictive performance among the different models.</div></div><div><h3>Results</h3><div>A total of 3034 patients with NSCLC were recruited in this study including 106 LVI+ patients. In the validation cohort, the Dual-head Res2Net_3D23F model achieved the highest AUC of 0.869, closely followed by the models of Dual-head Res2Net_3D3F (AUC, 0.868), Dual-head Res2Net_3D (AUC, 0.867), and EfficientNet-B0_2D (AUC, 0.857). There was no significant difference observed in the performance of the EfficientNet-B0_2D model when compared to the Dual-head Res2Net_3D3F and Dual-head Res2Net_3D23F.</div></div><div><h3>Conclusion</h3><div>Findings of this study suggest that utilizing deep convolutional neural network is a feasible approach for predicting pathological LVI in patients with NSCLC.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"31 12","pages":"Pages 5237-5247"},"PeriodicalIF":3.8,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141285280","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":"What Program Directors Think About Resident Education: Results of the 2023 Spring Survey of the Association of Program Directors in Radiology (APDR) Part II","authors":"Hillary W. Garner MD ,&nbsp;Priscilla J. Slanetz MD, MPH ,&nbsp;Jonathan O. Swanson MD, MBA ,&nbsp;Brent D. Griffith MD ,&nbsp;Carolynn M. DeBenedectis MD ,&nbsp;Jennifer E. Gould MD ,&nbsp;Tara L. Holm MD ,&nbsp;Michele Retrouvey MD ,&nbsp;Angelisa M. Paladin MD ,&nbsp;Anna Rozenshtein MD, MPH","doi":"10.1016/j.acra.2024.08.044","DOIUrl":"10.1016/j.acra.2024.08.044","url":null,"abstract":"<div><h3>Rationale and Objectives</h3><div>The Association of Program Directors in Radiology (APDR) administers an annual survey to assess issues and experiences related to residency program management and education. Response data from the 2023 survey provides insights on the impact of COVID-19 on resident recruitment (Part I) and education (Part II), which can be used to facilitate planning and resource allocation for the evolving needs of programs and their leadership.</div></div><div><h3>Materials and Methods</h3><div>An observational, cross-sectional study of the APDR membership was performed using a web-based survey consisting of 45 questions, 12 of which pertain to resident education in the post-pandemic era and are discussed in Part II of a two-part survey analysis. All active APDR members (n = 393) were invited to participate in the survey.</div></div><div><h3>Results</h3><div>The response rate was 32% (124 of 393). Results were tallied using Qualtrics software and qualitative responses were tabulated or summarized as comments.</div></div><div><h3>Conclusions</h3><div>The primary challenges to resident education are faculty burnout, rising case volumes, and remote instruction. However, most program leaders report that in-person readouts are much more common than remote readouts. The ability to offer both in-person and remote AIRP sessions is viewed positively. Most program leaders require Authorized User certification, although many do not think all residents need it. Assessment of procedural competence varies by the type of procedure and is similar to graduates’ self-assessment of competence.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"31 12","pages":"Pages 5331-5336"},"PeriodicalIF":3.8,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142331775","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":"Efficacy and Safety of Thermal Ablation for Patients With Stage I Non-small Cell Lung Cancer","authors":"Jin-ying He ,&nbsp;Ling Yang ,&nbsp;Dong-dong Wang","doi":"10.1016/j.acra.2024.05.038","DOIUrl":"10.1016/j.acra.2024.05.038","url":null,"abstract":"<div><h3>Rationale and Objectives</h3><div>The objective of this study was to measure the safety and efficacy of thermal ablation, including radiofrequency ablation<span> (RFA) and microwave ablation (MWA), for patients with stage I non-small cell lung cancer (NSCLC).</span></div></div><div><h3>Materials and Methods</h3><div>The databases PubMed was searched from inception to November 2023 to identify relevant studies. Statistical analyses were performed with R version 3. 6. 3.</div></div><div><h3>Results</h3><div><span><span><span>Thirty-three studies involving 1400 patients were finally included. According to our study, the incidence of patients with stage I NSCLC who were older than 60 years old was 98 % (95 % CI [94–100 %]); the lesions were mostly located in RUL (Right Upper Lobe) and LUL (Left Upper Lobe), and the incidence of the two sites was 29 % (95 % CI [23–35 %]) and 27 % (95 % CI [21–33 %]), respectively; the types of lung cancers mainly included adenocarcinoma, </span>squamous carcinoma, and large-cell lung cancer, of which adenocarcinoma accounted for the largest proportion of 63 % (95 % CI [56–70 %]); the causes of death were mainly categorized into cancer-related (57 %, 95 %CI[40–74 %]) and noncancer-related (40 %, 95 %CI [23–58 %]); the common complications in the </span>postoperative period<span> were pneumothorax and pain, with the incidence of 33 % (95 %CI[24–44 %]) and 33 % (95 %CI[19–50 %]), and the rate of the </span></span>postoperative complications<span> in MWA was slightly higher than those in RFA; the local recurrence rate was 23 % (95 %CI[17–29 %]) and the distant recurrence rate was 18 % (95 %CI[7–32 %]); the pooling result showed the rate of 1-, 2-, 3-, and 5-year survival rate were 96 %, 81 %, 68 %, and 42 %, the Cancer-specific survival (CSS) rates at 1, 2, 3, and 5 years were 98 %, 88 %, 75 %, and 58 %, Disease-free survival (DFS) rates at 1, 2, 3, and 5 years were 87 %, 63 %, 57 %, and 42 %, there were no significant differences existed between the RFA group and MWA group in survival rate, CSS and DFS.</span></div></div><div><h3>Conclusion</h3><div>Ablation therapy is safe and effective for stage I NSCLC patient. MWA and RFA have comparable efficacy, safety, and prognosis, which could be recommended for patients with stageⅠNSCLC, especially for patients who cannot tolerate open surgery.</div></div>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":"31 12","pages":"Pages 5269-5279"},"PeriodicalIF":3.8,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141472240","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}