The Diagnostic Value of Contrast-Enhanced Ultrasound and Enhanced CT Combined with Tumor Markers AFP and CA199 in Liver Cancer.
Journal of healthcare engineering
BACKGROUND:Early screening and diagnosis are of great significance to the treatment and prognosis of patients with liver cancer. This study aims to explore the application value of contrast-enhanced ultrasound and enhanced CT combined with tumor markers alpha-fetoprotein (AFP) and carbohydrate antigen 199 (CA199) in the diagnosis of liver cancer. METHODS:Liver cancer group ( = 256), benign disease group ( = 110), and control group ( = 50) participated in this study. The liver cancer and benign disease groups were diagnosed pathologically by contrast-enhanced ultrasound and enhanced CT before operation. The electrochemiluminescence method was used to detect the content of AFP and CA199. And the receiver operating characteristic (ROC) curve was drawn. RESULTS:The detection rate of contrast-enhanced ultrasound is higher than that of enhanced CT. Serum levels of AFP and CA199 in the liver cancer group were significantly higher than those in the benign lesion group and the control group. The ROC curve showed that the sensitivity, accuracy, and negative prediction rate of contrast-enhanced ultrasound and enhanced CT combined with tumor markers AFP and CA199 in the diagnosis of liver cancer were significantly higher than that of a single test. CONCLUSION:The combined detection of contrast-enhanced ultrasound and enhanced CT, AFP, and CA199 significantly improved the sensitivity and accuracy of liver cancer diagnosis. It has a significant effect on the early diagnosis of liver cancer and can be used as an important means of early screening.
A novel liver cancer diagnosis method based on patient similarity network and DenseGCN.
Liver cancer is the main malignancy in terms of mortality rate, accurate diagnosis can help the treatment outcome of liver cancer. Patient similarity network is an important information which helps in cancer diagnosis. However, recent works rarely take patient similarity into consideration. To address this issue, we constructed patient similarity network using three liver cancer omics data, and proposed a novel liver cancer diagnosis method consisted of similarity network fusion, denoising autoencoder and dense graph convolutional neural network to capitalize on patient similarity network and multi omics data. We compared our proposed method with other state-of-the-art methods and machine learning methods on TCGA-LIHC dataset to evaluate its performance. The results confirmed that our proposed method surpasses these comparison methods in terms of all the metrics. Especially, our proposed method has attained an accuracy up to 0.9857.
Application of artificial intelligence in the diagnosis and treatment of hepatocellular carcinoma: A review.
Jiménez Pérez Miguel,Grande Rocío González
World journal of gastroenterology
Although artificial intelligence (AI) was initially developed many years ago, it has experienced spectacular advances over the last 10 years for application in the field of medicine, and is now used for diagnostic, therapeutic and prognostic purposes in almost all fields. Its application in the area of hepatology is especially relevant for the study of hepatocellular carcinoma (HCC), as this is a very common tumor, with particular radiological characteristics that allow its diagnosis without the need for a histological study. However, the interpretation and analysis of the resulting images is not always easy, in addition to which the images vary during the course of the disease, and prognosis and treatment response can be conditioned by multiple factors. The vast amount of data available lend themselves to study and analysis by AI in its various branches, such as deep-learning (DL) and machine learning (ML), which play a fundamental role in decision-making as well as overcoming the constraints involved in human evaluation. ML is a form of AI based on automated learning from a set of previously provided data and training in algorithms to organize and recognize patterns. DL is a more extensive form of learning that attempts to simulate the working of the human brain, using a lot more data and more complex algorithms. This review specifies the type of AI used by the various authors. However, well-designed prospective studies are needed in order to avoid as far as possible any bias that may later affect the interpretability of the images and thereby limit the acceptance and application of these models in clinical practice. In addition, professionals now need to understand the true usefulness of these techniques, as well as their associated strengths and limitations.
Preoperative classification of primary and metastatic liver cancer via machine learning-based ultrasound radiomics.
Mao Bing,Ma Jingdong,Duan Shaobo,Xia Yuwei,Tao Yaru,Zhang Lianzhong
OBJECTIVE:To investigate the application of machine learning-based ultrasound radiomics in preoperative classification of primary and metastatic liver cancer. METHODS:Data of 114 consecutive histopathologically confirmed patients with liver cancer from January 2018 to November 2019 were retrospectively analyzed. All patients underwent liver ultrasonography within 1 week before hepatectomy or fine-needle biopsy. The liver lesions were manually segmented by two experts using ITK-SNAP software. Seven categories of radiomics features, including first-order, two-dimensional shape, gray-level co-occurrence matrices, gray-level run-length matrix, gray-level size-zone matrix, neighboring gray tone difference matrix, and gray-level dependence matrix, were extracted on the Pyradiomics platform. Fourteen filters were applied to the original images, and derived images were obtained. Then, the dimensions of radiomics features were reduced by least absolute shrinkage and selection operator (Lasso) method. Finally, k-nearest neighbor (KNN), logistic regression (LR), multilayer perceptron (MLP), random forest (RF), and support vector machine (SVM) were employed to distinguish primary liver cancer from metastatic liver cancer by a fivefold cross-validation strategy. The performance of the established model was mainly evaluated by the area under the receiver operating characteristic (ROC) curve (AUC) and accuracy. RESULTS:One thousand four hundred nine radiomics features were extracted from the original images and/or derived images for each patient. The mentioned five machine learning classifiers were able to differentiate primary liver cancer from metastatic liver cancer. LR outperformed other classifiers, with the accuracy of 0.843 ± 0.078 (AUC, 0.816 ± 0.088; sensitivity, 0.768 ± 0.232; specificity, 0.880 ± 0.117). CONCLUSIONS:Machine learning-based ultrasound radiomics features are able to non-invasively distinguish primary liver tumors from metastatic liver tumors. KEY POINTS:• Ultrasound-based radiomics was initially used for preoperative classification of primary versus metastatic liver cancer. • Multiple machine learning-based algorithms with cross-validation strategy were applied to extract machine learning-based ultrasound radiomics features. • Distinction between primary and metastatic tumors was obtained with a sensitivity of 0.768 and a specificity of 0.880.
Artificial Intelligence in Medical Imaging and Its Application in Sonography for the Management of Liver Tumor.
Nishida Naoshi,Kudo Masatoshi
Frontiers in oncology
Recent advancement in artificial intelligence (AI) facilitate the development of AI-powered medical imaging including ultrasonography (US). However, overlooking or misdiagnosis of malignant lesions may result in serious consequences; the introduction of AI to the imaging modalities may be an ideal solution to prevent human error. For the development of AI for medical imaging, it is necessary to understand the characteristics of modalities on the context of task setting, required data sets, suitable AI algorism, and expected performance with clinical impact. Regarding the AI-aided US diagnosis, several attempts have been made to construct an image database and develop an AI-aided diagnosis system in the field of oncology. Regarding the diagnosis of liver tumors using US images, 4- or 5-class classifications, including the discrimination of hepatocellular carcinoma (HCC), metastatic tumors, hemangiomas, liver cysts, and focal nodular hyperplasia, have been reported using AI. Combination of radiomic approach with AI is also becoming a powerful tool for predicting the outcome in patients with HCC after treatment, indicating the potential of AI for applying personalized medical care. However, US images show high heterogeneity because of differences in conditions during the examination, and a variety of imaging parameters may affect the quality of images; such conditions may hamper the development of US-based AI. In this review, we summarized the development of AI in medical images with challenges to task setting, data curation, and focus on the application of AI for the managements of liver tumor, especially for US diagnosis.
Application of artificial intelligence in preoperative imaging of hepatocellular carcinoma: Current status and future perspectives.
Feng Bing,Ma Xiao-Hong,Wang Shuang,Cai Wei,Liu Xia-Bi,Zhao Xin-Ming
World journal of gastroenterology
Hepatocellular carcinoma (HCC) is the most common primary malignant liver tumor in China. Preoperative diagnosis of HCC is challenging because of atypical imaging manifestations and the diversity of focal liver lesions. Artificial intelligence (AI), such as machine learning (ML) and deep learning, has recently gained attention for its capability to reveal quantitative information on images. Currently, AI is used throughout the entire radiomics process and plays a critical role in multiple fields of medicine. This review summarizes the applications of AI in various aspects of preoperative imaging of HCC, including segmentation, differential diagnosis, prediction of histopathology, early detection of recurrence after curative treatment, and evaluation of treatment response. We also review the limitations of previous studies and discuss future directions for diagnostic imaging of HCC.