Objectives: Acoustic radiation force impulse (ARFI) technology represents an innovative method for the quantification of tissue elasticity. The aims of this study were to evaluate elasticity by ARFI in both liver tumors and background liver tissue and to compare ARFI measurements with histologic data in liver tumors and background liver.
Methods: Seventy-nine tumors were prospectively studied: 43 benign and 36 malignant. Acoustic radiation force impulse measurements for each tumor type were expressed as mean ± standard deviation for both liver tumors and background liver; ARFI data were also correlated with histologic data.
Results: For liver tumors, the mean stiffness values were 1.90 ± 0.86 m/s for hepatocellular adenoma (n = 9), 2.14 ± 0.49 m/s for hemangioma (n = 15), 3.14 ± 0.63 m/s for focal nodular hyperplasia (n = 19), 2.4 ± 1.01 m/s for hepatocellular carcinoma (n = 24), and 3.0 ± 1.36 m/s for metastasis (n = 12). Important variations were observed within each tumor type or within a single tumor. These variations could have been due to necrosis, hemorrhage, or colloid. There was no statistically significant difference between the benign and malignant groups. Regarding background liver, it was possible to observe pathologic abnormalities in histologic analyses or liver function tests to explain the ARFI data. The degree of fibrosis was not the only determinant of liver stiffness in background liver; other factors such as portal embolization, sinusoidal obstruction syndrome caused by chemotherapy, and cholestasis, also could have interfered.
Conclusions: Acoustic radiation force impulse elastography could not allow differentiation between benign and malignant tumors. This study provides a better understanding of the correlation between ARFI and histologic data for both tumors and background liver.
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http://dx.doi.org/10.7863/jum.2013.32.1.121 | DOI Listing |
Nat Commun
January 2025
Department of Machine Learning, Moffitt Cancer Center, Tampa, FL, USA.
AI decision support systems can assist clinicians in planning adaptive treatment strategies that can dynamically react to individuals' cancer progression for effective personalized care. However, AI's imperfections can lead to suboptimal therapeutics if clinicians over or under rely on AI. To investigate such collaborative decision-making process, we conducted a Human-AI interaction study on response-adaptive radiotherapy for non-small cell lung cancer and hepatocellular carcinoma.
View Article and Find Full Text PDFInsights Imaging
January 2025
Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China.
Objectives: To develop and validate radiomics and deep learning models based on contrast-enhanced MRI (CE-MRI) for differentiating dual-phenotype hepatocellular carcinoma (DPHCC) from HCC and intrahepatic cholangiocarcinoma (ICC).
Methods: Our study consisted of 381 patients from four centers with 138 HCCs, 122 DPHCCs, and 121 ICCs (244 for training and 62 for internal tests, centers 1 and 2; 75 for external tests, centers 3 and 4). Radiomics, deep transfer learning (DTL), and fusion models based on CE-MRI were established for differential diagnosis, respectively, and their diagnostic performances were compared using the confusion matrix and area under the receiver operating characteristic (ROC) curve (AUC).
Sci Rep
January 2025
Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, China.
Hepatocellular carcinoma (HCC) is the most prevalent form of liver cancer, and ranks among the most lethal malignancies globally, primarily due to its high rates of recurrence and metastasis. Despite the urgency, no reliable biomarkers currently exist for predicting tumor recurrence in HCC. Telomerase reverse transcriptase (TERT) promoter mutations (TERTpm) and cellular tumor antigen p53 mutations (TP53m) have been frequently documented in HCC, but their combined clinical significance remains undefined.
View Article and Find Full Text PDFSci Rep
January 2025
Guangzhou Municipal and Guangdong Provincial Key Laboratory of Protein Modification and Disease, School of Basic Medical Sciences, Guangzhou Medical University, Guangzhou, 511436, China.
Hepatocellular carcinoma (HCC) is the most prevalent form of primary liver cancer, notoriously refractory to conventional chemotherapy. Historically, sulfane sulfur-based compounds have been explored for the treatment of HCC, but their efficacy has been underwhelming. We recently reported a novel sulfane sulfur donor, PSCP, which exhibited improved chemical stability and structural malleability.
View Article and Find Full Text PDFSci Rep
January 2025
Center for Informatics Science (CIS), School of Information Technology and Computer Science, Nile University, 26th of July Corridor, Sheikh Zayed City, Giza, 12588, Egypt.
Breast cancer, with its high incidence and mortality globally, necessitates early prediction of local and distant recurrence to improve treatment outcomes. This study develops and validates predictive models for breast cancer recurrence and metastasis using Recurrence-Free Survival Analysis and machine learning techniques. We merged datasets from the Molecular Taxonomy of Breast Cancer International Consortium, Memorial Sloan Kettering Cancer Center, Duke University, and the SEER program, creating a comprehensive dataset of 272, 252 rows and 23 columns.
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