Purpose 5-fluorouracil (5-FU), an effective chemotherapy drug, is commonly applied for colorectal cancer treatment. Nevertheless, its toxicity to normal tissues and the development of tumor resistance are the main obstacles to successful cancer chemotherapy and hence, its clinical application is limited. The use of resveratrol can increase 5-FU-induced cytotoxicity and mitigate the unwanted adverse effects. This study aimed to review the potential therapeutic effects of resveratrol in combination with 5-FU against colorectal cancer. Methods According to the PRISMA guideline, a comprehensive systematic search was carried out for the identification of relevant literature in four electronic databases of PubMed, Web of Science, Embase, and Scopus up to May 2021 using a pre-defined set of keywords in their titles and abstracts. We screened 282 studies in accordance with our inclusion and exclusion criteria. Thirteen articles were finally included in this systematic review. Results The in vitro findings showed that proliferation inhibition of colorectal cancer cells in the groups treated by 5-FU was remarkably higher than the untreated groups and the co-administration of resveratrol remarkably increased cytotoxicity induced by 5-FU. The in vivo results demonstrated a decrease in tumor growth of mice treated by 5-FU than the untreated group and a dramatic decrease was observed following combined treatment of resveratrol and 5-FU. It was also found that 5-FU alone and combined with resveratrol could regulate the cell cycle profile of colorectal cancer cells. Moreover, this chemotherapeutic agent induced the biochemical and histopathological changes in the cancerous cells/tissues and these alterations were synergized by resveratrol co-administration (for most of the cases), except for the inflammatory mediators. Conclusion The results obtained from this systematic review demonstrated that co-administration of resveratrol could sensitize the colorectal cancer cells to 5-FU treatment via various mechanisms, including regulation of cell cycle distribution, oxidant, apoptosis, anti-inflammatory effects.
To investigate the potential benefits of FDG PET radiomic feature maps (RFMs) for target delineation in non-small cell lung cancer (NSCLC) radiotherapy. Methods: Thirty-two NSCLC patients undergoing FDG PET/CT imaging were included. For each patient, nine grey-level co-occurrence matrix (GLCM) RFMs were generated. gross target volume (GTV) and clinical target volume (CTV) were contoured on CT (GTV CT , CTV CT ), PET (GTV PET40 , CTV PET40 ), and RFMs (GTV RFM , CTV RFM ,). Intratumoral heterogeneity areas were segmented as GTV PET50-Boost and radiomic boost target volume (RTV Boost ) on PET and RFMs, respectively. GTV CT in homogenous tumors and GTV PET40 in heterogeneous tumors were considered as GTV gold standard (GTV GS ). One-way analysis of variance was conducted to determine the threshold that finds the best conformity for GTV RFM with GTV GS . Dice similarity coefficient (DSC) and mean absolute percent error (MAPE) were calculated. Linear regression analysis was employed to report the correlations between the gold standard and RFM-derived target volumes. Results: Entropy, contrast, and Haralick correlation (H-correlation) were selected for tumor segmentation. The threshold values of 80%, 50%, and 10% have the best conformity of GTV RFM-entropy , GTV RFM-contrast , and GTV RFM-H-correlation with GTV GS , respectively. The linear regression results showed a positive correlation between GTV GS and GTV RFM-entropy (r = 0.98, p < 0.001), between GTV GS and GTV RFM-contrast (r = 0.93, p < 0.001), and between GTV GS and GTV RFM-H-correlation (r = 0.91, p < 0.001). The average threshold values of 45% and 15% were resulted in the best segmentation matching between CTV RFM-entropy and CTV RFM-contrast with CTV GS , respectively. Moreover, we used RFM to determine RTV Boost in the heterogeneous tumors. Comparison of RTV Boost with GTV PET50-Boost MAPE showed the volume error differences of 31.7%, 36%, and 34.7% in RTV Boost-entropy , RTV Boost-contrast , and RTV Boost-H-correlation , respectively. Conclusions: FDG PET-based radiomics features in NSCLC demonstrated a promising potential for decision support in radiotherapy, helping radiation oncologists delineate tumors and generate accurate segmentation for heterogeneous region of tumors.
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