Insights into Imaging最新文献

{"title":"Development and validation of a model based on preoperative dual-layer detector spectral computed tomography 3D VOI-based quantitative parameters to predict high Ki-67 proliferation index in pancreatic ductal adenocarcinoma.","authors":"Dan Zeng, Jiayan Zhang, Zuhua Song, Qian Li, Dan Zhang, Xiaojiao Li, Youjia Wen, Xiaofang Ren, Xinwei Wang, Xiaodi Zhang, Zhuoyue Tang","doi":"10.1186/s13244-024-01864-9","DOIUrl":"10.1186/s13244-024-01864-9","url":null,"abstract":"<p><strong>Objective: </strong>To develop and validate a model integrating dual-layer detector spectral computed tomography (DLCT) three-dimensional (3D) volume of interest (VOI)-based quantitative parameters and clinical features for predicting Ki-67 proliferation index (PI) in pancreatic ductal adenocarcinoma (PDAC).</p><p><strong>Materials and methods: </strong>A total of 162 patients with histopathologically confirmed PDAC who underwent DLCT examination were included and allocated to the training (114) and validation (48) sets. 3D VOI-iodine concentration (IC), 3D VOI-slope of the spectral attenuation curves, and 3D VOI-effective atomic number were obtained from the portal venous phase. The significant clinical features and DLCT quantitative parameters were identified through univariate analysis and multivariate logistic regression. The discrimination capability and clinical applicability of the clinical, DLCT, and DLCT-clinical models were quantified by the Receiver Operating Characteristic curve (ROC) and Decision Curve Analysis (DCA), respectively. The optimal model was then used to develop a nomogram, with the goodness-of-fit evaluated through the calibration curve.</p><p><strong>Results: </strong>The DLCT-clinical model demonstrated superior predictive capability and a satisfactory net benefit for Ki-67 PI in PDAC compared to the clinical and DLCT models. The DLCT-clinical model integrating 3D VOI-IC and CA125 showed area under the ROC curves of 0.939 (95% CI, 0.895-0.982) and 0.915 (95% CI, 0.834-0.996) in the training and validation sets, respectively. The nomogram derived from the DLCT-clinical model exhibited favorable calibration, as depicted by the calibration curve.</p><p><strong>Conclusions: </strong>The proposed model based on DLCT 3D VOI-IC and CA125 is a non-invasive and effective preoperative prediction tool demonstrating favorable predictive performance for Ki-67 PI in PDAC.</p><p><strong>Critical relevance statement: </strong>The dual-layer detector spectral computed tomography-clinical model could help predict high Ki-67 PI in pancreatic ductal adenocarcinoma patients, which may help clinicians provide appropriate and individualized treatments.</p><p><strong>Key points: </strong>Dual-layer detector spectral CT (DLCT) could predict Ki-67 in pancreatic ductal adenocarcinoma (PDAC). The DLCT-clinical model improved the differential diagnosis of Ki-67. The nomogram showed satisfactory calibration and net benefit for discriminating Ki-67.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"15 1","pages":"291"},"PeriodicalIF":4.1,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11621245/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142785484","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":"A multi-scene deep learning model for automated segmentation of acute vertebral compression fractures from radiographs: a multicenter cohort study.","authors":"Hao Zhang, Genji Yuan, Ziyue Zhang, Xiang Guo, Ruixiang Xu, Tongshuai Xu, Xin Zhong, Meng Kong, Kai Zhu, Xuexiao Ma","doi":"10.1186/s13244-024-01861-y","DOIUrl":"10.1186/s13244-024-01861-y","url":null,"abstract":"<p><strong>Objective: </strong>To develop a multi-scene model that can automatically segment acute vertebral compression fractures (VCFs) from spine radiographs.</p><p><strong>Methods: </strong>In this multicenter study, we collected radiographs from five hospitals (Hospitals A-E) between November 2016 and October 2019. The study included participants with acute VCFs, as well as healthy controls. For the development of the Positioning and Focus Network (PFNet), we used a training dataset consisting of 1071 participants from Hospitals A and B. The validation dataset included 458 participants from Hospitals A and B, whereas external test datasets 1-3 included 301 participants from Hospital C, 223 from Hospital D, and 261 from Hospital E, respectively. We evaluated the segmentation performance of the PFNet model and compared it with previously described approaches. Additionally, we used qualitative comparison and gradient-weighted class activation mapping (Grad-CAM) to explain the feature learning and segmentation results of the PFNet model.</p><p><strong>Results: </strong>The PFNet model achieved accuracies of 99.93%, 98.53%, 99.21%, and 100% for the segmentation of acute VCFs in the validation dataset and external test datasets 1-3, respectively. The receiver operating characteristic curves comparing the four models across the validation and external test datasets consistently showed that the PFNet model outperformed other approaches, achieving the highest values for all measures. The qualitative comparison and Grad-CAM provided an intuitive view of the interpretability and effectiveness of our PFNet model.</p><p><strong>Conclusion: </strong>In this study, we successfully developed a multi-scene model based on spine radiographs for precise preoperative and intraoperative segmentation of acute VCFs.</p><p><strong>Critical relevance statement: </strong>Our PFNet model demonstrated high accuracy in multi-scene segmentation in clinical settings, making it a significant advancement in this field.</p><p><strong>Key points: </strong>This study developed the first multi-scene deep learning model capable of segmenting acute VCFs from spine radiographs. The model's architecture consists of two crucial modules: an attention-guided module and a supervised decoding module. The exceptional generalization and consistently superior performance of our model were validated using multicenter external test datasets.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"15 1","pages":"290"},"PeriodicalIF":4.1,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11612108/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142768438","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":"Impact of uncertainty quantification through conformal prediction on volume assessment from deep learning-based MRI prostate segmentation.","authors":"Marius Gade, Kevin Mekhaphan Nguyen, Sol Gedde, Alvaro Fernandez-Quilez","doi":"10.1186/s13244-024-01863-w","DOIUrl":"10.1186/s13244-024-01863-w","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Objectives: &lt;/strong&gt;To estimate the uncertainty of a deep learning (DL)-based prostate segmentation algorithm through conformal prediction (CP) and to assess its effect on the calculation of the prostate volume (PV) in patients at risk of prostate cancer (PC).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Three-hundred seventy-seven multi-center 3-Tesla axial T2-weighted exams from biopsied males (66.64  &lt;math&gt;&lt;mo&gt;±&lt;/mo&gt;&lt;/math&gt;  7.47 years) at risk of PC were retrospectively included in the study. Assessment of PV based on PI-RADS 2.1 ellipsoid formula ( &lt;math&gt; &lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;PV&lt;/mi&gt;&lt;/mrow&gt; &lt;mrow&gt;&lt;mi&gt;r&lt;/mi&gt; &lt;mi&gt;e&lt;/mi&gt; &lt;mi&gt;f&lt;/mi&gt;&lt;/mrow&gt; &lt;/msub&gt; &lt;/math&gt; ) was available for included patients. Prostate segmentations were obtained from a DL model and used to calculate the PV ( &lt;math&gt; &lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;PV&lt;/mi&gt;&lt;/mrow&gt; &lt;mrow&gt;&lt;mi&gt;D&lt;/mi&gt; &lt;mi&gt;L&lt;/mi&gt;&lt;/mrow&gt; &lt;/msub&gt; &lt;/math&gt; ). CP was applied at a confidence level of 85% to flag unreliable pixel segmentations of the DL model. Subsequently, the PV ( &lt;math&gt; &lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;PV&lt;/mi&gt;&lt;/mrow&gt; &lt;mrow&gt;&lt;mi&gt;C&lt;/mi&gt; &lt;mi&gt;P&lt;/mi&gt;&lt;/mrow&gt; &lt;/msub&gt; &lt;/math&gt; ) was calculated when disregarding uncertain pixel segmentations. Agreement between &lt;math&gt; &lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;PV&lt;/mi&gt;&lt;/mrow&gt; &lt;mrow&gt;&lt;mi&gt;D&lt;/mi&gt; &lt;mi&gt;L&lt;/mi&gt;&lt;/mrow&gt; &lt;/msub&gt; &lt;/math&gt; and &lt;math&gt; &lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;PV&lt;/mi&gt;&lt;/mrow&gt; &lt;mrow&gt;&lt;mi&gt;C&lt;/mi&gt; &lt;mi&gt;P&lt;/mi&gt;&lt;/mrow&gt; &lt;/msub&gt; &lt;/math&gt; was evaluated against the reference standard &lt;math&gt; &lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;PV&lt;/mi&gt;&lt;/mrow&gt; &lt;mrow&gt;&lt;mi&gt;r&lt;/mi&gt; &lt;mi&gt;e&lt;/mi&gt; &lt;mi&gt;f&lt;/mi&gt;&lt;/mrow&gt; &lt;/msub&gt; &lt;/math&gt; . Intraclass correlation coefficient (ICC) and Bland-Altman plots were used to assess the agreement. The relative volume difference (RVD) was used to evaluate the PV calculation accuracy, and the Wilcoxon Signed-Rank Test was used to assess statistical differences. A p-value &lt; 0.05 was considered statistically significant.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Conformal prediction significantly reduced RVD when compared to the DL algorithm (RVD = - 2.81  &lt;math&gt;&lt;mo&gt;±&lt;/mo&gt;&lt;/math&gt;  8.85 and RVD = -8.01  &lt;math&gt;&lt;mo&gt;±&lt;/mo&gt;&lt;/math&gt;  11.50). &lt;math&gt; &lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;PV&lt;/mi&gt;&lt;/mrow&gt; &lt;mrow&gt;&lt;mi&gt;C&lt;/mi&gt; &lt;mi&gt;P&lt;/mi&gt;&lt;/mrow&gt; &lt;/msub&gt; &lt;/math&gt; showed a significantly larger agreement than &lt;math&gt; &lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;PV&lt;/mi&gt;&lt;/mrow&gt; &lt;mrow&gt;&lt;mi&gt;D&lt;/mi&gt; &lt;mi&gt;L&lt;/mi&gt;&lt;/mrow&gt; &lt;/msub&gt; &lt;/math&gt; when using the reference standard &lt;math&gt; &lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;PV&lt;/mi&gt;&lt;/mrow&gt; &lt;mrow&gt;&lt;mi&gt;r&lt;/mi&gt; &lt;mi&gt;e&lt;/mi&gt; &lt;mi&gt;f&lt;/mi&gt;&lt;/mrow&gt; &lt;/msub&gt; &lt;/math&gt; (mean difference (95% limits of agreement) &lt;math&gt; &lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;PV&lt;/mi&gt;&lt;/mrow&gt; &lt;mrow&gt;&lt;mi&gt;C&lt;/mi&gt; &lt;mi&gt;P&lt;/mi&gt;&lt;/mrow&gt; &lt;/msub&gt; &lt;/math&gt; : 1.27 mL (- 13.64; 16.17 mL) &lt;math&gt; &lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;PV&lt;/mi&gt;&lt;/mrow&gt; &lt;mrow&gt;&lt;mi&gt;D&lt;/mi&gt; &lt;mi&gt;L&lt;/mi&gt;&lt;/mrow&gt; &lt;/msub&gt; &lt;/math&gt; : 6.07 mL (- 14.29; 26.42 mL)), with an excellent ICC ( &lt;math&gt; &lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;PV&lt;/mi&gt;&lt;/mrow&gt; &lt;mrow&gt;&lt;mi&gt;C&lt;/mi&gt; &lt;mi&gt;P&lt;/mi&gt;&lt;/mrow&gt; &lt;/msub&gt; &lt;/math&gt; : 0.97 (95% CI: 0.97 to 0.98)).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusion: &lt;/strong&gt;Uncertainty quantification through CP increases the accuracy and reliability of DL-based PV assessment in patients at risk of PC.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Critic","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"15 1","pages":"286"},"PeriodicalIF":4.1,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11607187/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142755085","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":"Contrast-enhanced US Bosniak Classification: intra- and inter-rater agreement, confounding features, and diagnostic performance.","authors":"Dong-Dong Jin, Bo-Wen Zhuang, Ke Lin, Nan Zhang, Bin Qiao, Xiao-Yan Xie, Xiao-Hua Xie, Yan Wang","doi":"10.1186/s13244-024-01858-7","DOIUrl":"10.1186/s13244-024-01858-7","url":null,"abstract":"<p><strong>Background: </strong>The contrast-enhanced US (CEUS) Bosniak classification, proposed by the European Federation for Ultrasound in Medicine and Biology (EFSUMB) in 2020, predicts malignancy in cystic renal masses (CRMs). However, intra- and inter-rater reproducibility for CEUS features has not been well investigated.</p><p><strong>Purpose: </strong>To explore intra- and inter-rater agreement for US features, identify confounding features, and assess the diagnostic performance of CEUS Bosniak classification.</p><p><strong>Materials and methods: </strong>This retrospective study included patients with complex CRMs who underwent CEUS examination from January 2013 to August 2023. Radiologists (3 experts and 3 novices) evaluated calcification, echogenic content, wall, septa, and internal nodules of CRMs using CEUS Bosniak classification. Intra- and inter-rater agreements were assessed using the Gwet agreement coefficient (Gwet's AC). Linear regression identified features associated with discrepancies in Bosniak category assignment. Diagnostic performance was evaluated using the area under the receiver operating characteristic curve (AUC).</p><p><strong>Results: </strong>A total of 103 complex CRMs were analyzed in 103 patients (mean age, 50 ± 15 years; 66 males). Intra-rater agreement for the Bosniak category was substantial to almost perfect (Gwet's AC 0.73-0.87). Inter-rater agreement was substantial for the Bosniak category (Gwet's AC 0.75) and moderate to almost perfect for US features (Gwet's AC 0.44-0.94). Nodule variation (i.e., absence vs. obtuse margin vs. acute margin) explained 84% of the variability in the Bosniak category assignment. CEUS Bosniak classification showed good diagnostic performance, with AUCs ranging from 0.78 to 0.90 for each rater.</p><p><strong>Conclusions: </strong>CEUS Bosniak classification demonstrated substantial intra- and inter-rater reproducibility and good diagnostic performance in predicting the malignancy potential of CRMs. Nodule variations significantly predicted differences in Bosniak category assignments.</p><p><strong>Critical relevance statement: </strong>Contrast-enhanced US Bosniak classification reliably predicts malignancy in cystic renal masses, demonstrating substantial reproducibility and diagnostic accuracy. This improves clinical decision-making and patient management.</p><p><strong>Key points: </strong>Intra- and inter-rater reproducibility for contrast-enhance US features for Bosniak classification have not been well investigated. Substantial inter-rater agreements for the Bosniak category and variable agreements for determining imaging features were found. Contrast-enhanced US Bosniak classification is reproducible and has good diagnostic performance for predicting malignancy in cystic renal masses.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"15 1","pages":"285"},"PeriodicalIF":4.1,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11607359/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142755083","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":"Identification of neural alterations in patients with Crohn's disease with a novel multiparametric brain MRI-based radiomics model.","authors":"Ruo-Nan Zhang, Yang-di Wang, Hai-Jie Wang, Yao-Qi Ke, Xiao-di Shen, Li Huang, Jin-Jiang Lin, Wei-Tao He, Chen Zhao, Zhou-Lei Li, Ren Mao, Ye-Jun Wang, Guang Yang, Xue-Hua Li","doi":"10.1186/s13244-024-01859-6","DOIUrl":"10.1186/s13244-024-01859-6","url":null,"abstract":"<p><strong>Objectives: </strong>Gut-brain axis dysfunction has emerged as a key contributor to the pathogenesis of Crohn's disease (CD). The elucidation of neural alterations may provide novel insights into its management. We aimed to develop a multiparameter brain MRI-based radiomics model (RM) for characterizing neural alterations in CD patients and to interpret these alterations using multiomics traits.</p><p><strong>Methods: </strong>This prospective study enrolled 230 CD patients and 46 healthy controls (HCs). Participants voluntarily underwent brain MRI and psychological assessment (n = 155), blood metabolomics analysis (n = 260), and/or fecal 16S rRNA sequencing (n = 182). The RM was developed using 13 features selected from 13,870 first-order features extracted from multiparameter brain MRI in training cohort (CD, n = 75; HCs, n = 32) and validated in test cohort (CD, n = 34; HCs, n = 14). Multiomics data (including gut microbiomics, blood metabolomics, and brain radiomics) were compared between CD patients and HCs.</p><p><strong>Results: </strong>In the training cohort, area under the receiver operating characteristic curve (AUC) of RM for distinguishing CD patients from HCs was 0.991 (95% confidence interval (CI), 0.975-1.000). In test cohort, RM showed an AUC of 0.956 (95% CI, 0.881-1.000). CD-enriched blood metabolites such as triacylglycerol (TAG) exhibited significant correlations with both brain features detected by RM and CD-enriched microbiota (e.g., Veillonella). One notable correlation was found between Veillonella and Ctx-Lh-Middle-Temporal-CBF-p90 (r = 0.41). Mediation analysis further revealed that dysbiosis, such as of Veillonella, may regulate the blood flow in the middle temporal cortex through TAG.</p><p><strong>Conclusion: </strong>We developed a multiparameter MRI-based RM that characterized the neural alterations of CD patients, and multiomics data offer potential evidence to support the validity of our model. Our study may offer clues to help provide potential therapeutic targets.</p><p><strong>Critical relevance statement: </strong>Our brain-gut axis study developed a novel model using multiparameter MRI and radiomics to characterize brain changes in patients with Crohn's disease. We validated this model's effectiveness using multiomics data, making it a potential biomarker for better patient management.</p><p><strong>Key points: </strong>Utilizing multiparametric MRI and radiomics techniques could unveil Crohn's disease's neurophenotype. The neurophenotype radiomics model is interpreted using multiomics data. This model may serve as a novel biomarker for Crohn's disease management.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"15 1","pages":"289"},"PeriodicalIF":4.1,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11607295/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142755084","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":"Non-metastatic causes of multiple pulmonary nodules.","authors":"Esra Akçiçek, Gamze Durhan, Selin Ardalı Düzgün, Olcay Kurtulan, Meltem Gülsün Akpınar, Figen Demirkazık, Orhan Macit Arıyürek","doi":"10.1186/s13244-024-01856-9","DOIUrl":"10.1186/s13244-024-01856-9","url":null,"abstract":"<p><p>Various processes, including benign or malignant (mostly metastasis) processes, contribute to the occurrence of multiple pulmonary nodules. For differential diagnosis, metastasis must be excluded as an etiological factor in patients who have multiple pulmonary nodules with a known primary malignancy. However, differential diagnosis of multiple pulmonary nodules caused by benign diseases and malignant processes is challenging. Multiple pulmonary nodules resulting from metastasis may mimic those resulting from infections, inflammatory processes, and rare benign diseases. Some rare diseases, such as pulmonary sclerosing pneumocytoma and pulmonary epithelioid hemangioendothelioma, or common diseases with a rare presentation of multiple nodules must be considered in the differential diagnosis of metastasis. In addition to the clinical and laboratory findings, radiological features are crucial for differential diagnosis. The size, density, location, and border characteristics (well-defined or poorly defined) of pulmonary nodules, as well as their internal structure (solid, subsolid, or ground glass nodule), growth rate during follow-up, and associated pulmonary and extrapulmonary findings are important for differential diagnosis along with clinical and laboratory data. This article summarizes the general features and imaging findings of these diseases, which less frequently present with multiple pulmonary nodules, and the clues that can be used to distinguish these diseases from metastasis. CRITICAL RELEVANCE STATEMENT: The radiological features, clinical findings, and temporal changes during follow-up are important in distinguishing non-metastatic causes of multiple pulmonary nodules from metastatic causes and guiding diagnosis and early treatment, especially in patients with primary malignancy. KEY POINTS: Multiple pulmonary nodules have a wide range of etiologies, including metastatic disease. Metastasis as an etiology must be excluded in patients with multiple pulmonary nodules. Correlation of radiological findings (nodule size, position, and associated findings) with clinical history is crucial for differential diagnosis.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"15 1","pages":"288"},"PeriodicalIF":4.1,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11607223/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142755087","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":"Machine learning-based radiomics prognostic model for patients with proximal esophageal cancer after definitive chemoradiotherapy.","authors":"Linrui Li, Zhihui Qin, Juan Bo, Jiaru Hu, Yu Zhang, Liting Qian, Jiangning Dong","doi":"10.1186/s13244-024-01853-y","DOIUrl":"10.1186/s13244-024-01853-y","url":null,"abstract":"<p><strong>Objectives: </strong>To explore the role of radiomics in predicting the prognosis of proximal esophageal cancer and to investigate the biological underpinning of radiomics in identifying different prognoses.</p><p><strong>Methods: </strong>A total of 170 patients with pathologically and endoscopically confirmed proximal esophageal cancer from two centers were enrolled. Radiomics models were established by five machine learning approaches. The optimal radiomics model was selected using receiver operating curve analysis. Bioinformatics methods were applied to explore the potential biological mechanisms. Nomograms based on radiomics and clinical-radiomics features were constructed and assessed by receiver operating characteristics, calibration, and decision curve analyses net reclassification improvement, and integrated discrimination improvement evaluations.</p><p><strong>Results: </strong>The peritumoral models performed well with the majority of classifiers in the training and validation sets, with the dual-region radiomics model showing the highest integrated area under the curve values of 0.9763 and 0.9471, respectively, and outperforming the single-region models. The clinical-radiomics nomogram showed better predictive performance than the clinical nomogram, with a net reclassification improvement of 34.4% (p = 0.02) and integrated discrimination improvement of 10% (p = 0.007). Gene ontology enrichment analysis revealed that lipid metabolism-related functions are potentially crucial in the process by which the radiomics score could stratify patients.</p><p><strong>Conclusions: </strong>A combination of peritumoral radiomics features could improve the predictive performance of intratumoral radiomics to estimate overall survival after definitive chemoradiotherapy in patients with proximal esophageal cancer. Radiomics features could provide insights into the lipid metabolism associated with radioresistance and hold great potential to guide personalized care.</p><p><strong>Critical relevance statement: </strong>This study demonstrates that incorporating peritumoral radiomics features enhances the predictive accuracy of overall survival in proximal esophageal cancer patients after chemoradiotherapy, and suggests a link between radiomics and lipid metabolism in radioresistance, highlighting its potential for personalized treatment strategies.</p><p><strong>Key points: </strong>Peritumoral region radiomics features could predict the prognosis of proximal esophageal cancer. Dual-region radiomics features showed significantly better predictive performance. Radiomics features can provide insights into the lipid metabolism associated with radioresistance.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"15 1","pages":"284"},"PeriodicalIF":4.1,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11607220/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142755086","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":"Ultra-high gradient performance 3-Tesla MRI for super-fast and high-quality prostate imaging: initial experience.","authors":"Leon M Bischoff, Christoph Endler, Philipp Krausewitz, Joerg Ellinger, Niklas Klümper, Alexander Isaak, Narine Mesropyan, Dmitrij Kravchenko, Sebastian Nowak, Daniel Kuetting, Alois M Sprinkart, Petra Mürtz, Claus C Pieper, Ulrike Attenberger, Julian A Luetkens","doi":"10.1186/s13244-024-01862-x","DOIUrl":"10.1186/s13244-024-01862-x","url":null,"abstract":"<p><strong>Objectives: </strong>To implement and evaluate a super-fast and high-quality biparametric MRI (bpMRI) protocol for prostate imaging acquired at a new ultra-high gradient 3.0-T MRI system.</p><p><strong>Methods: </strong>Participants with clinically suspected prostate cancer prospectively underwent a multiparametric MRI (mpMRI) on a new 3.0-T MRI scanner (maximum gradient strength: 200 mT/m, maximum slew rate: 200 T/m/s). The bpMRI protocol was extracted from the full mpMRI protocol, including axial T2-weighted and diffusion-weighted (DWI) sequences (b0/800, b1500). Overall image quality was rated by two readers on a five-point Likert scale from (1) non-diagnostic to (5) excellent. PI-RADS 2.1 scores were assessed by three readers separately for the bpMRI and mpMRI protocols. Cohen's and Fleiss' κ were calculated for PI-RADS agreement between protocols and interrater reliability between readers, respectively.</p><p><strong>Results: </strong>Seventy-seven male participants (mean age, 66 ± 8 years) were included. Acquisition time of the bpMRI protocol was reduced by 62% (bpMRI: 5 min, 33 ± 21 s; mpMRI: 14 min, 50 ± 42 s). The bpMRI protocol showed excellent overall image quality for both the T2-weighted (median score both readers: 5 [IQR: 4-5]) and DWI (b1500) sequence (median score reader 1: 4 [IQR: 4-5]; reader 2: 4 [IQR: 4-4]). PI-RADS score agreement between protocols was excellent (Cohen's κ range: 0.91-0.95 [95% CI: 0.89, 0.99]) with an overall good interrater reliability (Fleiss' κ, 0.86 [95% CI: 0.80, 0.92]).</p><p><strong>Conclusion: </strong>Ultra-high gradient MRI allows the establishment of a high-quality and rapidly acquired bpMRI with high PI-RADS agreement to a full mpMRI protocol.</p><p><strong>Trials registration: </strong>Clinicaltrials.gov, NCT06244680, Registered 06 February 2024, retrospectively registered, https://classic.</p><p><strong>Clinicaltrials: </strong>gov/ct2/show/NCT06244680 .</p><p><strong>Critical relevance statement: </strong>A novel 3.0-Tesla MRI system with an ultra-high gradient performance enabled high-quality biparametric prostate MRI in 5.5 min while achieving excellent PI-RADS agreement with a standard multiparametric protocol.</p><p><strong>Key points: </strong>Multi- and biparametric prostate MRIs were prospectively acquired utilizing a maximum gradient of 200 mT/m. Super-fast biparametric MRIs showed excellent image quality and had high PI-RADS agreement with multiparametric MRIs. Implementation of high gradient MRI in clinical routine allows accelerated and high-quality biparametric prostate examinations.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"15 1","pages":"287"},"PeriodicalIF":4.1,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11607256/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142755088","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":"Intratumoral and peritumoral MRI-based radiomics for predicting extrapelvic peritoneal metastasis in epithelial ovarian cancer.","authors":"Xinyi Wang, Mingxiang Wei, Ying Chen, Jianye Jia, Yu Zhang, Yao Dai, Cai Qin, Genji Bai, Shuangqing Chen","doi":"10.1186/s13244-024-01855-w","DOIUrl":"10.1186/s13244-024-01855-w","url":null,"abstract":"<p><strong>Objectives: </strong>To investigate the potential of intratumoral and peritumoral radiomics derived from T2-weighted MRI to preoperatively predict extrapelvic peritoneal metastasis (EPM) in patients with epithelial ovarian cancer (EOC).</p><p><strong>Methods: </strong>In this retrospective study, 488 patients from four centers were enrolled and divided into training (n = 245), internal test (n = 105), and external test (n = 138) sets. Intratumoral and peritumoral models were constructed based on radiomics features extracted from the corresponding regions. A combined intratumoral and peritumoral model was developed via a feature-level fusion. An ensemble model was created by integrating this combined model with specific independent clinical predictors. The robustness and generalizability of these models were assessed using tenfold cross-validation and both internal and external testing. Model performance was evaluated by the area under the receiver operating characteristic curve (AUC). The Shapley Additive Explanation method was employed for model interpretation.</p><p><strong>Results: </strong>The ensemble model showed superior performance across the tenfold cross-validation, with the highest mean AUC of 0.844 ± 0.063. On the internal test set, the peritumoral and ensemble models significantly outperformed the intratumoral model (AUC = 0.786 and 0.832 vs. 0.652, p = 0.007 and p < 0.001, respectively). On the external test set, the AUC of the ensemble model significantly exceeded those of the intratumoral and peritumoral models (0.843 vs. 0.750 and 0.789, p = 0.008 and 0.047, respectively).</p><p><strong>Conclusion: </strong>Peritumoral radiomics provide more informative insights about EPM than intratumoral radiomics. The ensemble model based on MRI has the potential to preoperatively predict EPM in EOC patients.</p><p><strong>Critical relevance statement: </strong>Integrating both intratumoral and peritumoral radiomics information based on MRI with clinical characteristics is a promising noninvasive method to predict EPM to guide preoperative clinical decision-making for EOC patients.</p><p><strong>Key points: </strong>Peritumoral radiomics can provide valuable information about extrapelvic peritoneal metastasis in epithelial ovarian cancer. The ensemble model demonstrated satisfactory performance in predicting extrapelvic peritoneal metastasis. Combining intratumoral and peritumoral MRI radiomics contributes to clinical decision-making in epithelial ovarian cancer.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"15 1","pages":"281"},"PeriodicalIF":4.1,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11584833/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142686748","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":"Development and external evaluation of a self-learning auto-segmentation model for Colorectal Cancer Liver Metastases Assessment (COALA).","authors":"Jacqueline I Bereska, Michiel Zeeuw, Luuk Wagenaar, Håvard Bjørke Jenssen, Nina J Wesdorp, Delanie van der Meulen, Leonard F Bereska, Efstratios Gavves, Boris V Janssen, Marc G Besselink, Henk A Marquering, Jan-Hein T M van Waesberghe, Davit L Aghayan, Egidijus Pelanis, Janneke van den Bergh, Irene I M Nota, Shira Moos, Gunter Kemmerich, Trygve Syversveen, Finn Kristian Kolrud, Joost Huiskens, Rutger-Jan Swijnenburg, Cornelis J A Punt, Jaap Stoker, Bjørn Edwin, Åsmund A Fretland, Geert Kazemier, Inez M Verpalen","doi":"10.1186/s13244-024-01820-7","DOIUrl":"10.1186/s13244-024-01820-7","url":null,"abstract":"<p><strong>Objectives: </strong>Total tumor volume (TTV) is associated with overall and recurrence-free survival in patients with colorectal cancer liver metastases (CRLM). However, the labor-intensive nature of such manual assessments has hampered the clinical adoption of TTV as an imaging biomarker. This study aimed to develop and externally evaluate a CRLM auto-segmentation model on CT scans, to facilitate the clinical adoption of TTV.</p><p><strong>Methods: </strong>We developed an auto-segmentation model to segment CRLM using 783 contrast-enhanced portal venous phase CTs (CT-PVP) of 373 patients. We used a self-learning setup whereby we first trained a teacher model on 99 manually segmented CT-PVPs from three radiologists. The teacher model was then used to segment CRLM in the remaining 663 CT-PVPs for training the student model. We used the DICE score and the intraclass correlation coefficient (ICC) to compare the student model's segmentations and the TTV obtained from these segmentations to those obtained from the merged segmentations. We evaluated the student model in an external test set of 50 CT-PVPs from 35 patients from the Oslo University Hospital and an internal test set of 21 CT-PVPs from 10 patients from the Amsterdam University Medical Centers.</p><p><strong>Results: </strong>The model reached a mean DICE score of 0.85 (IQR: 0.05) and 0.83 (IQR: 0.10) on the internal and external test sets, respectively. The ICC between the segmented volumes from the student model and from the merged segmentations was 0.97 on both test sets.</p><p><strong>Conclusion: </strong>The developed colorectal cancer liver metastases auto-segmentation model achieved a high DICE score and near-perfect agreement for assessing TTV.</p><p><strong>Critical relevance statement: </strong>AI model segments colorectal liver metastases on CT with high performance on two test sets. Accurate segmentation of colorectal liver metastases could facilitate the clinical adoption of total tumor volume as an imaging biomarker for prognosis and treatment response monitoring.</p><p><strong>Key points: </strong>Developed colorectal liver metastases segmentation model to facilitate total tumor volume assessment. Model achieved high performance on internal and external test sets. Model can improve prognostic stratification and treatment planning for colorectal liver metastases.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"15 1","pages":"279"},"PeriodicalIF":4.1,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11584830/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142687009","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}