Dataset Information

Negative CT Contrast Agents for the Diagnosis of Malignant Osteosarcoma.

ABSTRACT: The current positive computed tomography (CT) contrast agents (PCTCAs) including clinical iodides, present high CT density value (CT-DV). However, they are incapable for the accurate diagnosis of some diseases with high CT-DV, such as osteosarcoma. Because bones and PCTCAs around osteosarcoma generate similar X-ray attenuations. Here, an innovative strategy of negative CT contrast agents (NCTCAs) to reduce the CT-DV of osteosarcoma is proposed, contributing to accurate detection of osteosarcoma. Hollow mesoporous silica nanoparticles, loading ammonia borane molecules and further modified by polyethylene glycol, are synthesized as NCTCAs for the diagnosis of osteosarcoma. The nanocomposites can produce H2 in situ at osteosarcoma areas by responding to the acidic microenvironment of osteosarcoma, resulting in nearly 20 times reduction of CT density in osteosarcoma. This helps form large CT density contrast between bones and osteosarcoma, and successfully achieves accurate diagnosis of osteosarcoma. Meanwhile, The NCTCAs strategy greatly expands the scope of CT application, and provides profound implications for the precise clinical diagnosis, treatment, and prognosis of diseases.

SUBMITTER: Meng X

PROVIDER: S-EPMC6891899 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Negative CT Contrast Agents for the Diagnosis of Malignant Osteosarcoma.

Meng Xianfu X Zhang Hua H Zhang Meng M Wang Baoming B Liu Yanyan Y Wang Yan Y Fang Xiangming X Zhang Jiawen J Yao Zhenwei Z Bu Wenbo W

Advanced science (Weinheim, Baden-Wurttemberg, Germany) 20191011 23

The current positive computed tomography (CT) contrast agents (PCTCAs) including clinical iodides, present high CT density value (CT-DV). However, they are incapable for the accurate diagnosis of some diseases with high CT-DV, such as osteosarcoma. Because bones and PCTCAs around osteosarcoma generate similar X-ray attenuations. Here, an innovative strategy of negative CT contrast agents (NCTCAs) to reduce the CT-DV of osteosarcoma is proposed, contributing to accurate detection of osteosarcoma. ...[more]

PMID: 31832312

Similar Datasets

Project description:BackgroundLiver cancer remains the leading cause of cancer death globally, and the treatment strategies are distinct for each type of malignant hepatic tumors. However, the differential diagnosis before surgery is challenging and subjective. This study aims to build an automatic diagnostic model for differentiating malignant hepatic tumors based on patients' multimodal medical data including multi-phase contrast-enhanced computed tomography and clinical features.MethodsOur study consisted of 723 patients from two centers, who were pathologically diagnosed with HCC, ICC or metastatic liver cancer. The training set and the test set consisted of 499 and 113 patients from center 1, respectively. The external test set consisted of 111 patients from center 2. We proposed a deep learning model with the modular design of SpatialExtractor-TemporalEncoder-Integration-Classifier (STIC), which take the advantage of deep CNN and gated RNN to effectively extract and integrate the diagnosis-related radiological and clinical features of patients. The code is publicly available at https://github.com/ruitian-olivia/STIC-model .ResultsThe STIC model achieved an accuracy of 86.2% and AUC of 0.893 for classifying HCC and ICC on the test set. When extended to differential diagnosis of malignant hepatic tumors, the STIC model achieved an accuracy of 72.6% on the test set, comparable with the diagnostic level of doctors' consensus (70.8%). With the assistance of the STIC model, doctors achieved better performance than doctors' consensus diagnosis, with an increase of 8.3% in accuracy and 26.9% in sensitivity for ICC diagnosis on average. On the external test set from center 2, the STIC model achieved an accuracy of 82.9%, which verify the model's generalization ability.ConclusionsWe incorporated deep CNN and gated RNN in the STIC model design for differentiating malignant hepatic tumors based on multi-phase CECT and clinical features. Our model can assist doctors to achieve better diagnostic performance, which is expected to serve as an AI assistance system and promote the precise treatment of liver cancer.

Project description:PurposeTo investigate the value of spectral CT in the differential diagnosis of benign from malignant pleural effusion.Method and materials14 patients with benign pleural effusion and 15 patients with malignant pleural effusion underwent non-contrast spectral CT imaging. These patients were later verified by the combination of disease history, clinical signs and other information with the consensus of surgeons and radiologists. Various Spectral CT image parameters measured for the effusion were as follows: CT numbers of the polychromatic 140kVp images, monochromatic images at 40keV and 100keV, the material density contents from the water, fat and blood-based material decomposition images, the effective atomic number and the spectral curve slope. These values were statistically compared with t test and logistic regression analysis between benign and malignant pleural effusion.ResultsThe CT value of benign and malignant pleural effusion in the polychromatic 140kVp images showed no differences (12.61±3.39HU vs. 14.71±5.03HU) (P>0.05), however, they were statistically different on the monochromatic images at 40keV (43.15±3.79 vs. 39.42±2.60, p = 0.005) and 100keV (9.11±1.38 vs. 6.52±2.04, p<0.001). There was difference in the effective atomic number value between the benign (7.87±0.08) and malignant pleural effusion (7.90±0.02) (P = 0.02). Using 6.32HU as the threshold for CT value measurement at 100keV, one could obtain sensitivity of 100% and specificity of 66.7% with area-under-curve of 0.843 for differentiating benign from malignant effusion. In addition, age and disease history were potential confounding factors for differentiating malignant pleural effusion from benign, since the older age (61.13±12.51 year-old vs48.57±12.33 year-old) as well as longer disease history (70.00±49.28 day vs.28.36±21.64 day) were more easily to be found in the malignant pleural effusion group than those in the benign pleural effusion group. By combining above five factors, one could obtain sensitivity of 100% and specificity of 71.4% with area-under-curve of 0.933 for differentiating benign from malignant effusion.ConclusionThe CT value measurement at both high and low energy levels and the effective atomic number obtained in a single spectral CT scan can assist the differential diagnosis of benign from malignant pleural effusion.Combining them with patient age and disease history can further improve diagnostic performance.Clinical relevance/applicationClinical findings and Spectral CT imaging can provide significant evidences about the nature of pleural effusion.

Dataset Information

Negative CT Contrast Agents for the Diagnosis of Malignant Osteosarcoma.

Publications

Negative CT Contrast Agents for the Diagnosis of Malignant Osteosarcoma.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets