European Journal of Cancer Prevention最新文献

Background: The incidence of male breast cancer has been increasing in recent years; however, the long-term survival outcomes of diagnosed patients remain uncertain. This study was designed to evaluate the conditional survival of male breast cancer patients and to predict the future survival of patients through the conditional nomogram, to provide important suggestions for clinical decision-making.

Methods: Retrospective data from the SEER database included 3600 male breast cancer patients, divided into training and validation groups (7 : 3 ratio). Overall survival rates were calculated using Kaplan-Meier analysis. Conditional survival analysis described survival at specific years. Time-dependent multivariate Cox analysis identified prognostic factors' impact. The conditional survival nomogram model predicted real-time survival rates.

Results: Over time, the 5-year real-time survival rate of patients gradually improved, increasing from 70.5 to 74.8, 79.4, 85.8, and 92.9% (respectively, representing 5-year survival rates of 1-4 years after diagnosis). In addition, the improvement in conditional survival rate CS5 showed a nonlinear trend. After 5 years of diagnosis, age, tumor size, and tumor stage had a sustained impact on patient prognosis. Finally, a conditional survival nomogram was constructed to predict the 10-year survival rate in real time.

Conclusion: Five years after diagnosis, the conditional survival rate of male patients with breast cancer has improved, but it is not nonlinear. In the first 5 years after diagnosis, patients with older age, larger tumor size, poorer tumor stage, and distant metastasis should be actively followed up and treated to improve their long-term survival.

Physical activity (PA) has an established role in the promotion of health and fitness and the prevention of disease. Expected overall benefits include reduction of all-cause morbidity and death, weight control, improved quality of life, improved bone health and decreased falls of elderly subjects, , deeper cognition, and reduced risk of depression, anxiety, and sleeplessness. Currently, PA is a mainstay in the management of cardiovascular diseases, metabolic syndrome, diabetes, and bone health. Recently, the perception of its role in primary and secondary prevention, interception, and treatment of cancer, however, is also gaining importance. Regular walking, the simplest type of PA, is associated with reduced all-cause and cardiovascular disease mortality, and a role in cancer prevention is of increasing interest. Furthermore, PA improves the quality of life of cancer patients, attenuating side effects of chemotherapy, decreasing sarcopenia, increasing fitness, and inhibiting the recurrence and progression of some cancer types. It promotes emotional and psychological benefits in patients, inducing positive changes. While mechanisms, effective levels and useful amount of PA practice are well established in cardiology, they are yet to be fully determined in oncology. Nevertheless, PA is recommended to reduce cancer risk in the general population, and it has been introduced in programs for the prevention of second cancers. In perspective, it will help as integrative therapy in cancer patients and for cancer survivors. The number of beneficial effects in the cancer continuum is highlighted in this review.

Background: Previous studies have focused on the risk of cardiovascular disease (CVD)-specific death in hematological cancers and in some single anatomical tumor sites, there remains a paucity of data on systematic analyses in solid tumors.

Objective: The objective of this study is to evaluate the distribution, risk, and trends of CVD-specific death in patients with solid tumors.

Methods: A total of 2 679 293 patients with solid tumors diagnosed between 1975 and 2019 were screened from the Surveillance, Epidemiology and End Results (SEER) program across 15 anatomical sites. Standardized mortality ratios (SMRs) and absolute excess risks (AERs) were used to describe the intensity of CVD-specific death, competing risk regression models were used to assess the risk of CVD-specific death, and restricted cubic spline analyses were employed to investigate the potential linear or nonlinear relationship between age and CVD death.

Results: CVD-specific death in patients with solid tumors accounted for 48.95% of non-cancer deaths. Compared with the general population, patients with solid tumors had higher SMR and AER of CVD death (SMR: 1.15; AER: 21.12), heart disease-related death (SMR: 1.13; AER: 13.96), and cerebrovascular disease-related death (SMR: 1.20; AER: 4.85). Additionally, the SMR exhibited a decreasing trend with increasing time to diagnosis. Furthermore, a nonlinear relationship was observed between age and CVD-specific death in patients with solid tumors of different systems.

Conclusion: CVD-specific death accounted for a large proportion of the cause of non-cancer deaths. Patients with solid tumors exhibit an elevated risk of CVD-specific death. Screening for CVD death and optimizing risk management in patients with solid tumors throughout anticancer treatment may be beneficial in preventing CVD death.

Breast cancer screening is crucial for early detection and treatment. Yet, underutilization persists due to various psychosocial factors. This manuscript delves into the multifaceted fears that hinder screening adherence. The literature provides a framework categorizing breast cancer screening fears into generalized cancer fear, fear of screening components, and fear of screening outcomes. In this review, we explore fear of screening components (concerns regarding radiation, discomfort, and pain) and fear of screening outcomes (disability and mortality apprehension, treatment fears, obligation anxiety, and financial concerns) as undesirable, and potentially addressable, aspects of breast cancer screening fear. False-positive results exacerbate these anxieties, prolonging distress and impacting patients' lives beyond the screening process. Addressing these concerns requires reframing current screening approaches to prioritize patient comfort, cultural sensitivity, and accessibility. To address current psychosocial challenges in breast cancer screening, this manuscript advocates for modifying breast cancer screening methods to improve adherence and patient well-being.

Objectives: This systematic review aims to synthesize the available literature to determine the association between birthweight and the risk of nonneurological childhood cancers.

Methods: We conducted a systematic search of PubMed, Web of Science, and Scopus databases up to May 2023 to identify observational studies. Heterogeneity between studies was evaluated using the I2 statistics. Publication bias was assessed using Begg and Egger tests. We calculated the odds ratio (OR) or risk ratio (RR) with a 95% confidence interval (CI) using a random-effects model.

Results: Of 11 034 studies retrieved from the search, 56 studies (including 10 568 091 participants) were eligible. The ORs (95% CI) of low (<2500 g) versus normal birthweight (2500-4000 g) and childhood cancers were as follows: leukemia, 0.92 (0.77-1.11); acute lymphoblastic leukemia, 0.82 (0.72-0.94); acute myeloid leukemia, 0.98 (0.77-1.24); lymphoma, 0.99 (0.47-2.10); Hodgkin, 0.79 (0.61-1.03); non-Hodgkin, 0.85 (0.60-1.20); neuroblastoma, 1.34 (1.14-1.58); retinoblastoma, 0.95 (0.68-1.32); rhabdomyosarcoma, 0.86 (0.61-1.20); embryonal, 0.97 (0.66-1.43); alveolar, 1.92 (0.43-8.51); and Wilms tumor, 1.01 (0.83-1.24). The ORs (95% CI) of high (>4000 g) versus normal birthweight and childhood cancers were as follows: leukemia, 1.30 (1.18-1.42); acute lymphoblastic leukemia, 1.27 (1.16-1.39); acute myeloid leukemia, 1.13 (0.98-1.30); lymphoma, 1.69 (0.72-3.94); Hodgkin, 1.22 (1.02-1.46); non-Hodgkin, 1.22 (0.80-1.86); neuroblastoma, 1.20 (1.02-1.41); retinoblastoma, 1.17 (0.93-1.48); rhabdomyosarcoma, 1.07 (0.90-1.27); embryonal, 1.22 (1.00-1.49); alveolar, 1.02 (0.46-2.27); and Wilms tumor, 1.49 (1.34-1.67).

Conclusion: This meta-analysis identified high birth weight as a potential risk factor for some childhood cancers, while low birth weight might be protective against a few.

Early-onset prostate cancer (EOPC) is relatively uncommon. It is unclear if the incidence of EOPC is evolving. Utilizing data from the SEER database from 2000 to 2020, the study identified prostate cancer cases in men under 55 years, focusing on trends in annual age-adjusted incidence rates (AAIR), stage at presentation, race/ethnicity, and local treatment patterns. The study encompassed 93 071 cases of EOPC, with the median age at diagnosis being 51 years. From 2000 to 2007, the AAIR of EOPC experienced a wave-like increase from 6.9 to 8.3 per 100 000 people. It then sharply declined to 5.4 by 2014, followed by 6 years of stability, and by 2020 it had dropped to its lowest point of 4.5. The trend observed across different racial groups was consistent with the overall pattern, where non-Hispanic Black patients consistently exhibited the highest incidence and the least reduction rate (annual percent change, -1.0; 95% confidence interval, -1.8 to -0.2; P  < 0.05). Stage II was the most commonly diagnosed, although its AAIR declined from 4.9 to 1.2 per 100 000 people. From 2010 through 2020, the proportion of receiving prostatectomy decreased from 63.0 to 43.6%. The declining rates of EOPC across diverse racial groups emphasize the critical need for focused research and interventions. Specifically, there is an urgent call to establish a tailored screening protocol for prostate cancer targeting Black youth.

Background: Ovarian cancer, the most devastating tumor in women globally, significantly impacts young women, compromising their daily lives and overall well-being. Ovarian cancer represents a significant public health concern due to its extensive physical and psychological consequences.

Material and methods: Data from the Global Burden of Disease were used to assess the global, regional, and national burden of ovarian cancer in young women aged 20-39 from 1990 to 2019. This analysis focused on trends measured by the estimated annual percentage change and explored the socioeconomic impacts via the socio-demographic index (SDI).

Results: During 1990-2019, the incidence and prevalence of ovarian cancer among young women increased globally, with annual rates of 0.74% and 0.89%, respectively. The mortality rate and disability-adjusted life years also rose annually by 0.20% and 0.23%, respectively. A significant burden shift was observed toward regions with lower SDI, with high fasting plasma glucose, BMI, and asbestos exposure identified as prominent risk factors, particularly in lower SDI regions.

Conclusion: Our findings underscore ovarian cancer in young women as an escalating global health challenge, with the burden increasingly shifting toward lower socioeconomic areas. This underscores the necessity for targeted prevention and control strategies for ovarian cancer, focusing on reducing the identified risk factors and ensuring equitable health resource distribution.

Objective: The objective of this study is to develop and validate a multiparametric MRI model employing machine learning to predict the effectiveness of treatment and the stage of breast cancer.

Methods: The study encompassed 400 female patients diagnosed with breast cancer, with 200 individuals allocated to both the control and experimental groups, undergoing examinations in Shenzhen, China, during the period 2017-2023. This study pertains to retrospective research. Multiparametric MRI was employed to extract data concerning tumor size, blood flow, and metabolism.

Results: The model achieved high accuracy, predicting treatment outcomes with an accuracy of 92%, sensitivity of 88%, and specificity of 95%. The model effectively classified breast cancer stages: stage I, 38% ( P  = 0.027); stage II, 72% ( P  = 0.014); stage III, 50% ( P  = 0.032); and stage IV, 45% ( P  = 0.041).

Conclusions: The developed model, utilizing multiparametric MRI and machine learning, exhibits high accuracy in predicting the effectiveness of treatment and breast cancer staging. These findings affirm the model's potential to enhance treatment strategies and personalize approaches for patients diagnosed with breast cancer. Our study presents an innovative approach to the diagnosis and treatment of breast cancer, integrating MRI data with machine learning algorithms. We demonstrate that the developed model exhibits high accuracy in predicting treatment efficacy and differentiating cancer stages. This underscores the importance of utilizing MRI and machine learning algorithms to enhance the diagnosis and individualization of treatment for this disease.