Insights into Imaging最新文献

{"title":"Strengthening lung cancer screening in Europe: fostering participation, improving outcomes, and addressing health inequalities through collaborative initiatives in the SOLACE consortium.","authors":"Hans-Ulrich Kauczor, Oyunbileg von Stackelberg, Emily Nischwitz, Joanna Chorostowska-Wynimko, Monika Hierath, Coline Mathonier, Helmut Prosch, Pamela Zolda, Marie-Pierre Revel, Ildikó Horváth, Martina Koziar Vašáková, Pippa Powell, Miroslav Samarzija, Torsten Gerriet Blum","doi":"10.1186/s13244-024-01814-5","DOIUrl":"10.1186/s13244-024-01814-5","url":null,"abstract":"<p><p>The Strengthening the Screening of Lung Cancer in Europe (SOLACE) initiative, supported by Europe's Beating Cancer Plan, is dedicated to advancing lung cancer screening. This initiative brings together the most extensive pan-European network of respiratory and radiology experts, involving 37 partners from 15 countries. SOLACE aims to enhance equitable access to lung cancer screening by developing targeted recruitment strategies for underrepresented and high-risk populations. Through comprehensive work packages, SOLACE integrates scientific research, pilot studies, and sustainability efforts to bolster regional and national screening efforts across EU member states. CRITICAL RELEVANCE STATEMENT: The SOLACE project aims to facilitate the optimization and implementation of equitable lung cancer screening programs across the heterogeneous healthcare landscape in EU member states. KEY POINTS: The effectiveness of lung cancer screening is supported by both scientific evidence and now increasing legislative support. SOLACE aims to develop, test, and disseminate tools to facilitate the realization of lung cancer screening at both a national and regional level. Previously underrepresented populations in lung cancer screening will be targeted by tailored recruitment strategies. SOLACE forms the first pan-European network of experts poised to drive real-world implementation of lung cancer screening.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"15 1","pages":"252"},"PeriodicalIF":4.1,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11496428/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142464385","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":"Risk stratification of thymic epithelial tumors based on peritumor CT radiomics and semantic features.","authors":"Lin Zhang, Zhihan Xu, Yan Feng, Zhijie Pan, Qinyao Li, Ai Wang, Yanfei Hu, Xueqian Xie","doi":"10.1186/s13244-024-01798-2","DOIUrl":"10.1186/s13244-024-01798-2","url":null,"abstract":"<p><strong>Objectives: </strong>To develop and validate nomograms combining radiomics and semantic features to identify the invasiveness and histopathological risk stratification of thymic epithelial tumors (TET) using contrast-enhanced CT.</p><p><strong>Methods: </strong>This retrospective multi-center study included 224 consecutive cases. For each case, 6764 intratumor and peritumor radiomics features and 31 semantic features were collected. Multi-feature selections and decision tree models were performed on radiomics features and semantic features separately to select the most important features for Masaoka-Koga staging and WHO classification. The selected features were then combined to create nomograms for the two systems. The performance of the radiomics model, semantic model, and combined model was evaluated using the area under the receiver operating characteristic curves (AUCs).</p><p><strong>Results: </strong>One hundred eighty-seven cases (56.5 years ± 12.3, 101 men) were included, with 62 cases as the external test set. For Masaoka-Koga staging, the combined model, which incorporated five peritumor radiomics features and four semantic features, showed an AUC of 0.958 (95% CI: 0.912-1.000) in distinguishing between early-stage (stage I/II) and advanced-stage (III/IV) TET in the external test set. For WHO classification, the combined model incorporating five peritumor radiomics features and two semantic features showed an AUC of 0.857 (0.760-0.955) in differentiating low-risk (type A/AB/B1) and high-risk (B2/B3/C) TET. The combined models showed the most effective predictive performance, while the semantic models exhibited comparable performance to the radiomics models in both systems (p > 0.05).</p><p><strong>Conclusion: </strong>The nomograms combining peritumor radiomics features and semantic features could help in increasing the accuracy of grading invasiveness and risk stratification of TET.</p><p><strong>Critical relevance statement: </strong>Peripheral invasion and histopathological type are major determinants of treatment and prognosis of TET. The integration of peritumoral radiomics features and semantic features into nomograms may enhance the accuracy of grading invasiveness and risk stratification of TET.</p><p><strong>Key points: </strong>Peritumor region of TET may suggest histopathological and invasive risk. Peritumor radiomic and semantic features allow classification by Masaoka-Koga staging (AUC: 0.958). Peritumor radiomic and semantic features enable the classification of histopathological risk (AUC: 0.857).</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"15 1","pages":"253"},"PeriodicalIF":4.1,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11496418/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142464384","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":"Automated segment-level coronary artery calcium scoring on non-contrast CT: a multi-task deep-learning approach.","authors":"Bernhard Föllmer, Sotirios Tsogias, Federico Biavati, Kenrick Schulze, Maria Bosserdt, Lars Gerrit Hövermann, Sebastian Stober, Wojciech Samek, Klaus F Kofoed, Pál Maurovich-Horvat, Patrick Donnelly, Theodora Benedek, Michelle C Williams, Marc Dewey","doi":"10.1186/s13244-024-01827-0","DOIUrl":"https://doi.org/10.1186/s13244-024-01827-0","url":null,"abstract":"<p><strong>Objectives: </strong>To develop and evaluate a multi-task deep-learning (DL) model for automated segment-level coronary artery calcium (CAC) scoring on non-contrast computed tomography (CT) for precise localization and quantification of calcifications in the coronary artery tree.</p><p><strong>Methods: </strong>This study included 1514 patients (mean age, 60.0 ± 10.2 years; 56.0% female) with stable chest pain from 26 centers participating in the multicenter DISCHARGE trial (NCT02400229). The patients were randomly assigned to a training/validation set (1059) and a test set (455). We developed a multi-task neural network for performing the segmentation of calcifications on the segment level as the main task and the segmentation of coronary artery segment regions with weak annotations as an auxiliary task. Model performance was evaluated using (micro-average) sensitivity, specificity, F1-score, and weighted Cohen's κ for segment-level agreement based on the Agatston score and performing interobserver variability analysis.</p><p><strong>Results: </strong>In the test set of 455 patients with 1797 calcifications, the model assigned 73.2% (1316/1797) to the correct coronary artery segment. The model achieved a micro-average sensitivity of 0.732 (95% CI: 0.710-0.754), a micro-average specificity of 0.978 (95% CI: 0.976-0.980), and a micro-average F1-score of 0.717 (95% CI: 0.695-0.739). The segment-level agreement was good with a weighted Cohen's κ of 0.808 (95% CI: 0.790-0.824), which was only slightly lower than the agreement between the first and second observer (0.809 (95% CI: 0.798-0.845)).</p><p><strong>Conclusion: </strong>Automated segment-level CAC scoring using a multi-task neural network approach showed good agreement on the segment level, indicating that DL has the potential for automated coronary artery calcification classification.</p><p><strong>Critical relevance statement: </strong>Multi-task deep learning can perform automated coronary calcium scoring on the segment level with good agreement and may contribute to the development of new and improved calcium scoring methods.</p><p><strong>Key points: </strong>Segment-level coronary artery calcium scoring is a tedious and error-prone task. The proposed multi-task model achieved good agreement with a human observer on the segment level. Deep learning can contribute to the automation of segment-level coronary artery calcium scoring.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"15 1","pages":"250"},"PeriodicalIF":4.1,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11484984/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142464364","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":"RadioComics-Santa Claus and the breakthrough reaction.","authors":"Paolo Lombardo, Knud Nairz, Ingrid Boehm","doi":"10.1186/s13244-024-01835-0","DOIUrl":"https://doi.org/10.1186/s13244-024-01835-0","url":null,"abstract":"","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"15 1","pages":"251"},"PeriodicalIF":4.1,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11484994/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142464367","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":"Chronic ankle instability: a cadaveric anatomical and 3D high-resolution MRI study for surgical reconstruction procedures.","authors":"Meng Dai, Hu Zhao, Peng Sun, Jiazheng Wang, Caixia Kong, Xiaoming Liu, Deyu Duan, Xi Liu","doi":"10.1186/s13244-024-01824-3","DOIUrl":"https://doi.org/10.1186/s13244-024-01824-3","url":null,"abstract":"<p><strong>Objectives: </strong>To quantitatively investigate the anatomy of the anterior talofibular ligament (ATFL) and calcaneofibular ligament (CFL) for surgical reconstruction procedures in chronic ankle instability (CAI).</p><p><strong>Methods: </strong>3D MRI was performed on five fresh-frozen cadaveric ankles using six different spatial resolutions (0.3 × 0.3 × 0.3 mm³, 0.45 × 0.45 × 0.45 mm³, 0.6 × 0.6 × 0.6 mm³, 0.75 × 0.75 × 0.75 mm³, 0.9 × 0.9 × 0.9 mm³, 1.05 × 1.05 × 1.05 mm³). After comparing the MRI results with cadaver dissection, a resolution of 0.45 × 0.45 × 0.45 mm³ was selected for bilateral ankles MRI on 24 volunteers. Classification of the ATFL and four distances of surgically relevant bony landmarkers were analyzed (distance 1 and 3, the fibular origin of the ATFL and CFL to the tip of fibula, respectively; distance 2, the talar insertion of the ATFL to the bare zone of talus; distance 4, the calcaneal insertion of the CFL to the peroneal tubercle).</p><p><strong>Results: </strong>In subjective evaluation, the interobserver ICC was 0.95 (95% confidence interval (CI): 0.94-0.97) between two readers. The spatial resolution of 0.3 × 0.3 × 0.3 mm³ and 0.45 × 0.45 × 0.45 mm³ received highest subjective score on average and demonstrated highest consistency with autopsy measurements in objective evaluation. Measurements on the 48 volunteer ankles, distance 1 in type I and II were 12.65 ± 2.08 mm, 13.43 ± 2.06 mm (superior-banded in Type II) and 7.69 ± 2.56 mm (inferior-banded in Type II) (means ± SD), respectively. Distance 2 in type I and II were 10.90 ± 2.24 mm, 11.07 ± 2.66 mm (superior-banded in Type II), and 18.44 ± 3.28 mm (inferior-banded in Type II), respectively. Distance 3 and 4 were 4.71 ± 1.04 mm and 14.35 ± 2.22 mm, respectively.</p><p><strong>Conclusion: </strong>We demonstrated the feasibility of quantifying the distances between bony landmarkers for surgical reconstruction surgery in CAI using high-resolution 3D MRI.</p><p><strong>Critical relevance statement: </strong>High-resolution 3D MRI examination may have a guiding effect on the preoperative evaluation of chronic ankle instability patients.</p><p><strong>Key points: </strong>Spatial resolutions of 0.3 × 0.3 × 0.3 mm³ and 0.45 × 0.45 × 0.45 mm³ demonstrated highest consistency with autopsy measurements. The spatial resolution of 0.45 × 0.45 × 0.45 mm³ was conformed more to clinical needs. 3D MRI can assist surgeons in developing preoperative plans for chronic ankle instability.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"15 1","pages":"249"},"PeriodicalIF":4.1,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11479647/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142464366","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":"Body composition as a potential imaging biomarker for predicting the progression risk of chronic kidney disease.","authors":"Zhouyan Liao, Guanjie Yuan, Kangwen He, Shichao Li, Mengmeng Gao, Ping Liang, Chuou Xu, Qian Chu, Min Han, Zhen Li","doi":"10.1186/s13244-024-01826-1","DOIUrl":"https://doi.org/10.1186/s13244-024-01826-1","url":null,"abstract":"<p><strong>Purpose: </strong>To investigate whether the body composition parameters can be employed as potential biomarkers for predicting the progression risk of chronic kidney disease (CKD).</p><p><strong>Materials and methods: </strong>Four hundred sixteen patients diagnosed with CKD were included in this retrospective study. Patients with a greater than 50% decline in estimated glomerular filtration rate or progression to end-stage kidney disease were in the high-risk group, otherwise, they were in a low-risk group. Body composition area, the index, and radiodensities in the Hounsfield unit (HU), which reflect the degree of X-ray absorption, were measured on abdominal CT images. Risk factors in body composition and clinical parameters of CKD were identified by Cox regression and utilized to construct the nomogram. The performance of the nomogram was assessed using time receiver operating characteristics curves, calibration curves, and decision curve analysis.</p><p><strong>Results: </strong>There were 254 patients in low-risk group and 162 in high-risk group (268 males, 148 females, mean age: 55.89 years). Urea, diabetes, 24 h-urinary protein, mean arterial pressure, and subcutaneous adipose tissue radiodensity (SATd) were valuable indicators for predicting the high-risk group. The area under curve values for the nomogram of training/validation set at 1 year, 2 years, and 3 years were 0.805/0.753, 0.784/0.783, and 0.846/0.754, respectively. For diabetic CKD patients, extra attention needs to be paid to visceral to subcutaneous fat ratio and renal sinus fat radiodensity.</p><p><strong>Conclusion: </strong>SATd was the most valuable noninvasive indicator of all body composition parameters for predicting high-risk populations with CKD. The nomogram we constructed has generalization with easily obtainable indicators, good performance, differentiation, and clinical practicability.</p><p><strong>Critical relevance statement: </strong>Radiodensity rather than an area of adipose tissue can be used as a new biomarker of prognosis for CKD patients, providing new insights into risk assessment, stratified management, and treatment for CKD patients.</p><p><strong>Key points: </strong>Obesity is an independent risk factor for the development and prognosis of CKD. Adipose tissue radiodensity is more valuable than fat area in prognosticating for kidney disease. Parameters that prognosticate in diabetic CKD patients are different from those in other CKD patients.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"15 1","pages":"247"},"PeriodicalIF":4.1,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11473763/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142464365","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":"Recommendations for the creation of benchmark datasets for reproducible artificial intelligence in radiology.","authors":"Nikos Sourlos, Rozemarijn Vliegenthart, Joao Santinha, Michail E Klontzas, Renato Cuocolo, Merel Huisman, Peter van Ooijen","doi":"10.1186/s13244-024-01833-2","DOIUrl":"https://doi.org/10.1186/s13244-024-01833-2","url":null,"abstract":"<p><p>Various healthcare domains have witnessed successful preliminary implementation of artificial intelligence (AI) solutions, including radiology, though limited generalizability hinders their widespread adoption. Currently, most research groups and industry have limited access to the data needed for external validation studies. The creation and accessibility of benchmark datasets to validate such solutions represents a critical step towards generalizability, for which an array of aspects ranging from preprocessing to regulatory issues and biostatistical principles come into play. In this article, the authors provide recommendations for the creation of benchmark datasets in radiology, explain current limitations in this realm, and explore potential new approaches. CLINICAL RELEVANCE STATEMENT: Benchmark datasets, facilitating validation of AI software performance can contribute to the adoption of AI in clinical practice. KEY POINTS: Benchmark datasets are essential for the validation of AI software performance. Factors like image quality and representativeness of cases should be considered. Benchmark datasets can help adoption by increasing the trustworthiness and robustness of AI.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"15 1","pages":"248"},"PeriodicalIF":4.1,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11473745/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142464368","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":"Cardiac MRI in infarct-like myocarditis: transmural extension of late gadolinium enhancement is associated with worse outcomes.","authors":"Alexander Isaak, Johannes Wirtz, Dmitrij Kravchenko, Narine Mesropyan, Leon M Bischoff, Simon Bienert, Leonie Weinhold, Claus C Pieper, Ulrike Attenberger, Can Öztürk, Sebastian Zimmer, Daniel Kuetting, Julian A Luetkens","doi":"10.1186/s13244-024-01832-3","DOIUrl":"10.1186/s13244-024-01832-3","url":null,"abstract":"<p><strong>Objectives: </strong>To assess the prognostic value of cardiac MRI (CMR) parameters for the occurrence of major adverse cardiac events (MACE) in patients with infarct-like myocarditis.</p><p><strong>Methods: </strong>In this retrospective single-center study, patients with CMR-confirmed acute myocarditis with infarct-like presentation were identified (2007-2020). Functional and structural parameters were analyzed including late gadolinium enhancement (LGE). The primary endpoint was the occurrence of MACE up to 5 years after discharge.</p><p><strong>Results: </strong>In total, 130 patients (mean age, 40 ± 19 years; 97 men, 75%) with infarct-like myocarditis were included. CMR was conducted a median of 3 days (interquartile range [IQR], 1-5) after symptom onset. MACE occurred in 18/130 patients (14%) during a median follow-up of 19.3 months (IQR, 4.5-53). The median extent of LGE was 7% (IQR, 4-10). LGE affected the subepicardium in 111/130 patients (85%), the midwall in 45/130 patients (35%), and both the subepicardium and midwall in 27/130 patients (21%). Transmural extension of non-ischemic LGE lesions was observed in 15/130 patients (12%) and septal LGE in 42/130 patients (32%). In univariable Cox regression analysis, a significant association was found between the occurrence of MACE and both, quantified LGE extent and transmural LGE pattern. In multivariable analysis, transmural extension of LGE was an independent predictor for MACE (hazard ratio, 6.34; 95% confidence interval: 2.29-17.49; p < 0.001). Patients with the transmural extension of LGE had a shorter event-free time on Kaplan-Meier analysis (log-rank p < 0.001).</p><p><strong>Conclusions: </strong>MACE occurred in 14% of patients with infarct-like myocarditis during follow-up. A transmural extension of non-ischemic LGE was associated with a worse long-term prognosis.</p><p><strong>Critical relevance statement: </strong>CMR-based assessment of transmural extension of non-ischemic LGE holds the potential to serve as an easily assessable marker for risk stratification in patients with infarct-like myocarditis.</p><p><strong>Key points: </strong>The prognostic value of CMR was studied in patients with infarct-like myocarditis. The extent of LGE and transmural extension were linked to adverse cardiac events. Transmural non-ischemic LGE can serve as an easily assessable prognostic marker.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"15 1","pages":"246"},"PeriodicalIF":4.1,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11469985/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142400170","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":"Prediction of allograft function in pre-transplant kidneys using sound touch elastography (STE): an ex vivo study.","authors":"Fu-Shun Pan, Dao-Peng Yang, Guo-Dong Zhao, Shu-Qi Huang, Yan Wang, Ming Xu, Jiang Qiu, Yan-Ling Zheng, Xiao-Yan Xie, Gang Huang","doi":"10.1186/s13244-024-01837-y","DOIUrl":"10.1186/s13244-024-01837-y","url":null,"abstract":"<p><strong>Background: </strong>The purpose of the study was to evaluate renal quality and predict posttransplant graft function using ex vivo sound touch elastography (STE).</p><p><strong>Methods: </strong>In this prospective study, 106 donor kidneys underwent ex vivo STE examination and biopsy from March 2022 to August 2023. The mean stiffness of the superficial cortex (STE_sc), deep cortex (STE_dc), and medulla (STE_me) was obtained and synthesized into one index (STE) through the factor analysis method. Additionally, 100 recipients were followed up for 6 months. A random forest algorithm was employed to explore significant predictive factors associated with the Remuzzi score and allograft function. The performance of parameters was evaluated by using the area under the receiver operating characteristic curve (AUC).</p><p><strong>Results: </strong>STE had AUC values of 0.803 for diagnosing low Remuzzi and 0.943 for diagnosing high Remuzzi. Meanwhile, STE had an AUC of 0.723 for diagnosing moderate to severe ATI. Random forest algorithm identified STE and Remuzzi score as significant predictors for 6-month renal function. The AUC for STE in predicting postoperative allograft function was 0.717, which was comparable with that of the Remuzzi score (AUC = 0.756). Nevertheless, the specificity of STE was significantly higher than that of Remuzzi (0.913 vs 0.652, p < 0.001). Given these promising results, donor kidneys can be transplanted directly without the need for biopsy when STE ≤ 11.741.</p><p><strong>Conclusions: </strong>The assessment of kidney quality using ex vivo STE demonstrated significant predictive value for the Remuzzi score and allograft function, which could help avoid unnecessary biopsy.</p><p><strong>Critical relevance statement: </strong>Pre-transplant kidney quality measured with ex vivo STE can be used to assess donor kidney quality and avoid unnecessary biopsy.</p><p><strong>Key points: </strong>STE has significant value for diagnosing low Remuzzi and high Remuzzi scores. STE achieved good performance in predicting posttransplant allograft function. Assessment of kidney quality using ex vivo STE could avoid unnecessary biopsies.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"15 1","pages":"245"},"PeriodicalIF":4.1,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11469982/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142400171","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":"Validation of the Mirai model for predicting breast cancer risk in Mexican women.","authors":"Daly Avendano, Maria Adele Marino, Beatriz A Bosques-Palomo, Yesika Dávila-Zablah, Pedro Zapata, Pablo J Avalos-Montes, Cecilio Armengol-García, Carmelo Sofia, Margarita Garza-Montemayor, Katja Pinker, Servando Cardona-Huerta, José Tamez-Peña","doi":"10.1186/s13244-024-01808-3","DOIUrl":"10.1186/s13244-024-01808-3","url":null,"abstract":"<p><strong>Objectives: </strong>To validate the performance of Mirai, a mammography-based deep learning model, in predicting breast cancer risk over a 1-5-year period in Mexican women.</p><p><strong>Methods: </strong>This retrospective single-center study included mammograms in Mexican women who underwent screening mammography between January 2014 and December 2016. For women with consecutive mammograms during the study period, only the initial mammogram was included. Pathology and imaging follow-up served as the reference standard. Model performance in the entire dataset was evaluated, including the concordance index (C-Index) and area under the receiver operating characteristic curve (AUC). Mirai's performance in terms of AUC was also evaluated between mammography systems (Hologic versus IMS). Clinical utility was evaluated by determining a cutoff point for Mirai's continuous risk index based on identifying the top 10% of patients in the high-risk category.</p><p><strong>Results: </strong>Of 3110 patients (median age 52.6 years ± 8.9), throughout the 5-year follow-up period, 3034 patients remained cancer-free, while 76 patients developed breast cancer. Mirai achieved a C-index of 0.63 (95% CI: 0.6-0.7) for the entire dataset. Mirai achieved a higher mean C-index in the Hologic subgroup (0.63 [95% CI: 0.5-0.7]) versus the IMS subgroup (0.55 [95% CI: 0.4-0.7]). With a Mirai index score > 0.029 (10% threshold) to identify high-risk individuals, the study revealed that individuals in the high-risk group had nearly three times the risk of developing breast cancer compared to those in the low-risk group.</p><p><strong>Conclusions: </strong>Mirai has a moderate performance in predicting future breast cancer among Mexican women.</p><p><strong>Critical relevance statement: </strong>Prospective efforts should refine and apply the Mirai model, especially to minority populations and women aged between 30 and 40 years who are currently not targeted for routine screening.</p><p><strong>Key points: </strong>The applicability of AI models to non-White, minority populations remains understudied. The Mirai model is linked to future cancer events in Mexican women. Further research is needed to enhance model performance and establish usage guidelines.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"15 1","pages":"244"},"PeriodicalIF":4.1,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11466924/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142400184","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}