APASL and AASLD Consensus Guidelines on Imaging Diagnosis of Hepatocellular Carcinoma: A Review

Consensus guidelines for radiological diagnosis of hepatocellular carcinoma (HCC) have been drafted by several large international working groups. This article reviews the similarities and differences between the most recent guidelines proposed by the American Association for Study of Liver Diseases and the Asian Pacific Association for the Study of the Liver. Current evidence for the various imaging modalities for diagnosis of HCC and their relevance to the consensus guidelines are reviewed

Medical Phrase Grounding with Region-Phrase Context Contrastive Alignment

Medical phrase grounding (MPG) aims to locate the most relevant region in a medical image, given a phrase query describing certain medical findings, which is an important task for medical image analysis and radiological diagnosis. However, existing visual grounding methods rely on general visual features for identifying objects in natural images and are not capable of capturing the subtle and specialized features of medical findings, leading to sub-optimal performance in MPG. In this paper, we propose MedRPG, an end-to-end approach for MPG. MedRPG is built on a lightweight vision-language transformer encoder and directly predicts the box coordinates of mentioned medical findings, which can be trained with limited medical data, making it a valuable tool in medical image analysis. To enable MedRPG to locate nuanced medical findings with better region-phrase correspondences, we further propose Tri-attention Context contrastive alignment (TaCo). TaCo seeks context alignment to pull both the features and attention outputs of relevant region-phrase pairs close together while pushing those of irrelevant regions far away. This ensures that the final box prediction depends more on its finding-specific regions and phrases. Experimental results on three MPG datasets demonstrate that our MedRPG outperforms state-of-the-art visual grounding approaches by a large margin. Additionally, the proposed TaCo strategy is effective in enhancing finding localization ability and reducing spurious region-phrase correlations

Multi-Phase Cross-modal Learning for Noninvasive Gene Mutation Prediction in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer and the fourth most common cause of cancer-related death worldwide. Understanding the underlying gene mutations in HCC provides great prognostic value for treatment planning and targeted therapy. Radiogenomics has revealed an association between non-invasive imaging features and molecular genomics. However, imaging feature identification is laborious and error-prone. In this paper, we propose an end-to-end deep learning framework for mutation prediction in APOB, COL11A1 and ATRX genes using multiphasic CT scans. Considering intra-tumour heterogeneity (ITH) in HCC, multi-region sampling technology is implemented to generate the dataset for experiments. Experimental results demonstrate the effectiveness of the proposed model.Comment: Accepted version to be published in the 42nd IEEE Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2020, Montreal, Canad

Effective Inhibition of Xenografts of Hepatocellular Carcinoma (HepG2) by Rapamycin and Bevacizumab in an Intrahepatic Model

10.1007/s11307-009-0213-4Molecular Imaging and Biology115334-342CPIM

Nonvirally Modified Autologous Primary Hepatocytes Correct Diabetes and Prevent Target Organ Injury in a Large Preclinical Model

BACKGROUND: Current gene- and cell-based therapies have significant limitations which impede widespread clinical application. Taking diabetes mellitus as a paradigm, we have sought to overcome these limitations by ex vivo electrotransfer of a nonviral insulin expression vector into primary hepatocytes followed by immediate autologous reimplantation in a preclinical model of diabetes. METHODS AND RESULTS: In a single 3-hour procedure, hepatocytes were isolated from a surgically resected liver wedge, electroporated with an insulin expression plasmid ex vivo and reimplanted intraparenchymally under ultrasonic guidance into the liver in each of 10 streptozotocin-induced diabetic Yorkshire pigs. The vector was comprised of a bifunctional, glucose-responsive promoter linked to human insulin cDNA. Ambient glucose concentrations appropriately altered human insulin mRNA expression and C-peptide secretion within minutes in vitro and in vivo. Treated swine showed correction of hyperglycemia, glucose intolerance, dyslipidemia and other metabolic abnormalities for > or = 47 weeks. Metabolic correction correlated significantly with the number of hepatocytes implanted. Importantly, we observed no hypoglycemia even under fasting conditions. Direct intrahepatic implantation of hepatocytes did not alter biochemical indices of liver function or induce abnormal hepatic lobular architecture. About 70% of implanted hepatocytes functionally engrafted, appeared histologically normal, retained vector DNA and expressed human insulin for > or = 47 weeks. Based on structural tissue analyses and transcriptome data, we showed that early correction of diabetes attenuated and even prevented pathological changes in the eye, kidney, liver and aorta. CONCLUSIONS: We demonstrate that autologous hepatocytes can be efficiently, simply and safely modified by electroporation of a nonviral vector to express, process and secrete insulin durably. This strategy, which achieved significant and sustained therapeutic efficacy in a large preclinical model without adverse effects, warrants consideration for clinical development especially as it could have broader future applications for the treatment of other acquired and inherited diseases for which systemic reconstitution of a specific protein deficiency is critical

Automated breast masses segmentation in digitized mammograms

In this paper, an automated segmentation method is proposed. The method is applied to the segmentation of breast masses in digitized mammograms and it operates on the whole mammograms instead of manually selected regions. Pixels with local maximum gray levels are flagged as seeds, from which many candidate objects are grown using modified region-growing technique. Following which False Positive (FP) reduction using decision tree is applied to discard the normal tissue regions. A total of 40 mammograms from Mammographic Image Analysis Society (MIAS) are analyzed. 36 masses are correctly segmented by the proposed method, resulting in 90% True Positive Rate at 1.3 FPs per image.Published versio