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6.
Fa Yi Xue Za Zhi ; 40(2): 128-134, 2024 Apr 25.
Article in English, Chinese | MEDLINE | ID: mdl-38847026

ABSTRACT

OBJECTIVES: To establish age estimation models of northern Chinese Han adults using cranial suture images obtained by CT and multiplanar reformation (MPR), and to explore the applicability of cranial suture closure rule in age estimation of northern Chinese Han population. METHODS: The head CT samples of 132 northern Chinese Han adults aged 29-80 years were retrospectively collected. Volume reconstruction (VR) and MPR were performed on the skull, and 160 cranial suture tomography images were generated for each sample. Then the MPR images of cranial sutures were scored according to the closure grading criteria, and the mean closure grades of sagittal suture, coronal sutures (both left and right) and lambdoid sutures (both left and right) were calculated respectively. Finally taking the above grades as independent variables, the linear regression model and four machine learning models for age estimation (gradient boosting regression, support vector regression, decision tree regression and Bayesian ridge regression) were established for northern Chinese Han adults age estimation. The accuracy of each model was evaluated. RESULTS: Each cranial suture closure grade was positively correlated with age and the correlation of sagittal suture was the highest. All four machine learning models had higher age estimation accuracy than linear regression model. The support vector regression model had the highest accuracy among the machine learning models with a mean absolute error of 9.542 years. CONCLUSIONS: The combination of skull CT-MPR and machine learning model can be used for age estimation in northern Chinese Han adults, but it is still necessary to combine with other adult age estimation indicators in forensic practice.


Subject(s)
Age Determination by Skeleton , Asian People , Cranial Sutures , Machine Learning , Tomography, X-Ray Computed , Humans , Cranial Sutures/diagnostic imaging , Middle Aged , Adult , Aged , Aged, 80 and over , Age Determination by Skeleton/methods , Retrospective Studies , Female , China/ethnology , Male , Skull/diagnostic imaging , Forensic Anthropology/methods , Bayes Theorem , Image Processing, Computer-Assisted/methods , Imaging, Three-Dimensional , Ethnicity , Linear Models , East Asian People
7.
Fa Yi Xue Za Zhi ; 40(2): 154-163, 2024 Apr 25.
Article in English, Chinese | MEDLINE | ID: mdl-38847030

ABSTRACT

OBJECTIVES: To develop a deep learning model for automated age estimation based on 3D CT reconstructed images of Han population in western China, and evaluate its feasibility and reliability. METHODS: The retrospective pelvic CT imaging data of 1 200 samples (600 males and 600 females) aged 20.0 to 80.0 years in western China were collected and reconstructed into 3D virtual bone models. The images of the ischial tuberosity feature region were extracted to create sex-specific and left/right site-specific sample libraries. Using the ResNet34 model, 500 samples of different sexes were randomly selected as training and verification set, the remaining samples were used as testing set. Initialization and transfer learning were used to train images that distinguish sex and left/right site. Mean absolute error (MAE) and root mean square error (RMSE) were used as primary indicators to evaluate the model. RESULTS: Prediction results varied between sexes, with bilateral models outperformed left/right unilateral ones, and transfer learning models showed superior performance over initial models. In the prediction results of bilateral transfer learning models, the male MAE was 7.74 years and RMSE was 9.73 years, the female MAE was 6.27 years and RMSE was 7.82 years, and the mixed sexes MAE was 6.64 years and RMSE was 8.43 years. CONCLUSIONS: The skeletal age estimation model, utilizing ischial tuberosity images of Han population in western China and employing the ResNet34 combined with transfer learning, can effectively estimate adult ischium age.


Subject(s)
Age Determination by Skeleton , Deep Learning , Imaging, Three-Dimensional , Ischium , Tomography, X-Ray Computed , Humans , Male , Female , Ischium/diagnostic imaging , Adult , Middle Aged , Tomography, X-Ray Computed/methods , Imaging, Three-Dimensional/methods , China , Retrospective Studies , Age Determination by Skeleton/methods , Aged , Young Adult , Aged, 80 and over , Reproducibility of Results
9.
Comput Biol Med ; 177: 108625, 2024 Jul.
Article in English | MEDLINE | ID: mdl-38823365

ABSTRACT

Liver segmentation is a fundamental prerequisite for the diagnosis and surgical planning of hepatocellular carcinoma. Traditionally, the liver contour is drawn manually by radiologists using a slice-by-slice method. However, this process is time-consuming and error-prone, depending on the radiologist's experience. In this paper, we propose a new end-to-end automatic liver segmentation framework, named ResTransUNet, which exploits the transformer's ability to capture global context for remote interactions and spatial relationships, as well as the excellent performance of the original U-Net architecture. The main contribution of this paper lies in proposing a novel fusion network that combines Unet and Transformer architectures. In the encoding structure, a dual-path approach is utilized, where features are extracted separately using both convolutional neural networks (CNNs) and Transformer networks. Additionally, an effective feature enhancement unit is designed to transfer the global features extracted by the Transformer network to the CNN for feature enhancement. This model aims to address the drawbacks of traditional Unet-based methods, such as feature loss during encoding and poor capture of global features. Moreover, it avoids the disadvantages of pure Transformer models, which suffer from large parameter sizes and high computational complexity. The experimental results on the LiTS2017 dataset demonstrate remarkable performance for our proposed model, with Dice coefficients, volumetric overlap error (VOE), and relative volume difference (RVD) values for liver segmentation reaching 0.9535, 0.0804, and -0.0007, respectively. Furthermore, to further validate the model's generalization capability, we conducted tests on the 3Dircadb, Chaos, and Sliver07 datasets. The experimental results demonstrate that the proposed method outperforms other closely related models with higher liver segmentation accuracy. In addition, significant improvements can be achieved by applying our method when handling liver segmentation with small and discontinuous liver regions, as well as blurred liver boundaries. The code is available at the website: https://github.com/Jouiry/ResTransUNet.


Subject(s)
Liver , Neural Networks, Computer , Tomography, X-Ray Computed , Humans , Liver/diagnostic imaging , Tomography, X-Ray Computed/methods , Liver Neoplasms/diagnostic imaging , Carcinoma, Hepatocellular/diagnostic imaging , Algorithms
10.
Comput Biol Med ; 177: 108659, 2024 Jul.
Article in English | MEDLINE | ID: mdl-38823366

ABSTRACT

Automatic abdominal organ segmentation is an essential prerequisite for accurate volumetric analysis, disease diagnosis, and tracking by medical practitioners. However, the deformable shapes, variable locations, overlapping with nearby organs, and similar contrast make the segmentation challenging. Moreover, the requirement of a large manually labeled dataset makes it harder. Hence, a semi-supervised contrastive learning approach is utilized to perform the automatic abdominal organ segmentation. Existing 3D deep learning models based on contrastive learning are not able to capture the 3D context of medical volumetric data along three planes/views: axial, sagittal, and coronal views. In this work, a semi-supervised view-adaptive unified model (VAU-model) is proposed to make the 3D deep learning model as view-adaptive to learn 3D context along each view in a unified manner. This method utilizes the novel optimization function that assists the 3D model to learn the 3D context of volumetric medical data along each view in a single model. The effectiveness of the proposed approach is validated on the three types of datasets: BTCV, NIH, and MSD quantitatively and qualitatively. The results demonstrate that the VAU model achieves an average Dice score of 81.61% which is a 3.89% improvement compared to the previous best results for pancreas segmentation in multi-organ dataset BTCV. It also achieves an average Dice score of 77.76% and 76.76% for the pancreas under the single organ non-pathological NIH dataset, and pathological MSD dataset.


Subject(s)
Imaging, Three-Dimensional , Humans , Imaging, Three-Dimensional/methods , Deep Learning , Abdomen/diagnostic imaging , Abdomen/anatomy & histology , Tomography, X-Ray Computed/methods , Pancreas/diagnostic imaging , Pancreas/anatomy & histology , Databases, Factual
11.
Phys Med ; 122: 103384, 2024 Jun.
Article in English | MEDLINE | ID: mdl-38824827

ABSTRACT

The dosimetry evaluation for the selective internal radiation therapy is currently performed assuming a uniform activity distribution, which is in contrast with literature findings. A 2D microscopic model of the perfused liver was developed to evaluate the effect of two different 90Y microspheres distributions: i) homogeneous partitioning with the microspheres equally distributed in the perfused liver, and ii) tumor-clustered partitioning where the microspheres distribution is inferred from the patient specific images. METHODS: Two subjects diagnosed with liver cancer were included in this study. For each subject, abdominal CT scans acquired prior to the SIRT and post-treatment 90Y positron emission tomography were considered. Two microspheres partitionings were simulated namely homogeneous and tumor-clustered partitioning. The homogeneous and tumor-clustered partitionings were derived starting from CT images. The microspheres radiation is simulated by means of Russell's law. RESULTS: In homogenous simulations, the dose delivery is uniform in the whole liver while in the tumor-clustered simulations a heterogeneous distribution of the delivered dose is visible with higher values in the tumor regions. In addition, in the tumor-clustered simulation, the delivered dose is higher in the viable tumor than in the necrotic tumor, for all patients. In the tumor-clustered case, the dose delivered in the non-tumoral tissue (NTT) was considerably lower than in the perfused liver. CONCLUSIONS: The model proposed here represents a proof-of-concept for personalized dosimetry assessment based on preoperative CT images.


Subject(s)
Carcinoma, Hepatocellular , Liver Neoplasms , Microspheres , Radiotherapy Dosage , Yttrium Radioisotopes , Liver Neoplasms/radiotherapy , Liver Neoplasms/diagnostic imaging , Carcinoma, Hepatocellular/radiotherapy , Carcinoma, Hepatocellular/diagnostic imaging , Humans , Yttrium Radioisotopes/therapeutic use , Models, Biological , Tomography, X-Ray Computed , Radiation Dosage , Microscopy
12.
Am J Case Rep ; 25: e944002, 2024 Jun 03.
Article in English | MEDLINE | ID: mdl-38825807

ABSTRACT

BACKGROUND Orbital metastasis originating from hepatocellular carcinoma (HCC), particularly as an initial manifestation in patients without a known history of HCC, is rare. Few reports exist on the treatment of patients having HCC with orbital metastasis using targeted therapy or immunotherapy. CASE REPORT We report a case of advanced-stage HCC in a 65-year-old man who first presented with progressive, painless blurred vision and proptosis of the right eye for 2 weeks. The patient had no history of chronic liver disease or cancer. Computed tomography revealed an enhancing hyperdense extraconal mass in the right orbit; a biopsy revealed metastatic HCC. Abdominal CT, which was performed to investigate the primary cancer, revealed a 1.2×1.6-cm arterial-enhancing nodule with venous washout in hepatic segment 5, associated with liver cirrhosis. The patient's serum alpha-fetoprotein level was 70.27 ng/dL. Chest computed tomography revealed lung metastasis. Thus, first-line systemic therapy combining durvalumab and tremelimumab was initiated alongside palliative radiotherapy targeting the right orbit, which began 1 week after the first dose of dual immunotherapy. The patient had significant clinical improvement, reduced proptosis, and serum alpha-fetoprotein levels. CONCLUSIONS Although orbital metastasis is a rare manifestation of HCC, physicians should recognize and consider aggressive investigations for early diagnosis, especially in patients with existing risk factors for HCC. Dual immunotherapy with durvalumab and tremelimumab in combination with radiotherapy can be considered a potential treatment option for managing advanced HCC with orbital metastasis.


Subject(s)
Antibodies, Monoclonal, Humanized , Carcinoma, Hepatocellular , Liver Neoplasms , Orbital Neoplasms , Humans , Male , Carcinoma, Hepatocellular/therapy , Carcinoma, Hepatocellular/secondary , Liver Neoplasms/secondary , Liver Neoplasms/therapy , Aged , Orbital Neoplasms/secondary , Orbital Neoplasms/therapy , Antibodies, Monoclonal, Humanized/therapeutic use , Antibodies, Monoclonal/therapeutic use , Immunotherapy , Antineoplastic Combined Chemotherapy Protocols/therapeutic use , Tomography, X-Ray Computed , Antineoplastic Agents, Immunological/therapeutic use
14.
Comput Biol Med ; 177: 108640, 2024 Jul.
Article in English | MEDLINE | ID: mdl-38833798

ABSTRACT

Graph convolutional neural networks (GCN) have shown the promise in medical image segmentation due to the flexibility of representing diverse range of image regions using graph nodes and propagating knowledge via graph edges. However, existing methods did not fully exploit the various attributes of image nodes and the context relationship among their attributes. We propose a new segmentation method with multi-similarity view enhancement and node attribute context learning (MNSeg). First, multiple views were formed by measuring the similarities among the image nodes, and MNSeg has a GCN based multi-view image node attribute learning (MAL) module to integrate various node attributes learnt from multiple similarity views. Each similarity view contains the specific similarities among all the image nodes, and it was integrated with the node attributes from all the channels to form the enhanced attributes of image nodes. Second, the context relationships among the attributes of image nodes are formulated by a transformer-based context relationship encoding (CRE) strategy to propagate these relationships across all the image nodes. During the transformer-based learning, the relationships were estimated based on the self-attention on all the image nodes, and then they were encoded into the learned node features. Finally, we design an attention at attribute category level (ACA) to discriminate and fuse the learnt diverse information from MAL, CRE, and the original node attributes. ACA identifies the more informative attribute categories by adaptively learn their importance. We validate the performance of MNSeg on a public lung tumor CT dataset and an in-house non-small cell lung cancer (NSCLC) dataset collected from the hospital. The segmentation results show that MNSeg outperformed the compared segmentation methods in terms of spatial overlap and the shape similarities. The ablation studies demonstrated the effectiveness of MAL, CRE, and ACA. The generalization ability of MNSeg was proved by the consistent improved segmentation performances using different 3D segmentation backbones.


Subject(s)
Lung Neoplasms , Tomography, X-Ray Computed , Humans , Lung Neoplasms/diagnostic imaging , Tomography, X-Ray Computed/methods , Neural Networks, Computer , Deep Learning
15.
Radiat Oncol ; 19(1): 69, 2024 May 31.
Article in English | MEDLINE | ID: mdl-38822385

ABSTRACT

BACKGROUND: Multiple artificial intelligence (AI)-based autocontouring solutions have become available, each promising high accuracy and time savings compared with manual contouring. Before implementing AI-driven autocontouring into clinical practice, three commercially available CT-based solutions were evaluated. MATERIALS AND METHODS: The following solutions were evaluated in this work: MIM-ProtégéAI+ (MIM), Radformation-AutoContour (RAD), and Siemens-DirectORGANS (SIE). Sixteen organs were identified that could be contoured by all solutions. For each organ, ten patients that had manually generated contours approved by the treating physician (AP) were identified, totaling forty-seven different patients. CT scans in the supine position were acquired using a Siemens-SOMATOMgo 64-slice helical scanner and used to generate autocontours. Physician scoring of contour accuracy was performed by at least three physicians using a five-point Likert scale. Dice similarity coefficient (DSC), Hausdorff distance (HD) and mean distance to agreement (MDA) were calculated comparing AI contours to "ground truth" AP contours. RESULTS: The average physician score ranged from 1.00, indicating that all physicians reviewed the contour as clinically acceptable with no modifications necessary, to 3.70, indicating changes are required and that the time taken to modify the structures would likely take as long or longer than manually generating the contour. When averaged across all sixteen structures, the AP contours had a physician score of 2.02, MIM 2.07, RAD 1.96 and SIE 1.99. DSC ranged from 0.37 to 0.98, with 41/48 (85.4%) contours having an average DSC ≥ 0.7. Average HD ranged from 2.9 to 43.3 mm. Average MDA ranged from 0.6 to 26.1 mm. CONCLUSIONS: The results of our comparison demonstrate that each vendor's AI contouring solution exhibited capabilities similar to those of manual contouring. There were a small number of cases where unusual anatomy led to poor scores with one or more of the solutions. The consistency and comparable performance of all three vendors' solutions suggest that radiation oncology centers can confidently choose any of the evaluated solutions based on individual preferences, resource availability, and compatibility with their existing clinical workflows. Although AI-based contouring may result in high-quality contours for the majority of patients, a minority of patients require manual contouring and more in-depth physician review.


Subject(s)
Artificial Intelligence , Radiotherapy Planning, Computer-Assisted , Tomography, X-Ray Computed , Humans , Radiotherapy Planning, Computer-Assisted/methods , Organs at Risk/radiation effects , Algorithms , Image Processing, Computer-Assisted/methods
16.
J Cardiothorac Surg ; 19(1): 307, 2024 May 31.
Article in English | MEDLINE | ID: mdl-38822379

ABSTRACT

BACKGROUND: Accurate prediction of visceral pleural invasion (VPI) in lung adenocarcinoma before operation can provide guidance and help for surgical operation and postoperative treatment. We investigate the value of intratumoral and peritumoral radiomics nomograms for preoperatively predicting the status of VPI in patients diagnosed with clinical stage IA lung adenocarcinoma. METHODS: A total of 404 patients from our hospital were randomly assigned to a training set (n = 283) and an internal validation set (n = 121) using a 7:3 ratio, while 81 patients from two other hospitals constituted the external validation set. We extracted 1218 CT-based radiomics features from the gross tumor volume (GTV) as well as the gross peritumoral tumor volume (GPTV5, 10, 15), respectively, and constructed radiomic models. Additionally, we developed a nomogram based on relevant CT features and the radscore derived from the optimal radiomics model. RESULTS: The GPTV10 radiomics model exhibited superior predictive performance compared to GTV, GPTV5, and GPTV15, with area under the curve (AUC) values of 0.855, 0.842, and 0.842 in the three respective sets. In the clinical model, the solid component size, pleural indentation, solid attachment, and vascular convergence sign were identified as independent risk factors among the CT features. The predictive performance of the nomogram, which incorporated relevant CT features and the GPTV10-radscore, outperformed both the radiomics model and clinical model alone, with AUC values of 0.894, 0.828, and 0.876 in the three respective sets. CONCLUSIONS: The nomogram, integrating radiomics features and CT morphological features, exhibits good performance in predicting VPI status in lung adenocarcinoma.


Subject(s)
Adenocarcinoma of Lung , Lung Neoplasms , Neoplasm Invasiveness , Neoplasm Staging , Nomograms , Tomography, X-Ray Computed , Humans , Male , Female , Lung Neoplasms/pathology , Lung Neoplasms/diagnostic imaging , Lung Neoplasms/surgery , Middle Aged , Tomography, X-Ray Computed/methods , Adenocarcinoma of Lung/surgery , Adenocarcinoma of Lung/diagnostic imaging , Adenocarcinoma of Lung/pathology , Neoplasm Staging/methods , Aged , Retrospective Studies , Pleura/diagnostic imaging , Pleura/pathology , Pleural Neoplasms/diagnostic imaging , Pleural Neoplasms/surgery , Pleural Neoplasms/pathology , Radiomics
17.
J Cardiothorac Surg ; 19(1): 308, 2024 May 31.
Article in English | MEDLINE | ID: mdl-38822419

ABSTRACT

BACKGROUND: Bronchopleural fistula (BPF) is a rare but fatal complication after pneumonectomy. When a BPF occurs late (weeks to years postoperatively), direct resealing of the bronchial stump through the primary thoracic approach is challenging due to the risks of fibrothorax and injury to the pulmonary artery stump, and the surgical outcome is generally poor. Here, we report a case of late left BPF following left pneumonectomy successfully treated using a right thoracic approach assisted by extracorporeal membrane oxygenation (ECMO). CASE PRESENTATION: We report the case of a 57-year-old male patient who underwent left lower and left upper lobectomy, respectively, for heterochronic double primary lung cancer. A left BPF was diagnosed at the 22nd month postoperatively, and conservative treatment was ineffective. Finally, the left BPF was cured by minimally invasive BPF closure surgery via the right thoracic approach with the support of veno-venous extracorporeal membrane oxygenation (VV-ECMO). CONCLUSIONS: Advanced BPF following left pneumonectomy can be achieved with an individualized treatment plan, and the right thoracic approach assisted by ECMO is a relatively simple and effective method, which could be considered as an additional treatment option for similar patients.


Subject(s)
Bronchial Fistula , Extracorporeal Membrane Oxygenation , Lung Neoplasms , Pleural Diseases , Pneumonectomy , Humans , Male , Pneumonectomy/adverse effects , Extracorporeal Membrane Oxygenation/methods , Middle Aged , Bronchial Fistula/etiology , Bronchial Fistula/surgery , Pleural Diseases/etiology , Pleural Diseases/surgery , Lung Neoplasms/surgery , Postoperative Complications/surgery , Postoperative Complications/therapy , Tomography, X-Ray Computed
18.
Niger Postgrad Med J ; 31(2): 147-155, 2024 Apr 01.
Article in English | MEDLINE | ID: mdl-38826018

ABSTRACT

BACKGROUND: The thickness of extraocular muscles (EOMs) is important in the management of several conditions associated with EOM enlargement. This study determined the normative values of EOM diameters in adult patients seen at a teaching hospital in Nigeria. MATERIALS AND METHODS: The study measured the thickness of the EOMs and the interzygomatic line (IZL) on brain images of 300 patients with non-orbital conditions (150 computed tomography [CT] and 150 magnetic resonance imaging [MRI]) archived in the radiological database of Delta State University Hospital, Nigeria, after ethical clearance. The Statistical Package for the Social Sciences (version 23) was used to obtain descriptive statistics and further compare the variables based on gender, age groups and laterality. The association between parameters was tested using Pearson's correlation test. A probability value of <5% was considered significant. RESULTS: The thickest muscles were the medial rectus (0.42 ± 0.08 cm) and superior muscle group (0.42 ± 0.33 cm) on CT and the inferior rectus (0.40 ± 0.08 cm) on MRI. The diameters were symmetrical with sexual dimorphism in the superior muscle group on CT, medial and lateral recti on MRI and sum of all EOMs on both imaging groups (P < 0.05). The superior muscle group and the sum of all EOMs showed significant age group variations and a positive correlation with age. We noted a positive correlation between each EOM diameter and the sum of all EOMs besides the IZL (P < 0.05). CONCLUSION: This study offers normative data regarding EOMs that radiologists and ophthalmologists can use to diagnose disease conditions that cause EOM enlargement and further assess their response to treatment.


Subject(s)
Magnetic Resonance Imaging , Oculomotor Muscles , Tomography, X-Ray Computed , Humans , Oculomotor Muscles/diagnostic imaging , Oculomotor Muscles/anatomy & histology , Male , Female , Adult , Nigeria , Retrospective Studies , Middle Aged , Magnetic Resonance Imaging/methods , Aged , Reference Values , Young Adult , Adolescent
20.
Rev Med Suisse ; 20(877): 1132-1134, 2024 Jun 05.
Article in French | MEDLINE | ID: mdl-38836397

ABSTRACT

A 50-year-old individual identified as a 'frequent user' of emergency services due to chronic abdominal pain was transported to the emergency department by ambulance during a new episode of abdominal pain. Despite being initially deemed stable by paramedics, the patient was not reassessed by the triage nurse upon arrival. Subsequently, the patient presented with severe pain, arterial hypotension, and tachycardia. Following a multidisciplinary protocol for pain management, analgesic treatment was initiated. Despite several hours of management and repeated assessments, an abdominal CT-scan was eventually conducted, revealing a perforated small intestine. The application of the 'frequent user' label may have contributed to a delay in the provision of timely care for this patient.


Subject(s)
Abdominal Pain , Humans , Middle Aged , Abdominal Pain/etiology , Abdominal Pain/therapy , Abdominal Pain/diagnosis , Intestinal Perforation/etiology , Intestinal Perforation/diagnosis , Tomography, X-Ray Computed/methods , Male , Emergency Medical Services/methods , Emergency Medical Services/standards , Emergency Service, Hospital/organization & administration
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