Background/Objectives: Cervical cancer remains a major cause of morbidity and mortality among women worldwide, marked by stark geographic and socioeconomic disparities. Preventable via HPV vaccination and screening, progress toward elimination varies widely across and within countries. This narrative review synthesizes the epidemiology, including incidence, mortality, survival, and stage distribution, as well as risk factors and the coverage/equity of HPV screening and vaccination programs. Methods: Comprehensive searches were performed in PubMed, Web of Science, Scopus, and Google Scholar (no date restrictions; English only). Included were original epidemiological studies, systematic reviews, meta-analyses, and international reports on burden, risk factors, or prevention indicators. Data were qualitatively synthesized into three themes: epidemiological patterns, risk factors, and screening/prevention programs. Results: Persistent high-risk HPV infection causes nearly all cervical cancers, predominantly HPV16/18, with regional variation in other types. Strong co-factors include HIV immunosuppression, early sexual debut, multiple partners, high parity, long-term oral contraceptive use, and smoking. Inequalities in incidence, late diagnosis, and survival are driven by socioeconomic disadvantages, low education, rural residence, and poor health system access. Screening ranges from cytology/VIA to primary HPV testing, but coverage is low and inequitable in high-burden settings. HPV vaccination has expanded yet faces major gaps in low- and middle-income countries. Conclusions: Cervical cancer burden concentrates in low-resource and marginalized populations. Global elimination demands accelerated, equitable scale-up of HPV vaccination and screening, alongside health system strengthening and barrier reduction.

Background and Objectives: Esophageal cancer (EC) remains a major global public health challenge due to its aggressiveness and poor survival rates. Therefore, this study aims to summarize the incidence, mortality, prevalence, and global burden of EC based on sex, age, and geographical divisions and to investigate the correlation of some risk factors including the Sociodemographic Index (SDI) and important health indicators to identify high-risk populations. Materials and Methods: We extracted the number and age-standardized rates (ASRs) of EC incidence, mortality, disability-adjusted life years (DALYs), and targeted risk factors for 204 countries and territories from the Global Burden of Disease 2021 study. Correlations between the ASRs of incidence, death, and DALYs and risk factors were investigated using SPSS 22 with Spearman’s correlation coefficient at a 0.05 significance level. Results: In 2021, the global age-standardized incidence rate (ASIR), death rate (ASDR), and DALY rate for EC were 6.65 (95% UI: 5.88–7.45), 6.25 (95% UI: 5.53–7.00), and 148.56 (95% UI: 131.71–166.82) per 100,000, respectively. Middle-SDI and high–middle-SDI regions showed the highest and lowest ASIR, ASDR, and DALY ASRs of EC. SDI correlated negatively with ASIR (–0.363), ASDR (–0.414), and DALY ASRs (–0.422). Male-to-female ratios for ASIR, ASDR, and DALY ASRs were 3.32, 3.37, and 3.51, respectively. As age increased, the incidence, death, and DALYs of EC also increased. East Asia recorded the highest incidence, death, and DALY values and ASRs of EC. The ASIR, ASDR, and DALY ASRs also increased with dietary risks, including the low intake of calcium, fruits, omega-6 polyunsaturated fatty acids, seafood omega-3 fatty acids, and vegetables. Conclusions: Considering the incidence, mortality, and high burden of EC in some regions, alongside the presence of modifiable risk factors, major interventions are needed to reduce these risks. Therefore, identifying high-risk areas and factors of EC, promoting lifestyle changes, and lowering the screening age could enable earlier detection and reduce the mortality of EC.

Background and Objectives: This scoping review focuses on gathering and examining the literature regarding modifiable and nonmodifiable risk factors for breast cancer. Materials and Methods: A comprehensive search was conducted in the PubMed/MEDLINE, Scopus, and Web of Science Core Collection databases with the keywords “breast cancer”, “risk”, and “risk factor” and a combination of these words to find quality articles published from 2010 to January 2025. Results: In total, 169 articles published in English were included in the study. The findings of this study revealed that family history, genetics, blood group, race, previous history of breast cancer, sex hormones, and breast density are nonmodifiable risk factors for this cancer. Smoking, alcohol, coffee and tea consumption, a low level of physical activity, dietary regimens, hormone replacement therapy, contraceptive pills, reproductive factors, no breastfeeding, metabolic syndrome, obesity, diabetes, some medications, work status, and other diseases are modifiable risk factors that play a role in the occurrence of breast cancer. Discussion: Lifestyle modifications such as improving one′s diet, engaging in regular physical activity, avoiding smoking/passive smoking, limiting alcohol consumption, maintaining a healthy weight after menopause, optimizing long‐term nonhormonal fertility choices, avoiding medically unnecessary hormone increases, and performing genetic screening when necessary can help in the prevention of breast cancer.

Background and Objectives: Female breast cancer (FBC) is an increasing public health concern in Iran, with notable geographic disparities that necessitate comprehensive burden assessments at national and provincial levels. This study presented the national and subnational burden and changes in FBC burden from 2010 to 2021 in Iran in comparison with global data. Materials and Methods: The GBD (2021) data on female BC were extracted from the Global Health Data Exchange (GHDx) query tool. Age-standardized incidence, deaths, prevalence, and adjusted years of life with disabilities (DALYs) rates (per 100,000) of FBC were extracted. Data were extracted globally, by continents, for Iran and its provinces, from 2010 to 2021. Results: Although the global FBC burden indicators remained almost stable, in Iran, there was a nearly twofold rise in incidence and prevalence and notable rises in mortality and DALYs. This study showed significant variation at the provincial level; Tehran, Qom, and Alborz consistently had the highest incidence, prevalence, mortality, and DALY rates, whereas Sistan and Baluchistan, Chahar Mahaal and Bakhtiari, Kohgiluyeh and Boyer-Ahmad, and Zanjan had the lowest rates. During 2010–2021, the provinces of Golestan, Ardebil, Sistan and Baluchistan, West Azarbayejan, Kohgiluyeh and Boyer-Ahmad, and North Khorasan experienced the most increasing trend in BC burden, while Yazd and Semnan showed smaller increases or modest decreases. Conclusions: The rising FBC burden in Iran underscores the urgent need to strengthen cancer registries, expand screening programs, ensure equitable resource distribution, and implement targeted regional interventions focused on modifiable risk factors and early detection to reduce health disparities nationwide.

Global Burden of Tracheal, Bronchus, and Lung Cancer in Adults Over 55 Years Old Based on Socio demographic Status and Geographical and Gender Differences from 2010-2021 - Thoracic Research and Practice

Objective: This study is aimed at developing and evaluating a machine learning–based model for breast cancer classification using integrated clinical, demographic, reproductive, and lifestyle data. Methods: A retrospective machine learning framework was developed using data from a case–control study conducted in Tehran. The dataset included demographic, clinical, reproductive, lifestyle, and screening‐related variables. Data preprocessing was performed within machine learning pipelines to ensure data quality and prevent data leakage. Duplicate records and noninformative variables were removed. Missing values were imputed using median values for numerical variables and the most frequent value for categorical variables. Categorical features were encoded appropriately, and Min–Max normalization was applied where required. Feature selection was conducted using mutual information (MI) and analysis of variance (ANOVA) within a stratified cross‐validation framework. The data were split into training (80%) and test (20%) sets. Several supervised learning algorithms, including Gaussian Naive Bayes (GNB), K‐nearest neighbors (KNN), decision tree (DT), random forest (RF), support vector machine (SVM), logistic regression (LR), and artificial neural network (ANN), were trained and evaluated. Model performance was assessed using accuracy, precision, recall (sensitivity), F1‐score, and receiver operating characteristic–area under the curve (ROC‐AUC), with stratified 5‐fold cross‐validation and final evaluation on an independent test set. Results: Significant differences were observed between breast cancer patients and healthy controls across multiple demographic and clinical variables. Patients were generally older and more likely to be widowed, belong to higher socioeconomic classes, and be housewives, whereas higher education levels and employment were more frequent among healthy individuals ( p < 0.001). Reproductive factors, including age at first marriage and breastfeeding duration, also showed significant differences. Feature selection reduced 414 initial variables to 40 key predictors. The most influential features included genetic factors (BRCA1/2 mutations and family history), reproductive and hormonal characteristics (age at menarche, menopause, and infertility), lifestyle behaviors (dietary patterns and physical activity), anthropometric measures (BMI and weight at age 30), and screening‐related variables (mammography, ultrasound, and biopsy). All models demonstrated strong and stable performance with minimal differences between cross‐validation and test results, indicating good generalization. RF achieved the highest performance (accuracy: 0.9897, precision: 0.9946, recall: 0.9840, F1‐score: 0.9892), followed by SVM and LR, whereas ANN showed the lowest overall performance. Conclusion: Machine learning models can effectively classify breast cancer using multidimensional patient data. Ensemble methods, particularly RF, demonstrated superior accuracy and robustness, highlighting their ability to capture complex nonlinear relationships. The identified predictors are consistent with established clinical and epidemiological risk factors, supporting the validity of the proposed models. These findings suggest that machine learning approaches hold strong potential for personalized risk assessment and early detection of breast cancer; however, external validation across diverse populations is necessary to confirm generalizability.