Technology in Cancer Research & Treatment最新文献

{"title":"Optimizing Helical Tomotherapy for Left-Sided Breast Cancer: A Retrospective Dosimetric Study of a Novel Virtual Organ-Arc Block.","authors":"Yingtao Fang, Wenliang Yu, Jian Qiao, Yanju Yang, Jing Mi, Lei Yu, Ying Guo, Jiazhou Wang, Weigang Hu","doi":"10.1177/15330338251363288","DOIUrl":"10.1177/15330338251363288","url":null,"abstract":"<p><p>IntroductionLeft-sided breast cancer radiotherapy requires precise dose modulation to balance target coverage and organ-at-risk (OAR) sparing. This study evaluates a novel Organ and Arc-based Directional Block (OABD Block) in helical tomotherapy planning to address this challenge.MethodsIn this single-institutional retrospective study, 10 post-mastectomy patients with left-sided breast cancer receiving adjuvant radiotherapy were studied. Target volumes included chest wall, internal mammary, axillary, and supraclavicular lymph nodes, with a dose of 50 Gy over 25 fractions. Using a tomotherapy planning system, an OABD Block was configured to incorporate arc structures and protect organs-at-risk. For each patient, helical tomotherapy plans were prepared with and without the OABD Block, keeping field width, pitch, and modulation factors identical. Additionally, static intensity-modulated radiotherapy (IMRT) plans were created on a United Imaging system. Treatment plans were evaluated by dose-volume parameters, conformity and homogeneity indices, and mean doses to targets and normal tissues.ResultsHelical tomotherapy with the OABD Block provided a mean conformity Index of 0.79 for the Planning Target Volume, higher than plans without the block (0.73) but below IMRT plans (0.88). The homogeneity Index averaged 0.14 with the block, 0.18 without, and 0.11 in IMRT. For the internal mammary lymph node region, D95% reached 5007.7 cGy with the block, compared to 5001.1 cGy without and 4897.9 cGy in IMRT. The OABD Block reduced the mean heart dose to 478.7 cGy, compared to 533.5 cGy without and 638.9 cGy in IMRT. Left lung V5 was 48.0% with the block, 52.7% without, and 53.2% in IMRT; V20 was also lowest with the block (17.5%) versus without (20.3%) and IMRT (24.3%).ConclusionAdding the OABD Block to helical tomotherapy improved internal mammary lymph node dose coverage and reduced exposure to organs at risk.</p>","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"24 ","pages":"15330338251363288"},"PeriodicalIF":2.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12304599/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144708772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and Validation of Predictive Models for Differentiating Resectable Stage III Peripheral SCLC from NSCLC Using Radiomic Features and Clinical Parameters.","authors":"Junjie Zhang, Ligang Hao, Qiuxu Zhang, Lina Zheng, Qian Xu, Fengxiao Gao","doi":"10.1177/15330338251368956","DOIUrl":"https://doi.org/10.1177/15330338251368956","url":null,"abstract":"<p><p>ObjectiveLung cancer is primarily categorized into small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC), each characterized by distinct therapeutic approaches and prognostic outcomes, particularly in stage III peripheral cases. This study aimed to develop predictive models utilizing clinical and radiomic data to preoperatively differentiate stage III peripheral SCLC from NSCLC.MethodWe conducted a retrospective analysis of 33 stage III peripheral SCLC cases and 99 stage III peripheral NSCLC cases treated at our hospital between January 2016 and July 2024. A total of 1037 radiomic features were extracted from contrast-enhanced CT scans. The cohort was divided into a training set (n = 92) and a test set (n = 40). Radiomic feature selection was performed using the LASSO algorithm, and nine machine learning models were evaluated. The optimal model was employed to compute the radiomics score (Rad-score) and construct a clinical model. A combined model, integrating clinical factors and radiomic features, was assessed for clinical utility through receiver operating characteristic (ROC) curve analysis (area under the curve, AUC), KS statistics and decision curve analysis (DCA). We externally validated the combined model in a group of 84 patients from another hospital.ResultsThe logistic regression-based combined model exhibited superior performance, achieving AUC values of 0.956, 0.775, and 0.841 for the combined, clinical, and radiomics models, respectively, within the training cohort, and 0.905, 0.864, and 0.732 in the test cohort. AUC for the combined model was 0.843 in the external validation cohort. The KS statistics and DCA indicated the clinical utility of the combined model, as evidenced by a Brier score of 0.115.ConclusionThe integration of clinical parameters and radiomics features within the combined model may hold significant potential for the preoperative differentiation of stage III peripheral SCLC from NSCLC.</p>","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"24 ","pages":"15330338251368956"},"PeriodicalIF":2.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12374101/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144969979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Lightweight Dual-Output Vision Transformer for Enhanced Lung Nodule Classification Using CT Images.","authors":"Menna Allah Mahmoud, Yanhua Wen, Yuling Liufu, Xiaohuan Pan, Ruihua Su, Yubao Guan","doi":"10.1177/15330338251370439","DOIUrl":"https://doi.org/10.1177/15330338251370439","url":null,"abstract":"<p><p>IntroductionThis study evaluates the effectiveness of a lightweight vision transformer (EfficientFormerV2-S2) with a dual-output architecture for lung nodule classification, assessing its performance and generalizability across multiple datasets.MethodsThe study utilized datasets from three sources: Institution 1 (936 images), Institution 2 (280 images), and a public Zenodo dataset (308 images), comprising adenocarcinoma, squamous cell carcinoma, and benign lesions. Model evaluation included holdout validation, five-fold cross-validation, and benchmarking against the PneumoniaMedMNIST dataset. Comprehensive image preprocessing and augmentation techniques were implemented.ResultsThe model demonstrated robust performance across all datasets, achieving test accuracies of 92.62 ± 1.65%, 97.14 ± 1.78%, and 95.74 ± 1.35% for Institutions 1, 2, and Zenodo respectively. Cross-validation results showed consistent performance with minimal variability (standard deviations <2%). On the PneumoniaMedMNIST benchmark, our optimized model achieved superior performance (accuracy: 0.936, AUC: 0.981) compared to ResNet18 and ResNet50 benchmarks.ConclusionThe lightweight transformer-based model demonstrates excellent performance and generalizability across multiple institutional datasets, suggesting its potential for efficient clinical implementation in lung nodule classification tasks.</p>","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"24 ","pages":"15330338251370439"},"PeriodicalIF":2.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12374121/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144969994","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting the Efficacy of Neoadjuvant Chemotherapy Combined with Immunotherapy for Esophageal Squamous Cell Carcinoma via Enhanced CT Radiomics Combined with Clinical Features.","authors":"Xiang Qin, Fen Wang, Shaohong Wu, Dong Han, Genji Bai, Lili Guo","doi":"10.1177/15330338251370437","DOIUrl":"10.1177/15330338251370437","url":null,"abstract":"<p><p>IntroductionTo evaluate the predictive efficacy of enhanced Computed Tomograph(CT) radiomics combined with clinical features for assessing treatment response to neoadjuvant chemotherapy plus immunotherapy in esophageal squamous cell carcinoma (ESCC) patients.MethodsWe retrospectively analyzed 189 pathologically confirmed esophageal squamous cell carcinoma patients (treated between January 2020 and October 2024) who underwent neoadjuvant chemoimmunotherapy. Patients were stratified into remission and non-remission groups based on pathological response and randomly divided into training (n = 114) and testing (n = 75) sets (6:4 ratio). Clinical predictors were identified using logistic regression to construct a clinical model. Radiomic features were extracted from manually delineated tumor regions on contrast-enhanced CT scans, and a radiomics model was developed. A combined model integrating clinical variables and radiomics probabilities was then built and presented as a nomogram. Model performance was assessed using receiver operating characteristic (ROC) curves (AUC, Area Under the Curve) comparison via Delong test), calibration curves, and decision curve analysis (DCA).ResultsMultivariable analysis identified treatment cycle number as a significant clinical predictor. Ten radiomic features were selected for the final model. In the training set, the clinical model achieved an AUC of 0.705 (95% CI 0.607-0.802), while the radiomics and combined models showed superior performance with AUCs of 0.905 (95% CI 0.843-0.967) and 0.914 (95% CI 0.857-0.970), respectively. Similar trends were observed in the testing set, where the combined model (AUC 0.859, 95% CI 0.768-0.950) outperformed both the radiomics (AUC 0.815) and clinical (AUC 0.644) models.ConclusionThe enhanced CT radiomics model has better predictive efficacy for remission with neoadjuvant chemotherapy combined with immunotherapy in esophageal squamous cell carcinoma patients, and the combined model has greater predictive value.</p>","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"24 ","pages":"15330338251370437"},"PeriodicalIF":2.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12361841/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144875335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Prognostic Risk Signature Based on Myeloid-Derived Suppressor Cells and Regulatory T Cells in Clear Cell Renal Cell Carcinoma.","authors":"Zhaoyu Xu, Ken Liu, Qiangqiang Xu, Peng Li, Qi Wu, Junjie Ye","doi":"10.1177/15330338251382839","DOIUrl":"10.1177/15330338251382839","url":null,"abstract":"<p><p>IntroductionClear cell renal cell carcinoma (ccRCC) is the most prevalent histological subtype of renal carcinoma. To diagnose ccRCC and assess its prognosis more accurately, it is essential to screen novel prognostic biomarkers and construct prognostic signatures.MethodsImmune infiltration analysis of the TCGA cohort was performed via single-sample gene set enrichment analysis (ssGSEA). The ccRCC cohort from the TCGA database was used to identify MDSC/Treg-related genes. Hub genes were selected from the common genes in the MDSC/Treg-related gene list via machine learning approaches. These hub genes were then employed to construct the risk signature through multivariate analysis.The prognostic performance, immune performance, and functional analysis of the signature were comprehensively assessed. Two independent GEO datasets were used to verify the major findings above. Potential drugs were screened to promote clinical transformation via the CellMiner platform. Finally, the expression levels of six markers were validated through RT-qPCR analysis of clinical tissue samples.ResultsSix MDSC/Treg-related DEGs were identified via machine learning approaches based on the Cancer Genome Atlas cohort. A novel signature (risk score = -0.5579*<i>wdfy4</i>-0.2198*<i>il16</i> + 0.8014*<i>fcgr1b</i> + 0.3344*<i>nod2</i> + 0.4111*<i>relt</i> + 0.1131*<i>mki67</i>) was subsequently constructed. More advanced clinical subgroups had higher scores. In addition, the signature was an independent prognostic indicator (HR = 2.0, 95% CI: 1.6-2.4, <i>p</i> value <0.0001), and the AUC values of the signature at 1, 2, and 3 years were 0.8, 0.74, and 0.76, respectively. The high-risk group presented greater MDSC/Treg infiltration and higher expression levels of PD1 (<i>p</i> < 0.0001)/PDL1 (<i>p</i> < 0.05) and HLA-related genes. Moreover, patients with a high risk score demonstrated a poorer response to anti-PD1/PDL1 therapy (NIVOLUMAB), along with worse progression-free survival (PFS, <i>p</i> = 0.0042). Moreover, two independent cohorts were used to validate the major conclusions. Twelve potential FDA-approved drugs were screened to promote clinical transformation. ill6 (<i>p</i> < 0.05), mki67 (<i>p</i> < 0.001), nod2 (<i>p</i> < 0.01), wdfy4 (<i>p</i> < 0.01), and relt (<i>p</i> < 0.01) were validated through RT-qPCR, with the exception of fcgr1b (<i>p</i> > 0.05).ConclusionA signature related to MDSC/Treg DEGs was constructed. This signature can differentiate between immune and clinical features, enabling the prediction of both clinical and immunotherapy prognoses. However, some PCR experiments did not fully validate the bioinformatics results.</p>","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"24 ","pages":"15330338251382839"},"PeriodicalIF":2.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12501446/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145239719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Retraction: LncRNA BANCR Promotes Pancreatic Cancer Tumorigenesis via Modulating MiR-195-5p/Wnt/β-Catenin Signaling Pathway.","authors":"","doi":"10.1177/15330338251388847","DOIUrl":"10.1177/15330338251388847","url":null,"abstract":"","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"24 ","pages":"15330338251388847"},"PeriodicalIF":2.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12536185/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145309157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Screening of Germline BRCA1 and BRCA2 Variants in Nigerian Breast Cancer Patients.","authors":"Abimbola F Onyia, Paul Jibrin, Temitope Olatunji-Agunbiade, Ademola Oyekan, AbdulRazzaq Lawal, Adewumi Alabi, Anthonia C Sowunmi, Eben A Aje, Oluwabusayo B Ogunniyi, Ebenezer S Nkom, Opeyemi C De Campos, Oluwakemi A Rotimi, Jelili O Oyelade, Solomon O Rotimi","doi":"10.1177/15330338251333012","DOIUrl":"https://doi.org/10.1177/15330338251333012","url":null,"abstract":"<p><p>BackgroundBreast cancer remains a leading cause of mortality among Nigerian women, with triple-negative breast cancer (TNBC) being particularly prevalent. Variations in BRCA1 and BRCA2 genes remain key risk factors for this disease. However, there are gaps in the frequency and spectrum of these variants in Nigerian populations, as well as a dearth in the local capacity to characterize these variations.ObjectiveThis study aimed at identifying and characterizing the germline variations in BRCA1/2 in Nigerian breast cancer patients and healthy age-matched controls to understand the genetic risk profile of breast cancer in this population.MethodsA prospective case-control study was conducted involving 45 breast cancer patients and 51 controls recruited from four major hospitals. DNA was extracted from blood samples, followed by targeted sequencing of BRCA1/2 exonic and intronic regions using the Ampliseq BRCA panel and Illumina MiSeq platform. Variant calling was performed, clinical significance was evaluated on ClinVar and BRCA Exchange databases, and haplotype analysis was performed using NIH LDlink and Haploview 4.2 software.ResultsPathogenic BRCA1/2 variants were identified in 6.7% of breast cancer patients, all with TNBC and a family history of cancer. Two pathogenic BRCA1 variants were detected: a frameshift deletion BRCA1 c.133_134delAA (p.Lys45 fs) (rs397508857) and a missense variant BRCA1 c.5324T > A (p.Met1775Arg) (rs41293463). A BRCA2 frameshift deletion BRCA2 c.8817_8820del (p.Lys2939 fs) (rs397508010) was also identified. These variants were absent in controls. Haplotype analysis revealed distinct BRCA1 and BRCA2 haplotypes in the breast cancer group.ConclusionThis study identifies key BRCA1/2 pathogenic variants and unique haplotypes in Nigerian breast cancer patients, highlighting the need for population-specific genetic screening. Integrating genetic testing into breast cancer management strategies could facilitate early detection, personalized treatment planning, and genetic counseling in Nigeria.</p>","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"24 ","pages":"15330338251333012"},"PeriodicalIF":2.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12033648/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143987012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison of Deep Learning-Based Auto-Segmentation Results on Daily Kilovoltage, Megavoltage, and Cone Beam CT Images in Image-Guided Radiotherapy.","authors":"Zhixing Wang, Chengyu Shi, Carson Wong, Seyi M Oderinde, William T Watkins, Kun Qing, Bo Liu, Terence M Williams, An Liu, Chunhui Han","doi":"10.1177/15330338251344198","DOIUrl":"10.1177/15330338251344198","url":null,"abstract":"<p><p>IntroductionThis study aims to evaluate auto-segmentation results using deep learning-based auto-segmentation models on different online CT imaging modalities in image-guided radiotherapy.MethodsPhantom studies were first performed to benchmark image quality. Daily CT images for sixty patients were retrospectively retrieved from fan-beam kilovoltage CT (kVCT), kV cone-beam CT (kV-CBCT), and megavoltage CT (MVCT) scans. For each imaging modality, half of the patients received CT scans in the pelvic region, while the other half in the thoracic region. Deep learning auto-segmentation models using a convolutional neural network algorithm were used to generate organs-at-risk contours. Quantitative metrics were calculated to compare auto-segmentation results with manual contours.ResultsThe auto-segmentation contours on kVCT images showed statistically significant difference in Dice similarity coefficient (DSC), Jaccard similarity coefficient, sensitivity index, inclusiveness index, and the 95^th percentile Hausdorff distance, compared to those on kV-CBCT and MVCT images for most major organs. In the pelvic region, the largest difference in DSC was observed for the bowel volume with an average DSC of 0.84 ± 0.05, 0.35 ± 0.23, and 0.48 ± 0.27 for kVCT, kV-CBCT, and MVCT images, respectively (<i>p</i>-value < 0.05); in the thoracic region, the largest difference in DSC was found for the esophagus with an average DSC of 0.63 ± 0.16, 0.18 ± 0.13, and 0.22 ± 0.08 for kVCT, kV-CBCT, and MVCT images, respectively (<i>p</i>-value < 0.05).ConclusionDeep learning-based auto-segmentation models showed better agreement with manual contouring when using kVCT images compared to kV-CBCT or MVCT images. However, manual correction remains necessary after auto-segmentation with all imaging modalities, particularly for organs with limited contrast from surrounding tissues. These findings underscore the potential and limits in applying deep learning-based auto-segmentation models for adaptive radiotherapy.</p>","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"24 ","pages":"15330338251344198"},"PeriodicalIF":2.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12099101/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144111982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"miR-10b as a Clinical Marker and a Therapeutic Target for Metastatic Breast Cancer.","authors":"Alan Halim, Bryan Kim, Elizabeth Kenyon, Anna Moore","doi":"10.1177/15330338251339256","DOIUrl":"10.1177/15330338251339256","url":null,"abstract":"<p><p>Despite advances in cancer detection and treatment, metastatic breast cancer continues to carry a poor prognosis due to the lack of diagnostic and therapeutic resources that are specific to the metastatic process. MicroRNA-10b (miR-10b) is a small, noncoding RNA that is the focus of many studies due to its unique role as a driver of metastasis. The pathways it is involved in and the properties it confers have been reviewed previously and, collectively, are suggestive of the potential of miR-10b as a clinical marker and as a therapeutic target specific to metastatic disease. With the goal of application of our understanding of miR-10b to the clinic, in this mini-review, we highlight the studies that support the utility of miR-10b for these translational purposes.</p>","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"24 ","pages":"15330338251339256"},"PeriodicalIF":2.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12099151/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144111987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamics of VEGF-А, Аngiopoietin-2 and HIF-1α Levels in Patients with Brain Metastases Treated with Cyberknife Radiosurgery.","authors":"Veselin Popov, Gabriela Raycheva, Zhanet Grudeva-Popova","doi":"10.1177/15330338251313945","DOIUrl":"10.1177/15330338251313945","url":null,"abstract":"<p><p>The contemporary concept of carcinogenesis summarizes the role of hypoxia, neoangiogenesis, and hemostasis, including in the stage of progression and metastasis of the tumor process. Metastatic disease is a serious therapeutic challenge for any oncological condition. The purpose of this study was to evaluate the dynamics of specific indicators of neoangiogenesis and hypoxia as potential biomarkers for therapeutic efficacy or risk of disease progression in patients with brain metastases (BM) undergoing robotic stereotactic radiosurgery. Two groups of patients (lung cancer and other types of cancers) with oligometastatic disease and brain metastases were included. The patients (n = 66) were treated CyberKnife system. Human Angiopoietin-2, Hypoxia inducible factor 1 α (HIF-1α) and human Vascular Endothelial Growth Factor-А (VEGF-А) were measured in this prospective longitudinal study. Analysis of human Angiopoietin-2, HIF-1α, human VEGF-A in the post-treatment period showed a statistically significant decrease between the baseline and the 6 months post-treatment time point in both patient groups. The baseline value of serum VEGF-А in the group with lung cancer decreased by 40%, Аngiopoietin-2-by 48%, HIF-1α -by 43%. In the group with other types of cancers, VEGF-А decreased by 54.75%, Аngiopoietin-2-by 52%, HIF-1α -by 39.5%. Despite the significant reduction, the levels remained significantly higher in both groups than in healthy controls. This study underscores the potential of integrating molecular markers like VEGF-A, Angiopoietin-2, and HIF-1α into clinical decision-making to enhance outcomes for patients with brain metastases undergoing RSRS.</p>","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"24 ","pages":"15330338251313945"},"PeriodicalIF":2.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11726525/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142971965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}