BMC Medical Imaging最新文献

{"title":"Computed tomography enterography radiomics and machine learning for identification of Crohn's disease.","authors":"Qiao Shi, Yajing Hao, Huixian Liu, Xiaoling Liu, Weiqiang Yan, Jun Mao, Bihong T Chen","doi":"10.1186/s12880-024-01480-5","DOIUrl":"10.1186/s12880-024-01480-5","url":null,"abstract":"<p><strong>Background: </strong>Crohn's disease is a severe chronic and relapsing inflammatory bowel disease. Although contrast-enhanced computed tomography enterography is commonly used to evaluate crohn's disease, its imaging findings are often nonspecific and can overlap with other bowel diseases. Recent studies have explored the application of radiomics-based machine learning algorithms to aid in the diagnosis of medical images. This study aims to develop a non-invasive method for detecting bowel lesions associated with Crohn's disease using CT enterography radiomics and machine learning algorithms.</p><p><strong>Methods: </strong>A total of 139 patients with pathologically confirmed Crohn's disease were retrospectively enrolled in this study. Radiomics features were extracted from both arterial- and venous-phase CT enterography images, representing both bowel lesions with Crohn's disease and segments of normal bowel. A machine learning classification system was constructed by combining six selected radiomics features with eight classification algorithms. The models were trained using leave-one-out cross-validation and evaluated for accuracy.</p><p><strong>Results: </strong>The classification model demonstrated robust performance and high accuracy, with an area under the curve of 0.938 and 0.961 for the arterial- and venous-phase images, respectively. The model achieved an accuracy of 0.938 for arterial-phase images and 0.961 for venous-phase images.</p><p><strong>Conclusions: </strong>This study successfully identified a radiomics machine learning method that effectively differentiates Crohn's disease bowel lesions from normal bowel segments. Further studies with larger sample sizes and external cohorts are needed to validate these findings.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11542238/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142590063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction: CT coronary fractional flow reserve based on artificial intelligence using different software: a repeatability study.","authors":"Jing Li, Zhenxing Yang, Zhenting Sun, Lei Zhao, Aishi Liu, Xing Wang, Qiyu Jin, Guoyu Zhang","doi":"10.1186/s12880-024-01485-0","DOIUrl":"10.1186/s12880-024-01485-0","url":null,"abstract":"","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11539802/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142581837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diagnostic significance of ultrasound characteristics in discriminating follicular thyroid carcinoma from adenoma.","authors":"Ruifang Xu, Wanwan Wen, Yanning Zhang, Linxue Qian, Yujiang Liu","doi":"10.1186/s12880-024-01477-0","DOIUrl":"10.1186/s12880-024-01477-0","url":null,"abstract":"<p><strong>Background: </strong>Follicular thyroid carcinoma (FTC) is the second most common cancer of the thyroid gland and has a greater propensity for haematogenous metastasis. However, the preoperative differentiation of FTC from follicular thyroid adenoma (FTA) is not well established. Certain ultrasound characteristics are associated with an increased risk of thyroid malignancy, but mainly for papillary thyroid cancers and not for FTC.</p><p><strong>Objectives: </strong>This retrospective study aimed to evaluate the ultrasound characteristics of FTC and the value of ultrasound characteristics in differentiating FTC from FTA.</p><p><strong>Methods: </strong>A total of 96 patients with pathologically confirmed FTC or FTA who underwent preoperative thyroid ultrasound were included in this study. The ultrasound and pathological characteristics were evaluated.</p><p><strong>Results: </strong>Our data revealed that the incidences of lesions with tubercle-in-nodule, spiculated/microlobulated margins, mixed vascularization, egg-shell calcification, central stellate scarring, extension toward the capsule and chronic lymphocytic thyroiditis were significantly higher in the FTC group (all p < 0.05). After adjusting for confounding factors, lesions with mixed vascularization (odds ratio [OR]: 2.038, P = 0.019), central stellate scarring (OR: 87.992, P = 0.007), extension toward the capsule (OR: 22.587, P = 0.010), and chronic lymphocytic thyroiditis (OR: 9.195, P = 0.006) were independently associated with FTC. Furthermore, combined with chronic lymphocytic thyroiditis, mixed vascularization, central stellate scarring, and extension toward the capsule showed high discriminatory accuracy in predicting FTC (AUC: 0.914; sensitivity: 96.5%; specificity: 71.8%; p < 0.001).</p><p><strong>Conclusions: </strong>In combination with chronic lymphocytic thyroiditis, mixed vascularization, central stellate scarring, and extension toward the capsule have greater accuracy in differentiating FTCs from FTAs.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11536847/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142581842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computer vision applications for the detection or analysis of tuberculosis using digitised human lung tissue images - a systematic review.","authors":"Kapongo D Lumamba, Gordon Wells, Delon Naicker, Threnesan Naidoo, Adrie J C Steyn, Mandlenkosi Gwetu","doi":"10.1186/s12880-024-01443-w","DOIUrl":"10.1186/s12880-024-01443-w","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;To conduct a systematic review of the computer vision applications that detect, diagnose, or analyse tuberculosis (TB) pathology or bacilli using digitised human lung tissue images either through automatic or semi-automatic methods. We categorised the computer vision platform into four technologies: image processing, object/pattern recognition, computer graphics, and deep learning. In this paper, the focus is on image processing and deep learning (DL) applications for either 2D or 3D digitised human lung tissue images. This review is useful for establishing a common practice in TB analysis using human lung tissue as well as identifying opportunities for further research in this space. The review brings attention to the state-of-art techniques for detecting TB, with emphasis on the challenges and limitations of the current techniques. The ultimate goal is to promote the development of more efficient and accurate algorithms for the detection or analysis of TB, and raise awareness about the importance of early detection.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Design: &lt;/strong&gt;We searched five databases and Google Scholar for articles published between January 2017 and December 2022 that focus on Mycobacterium tuberculosis detection, or tuberculosis pathology using digitised human lung tissue images. Details regarding design, image processing and computer-aided techniques, deep learning models, and datasets were collected and summarised. Discussions, analysis, and comparisons of state-of-the-art methods are provided to help guide future research. Further, a brief update on the relevant techniques and their performance is provided.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Several studies have been conducted to develop automated and AI-assisted methods for diagnosing Mtb and TB pathology from digitised human lung tissue images. Some studies presented a completely automated method of diagnosis, while other studies developed AI-assisted diagnostic methods. Low-level focus areas included the development of a novel &lt;math&gt;&lt;mi&gt;μ&lt;/mi&gt;&lt;/math&gt; CT scanner for soft tissue image contract, and use of multiresolution computed tomography to analyse the 3D structure of the human lung. High-level focus areas included the investigation the effects of aging on the number and size of small airways in the lungs using CT and whole lung high-resolution &lt;math&gt;&lt;mi&gt;μ&lt;/mi&gt;&lt;/math&gt; CT, and the 3D microanatomy characterisation of human tuberculosis lung using &lt;math&gt;&lt;mi&gt;μ&lt;/mi&gt;&lt;/math&gt; CT in conjunction with histology and immunohistochemistry. Additionally, a novel method for acquiring high-resolution 3D images of human lung structure and topology is also presented.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusion: &lt;/strong&gt;The literature indicates that post 1950s, TB was predominantly studied using animal models even though no animal model reflects the full spectrum of human pulmonary TB disease and does not reproducibly transmit Mtb infection to other animals (Hunter, 2011). This explains why there are very f","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11536899/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142575443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computerized tomography features acting as predictors for invasive therapy in the management of Crohn's disease-related spontaneous intra-abdominal abscess: experience from long-term follow-up.","authors":"Yinghao Sun, Wei Liu, Ye Ma, Hong Yang, Yue Li, Bei Tan, Ji Li, Jiaming Qian","doi":"10.1186/s12880-024-01475-2","DOIUrl":"10.1186/s12880-024-01475-2","url":null,"abstract":"<p><strong>Background: </strong>Decision-making in the management of Crohn's disease (CD)-related spontaneous intra-abdominal abscess (IAA) is challenging. This study aims to reveal predictive factors for percutaneous drainage and/or surgery in the treatment of CD-related spontaneous IAA through long-term follow-up.</p><p><strong>Methods: </strong>Data were collected, including clinical manifestations, radiography and treatment strategies, in Chinese patients with CD-related IAA in a tertiary medical center. Univariate and Multivariate Cox analysis were conducted to identify predictors for invasive therapy.</p><p><strong>Results: </strong>Altogether, 48 CD patients were identified as having IAA through enhanced CT scans. The median follow-up time was 45.0 (23.3, 58.0) months. 23 (47.9%) patients underwent conservative medical treatment, and 25 (52.1%) patients underwent percutaneous drainage and/or surgical intervention (invasive treatment group). The 1-, 2-, and 5-year overall survival rates without invasive treatment were 75.0%, 56.1%, and 46.1%, respectively. On univariate Cox analysis, the computerized tomography (CT) features including nonperienteric abscess (HR: 4.22, 95% CI: 1.81-9.86, p = 0.001), max abscess diameter (HR: 1.01, 95% CI: 1.00-1.02, p<0.001) and width of sinus (HR: 1.27, 95% CI: 1.10-1.46, p = 0.001) were significantly associated with invasive treatment. Nonperienteric abscess was significantly associated with invasive treatment on multivariate Cox analysis (HR: 3.11, 95% CI: 1.25-7.71, p = 0.015). A score model was built by width of sinus, location of abscess and max abscess diameter to predict invasive treatment. The AUC of ROC, sensitivity and specificity were 0.892, 80.0% and 90.9% respectively.</p><p><strong>Conclusions: </strong>More than half of CD-related IAA patients needed invasive therapy within 5-year follow-up. The CT features including nonperienteric abscess, larger maximum abscess diameter and width of sinus suggested a more aggressive approach to invasive treatment.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11536533/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142581833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Imaging and clinical manifestations of hematogenous dissemination in melioidosis.","authors":"Anle Yu, Lanfang Su, Qun Li, Xiaohua Li, Sile Tao, Feng Li, Danqiong Deng","doi":"10.1186/s12880-024-01471-6","DOIUrl":"10.1186/s12880-024-01471-6","url":null,"abstract":"<p><strong>Background: </strong>Although there is a high incidence of hematogenous infections in melioidosis, a tropical infectious disease, there are few systematic analyses of hematogenous melioidosis in imaging articles. A comprehensive clinical and imaging evaluation of hematogenous melioidosis be conducted in order to achieve early diagnosis of the disease.</p><p><strong>Materials and methods: </strong>We conducted an analysis of 111 cases of melioidosis diagnosed by bacteriological culture between August 2001 and September 2022. The analysis focused on observing the main manifestations of chest imaging and clinical data, including nodules, cavities, consolidation, ground glass opacity(GGO), pleural effusion, centrilobular nodules, and temperature, leucocyte count, diabetes, etc. Our study involved univariate and multivariate analyses to identify significant diagnostic variables and risk predictive factors.</p><p><strong>Results: </strong>A total of 71.2% (79/111) of melioidosis cases were caused by hematogenous infection, and the most common organ involved was the lungs (88.5%, 100/113). The incidence of sepsis in patients with lung abnormalities was high (73%, 73/100), and the mortality rate of septic shock was 22% (22/100). Univariate analysis showed that the radiologic signs of blood culture-positive cases were more likely to have bilateral pulmonary and subpleural nodules (p = 0.003), bilateral GGO (p = 0.001), bilateral hydrothorax (p = 0.011). The multivariate analysis revealed a significant improvement in the area under the receiver operating characteristic curve (AUC) when comparing the model that included both clinical and radiologic variables to the model with clinical variables alone. The AUC increased from 0.818 to 0.932 (p = 0.012). The most important variables in the logistic regression with backward elimination were found to be nodule, GGO, and diabetes.</p><p><strong>Conclusion: </strong>The combination of CT features and clinical variables provided a valuable and timely warning for blood borne infectious melioidosis.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11529265/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142563798","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficient brain tumor grade classification using ensemble deep learning models.","authors":"Sankar M, Baiju Bv, Preethi D, Ananda Kumar S, Sandeep Kumar Mathivanan, Mohd Asif Shah","doi":"10.1186/s12880-024-01476-1","DOIUrl":"10.1186/s12880-024-01476-1","url":null,"abstract":"<p><p>Detecting brain tumors early on is critical for effective treatment and life-saving efforts. The analysis of the brain with MRI scans is fundamental to the diagnosis because it contains detailed structural views of the brain, which is vital in identifying any of its abnormalities. The other option of performing an invasive biopsy is very painful and uncomfortable, which is not the case with MRI as it is free from surgically invasive margins and pieces of equipment. This helps patients to feel more at ease and hasten the diagnostic procedure, allowing physicians to formulate and practice action plans quicker. It is very difficult to locate a human brain tumor by manual because MRI scans produce large numbers of three-dimensional images. Complete applicability of pre-written computerized diagnostics, affords high possibilities in providing areas of interest earlier through the application of machine learning techniques and algorithms. The proposed work in the present study was to develop a deep learning model which will classify brain tumor grade images (BTGC), and hence enhance accuracy in diagnosing patients with different grades of brain tumors using MRI. A MobileNetV2 model, was used to extract the features from the images. This model increases the efficiency and generalizability of the model further. In this study, six standard Kaggle brain tumor MRI datasets were used to train and validate the developed and tested model of a brain tumor detection and classification algorithm into several types. This work consists of two key components: (i) brain tumor detection and (ii) classification of the tumor. The tumor classifications are conducted in both three classes (Meningioma, Pituitary, and glioma) and two classes (malignant, benign). The model has been reported to detect brain tumors with 99.85% accuracy, to distinguish benign and malignant tumors with 99.87% accuracy, and to type meningioma, pituitary, and glioma tumors with 99.38% accuracy. The results of this study indicate that the described technique is useful in the detection and classification of brain tumors.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11529038/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142563795","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"¹⁸F-FDG PET/CT for predicting inferior vena cava wall invasion in patients of renal cell carcinoma with the presence of inferior vena cava tumor thrombus.","authors":"Anhui Zhu, Xiaoyan Hou, Na Guo, Weifang Zhang","doi":"10.1186/s12880-024-01466-3","DOIUrl":"10.1186/s12880-024-01466-3","url":null,"abstract":"<p><strong>Introduction: </strong>Preoperative evaluation of inferior vena cava (IVC) wall invasion is very important to improve outcomes of patients with renal cell carcinoma (RCC), and may allow surgical urologists to treat the IVC more effectively. The objective of this study was to evaluate preoperative ¹⁸F-FDG PET/CT in patients with RCC and IVC tumor thrombus (IVCTT) for the diagnosis of IVC wall invasion.</p><p><strong>Methods: </strong>This retrospective case-control study evaluated 68 patients with RCC with level I-IV tumor thrombus. According to the histopathologic examination result, the patients were divided into IVC wall invasion group and non-invasion group. The ¹⁸F-FDG PET/CT features between two groups were analyzed. Furthermore, a logistic regression model was used to determine if there was an association between PET/CT features and IVC wall invasion.</p><p><strong>Results: </strong>Sixty-eight patients were evaluated, and 55.9% (38/68) had IVC wall invasion. Compared with non-invasion group, invasion group had higher SUVmax of RCC, higher SURmax (tumor to tumor thrombus ratio, Tu/Th), higher IVCTT coronal diameter, and longer IVCTT craniocaudal extent (all p < 0.05). Multivariate analysis showed that SURmax (Tu/Th) (OR 8.760 [95%CI, 1.019-75.310]; p = 0.048) and the maximum coronal diameter of IVCTT (OR 1.143 [95%CI, 1.029-1.269]; p = 0.028) were predictors of IVC wall invasion. A model combining SURmax (Tu/Th) and the maximum coronal diameter of IVCTT achieved an AUC of 0.855 (95%CI, 0.757-0.954). The specificity and sensitivity for assessing IVC wall invasion was 92.1% and 76.7%, respectively.</p><p><strong>Conclusions: </strong>Increases in SURmax (Tu/Th) and the maximum coronal diameter of IVCTT are associated with a higher probability of IVC wall invasion. Preoperative ¹⁸F-FDG PET/CT imaging may be used to assess IVC wall invasion.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11526541/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142557128","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Does the FARNet neural network algorithm accurately identify Posteroanterior cephalometric landmarks?","authors":"Merve Gonca, İbrahim Şevki Bayrakdar, Özer Çelik","doi":"10.1186/s12880-024-01478-z","DOIUrl":"10.1186/s12880-024-01478-z","url":null,"abstract":"<p><strong>Background: </strong>We explored whether the feature aggregation and refinement network (FARNet) algorithm accurately identified posteroanterior (PA) cephalometric landmarks.</p><p><strong>Methods: </strong>We identified 47 landmarks on 1,431 PA cephalograms of which 1,177 were used for training, 117 for validation, and 137 for testing. A FARNet-based artificial intelligence (AI) algorithm automatically detected the landmarks. Model effectiveness was calculated by deriving the mean radial error (MRE) and the successful detection rates (SDRs) within 2, 2.5, 3, and 4 mm. The Mann-Whitney U test was performed on the Euclidean differences between repeated manual identifications and AI trials. The direction in differences was analyzed, and whether differences moved in the same or opposite directions relative to ground truth on both the x and y-axis.</p><p><strong>Results: </strong>The AI system (web-based CranioCatch annotation software (Eskişehir, Turkey)) identified 47 anatomical landmarks in PA cephalograms. The right gonion SDRs were the highest, thus 96.4, 97.8, 100, and 100% within 2, 2.5, 3, and 4 mm, respectively. The right gonion MRE was 0.94 ± 0.53 mm. The right condylon SDRs were the lowest, thus 32.8, 45.3, 54.0, and 67.9% within the same thresholds. The right condylon MRE was 3.31 ± 2.25 mm. The AI model's reliability and accuracy were similar to a human expert's. AI was better at four skeleton points than the expert, whereas the expert was better at one skeletal and seven dental points (P < 0.05). Most of the points exhibited significant deviations along the y-axis. Compared to ground truth, most of the points in AI and the second trial showed opposite movement on the x-axis and the same on the y-axis.</p><p><strong>Conclusions: </strong>The FARNet algorithm streamlined orthodontic diagnosis.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11526671/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142543505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An artificial intelligence algorithm for the detection of pulmonary ground-glass nodules on spectral detector CT: performance on virtual monochromatic images.","authors":"Zhong-Yan Ma, Hai-Lin Zhang, Fa-Jin Lv, Wei Zhao, Dan Han, Li-Chang Lei, Qin Song, Wei-Wei Jing, Hui Duan, Shao-Lei Kang","doi":"10.1186/s12880-024-01467-2","DOIUrl":"10.1186/s12880-024-01467-2","url":null,"abstract":"<p><strong>Background: </strong>This study aims to assess the performance of an established an AI algorithm trained on conventional polychromatic computed tomography (CT) images (CPIs) to detect pulmonary ground-glass nodules (GGNs) on virtual monochromatic images (VMIs), and to screen the optimal virtual monochromatic energy for the clinical evaluation of GGNs.</p><p><strong>Methods: </strong>Non-enhanced chest SDCT images of patients with pulmonary GGNs in our clinic from January 2022 to December 2022 were continuously collected: adenocarcinoma in situ (AIS, n = 40); minimally invasive adenocarcinoma (MIA, n = 44) and invasive adenocarcinoma (IAC, n = 46). A commercial CAD system based on deep convolutional neural networks (DL-CAD) was used to process the CPIs, 40, 50, 60, 70, and 80 keV monochromatic images of 130 spectral CT images. AI-based histogram parameters by logistic regression analysis. The diagnostic performance was evaluated by the receiver operating characteristic (ROC) curves, and Delong's test was used to compare the CPIs group with the VMIs group.</p><p><strong>Results: </strong>When distinguishing IAC from MIA, the diagnostic efficiency of total mass was obtained at 80 keV, which was superior to those of other energy levels (P < 0.05). And Delong's test indicated that the differences between the area-under-the-curve (AUC) values of the CPIs group and the VMIs group were not statistically significant (P > 0.05).</p><p><strong>Conclusion: </strong>The AI algorithm trained on CPIs showed consistent diagnostic performance on VMIs. When pulmonary GGNs are encountered in clinical practice, 80 keV could be the optimal virtual monochromatic energy for the identification of preoperative IAC on a non-enhanced chest CT.</p>","PeriodicalId":9020,"journal":{"name":"BMC Medical Imaging","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11523583/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142543502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}