American journal of cancer research最新文献

Pancreatic cancer (PC) is the third leading cause of all cancer-related fatalities and accounts for approximately 3% of cancer cases in the United States. PC survival rates are lower in Blacks compared to other races, and this has been attributed to socioeconomic and genetic factors. In this study, we evaluated sociodemographic and genetic characteristics associated with PC incidence and mortality among Blacks. Data from the SEER 22 registries (2000-2020) were used to calculate the incidence rates and relative survival. County mortality rates from 2017 to 2021 were analyzed. Incidence rate ratios based on gender, age, primary disease site, stage, level of education, and poverty were calculated. Survival analysis was conducted using the Kaplan-Meier method. Mutant gene expression was obtained from the MSK-CHORD tumor registry. Overall, 48,606 Black patients were diagnosed with malignant PC between 2000 and 2020: females (53.53%) and males (46.47%). Both males and females experienced a slight increase in Annual Percent Change (APC) of PC incidence (0.24, 95% CI, -0.02-0.53) and (0.22, 95% CI, -0.05-0.51), respectively, from 2000 to 2020. Males aged 55 to 75 years were most frequently affected. Overall incidence risk from 2000-2020 by age was higher in Black males IRR > 1 (1.18, 95% CI, 1.16-1.21). The most common primary PC site for Black males and females was the head of the pancreas, 49.06% and 49.88%, respectively. By staging, distant PC had the highest frequency in Blacks. Poverty level was associated with PC incidence among females and PC mortality among both males and females. Stage was associated with survival among males with localized and regional PC. The 5-year relative survival was less than 11% across combined PC stages for both sexes. Black males had a relatively lower 5-year survival than Black females in localized (31.7 vs. 37.2%) and distant PC (2.6% vs. 2.90%). Mutant KRAS expression was higher in Black males. PC incidence and mortality were significantly higher in Black males. Our analysis points to the importance of poverty alleviation programs that target females are likely to reduce PC incidence. Furthermore, receiving recommended screening for PC and early-stage diagnostics is important to lower PC mortality.

Objectives: This study aimed to explore the relationship between psychological resilience (PR) and symptom burden in postoperative brain glioma (BG) patients and to identify factors influencing this relationship.

Methods: A total of 296 postoperative BG patients were included in this study. Various scales were employed, including the General Information Questionnaire, the Psychological Resilience Scale for PR, the M.D. Anderson Symptom Inventory for Brain Tumors to assess symptom burden, the Social Support Rating Scale, and the General Self-Efficacy Scale. Pearson correlation and multifactor linear regression analyses were used to examine the relationship between PR and symptom burden, and to assess the impacts of social support and self-efficacy.

Results: Higher PR was associated with younger age, higher educational level, and greater family income. A significant inverse correlation was found between PR and symptom burden (r=-0.827, P<0.001). Social support (r=-0.832, P<0.001) and self-efficacy (r=-0.116, P=0.046) were also negatively correlated with symptom burden. Multifactorial analysis revealed that both PR and social support independently influenced symptom burden.

Conclusions: Enhancing PR and social support in postoperative BG patients may reduce symptom burden and improve quality of life. Future research should investigate interventions to improve PR and evaluate their long-term effects on symptom management and recovery.

This study evaluated the predictive value of the prognostic nutritional index (PNI), fibrinogen-to-albumin ratio (FAR), and neutrophil-to-lymphocyte ratio (NLR) for overall survival in hepatocellular carcinoma (HCC) patients. A total of 283 HCC cases from Hunan Provincial People's Hospital were included in the analysis, with 45 additional patients as external validation. The relationship between these indices and patient prognosis was further evaluated using the Kaplan-Meier method and Cox regression analysis. Receiver operating characteristic (ROC) curve analysis was performed to assess the predictive performance of these indices for overall survival (OS) and to determine the optimal cutoff values. ROC curve analysis revealed that the area under the curve (AUC) for PNI, FAR, and NLR was 0.723, 0.857, and 0.872, respectively. Multivariate analysis identified hepatitis history, intraoperative blood transfusion, FAR, NLR, and PNI as independent prognostic factors (all P<0.05). The resulting prediction model demonstrated strong performance in both the training (C-index =0.917) and external validation (C-index =0.853) cohorts, with AUCs of 0.889 and 0.931 for 6-month and 1-year prediction in the validation set, respectively. These findings suggest that preoperative levels of peripheral blood PNI, FAR, and NLR are closely associated with the surgical prognosis of HCC patients. The prognostic prediction model developed based on these indices exhibits good predictive efficacy.

Unfavorable T2 glottic squamous cell carcinoma with impaired vocal cord mobility and/or bulky disease has been a real treatment challenge with high local failure rates. The purpose of this study is to compare the oncological outcome of unfavorable T2 glottic carcinoma in patients treated with radical radiotherapy versus concurrent chemoradiation. This study is a prospective, open label, randomized trial, in which all patients with unfavorable T2 glottic cancer were treated with either single modality radiotherapy using hypofractionation protocol 65.25 Gy (arm A) or concurrent chemoradiation (arm B) between 2019 and 2023. The primary end points were local control and local progression free survival (PFS). Sixty-two patients were recruited in the study. Local control was significantly higher in concurrent chemoradiation (CCRT) group compared to radiotherapy (RT) group. The 3-year local progression free survival rates were significantly higher in CCRT arm (85.5%) compared to RT arm (57.8%) (P=0.015). Concurrent chemoradiation should be considered for selected patients with T2 glottic squamous cell carcinoma with impaired vocal cord mobility and/or bulky disease due to high rate of local failure with radiotherapy alone.

1q gain/amplification (1q+) is the most common cytogenetic abnormality (CA), with a frequency of 30-50% in patients with newly diagnosed multiple myeloma (NDMM). Although accumulating evidence supports 1q+ as a "high-risk" CA (HRCA), several issues remain to be addressed to understand its true prognostic property. We retrospectively analyzed a cohort of 934 patients with NDMM from three centers in China, who had baseline data available for 1q+ [including 1q21 gain (3 copies) and amplification (> 3 copies)] detected by fluorescence in situ hybridization in isolated CD138⁺ cells, and who received first-line treatment with novel agents including proteasome inhibitors, immunomodulatory drugs, or both. Minimal residue disease (MRD) was assessed using next-generation flow cytometry. In this cohort, 1q+ patients accounted for 53% of all patients. 1q+ patients were characterized by larger tumor burden, more advanced diseases, adverse complications, and frequent concurrence of other CAs (particularly HRCAs) at diagnosis. Concurrence of HRCAs [del(17p), t(4;14), and t(14;16); known as double-hit MM], but not standard-risk CA, markedly worsened the outcome of 1q+ patients, compared to those with 1q+ only (progression-free survival/PFS: hazard ratio/HR 1.63, 95% confidence interval/CI 1.21-2.20, P = 0.0013; overall survival/OS: HR 1.96, 95% CI 1.40-2.74, P < 0.0001). 1q+ modulated the risk levels defined by the Revised International Staging System (R-ISS). Although the overall response rate was not significantly different between patients with or without 1q+, fewer 1q+ patients achieved complete response or better and minimal residue disease negativity (MRD^-). MRD^- attainment substantially prolonged PFS (HR 4.03, 95% CI 2.59-6.29, P < 0.0001) and OS (HR 3.72, 95% CI 2.24-6.19, P < 0.0001) of 1q+ patients. While 1q+ patients had relatively shorter MRD^- duration, sustained MRD^- significantly improved the PFS and OS of 1q+ patients. Together, 1q+ is an HRCA and a major component of double-hit MM, while the risk-adapted and MRD-tailored therapy may best help manage this high-risk population.

Objectives: To evaluate the efficacy of a combination of Ovarian-Adnexal Reporting and Data System (O-RADS), contrast-enhanced ultrasound (CEUS), and cancer antigen 125 (CA125) in identifying malignant ovarian-adnexal lesions and improving diagnostic accuracy, providing a reliable reference for the evaluation and management of ovarian-adnexal malignancies to enhance patient prognosis.

Methods: The study analyzed the diagnostic performance of O-RADS alone and in combination with CEUS and CA125 for distinguishing between benign and malignant ovarian-adnexal lesions. Sensitivity, specificity, accuracy, and Kappa values were calculated to assess the efficacy of these diagnostic approaches.

Results: O-RADS alone showed a diagnostic sensitivity of 88.24%, specificity of 80.77%, and accuracy of 82.61% (Kappa = 0.719). When combined with CEUS, the diagnostic accuracy and Kappa value significantly improved. The combination of O-RADS, CEUS, and CA125 further enhanced the diagnostic performance, achieving sensitivity, specificity, and accuracy of 82.35%, 98.08%, and 94.20%, respectively (Kappa = 0.804).

Conclusions: The combination of O-RADS, CEUS, and CA125 significantly improves the diagnostic accuracy of ovarian-adnexal malignant lesions, providing new clinical references for the evaluation and management of ovarian-adnexal malignancies and contributing to better patient prognosis.

Background: Hepatocellular carcinoma (HCC) is a prevalent malignancy worldwide, with portal vein tumor thrombosis (PVTT) worsening its prognosis and complicating management. The combination of transarterial chemoembolization (TACE) and the targeted agent sorafenib has been proposed to improve treatment outcomes. This study investigates the prognostic factors influencing the effectiveness of this combined treatment in HCC patients with PVTT.

Methods: A retrospective cohort study was conducted on 299 patients diagnosed with HCC and PVTT who underwent TACE and sorafenib treatment between January 2018 and December 2022. Patients were categorized into good-prognosis (n = 197) and poor-prognosis (n = 102) groups based on Response Evaluation Criteria in Solid Tumors (RECIST) assessed four weeks post-treatment. Prognostic factors were analyzed using univariate and multivariate analyses to identify significant determinants affecting therapeutic outcomes.

Results: Key prognostic factors included tumor number, differentiation, size, PVTT extent, Child-Pugh class, ECOG performance status, hospitalization duration, and AFP levels. Patients with a single tumor had better outcomes (OR 0.358, P = 0.002), whereas poor differentiation (OR 4.561, P = 0.005) and larger tumor size (OR 0.347, P < 0.001) were associated with worse prognosis. A higher Child-Pugh class (OR 0.563, P = 0.035) and better ECOG performance (OR 2.710, P = 0.025) improved prognosis, while prolonged hospitalization and elevated AFP levels were linked to poorer outcomes. ASA classification and HCC morphology did not significantly impact prognosis.

Conclusion: The prognosis of HCC with PVTT treated with TACE and sorafenib is significantly influenced by tumor characteristics, liver function, and overall patient health. Identifying these factors can aid in refining personalized treatment strategies to improve survival outcomes.

Objective: To develop an individualized prediction model for myelosuppression risk in lung cancer patients undergoing platinum-based doublet chemotherapy and validate its predictive efficacy.

Methods: A retrospective analysis was conducted on the clinical data of 584 lung cancer patients who received platinum-based doublet chemotherapy at The Affiliated Hospital of Qingdao University between January 2016 and December 2020. Patients were randomly assigned to a training cohort (n=391) and a validation cohort (n=193). Myelosuppression occurred in 280 (71.6%) patients in the training cohort and 132 (68.4%) in the validation cohort. Univariate analysis and LASSO regression were used to identify independent risk factors for myelosuppression. Prediction models were developed using Support Vector Machine (SVM), Random Forest, Extreme Gradient Boosting (XGBoost), and Adaptive Boosting (Adaboost). Model performance was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and Decision Curve Analysis (DCA). The SHAP algorithm was employed to evaluate feature importance, and a nomogram was developed for individual risk prediction.

Results: LASSO regression identified 10 independent risk factors for myelosuppression: age, body mass index (BMI), white blood cell count, neutrophil count, platelet count, total protein, gender, treatment regimen, targeted therapy, and first chemotherapy cycle. In the training cohort, the XGBoost model exhibited the best performance, with an area under the curve (AUC) of 0.855 (95% CI: 0.813-0.897), while the AUC in the validation cohort was 0.793. SHAP analysis identified white blood cell count, platelet count, neutrophil count, BMI, and age as the most influential predictors. The SHAP analysis based on the XGBoost model demonstrated substantial value.

Conclusion: This study successfully developed an individualized prediction model for myelosuppression risk in lung cancer patients following platinum-based doublet chemotherapy, with the XGBoost model achieving high predictive accuracy and clinical utility. The model provides a valuable tool for guiding precision medicine.

Microporous annealed particle (MAP) hydrogels consist of densely crosslinked and annealed hydrogel particles. Compared to common hydrogels, the inherent porosity within and among these hydrogel particles offers interconnected channels for substance exchange in addition to sufficient growth space for cells, thereby forming a three-dimensional culture system that highly mimics the in vivo microenvironment. Such characteristics enable MAP hydrogels to adapt to various requirements of biomedical applications, along with their excellent injectability and mechanical properties. This review initially provides a comprehensive summary of the fabrication methods and material types of MAP hydrogels, alongside an assessment of their mechanical properties and porosity. In vitro studies are evaluated based on the impact of MAP hydrogels on cellular behaviors, focusing on cell proliferation, differentiation, migration, activity, and phenotype. In vivo research highlights the promising applications of MAP hydrogels in tissue regeneration, as well as their innovative use in cancer immunotherapy. Current challenges and future research directions are outlined, underscoring the potential of MAP hydrogels to significantly improve clinical outcomes in cancer treatment and regenerative medicine.

Breast cancer is a disorder affecting women globally, and hence an early and precise classification is the best possible treatment to increase the survival rate. However, the breast cancer classification faced difficulties in scalability, fixed-size input images, and overfitting on limited datasets. To tackle these issues, this work proposes a Patho-Net model for breast cancer classification that overcomes the problems of scalability in color normalization, integrates the Gated Recurrent Unit (GRU) network with the U-Net architecture to process images without the need for resizing and computational efficiency, and addresses the overfitting problems. The proposed model collects and normalizes histopathology images using automated reference image selection with the Reinhard method for color standardization. Also, the Enhanced Adaptive Non-Local Means (EANLM) filtering is utilized for noise removal to preserve image features. These preprocessed images undergo semantic segmentation to isolate specific parts of an image, followed by feature extraction using an Improved Gray Level Co-occurrence Matrix (I-GLCM) to reveal fine patterns and textures in images. These features serve as input into the classification U-Net model integrated with GRU networks to improve the model performance. Finally, the classification result is expanded, and XAI is used for clear visual explanations of the model's predictions. The proposed Patho-Net model, which uses the 100X BreakHis dataset, achieves an accuracy of 98.90% in the classification of breast cancer.