Insights into Imaging最新文献

{"title":"Fully automated MRI-based convolutional neural network for noninvasive diagnosis of cirrhosis.","authors":"Tianying Zheng, Yajing Zhu, Yidi Chen, Shengshi Mai, Lixin Xu, Hanyu Jiang, Ting Duan, Yuanan Wu, Yali Qu, Yinan Chen, Bin Song","doi":"10.1186/s13244-024-01872-9","DOIUrl":"10.1186/s13244-024-01872-9","url":null,"abstract":"<p><strong>Objectives: </strong>To develop and externally validate a fully automated diagnostic convolutional neural network (CNN) model for cirrhosis based on liver MRI and serum biomarkers.</p><p><strong>Methods: </strong>This multicenter retrospective study included consecutive patients receiving pathological evaluation of liver fibrosis stage and contrast-enhanced liver MRI between March 2010 and January 2024. On the training dataset, an MRI-based CNN model was constructed for cirrhosis against pathology, and then a combined model was developed integrating the CNN model and serum biomarkers. On the testing datasets, the area under the receiver operating characteristic curve (AUC) was computed to compare the diagnostic performance of the combined model with that of aminotransferase-to-platelet ratio index (APRI), fibrosis-4 index (FIB-4), and radiologists. The influence of potential confounders on the diagnostic performance was evaluated by subgroup analyses.</p><p><strong>Results: </strong>A total of 1315 patients (median age, 54 years; 1065 men; training, n = 840) were included, 855 (65%) with pathological cirrhosis. The CNN model was constructed on pre-contrast T1- and T2-weighted imaging, and the combined model was developed integrating the CNN model, age, and eight serum biomarkers. On the external testing dataset, the combined model achieved an AUC of 0.86, which outperformed FIB-4, APRI and two radiologists (AUC: 0.67 to 0.73, all p < 0.05). Subgroup analyses revealed comparable diagnostic performances of the combined model in patients with different sizes of focal liver lesions.</p><p><strong>Conclusion: </strong>Based on pre-contrast T1- and T2-weighted imaging, age, and serum biomarkers, the combined model allowed diagnosis of cirrhosis with moderate accuracy, independent of the size of focal liver lesions.</p><p><strong>Critical relevance statement: </strong>The fully automated convolutional neural network model utilizing pre-contrast MR imaging, age and serum biomarkers demonstrated moderate accuracy, outperforming FIB-4, APRI, and radiologists, independent of size of focal liver lesions, potentially facilitating noninvasive diagnosis of cirrhosis pending further validation.</p><p><strong>Key points: </strong>This fully automated convolutional neural network (CNN) model, using pre-contrast MRI, age, and serum biomarkers, diagnoses cirrhosis. The CNN model demonstrated an external testing dataset AUC of 0.86, independent of the size of focal liver lesions. The CNN model outperformed aminotransferase-to-platelet ratio index, fibrosis-4 index, and radiologists, potentially facilitating noninvasive diagnosis of cirrhosis.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"15 1","pages":"298"},"PeriodicalIF":4.1,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11638457/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142812932","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":"CT imaging findings of invasive pulmonary fungal infections in hemato-oncologic children.","authors":"Leonor Alamo, Francesco Ceppi, Estelle Tenisch, Catherine Beigelman-Aubry","doi":"10.1186/s13244-024-01871-w","DOIUrl":"10.1186/s13244-024-01871-w","url":null,"abstract":"<p><p>Hemato-oncologic children form a heterogeneous group with a wide spectrum of ages, malignancy types, and immunosuppression grades during the different phases of their treatment. Immunosuppression is caused by multiple factors, including the malignancy itself, bone marrow suppression secondary to therapy, and wide use of steroids and antibiotics, among others. At the same time, the risk of infections in these patients remains high because of prolonged hospitalizations or the need for long-timing implanted devices between other features. In this context, a pulmonary fungal infection can rapidly turn into a life-threatening condition that requires early diagnosis and appropriate management. This pictorial essay illustrates the main imaging findings detected in chest computed tomography examinations performed in pediatric hemato-oncologic patients with proven pulmonary invasive fungal infections caused by Candida, Aspergillus, or Mucor. In addition, it describes useful clues for limiting differential diagnoses, reviews the literature on pediatric patients, and compares imaging findings in adults and children. CRITICAL RELEVANCE STATEMENT: The main fungal pathogens causing invasive fungal infections (IFI) in hemato-oncologic children are Candida, Aspergillus, and Mucor. This review describes the most frequently affected organs and the most common imaging findings detected in chest CT exams in children with pulmonary IFI. KEY POINTS: To review the main computed tomography imaging findings suggesting pulmonary invasive fungal infection (IFI) in hemato-oncologic children. To describe differences between pediatric and adult patients with proven pulmonary IFI. To provide useful clues for limiting the differential diagnosis of pulmonary IFI in pediatric patients.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"15 1","pages":"296"},"PeriodicalIF":4.1,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11638445/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142812928","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":"The top 100 most-cited articles on artificial intelligence in breast radiology: a bibliometric analysis.","authors":"Sneha Singh, Nuala A Healy","doi":"10.1186/s13244-024-01869-4","DOIUrl":"10.1186/s13244-024-01869-4","url":null,"abstract":"<p><strong>Introduction: </strong>Artificial intelligence (AI) in radiology is a rapidly evolving field. In breast imaging, AI has already been applied in a real-world setting and multiple studies have been conducted in the area. The aim of this analysis is to identify the most influential publications on the topic of artificial intelligence in breast imaging.</p><p><strong>Methods: </strong>A retrospective bibliometric analysis was conducted on artificial intelligence in breast radiology using the Web of Science database. The search strategy involved searching for the keywords 'breast radiology' or 'breast imaging' and the various keywords associated with AI such as 'deep learning', 'machine learning,' and 'neural networks'.</p><p><strong>Results: </strong>From the top 100 list, the number of citations per article ranged from 30 to 346 (average 85). The highest cited article titled 'Artificial Neural Networks In Mammography-Application To Decision-Making In The Diagnosis Of Breast-Cancer' was published in Radiology in 1993. Eighty-three of the articles were published in the last 10 years. The journal with the greatest number of articles was Radiology (n = 22). The most common country of origin was the United States (n = 51). Commonly occurring topics published were the use of deep learning models for breast cancer detection in mammography or ultrasound, radiomics in breast cancer, and the use of AI for breast cancer risk prediction.</p><p><strong>Conclusion: </strong>This study provides a comprehensive analysis of the top 100 most-cited papers on the subject of artificial intelligence in breast radiology and discusses the current most influential papers in the field.</p><p><strong>Clinical relevance statement: </strong>This article provides a concise summary of the top 100 most-cited articles in the field of artificial intelligence in breast radiology. It discusses the most impactful articles and explores the recent trends and topics of research in the field.</p><p><strong>Key points: </strong>Multiple studies have been conducted on AI in breast radiology. The most-cited article was published in the journal Radiology in 1993. This study highlights influential articles and topics on AI in breast radiology.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"15 1","pages":"297"},"PeriodicalIF":4.1,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11638451/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142812947","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":"Pediatric menisci: normal aspects, anatomical variants, lesions, tears, and postsurgical findings.","authors":"Flávia Ferreira Araújo, Júlio Brandão Guimarães, Isabela Azevedo Nicodemos da Cruz, Leticia Dos Reis Morimoto, Alípio Gomes Ormond Filho, Marcelo Astolfi Caetano Nico","doi":"10.1186/s13244-024-01867-6","DOIUrl":"10.1186/s13244-024-01867-6","url":null,"abstract":"<p><p>The reported incidence of meniscal tears in the pediatric age group has increased because of increased sports participation and more widespread use of MRI. Meniscal injury is one of the most commonly reported internal derangements in skeletally immature knees and can be associated with early degenerative joint disease leading to disability. The pediatric meniscus has particularities, and knowledge of normal anatomy, anatomical variations, and the patterns of meniscal injury in the pediatric age group is essential to provide a correct diagnosis. CRITICAL RELEVANCE STATEMENT: Accurate MRI interpretation of pediatric meniscal injuries is crucial. Understanding age-specific anatomy, vascularity, and variations can improve diagnostic precision, guiding targeted treatments to prevent early joint degeneration and disability. KEY POINTS: Meniscal lesions are common injuries in skeletally immature knees. Awareness of anatomical meniscus variants, patterns of injury, and associated injuries is essential. Meniscal tears in pediatric patients should be repaired if possible.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"15 1","pages":"295"},"PeriodicalIF":4.1,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11638453/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142812939","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":"Preoperative detection of extraprostatic tumor extension in patients with primary prostate cancer utilizing [⁶⁸Ga]Ga-PSMA-11 PET/MRI.","authors":"Clemens P Spielvogel, Jing Ning, Kilian Kluge, David Haberl, Gabriel Wasinger, Josef Yu, Holger Einspieler, Laszlo Papp, Bernhard Grubmüller, Shahrokh F Shariat, Pascal A T Baltzer, Paola Clauser, Markus Hartenbach, Lukas Kenner, Marcus Hacker, Alexander R Haug, Sazan Rasul","doi":"10.1186/s13244-024-01876-5","DOIUrl":"10.1186/s13244-024-01876-5","url":null,"abstract":"<p><strong>Objectives: </strong>Radical prostatectomy (RP) is a common intervention in patients with localized prostate cancer (PCa), with nerve-sparing RP recommended to reduce adverse effects on patient quality of life. Accurate pre-operative detection of extraprostatic extension (EPE) remains challenging, often leading to the application of suboptimal treatment. The aim of this study was to enhance pre-operative EPE detection through multimodal data integration using explainable machine learning (ML).</p><p><strong>Methods: </strong>Patients with newly diagnosed PCa who underwent [⁶⁸Ga]Ga-PSMA-11 PET/MRI and subsequent RP were recruited retrospectively from two time ranges for training, cross-validation, and independent validation. The presence of EPE was measured from post-surgical histopathology and predicted using ML and pre-operative parameters, including PET/MRI-derived features, blood-based markers, histology-derived parameters, and demographic parameters. ML models were subsequently compared with conventional PET/MRI-based image readings.</p><p><strong>Results: </strong>The study involved 107 patients, 59 (55%) of whom were affected by EPE according to postoperative findings for the initial training and cross-validation. The ML models demonstrated superior diagnostic performance over conventional PET/MRI image readings, with the explainable boosting machine model achieving an AUC of 0.88 (95% CI 0.87-0.89) during cross-validation and an AUC of 0.88 (95% CI 0.75-0.97) during independent validation. The ML approach integrating invasive features demonstrated better predictive capabilities for EPE compared to visual clinical read-outs (Cross-validation AUC 0.88 versus 0.71, p = 0.02).</p><p><strong>Conclusion: </strong>ML based on routinely acquired clinical data can significantly improve the pre-operative detection of EPE in PCa patients, potentially enabling more accurate clinical staging and decision-making, thereby improving patient outcomes.</p><p><strong>Critical relevance statement: </strong>This study demonstrates that integrating multimodal data with machine learning significantly improves the pre-operative detection of extraprostatic extension in prostate cancer patients, outperforming conventional imaging methods and potentially leading to more accurate clinical staging and better treatment decisions.</p><p><strong>Key points: </strong>Extraprostatic extension is an important indicator guiding treatment approaches. Current assessment of extraprostatic extension is difficult and lacks accuracy. Machine learning improves detection of extraprostatic extension using PSMA-PET/MRI and histopathology.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"15 1","pages":"299"},"PeriodicalIF":4.1,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11638435/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142812943","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":"MRI analysis of relative tumor enhancement in liver metastases and correlation with immunohistochemical features.","authors":"Felix Barajas Ordonez, Sebastian Gottschling, Kai Ina Eger, Jan Borggrefe, Dörthe Jechorek, Alexey Surov","doi":"10.1186/s13244-024-01866-7","DOIUrl":"10.1186/s13244-024-01866-7","url":null,"abstract":"<p><strong>Objective: </strong>Investigate the association between the relative tumor enhancement (RTE) of gadoxetic acid across various MRI phases and immunohistochemical (IHC) features in patients with liver metastases (LM) from colorectal cancer (CRC), breast cancer (BC), and pancreatic cancer (PC).</p><p><strong>Methods: </strong>A retrospective analysis was conducted on 68 patients with LM who underwent 1.5-T MRI scans. Non-contrast and contrast-enhanced T1-weighted (T1-w) gradient echo (GRE) sequences were acquired before LM biopsy. RTE values among LM groups were compared by cancer type using analysis of variance. The relationships between RTE and IHC features tumor stroma ratio, cell count, Ki67 proliferation index, and CD45 expression were evaluated using Spearman's rank correlation coefficients.</p><p><strong>Results: </strong>Significant differences in RTE were observed across different MRI phases among patients with BCLM, CRCLM, and PCLM: arterial phase (0.75 ± 0.42, 0.37 ± 0.36, and 0.44 ± 0.19), portal venous phase (1.09 ± 0.41, 0.59 ± 0.44, and 0.53 ± 0.24), and venous phase (1.11 ± 0.45, 0.65 ± 0.61, and 0.50 ± 0.20). In CRCLM, RTE inversely correlated with mean Ki67 (r = -0.50, p = 0.01) in the hepatobiliary phase. Negative correlations between RTE and CD45 expression were found in PCLM and CRCLM in the portal venous phase (r = -0.69, p = 0.01 and r = -0.41, p = 0.04) and the venous phase (r = -0.65, p = 0.01 and r = -0.44, p = 0.02).</p><p><strong>Conclusion: </strong>Significant variations in RTE were identified among different types of LM, with correlations between RTE values and IHC markers such as CD45 and Ki67 suggesting that RTE may serve as a non-invasive biomarker for predicting IHC features in LM.</p><p><strong>Critical relevance statement: </strong>RTE values serve as a predictive biomarker for IHC features in liver metastasis, potentially enhancing non-invasive patient assessment, disease monitoring, and treatment planning.</p><p><strong>Key points: </strong>Few studies link gadoxetic acid-enhanced MRI with immunohistochemistry in LM. RTE varies by liver metastasis type and correlates with CD45 and Ki67. RTE reflects IHC features in LM, aiding non-invasive assessment.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"15 1","pages":"294"},"PeriodicalIF":4.1,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11621246/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142785489","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":"Assessment of vascular invasion of pancreatic ductal adenocarcinoma based on CE-boost black blood CT technique.","authors":"Yue Lin, Tongxi Liu, Yingying Hu, Yinghao Xu, Jian Wang, Sijia Guo, Sheng Xie, Hongliang Sun","doi":"10.1186/s13244-024-01870-x","DOIUrl":"10.1186/s13244-024-01870-x","url":null,"abstract":"<p><strong>Objectives: </strong>To explore the diagnostic efficacy of advanced intelligent clear-IQ engine (AiCE) and adaptive iterative dose reduction 3D (AIDR 3D), combination with and without the black blood CT technique (BBCT), for detecting vascular invasion in patients diagnosed with nonmetastatic pancreatic ductal adenocarcinoma (PDAC).</p><p><strong>Methods: </strong>A total of 35 consecutive patients diagnosed with PDAC, proceeding with contrast-enhanced abdominal CT scans, were enrolled in this study. The arterial and portal venous phase images were reconstructed using AiCE and AIDR 3D. The corresponding BBCT images were established as AiCE-BBCT and AIDR 3D-BBCT, respectively. Two observers scored the image quality independently. Cohen's kappa (k) value or intraclass correlation coefficient (ICC) was used to analyze consistency. The diagnostic performance of four algorithms in detecting vascular invasion in PDAC patients was assessed using the area under the curve (AUC).</p><p><strong>Results: </strong>The AiCE and AiCE-BBCT groups demonstrated superior image noise and diagnostic acceptability compared with AIDR 3D and AIDR 3D-BBCT groups (all p < 0.001), and the k value was 0.861-0.967 for both reviewers. In terms of diagnostic capability for vascular invasion in PDAC, the AiCE-BBCT group exhibited higher specificity (95.0%) and sensitivity (93.3%) compared to the AIDR 3D and AIDR 3D-BBCT groups, with an AUC of 0.942 (95% CI: 0.849-1.000, p < 0.05). Furthermore, all vascular evaluations conducted using AiCE-BBCT demonstrated better consistency (ICC: 0.847-0.935).</p><p><strong>Conclusion: </strong>The BBCT technique in conjunction with AiCE could lead to notable enhancements in both the image quality of PDAC images and the diagnostic performance for tumor vascular invasion.</p><p><strong>Critical relevance statement: </strong>Better diagnostic accuracy of vascular invasion of PDAC based on BBCT in combination with an AiCE is a critical factor in determining treatment strategies and patient outcomes.</p><p><strong>Key points: </strong>Identifying vascular invasion of PDAC is important for prognostication. Combined images provide improved image quality and higher diagnostic accuracy. Combined images can excellently display the vascular wall and invasion.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"15 1","pages":"293"},"PeriodicalIF":4.1,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11621291/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142785479","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 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}