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

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.

Objectives: Outcomes for upper gastrointestinal (UGI) cancers are poor except for those patients whose cancers are diagnosed at a very early stage. Unique socioeconomic factors may result in worse outcomes in the safety-net setting given that these patients often seek care later in the disease course. This study aims to understand the survival outcomes of patients with UGI cancers in a safety-net health care (SNH) setting.

Methods: Patients diagnosed with esophageal squamous (ES), esophageal adenocarcinoma (EA), and gastric carcinomas (G) at JPS Health Network in Fort Worth, Texas from January 1, 2018, to December 31, 2022, were identified in the tumor registry database. The electronic health record was queried for clinical characteristics, pathology variables, and management outcomes. Kaplan-Meier curve was used to illustrate the difference in survival time across cancer stages from an index date of diagnosis with censoring at date of last contact.

Results: A total of 171 patients were included: the median age was 57 years, but 15 patients were under 40 years. By ethnicity, 40% were Hispanic. The majority were male (71%) and uninsured (65%). G was the most common primary site (n = 92, 54%), followed by EA (n = 58, 34%) and ES (n = 21, 12%). Of the 125 stage 4B patients, 69 (55%) did not receive any treatment, 6 received only palliative radiation, and the remainder received systemic therapy. In patients with stage 4B, the median overall survival for patients with systemic treatment was 7.9 months (95% CI: 6.7-11), compared with 2.1 months (95% CI: 1.5-3.0) without treatment.

Conclusions: In an urban safety-net population with a high percentage of Hispanic population, most patients with advanced UGI cancers did not receive systemic therapy. Interventions to improve outcomes must consider the unique socioeconomic needs of this vulnerable population to translate clinical trial results into improved outcomes.

Objectives: The effect of comorbidity on kidney and urinary complications after surgery for renal cell carcinoma (RCC) is unclear. We aimed to assess the effect of comorbidity on postoperative complications and readmissions among patients with RCC in the Military Health System, which provides universal access to care to eligible beneficiaries.

Methods: We identified a cohort of patients aged 18 and older diagnosed with stage I to III RCC between 2001 and 2014 who received nephrectomy in the MilCanEpi database. Outcomes included 90-day general and kidney and urinary complications and hospital readmissions. The adjusted rate ratios (ARRs) with 95% CIs for Elixhauser comorbidity (0, 1 to 2, 3 to 4, ≥5) and outcomes were estimated using multivariable Poisson regression.

Results: The study included 1470 patients with a median (IQR) comorbidity count of 2 (0 to 3). Overall, patients with ≥5 comorbidities had elevated rates of general complications (ARR=1.47, 95% CI=0.96, 2.23) relative to patients with no comorbidity. For kidney and urinary complications, patients with 1 to 2 (ARR=1.65, 95% CI=1.07, 2.54), 3 to 4 (ARR=2.29, 95% CI=1.45, 3.62), and ≥5 comorbidities (ARR=2.64, 95% CI=1.60, 4.34) had statistically significant higher risks relative to patients with no comorbidity. Patients with ≥5 comorbidities had higher risk of readmission (ARR=1.65, 95% CI=1.08, 2.54 vs. no comorbidity), while the risks were not statistically different for patients with lower comorbidity.

Conclusions: The results demonstrate the increased risk for postoperative kidney and urinary complications and readmissions for patients with RCC and comorbidity and highlights the importance of comorbidity management in surgical care among patients with RCC.

Objectives: Previous meta-analyses have assessed the benefits and safety profile of immune checkpoint inhibitors in hepatocellular carcinoma patients. This meta-analysis provides an updated synthesis by incorporating the newly published studies and previous studies with the revised data.

Methods: A systematic review and meta-analysis were conducted following PRISMA guidelines. Databases (PubMed, Google Scholar, and Cochrane Library) were searched for randomized controlled trials (RCTs) comparing Immune Checkpoint Inhibitors to Standard Therapy or Placebo in patients with Hepatocellular Carcinoma. Studies were selected based on predefined eligibility criteria, and odds ratios (ORs) and hazard ratios (HRs) for outcomes were calculated using a random effects model.

Results: Eighteen RCTs involving 9244 patients were included in this study. Compared with the control group, ICIs were associated with a significantly improved objective response rate (ORR) (OR=3.20, 95% CI: 2.44-4.20, P=<0.00001), disease control rate (DCR) (OR=1.40, 95% CI: 1.08-1.81, P=0.01), stable disease (SD) (OR=2.15, 95% CI: 1.16-3.98, P=0.02), overall survival (OS) (HR=0.79, 95% CI: 0.73-0.86, P=<0.00001), progression-free survival (PFS) (HR=0.77, 95% CI: 0.69-0.87, P=<0.00001) and all-cause grade ≥3 adverse events (OR=1.36, 95% CI: 1.10-1.67, P=0.005). No significant differences were observed between the 2 groups in terms of progressive disease (PD) (OR=0.88, 95% CI: 0.67-1.15, P=0.34), all-cause any-grade adverse events (OR=1.04, 95% CI: 0.51-2.11, P=0.91), treatment-related any-grade adverse events (OR=1.32, 95% CI: 0.67-2.59, P=0.42), and treatment-related grade ≥3 adverse events (OR=1.16, 95% CI: 0.65-2.09, P=0.61).

Conclusions: By incorporating the most recent data and the newly published studies, this updated meta-analysis offers a clearer understanding of immune checkpoint inhibitors and reinforces their advantage over other therapeutic options in the treatment of hepatocellular carcinoma. Continued research is encouraged that will further validate these findings.

Lung cancer is the leading cause of cancer-related mortality worldwide, with non-small cell lung carcinoma (NSCLC) comprising over 85% of cases. Advances in molecular diagnostics and precision oncology have shifted treatment toward mutation-driven strategies, resulting in significantly improved patient outcomes. This review synthesizes current evidence on the most clinically relevant genetic alterations in NSCLC and their corresponding targeted therapies, including epidermal growth factor receptor (EGFR) mutations, anaplastic lymphoma kinase (ALK) rearrangements, ROS1 fusions, BRAF V600E mutations, MET exon 14 skipping mutations, RET and NTRK fusions, HER2 alterations, and KRAS G12C mutations. We outline mechanisms of action, clinical efficacy, and limitations of FDA-approved tyrosine kinase inhibitors (TKIs) and emerging agents, with emphasis on resistance pathways, both on-target and bypass-mediated, observed during treatment with drugs such as osimertinib, crizotinib, sotorasib, and entrectinib. The role of next-generation sequencing (NGS), liquid biopsy, and comprehensive biomarker profiling in guiding personalized therapy selection is also discussed, along with strategies for sequential therapy and rational combination approaches.

Objectives: Human epidermal growth factor receptor-2 (HER2)-positive breast cancer is rapidly growing with poor outcomes if left untreated. Research evidence indicates that chemotherapy-free regimens can improve outcomes of HER2-positive early breast cancer while maintaining high oncogenic safety. The main objective of our study was to examine the efficacy and safety of chemotherapy-free regimens in patients with HER2-positive early breast cancer.

Methods: A systematic literature search was conducted using the PubMed, CENTRAL, Google Scholar, and Embase databases to identify relevant randomized trials published until July 2024.

Results: Five randomized trials, including 1489 patients with early-stage HER2-positive breast cancer, met the inclusion criteria for this meta-analysis. The pooled results showed that about 26.1% (182/698) of patients receiving chemo-free therapies achieve pathologic complete response (pCR). However, the subgroup analysis revealed that patients treated with trastuzumab plus pertuzumab and those treated with HER2-directed drugs plus endocrine therapy had significantly inferior pCR than those receiving chemotherapy combined with anti-HER2 drugs (OR: 0.22, P=0.0002 and OR: 0.25, P<0.00001). The meta-analytic results also demonstrated that patients receiving chemo-free therapies have high 3-year and 5-year invasive disease-free survival (iDFS) (95.40% and 85.71%) and 5-year event-free survival (EFS) (81.13). Regarding safety, the pooled results revealed that the discontinuation rates due to adverse events were statistically similar between the 2 groups (OR: 0.29, P=0.18).

Conclusions: Chemotherapy-free regimens demonstrate promising responses and survival outcomes in patients with HER2-positive early breast cancer. However, the combination of anti-HER2 drugs with chemotherapy still demonstrates superior overall clinical outcomes.

Objectives: Despite a good response to first-line chemotherapy, small-cell lung cancer (SCLC) has high relapse rates and a poor prognosis. We conducted a systematic review and meta-analysis to assess the role of immune checkpoint inhibitors (ICIs) in the treatment of extended stage SCLC (ES-SCLC), in different lines of therapy.

Methods: Medline (PubMed), EMBASE, and Cochrane Library databases between January 2010 and March 2025 and conference proceedings between 2018 and 2025 were searched for RCTs assessing ICIs versus chemotherapy in patients with ES-SCLC. Primary endpoints were overall survival (OS) and progression-free survival (PFS). Secondary endpoints included objective response rate (ORR) and grade 3+ adverse events. Pooled hazard ratios (HR) for OS and PFS were meta-analyzed using the generic inverse variance method, and random-effect models were used to compute pooled estimates. Subgroup analyses compared survival by line of therapy, sex, age, and ECOG status.

Results: ICIs decreased risk of death by 19% (HR: 0.81, 95% CI: 0.76-0.86). OS benefit was regardless of age, sex, or ECOG, but only in first-line treatment. ICIs decreased the risk of disease progression by 22% (HR: 0.78, 95% CI: 0.67-0.91), with PFS benefit restricted to first-line treatment with a detrimental effect in the second line. ICIs improved ORR (OR: 0.79, 95% CI: 0.66-0.95), but were associated with increased grade 3+diarrhea (OR: 3.63, 95% CI: 1.46-9.02).

Conclusions: ICIs conferred efficacy benefits and an acceptable safety profile in the treatment of patients with ES-SCLC in the first-line, but should not be used in the second-line as single agents. Biomarkers predicting long-term benefit are needed to further improve outcomes.