Due to the low percentage of resectable liver tumors, new alternative treatment modalities are used. Among them, radioablation, that is, by using a limited number of high dose radiation. The aim of this study was to evaluate the effectiveness of liver tumor radioablation at 36 Gy delivered in three fractions. The analyzed material comprised of 65 liver tumors. In 61 cases, we irradiated metastases (20 rectal cancers) and in 4 primary liver tumors. Radioablation, was done using 6 and 20 MV photons with a fraction dose of 12 Gy once a week up to a total dose of 36 Gy. During the follow-up we measured tumor diameters, and for our statistics we used a classical linear regression and the Bayesian approach. Mild and moderate late toxicity was observed. We found a significant absolute and relative decrease in tumor size during the first 6 months from the whole analyzed group. In subgroups with adenocarcinomas, metastases of gastrointestinal tract (GI) cancers, metastases of cancers other than GI cancers, and in the subgroup in which 2D-2D kV system (IGRT) and respiratory gating was used. The percentage of tumors with local control (lack of "in field" progression) after 6 months was 89%. The obtained results permit us to conclude that gated SBRT of liver tumors is an effective and safe treatment modality resulting in a significant regression of liver tumors and that the highest degree of tumor size reduction can be expected for metastases of non-gastro intestinal tract cancers.
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http://dx.doi.org/10.7785/tcrt.2012.500311 | 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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