American Journal of Clinical Oncology-Cancer Clinical Trials最新文献

To systematically evaluate the risk factors for postoperative complications of venous thromboembolism in patients with gynecologic malignancies. Cohort studies and case-control studies on the risk factors of postoperative venous thromboembolism in gynecologic malignancy patients were included in the search of China Knowledge, Wanfang, Wipro, China Biomedical Literature Database, PubMed, Cochrane Library, Embase, and Web of Science databases from inception to March 2025, and were analyzed. Studies. Data were statistically analyzed using RevMan 5.2 software. A total of 19 studies involving 123,329 patients with gynecologic malignancies were included. The analysis showed that advanced age (OR=3.08, 95% CI=2.85-3.32, P <0.00001), open surgery (OR=9.18, 95% CI=2.38-35.34, P =0.001), high surgical complexity (OR=9.97, 95% CI=5.80-17.15, P <0.00001), and surgical duration (OR=3.33, 95% CI=2.97-3.73, P <0.00001), high BMI (OR=4.77, 95% CI=3.47-6.57, P <0.00001), comorbidities (OR=21.02, 95% CI=8.72-50.70, P <0.00001), and prolonged bed rest in the postoperative period ( OR=25.16, 95% CI=10.32-61.32, P <0.00001), high intraoperative bleeding (OR=107.53, 95% CI=17.71-652.85, P <0.00001), and high D-dimer level (OR=5.55, 95% CI=3.27-9.43, P <0.00001), advanced tumor stage (OR=7.58, 95% CI=2.22-25.90, P =0.001), high tumor grade (OR=27.67, 95% CI=8.39-91.18, P <0.00001), and occurrence of lymph node metastasis (OR=31.21, 95% CI=9.54-102.15, P <0.00001) were all were risk factors for postoperative venous thrombosis in patients with gynecologic malignancies. Clinical staff should take into account the 12 risk factors identified in this study to actively identify gynecologic malignant tumor patients at high risk for venous thromboembolism after surgery and provide targeted measures to prevent or reduce the risk of postoperative DVT.

Objectives: This practice parameter was revised collaboratively by the American College of Radiology (ACR) and American Radium Society (ARS). Stereotactic body radiation therapy (SBRT) precisely delivers higher dose(s) of radiation in 5 of fewer fractions, compared with conventional radiation. Given the complexity and technical nature of this treatment technique, practice parameters are needed to provide guidance to physicians and physicists.

Methods: This practice parameter was developed according to the process described under the heading The Process for Developing ACR Practice Parameters and Technical Standards on the ACR website ( https://www.acr.org/Clinical-Resources/Practice-Parameters-and-Technical-Standards ) by the Committee on Practice Parameters-Radiation Oncology of the ACR Commission on Radiation Oncology in collaboration with the ARS.

Results: Workflow, qualifications/responsibilities of personnel, quality control, and treatment delivery/verification are reviewed. Notable elements of SBRT include image guidance, immobilization, and motion management, with the treatment planning goal of minimizing the volume of normal tissue exposed to medium and high dose levels and maximizing dose safely to the target. Specialized training is encouraged, as some technologies are not used in standard treatments.

Conclusions: This practice parameter provides direction on key components recommended for SBRT and may be used as a guide to physicians and physicists wanting to provide this treatment to their patients.

Objectives: The practice parameter was revised collaboratively by the American College of Radiology (ACR), the American Brachytherapy Society (ABS), the American College of Nuclear Medicine (ACNM), the American Radium Society (ARS), the Society of Interventional Radiology (SIR), and the Society of Nuclear Medicine and Molecular Imaging (SNMMI). This document summarizes current evidence-based guidelines for the administration of Yttrium radioembolic therapy to the liver, including training requirements, evidence-based guidelines for administration, and safe practice for administration.

Methods: This practice parameter was revised according to the process described under the heading The Process for Developing ACR Practice Parameters and Technical Standards on the ACR website ( https://www.acr.org/ClinicalResources/Practice-Parameters-and-Technical-Standards ) by the Committee on Practice Parameters-Interventional and Cardiovascular Radiology of the ACR Commission on Interventional and Cardiovascular, Committee on Practice Parameters and Technical Standards-Nuclear Medicine and Molecular Imaging of the ACR Commission on Nuclear Medicine and Molecular Imaging and the Committee on Practice Parameters-Radiation Oncology of the ACR Commission on Radiation Oncology in collaboration with the ABS, the ACNM, the ARS, the SIR, and the SNMMI.

Results: This review seeks not to be a comprehensive discussion of radiotherapy to the liver, but rather, seeks to provide a parameter for safe and effective therapy. We discuss the qualifications of physicians involved in this therapy, basic indications, contraindications, procedural work-up, safe-handling, and regulatory requirement for the administration of selective internal radiation therapy to patients that are likely to benefit. The goal of this document is not to define which patients are best treated by these therapies, as this is best determined for individual patients after multidisciplinary review. A consistent and evidence-based approach to therapy, however, would benefit all patients who are offered this therapy. This document seeks to provide a framework for current best practices for the administration of the 2 currently available radioembolization devices.

Conclusions: As Yttrium-90 radiotherapy to the liver occupies a growing role in the treatment of primary and metastatic liver cancer, this review seeks to assist clinicians of all involved specialties to optimize the efficacy and safety of these procedures.

Objectives: The vascular endothelial growth factor (VEGF) pathway plays a crucial part in tumor angiogenesis by enhancing the creation of new blood vessels that supply oxygen. Breast cancer cells with overexpressed human epidermal growth factor receptor 2 (HER2) usually produce high levels of VEGF, because HER2 signaling upregulates VEGF expression. We aim to investigate the clinical benefit of VEGF and HER2 inhibitors in the treatment of breast cancer.

Methods: A systematic search for records from inception until January 2025 was conducted in PubMed, Web of Science, MEDLINE, Scopus, and Google Scholar. The primary endpoint of the present review was the overall response rate (ORR), and the secondary endpoints were complete response (CR) and partial response (PR).

Results: Five distinct clinical trials enrolling 307 women with HER2-positive breast cancer were included in the present meta-analysis. The pooled analysis revealed that the ORR of breast cancer patients treated with anti-HER2 combined with anti-VEGF was 31.9% (95% CI: 21.6%-44.2%). Moreover, 4.9% of patients treated with anti-HER2 combined with anti-VEGF achieved CR, and 32.6% achieved PR. Data from 2 included trials also showed that patients treated with lapatinib and pazopanib had significantly higher response rates than patients receiving lapatinib alone (OR: 2.21; 95% CI: 1.15-4.22; P = 0.017).

Conclusions: Dual inhibition of HER2 and VEGF demonstrated promising responses, with 31.9% of patients achieving ORR. Furthermore, the combined targeting of HER2 and VEGF, with lapatinib and pazopanib results in better responses than monotherapy targeting of HER2 with lapatinib.

Objectives: Chemotherapy-induced cardiotoxicity is still a major clinical problem, usually appearing subclinically before structural or symptomatic cardiac dysfunction appears. Standard surveillance methods use imaging and biomarkers, which are time-intensive and money-intensive and can only identify damage at more advanced levels. Electrocardiography (ECG) provides a low-cost, non-invasive method that can detect early electrophysiological changes but is not fully utilized in cardio-oncology. The present work was designed to build an explainable machine learning model for predicting chemo-like cardiotoxicity patterns at an early stage from single-lead ECG signals.

Methods: A public ECG data set (n=4997 segments) underwent preprocessing and was converted to 18 temporal, morphologic, and spectral features. Two ensemble learning algorithms-Random Forest and XGBoost-were trained and validated with stratified splits. Model performance was assessed with ROC-AUC, PR-AUC, and F1-score with 1000 bootstrap resampling. Feature interpretability was evaluated through permutation importance and SHAP analysis.

Results: Both models scored near-perfect classification (ROC-AUC and PR-AUC>0.99, F1-score ≈ 0.986). Spectral entropy, band3 (high-energy frequency), QT surrogate, and peak count were the top features ranking alongside early cardiotoxicity indicators like repolarization instability and autonomic imbalance.

Conclusions: The feature-driven, interpretable ML architecture suggested here shows that single-lead ECG has the potential to be an affordable and clinically relevant tool for the early detection of chemotherapy-induced cardiotoxicity. The method provides a feasible route toward implementation in precision cardio-oncology, particularly in resource-poor or ambulatory environments.

Objectives: Accurate lung cancer prediction from CT scans using advanced deep learning methods is crucial for improving early diagnosis and treatment outcomes, as it harnesses innovative algorithms to enhance the detection and classification of malignant lesions in imaging data. The comprehensive approach for accurate lung cancer prediction from CT scans using advanced deep learning methods. Lung cancer remains one of the leading causes of cancer-related deaths globally, emphasizing the need for early and precise diagnosis.

Methods: They propose a multistage framework that integrates state-of-the-art techniques, including hybrid Graph Convolutional Networks (GCNs) and Conditional Random Fields (CRFs) for image segmentation, followed by an innovative feature extraction pipeline utilizing Capsule Networks (CapsNets), Siamese Neural Networks, and Hybrid Deep Autoencoders. This combination allows for the effective identification of lung regions and the detection of potential lesions, ensuring high segmentation accuracy and robustness against noise.

Results: The feature extraction implements a refined classification strategy that merges a Hybrid CNN-Transformer Model with Graph Neural Networks (GNNs). This dual approach leverages CNNs for capturing local patterns and transformers for modelling long-range dependencies, enhancing the ability to recognize subtle features indicative of malignancies. GNNs further contribute by modelling spatial and relational information among extracted features, facilitating a deeper understanding of the lung's complex anatomic structures.

Conclusions: The proposed technique also leads with 91%, compared with LSTM's 80%, FNN's 70%, and RNN's 70%, highlighting its ability to minimize false positives, implemented using Python software. The future scope for accurate lung cancer prediction from CT scans using advanced deep learning methods includes the development of more sophisticated algorithms that integrate multimodal imaging data, enhancing diagnostic precision, and personalization of treatment plans.

Cancer remains a growing global health burden, with many survivors experiencing significant psychological symptoms such as fatigue, pain, anxiety, and depression. Noninvasive brain stimulation techniques such as rTMS have gained attention for their potential to modulate neural circuits implicated in pain perception, mood regulation, and fatigue. This scoping review aims to explore the current application, safety, and effectiveness of Transcranial Magnetic Stimulation (TMS) as a potential intervention to alleviate cancer-related and treatment-induced psychological symptoms. This scoping review followed Arksey and O'Malley's 5-stage framework and adhered to PRISMA-ScR guidelines. The review identified, selected, and charted data from eligible studies across 5 databases to explore the effects of repeated transcranial magnetic stimulation on individuals living with cancer. Between 2010 and 2025, 17 studies investigated rTMS in cancer populations, including single-arm trials, sham-controlled RCTs, case studies, and retrospective observational studies, with sample sizes ranging from 1 to 66 participants (total n=406). Participants were predominantly female (65.9%) and had diverse cancer types, stages, and treatment statuses, including completed treatment, active therapy, and palliative care. rTMS protocols varied in duration (5 d to 6 wk), session frequency, intensity (70% to 120% RMT), and coil placement, targeting motor cortex, dorsolateral prefrontal cortex, or frontoparietal networks. Safety outcomes were favorable, with no serious adverse events reported and only mild, transient side effects, though one case of postoperative seizure was noted. rTMS was generally feasible and well-tolerated, with participants reporting positive experiences and high adherence. Key efficacy findings included improvements in depression, anxiety, pain, quality of life, motor function, and chemotherapy-induced neuropathy, although follow-up periods and outcome measures were heterogeneous across studies. rTMS appears safe and promising for managing cancer-related symptoms, but larger, standardized, sham-controlled trials with long-term follow-up are needed to confirm its clinical value.

Objectives: Lung cancer remains one of the leading causes of cancer-related mortality worldwide, underscoring the urgent need for improved diagnostic and predictive methodologies. The several challenges in the complexity and high dimensionality of genomic data can lead to overfitting and computational inefficiencies, making it difficult to extract relevant features. The objective of this study is to develop a hybrid deep learning model that effectively integrates genomic data and imaging to enhance the accuracy of lung cancer prediction.

Methods: The study utilizes the LIDC-IDRI data set for comprehensive data collection, focusing on both imaging and genomic data relevant to lung cancer prediction. In the data preprocessing phase, a LoGF is applied to refine the images, emphasizing edges and enhancing the detection of critical features, which supports more accurate predictions of lung cancer outcomes.

Results: Imaging features are extracted from CT scans using various techniques, including texture analysis, shape descriptors, and deep learning-based methods, such as DCE imaging, which offers valuable insights into tumor vascularity and perfusion characteristics. The lung cancer prediction is conducted using hybrid deep learning techniques, employing the Inception-ResNet-v2 architecture, aimed at significantly enhancing diagnostic accuracy and facilitating early detection of lung cancer.

Conclusions: The result shows that accuracy is the exactness of the models, with Inception-ResNet-v2 achieving the highest at 92.5%, implemented using Python software. Future research can explore the integration of additional multimodal data sources, such as electronic health records and lifestyle factors, to further enhance lung cancer prediction models.

Objectives: Lung cancer remains a leading cause of cancer-related deaths worldwide, largely due to late diagnosis and the complexity of its risk factors. Early detection and accurate risk prediction are critical to improving patient survival and reducing treatment costs.

Methods: This study presents a novel deep learning framework combining advanced techniques such as the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI), Whale Optimization Algorithm with Adaptive Particle Swarm Optimization (WOA-APSO), convolutional neural networks (CNN), and Kernel-based non-Gaussian CNN (KNG-CNN) implemented in PYTHON to enhance lung cancer risk prediction.

Results: The proposed model effectively optimizes feature selection and achieves a high prediction accuracy of 99.25%. These findings demonstrate the potential of integrating deep learning and optimization algorithms for precise risk stratification, facilitating early diagnosis, and personalized treatment.

Conclusions: This work underscores the transformative impact of AI-driven approaches in lung cancer prognosis and highlights future opportunities for improving clinical outcomes.

Worldwide, the incidence of lung cancer is projected to continue its upward trend, with an estimated 2.5 million new cases annually. Non-small cell lung cancer (NSCLC) is the most prevalent form of lung cancer, accounting for ~85% of all cases. One of the challenges associated with NSCLC management is incomplete understanding of the underlying molecular mechanisms. Tumors often harbor multiple genetic changes that interact in complex ways, influencing tumor behavior, including the growth rate, metastatic potential as well as response and resistance to therapies. Identification of genetic alterations is desirable to anticipate resistance mechanisms and guide the development of combination therapies to overcome them. It also allows better stratification of patients in clinical trials, ensuring that the therapies are tested in the most appropriate populations, improving the chances of identifying effective treatments and tailor treatment plans based on the specific genetic profile of a patient's tumor. This review summarizes the established genetic and epigenetic alterations associated with NSCLC and discusses the need for understanding the molecular pathogenesis.