Users can access data about cancer incidence and mortality for all the countries of the world as of 2008. Background GloboCan is a project of the International Agency for Research on Cancer and the World Health Organization (WHO). GloboCan presents estimates of the burden of cancer in 184 countries or territories around the world. User functionality GloboCan provides access to the most recent estimates (from 2008) of the incidence and mortality of 27 major cancers. Users can create fact sheets or do online analysis to create tables, graphs, maps, and predictions. Users c an choose to create tables by population or by cancer type. Covariates for analysis include age group, sex, and continent. Users are able to choose between mortality and incidence statistics. Users can choose to create age specific cancer curves, bar charts, maps, and pie charts. The prediction option allows the user to estimate the future burden of a selected cancer in selected population for a selected year. Data Notes Data sources and methods are clearly outlined on the “Data Sources and Methods” section of the website. Users are able to download their online analysis in PDF or html format. GloboCan uses the definitions outlined in the United Nations, World Population Prospects, 2008 revision (except Cyprus located in Southern Europe and Taiwan is located in Eastern Asia).

Colorectal cancer (CRC) is one of the most common cancer types worldwide. Its increasing mortality trends, especially in emerging countries, are a concern. The aim of this study was to analyse mortality trends and spatial patterns of CRC in the state of Sergipe, Brazil, from 1990 to 2019. Trends were calculated using data from the Online Mortality Atlas and Joinpoint Regression Program 4.8.0.1. Spatial analyses were performed using the empirical Bayesian model and Moran indices calculated by TerraView 4.2.2 between 1990 to 1999, 2000 to 2009 and 2010 to 2019. A total of 1585 deaths were recorded during the study period, with 58.42% among females. Trends were increasing and constant for both sexes and all age groups studied. The highest mean annual percent change was 6.2 {95% Confidence interval (CI) 3.4;9.0} for males aged +65 years and 4.5 (95% CI 3.2;5.8) for females aged 50–64 years. There was positive spatial autocorrelation for both sexes in all periods studied when using the Moran index for Bayesian rates. In summary, a consistent trend of increasing colorectal cancer (CRC) mortality has been observed overall. Nevertheless, an altered spatial distribution among males has emerged over the studied period.

Background: Non-melanoma skin cancer (NMSC) incidence has been increasing steadily around the world. The aim of the study is to describe geographic trends in incidence and mortality of NMSC in Russia between 2007 and 2017 and compare findings to other European countries. Methods: We used geospatial analysis to map the incident cases and descriptive statistical analysis to analyze trends. Additionally, we assessed the relationship between ethnicity, geographic latitude/longitude, and NMSC incidence/mortality rates. We retrospectively analyzed the data from the Moscow Oncology Research Institute, Ministry of Health of the Russian Federation, for 2007–2017. Routine methods of descriptive epidemiology were used to study incidence and mortality rates by age groups, years, and jurisdictions (i.e., Federal Districts and Federal Subjects). Results: In total, 733,723 patients were diagnosed with NMSC in Russia over the period 2007–2017, of whom 63% were women. The overall age-standardized incidence and mortality rates were 29.64/100,000 and 0.70/100,000, respectively. There was a consistent increase in age-standardized incidence rates over the study period, with a decreasing mortality rate. Geographic mapping revealed north-to-south and east-to-west gradients for NMSC. Conclusions: This study demonstrated longitudinal trends for NMSC incidence in Russia documenting that skin phototype, latitude/longitude, climate zones, and cultural practices remain dominant risk factors defining the epidemiology of NMSC. Moreover, this work identified several regions in the country (i.e., Republic of Adygea, Samara, Krasnodar Krai, etc.), where patient education/sun awareness campaigns will be useful to help reduce the risk of this malignancy.

Breast cancer is the second most common cancer among women in the United States (some kinds of skin cancer are the most common). Black women and White women get breast cancer at about the same rate, but Black women die from breast cancer at a higher rate than White women (Centers for Disease Control and Prevention).Mammograms are non-invasive tests that can detect breast cancer and other abnormalities early (when most treatable), even before any symptoms appear. Mammograms are the only test proven to reduce breast cancer deaths.This map shows the overall mammogram screening percentage of female Medicare enrollees age 65-74, as well as the racial/ethnic group least likely to receive mammogram screenings. Data from County Health Rankings (in this layer and referenced below), available for nation, state, and county, and available in ArcGIS Living Atlas of the World.

Joinpoint analysis for colorectal cancer mortality in males and females.

Please cite our data paper published in "Data in Brief": https://www.sciencedirect.com/science/article/pii/S2352340923007473

BackgroundLiver cancer ranks as the third leading cause of cancer-related mortality worldwide [1] and alarmingly, both the incidence and mortality rates of liver cancer are increasing [2; 3]. Among the various types of primary liver cancer, hepatocellular carcinoma (HCC) stands out as the most prevalent, accounting for approximately 70-85% of liver cancer cases [4]. Leveraging the advantages of magnetic resonance (MR) imaging, HCC can be reliably detected and diagnosed without the requirement of an invasive biopsy [5]. MR imaging offers high tissue contrast, which can be further enhanced through contrast-enhanced multiphasic magnetic resonance imaging (mpMRI) techniques. This enables accurate identification and non-invasive diagnosis of HCC [6].

ObjectivePrecise segmentation of the liver plays a crucial role in volumetry assessment and serves as a vital pre-processing step for subsequent tumor detection algorithms [7]. However, accurate liver segmentation can be particularly challenging in patients with cancer-related tissue alterations and deformations in shape [8]. Accurate HCC tumor segmentation is essential for the extraction of quantitative imaging biomarkers such as radiomics and can be used for studies on treatment response assessment and prognosis evaluation and provides critical information about the tumor biology. In order to enhance the reproducibility of liver and tumor segmentation, automated methods utilizing image analysis techniques and machine learning have been developed. These methods have demonstrated promising results [7; 8]; however, most algorithms were tested only on small internal test sets and therefore do not guarantee generalizable and consistent performance on external data. Publicly available datasets allow for fair and objective comparisons between different algorithms, techniques, or approaches. Researchers can evaluate the strengths and weaknesses of their methods in relation to existing solutions and establish benchmarks for performance evaluation. In addition to providing a benchmark with this dataset, we also assess the inter-rater variability between two different sets of tumor segmentations. This analysis serves as a measure of reproducibility for human segmentations, highlighting the consistency or variability that may exist among different human raters. Understanding the reproducibility of human segmentations is essential in assessing the reliability of manual annotations and establishing a baseline for algorithm performance comparison. By introducing LiverHccSeg, we aim to fill the gap of lacking publicly available mpMRI HCC datasets and offer researchers and developers a valuable resource for algorithmic evaluation on external data and imaging biomarker analyzes.

Materials and Methods Inclusion of PatientsAll available scans from The Cancer Genome Atlas Liver Hepatocellular Carcinoma Collection (TCGA-LIHC) (https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=6885436) were downloaded [9]. One multiphasic MRI scan (pre and triphasic post contrast) per patient was included. Patients who did not exhibit a tumor or residual tumor were excluded from the tumor segmentation dataset; however, they were included in the liver segmentation dataset.

MR Imaging DataSubsequently, all imaging data was converted to the Neuroimaging Informatics Technology Initiative (NIfTI) format with the dcm2nii (v2.1.53) package [10] and available header information was extracted using the pydicom (v.2.1.2) package [11]. Multiparametric MR sequences were labeled with a consistent syntax ('pre', 'art', 'pv', 'del', for the pre-contrast, arterial, portal-venous and delayed contrast phases, respectively). All images were already de-identified by the TCIA website. Images were acquired between the years 1993 and 2007 on Philips and Siemens scanners with field strengths of 1.5 and 3 Tesla. Full details of the imaging parameters can be found in Table 5. Briefly, the median repetition time (TR) and median echo time (TE) were 365.8 ms and 26.4 ms, respectively. The median slice thickness was 9.5 mm, the median bandwidth 536.9 Hz.

Scientific ReadingAfter conversion, all images were read in a scientific reading by two board-certified abdominal radiologists (S.A. and S.H with 9 and 10 years of experience, respectively). Any disagreement between the two raters was discussed in a consensus meeting. All HCC lesions were classified according to LI-RADS criteria [6].

Image RegistrationThe co-registration of pre-contrast, portal-venous, and delayed-phase images with arterial phase images was performed using the software BioImage Suite (v3.5) [12]. A non-rigid intensity-based registration approach was applied, employing a parameterized free-form deformation (FFD) with 3D B-splines [13]. The optimal FFD transformation was estimated by maximizing the normalized mutual information similarity metric [14] through gradient descent optimization. To enhance the optimization process, a multi-resolution image pyramid with three levels was utilized. The final B-spline control point spacing was set to 80 mm. The estimated transformation was then employed to warp the moving images (pre-contrast, portal-venous, and delayed-phase) into the reference image space, specifically the arterial phase image.

Liver and Tumor Segmentation and Statistical AnalysisAll livers and tumors were manually segmented under the supervision of two board-certified abdominal radiologists using the software 3D Slicer (v4.10.2) [15]. To compare the segmentation agreement between the two sets of liver and tumor segmentations, we calculated segmentation metrics using the Python package seg-metrics (v1.0.0) [16]. All segmentation metrics and statistics were calculated in Python (v3.7).

Data descriptionThe data that appears in this article include:

dicoms.zip: This zip file contains all the raw MR images from The Cancer Genome Atlas Liver Hepatocellular Carcinoma Collection (TCGA-LIHC) [1] in the Digital Imaging and Communications in Medicine (DICOM) format used for the curation of this dataset. The data is structured as Patient-ID/DATE/SEQUENCE where Patient-ID is the unique unidentified patient ID, DATE is the date of the image acquisition, and SEQUENCE is the name of the MR sequence. LiverHccSeg_MetaData.xlsx: This spreadsheet contains all the metadata from the DICOM headers along with the data from the scientific image readings. nifti_and_segms.zip: This zip file contains all MR images along with the liver and tumor segmentations in the Neuroimaging Informatics Technology Initiative (NIfTI) format.The data is structured as Patient-ID/DATE/SEQUENCE where Patient-ID is the unique anonymized patient identifier, DATE is the date of the image acquisition, and SEQUENCE is the name of the MRI sequence or segmentation image.The NIfTI files are named as follows:pre.nii.gz : Pre-contrast T1-weighted MRIart.nii.gz: Arterial-phase T1-weighted MRIpv.nii.gz: Portal-venous-phase T1-weighted MRIdel.nii.gz: Delayed-phase T1-weighted MRIart_pre.nii.gz: Pre-contrast T1-weighted MRI registered to the corresponding arterial-phase T1-weighted imageart_pv.nii.gz: Portal-venous-phase T1-weighted MRI registered to the corresponding arterial-phase T1-weighted MRIart_del.nii.gz: Delayed-phase T1-weighted MRI registered to the corresponding arterial-phase T1-weighted MRIThe corresponding manual segmentations are named after the rater and the type of segmentation and follow the format 'RATER_ROI.nii.gz' where RATER denotes the human rater and ROI denotes the region of interest that was segmented, for example, 'rater1_liver.nii.gz', 'rater2_liver.nii.gz', 'rater1_tumor1.nii.gz', and 'rater2_tumor1.nii.gz'. For tumor segmentations, an integer indicates the tumor identification number for different tumor ROIs, for example, 'rater1_tumor1.nii.gz' and 'rater2_tumor1.nii.gz'. The segmentations can be used for the arterial phase NIfTI file as well as the corresponding co-registered pre-contrast (art_pre.nii.gz), portal-venous (art_pv.nii.gz), and delayed-phase (art_del.nii.gz) images. segm_metrics.xlsx: This spreadsheet summarizes the segmentation agreement between the two sets of liver and tumor segmentations by the two board-certified abdominal radiologists.

References 1 Sung H, Ferlay J, Siegel RL et al (2021) Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin 71:209-249 2 Siegel RL, Miller KD, Jemal A (2019) Cancer statistics, 2019. CA Cancer J Clin 69:7-34 3 White DL, Thrift AP, Kanwal F, Davila J, El-Serag HB (2017) Incidence of Hepatocellular Carcinoma in All 50 United States, From 2000 Through 2012. Gastroenterology 152:812-820.e815 4 Perz JF, Armstrong GL, Farrington LA, Hutin YJ, Bell BP (2006) The contributions of hepatitis B virus and hepatitis C virus infections to cirrhosis and primary liver cancer worldwide. J Hepatol 45:529-538 5 Hamer OW, Schlottmann K, Sirlin CB, Feuerbach S (2007) Technology insight: advances in liver imaging. Nat Clin Pract Gastroenterol Hepatol 4:215-228 6 Chernyak V, Fowler KJ, Kamaya A et al (2018) Liver Imaging Reporting and Data System (LI-RADS) Version 2018: Imaging of Hepatocellular Carcinoma in At-Risk Patients. Radiology 289:816-830 7 Bousabarah K, Letzen B, Tefera J et al (2020) Automated detection and delineation of hepatocellular carcinoma on multiphasic contrast-enhanced MRI using deep learning. Abdom Radiol. 10.1007/s00261-020-02604-5 8 Gross M, Spektor M, Jaffe A et al (2021) Improved performance and consistency of deep learning 3D liver segmentation with heterogeneous cancer stages in magnetic resonance imaging. PLoS One 16:e0260630 9 Erickson BJ, Kirk S, Lee Y et al (2016) Radiology Data from The Cancer Genome Atlas

Mortality_Incidence Ratio and their respective confidence intervals.

Supplementary Material 2: Figure S1. Association between older TBL cancer patients (aged 70 years and older) with ASIRs, ASDRs, and ASMRs and SDIs in 204 countries and territories from 1990 to 2021. SDI vs ASIRs: (A) both sexes, (B) males, (C) females; SDI vs ASMRs: (D) both sexes, (E) males, (F) females; SDI vs ASDRs: (G) both sexes, (H) males, (I) females. Abbreviations: ASIR = age-standardized incidence rate, ASMR = age-standardized mortality rate, ASDR = age-standardized rate of DALYs, DALYs = disability-adjusted life years, SDI = sociodemographic index, TBL = tracheal, bronchial, and lung. Figure S2. AAPCs of the ASIR (A), ASMR (B), and ASDR (C) from 1990 to 2021 in 204 countries and territories according to the SDI in 2021. Abbreviations: ASIR = age-standardized incidence rate, ASMR = age-standardized mortality rate, ASDR = age-standardized rate of DALYs, AAPCs = average annual percent changes, DALYs = disability-adjusted life years, SDI = sociodemographic index, TBL = tracheal, bronchial, and lung. Figure S3. Comparison of the ASIR, ASMR, and ASDR for older TBL cancer patients (aged 70 years and older) in both sexes across 21 geographical GBD regions by the SDI for 1990, 2004, 2015 and 2021. (A) ASIR, (B) ASMR, (C) ASDR. Abbreviations: ASIRs = age-standardized incidence rate, ASMRs = age-standardized mortality rate, ASDRs = age-standardized rate of DALYs, DALYs = disability-adjusted life years, GBD = global burden of disease, SDI = socialdemographic index, TBL = tracheal, bronchial, and lung. Figure S4. Comparison of the ASIR, ASMR, and ASDR for older TBL cancer patients (aged 70 years and older) in male across 21 geographical GBD regions by the SDI for 1990, 2004, 2015 and 2021. (A) ASIR, (B) ASMR, (C) ASDR. Abbreviations: ASIRs = age-standardized incidence rate, ASMRs = age-standardized mortality rate, ASDRs = age-standardized rate of DALYs, DALYs = disability-adjusted life years, GBD = global burden of disease, SDI = socialdemographic index, TBL = tracheal, bronchial, and lung. Figure S5. Comparison of the ASIR, ASMR, and ASDR for older TBL cancer patients (aged 70 years and older) in female across 21 geographical GBD regions by the SDI for 1990, 2004, 2015 and 2021. (A) ASIR, (B) ASMR, (C) ASDR. Abbreviations: ASIRs = age-standardized incidence rate, ASMRs = age-standardized mortality rate, ASDRs = age-standardized rate of DALYs, DALYs = disability-adjusted life years, GBD = global burden of disease, SDI = socialdemographic index, TBL = tracheal, bronchial, and lung. Figure S6. The global distribution of the ASIR, ASMR, and ASDR of TBL cancer in older male patients (aged 70 years and older) in 1990 and 2021. Global maps of ASIR (A), ASMR (B), and ASDR (C) in 1990; and global maps of ASIR (D), ASMR (E), and ASDR (F) in 2021. Abbreviations: ASIR = age-standardized incidence rate, ASMR = age-standardized mortality rate, ASDR = age-standardized rate of DALYs, DALYs = disability-adjusted life years, TBL = tracheal, bronchial, and lung. Figure S7. The global distribution of the ASIR, ASMR, and ASDR of TBL cancer in older female patients (aged 70 years and older) in 1990 and 2021. Global maps of ASIR (A), ASMR (B), and ASDR (C) in 1990; and global maps of ASIR (D), ASMR (E), and ASDR (F) in 2021. Abbreviations: ASIR = age-standardized incidence rate, ASMR = age-standardized mortality rate, ASDR = age-standardized rate of DALYs, DALYs = disability-adjusted life years, TBL = tracheal, bronchial, and lung. Figure S8. Proportion of TBL cancer in both sexes older patients (aged 70 years and older) DALYs attributable to 16 risk factors globally and classified by SDI levels in 2021. Abbreviations: DALYs = disability-adjusted life years, TBL = tracheal, bronchial, and lung, SDI = sociodemographic index. Figure S9. Proportion of TBL cancer in older male patients (aged 70 years and older) DALYs attributable to 16 risk factors globally and classified by SDI levels in 2021. Abbreviations: DALYs = disability-adjusted life years, TBL = tracheal, bronchial, and lung, SDI = sociodemographic index. Figure S10. Proportion of TBL cancer in older female patients (aged 70 years and older) DALYs attributable to 16 risk factors globally and classified by SDI levels in 2021. Abbreviations: DALYs = disability-adjusted life years, TBL = tracheal, bronchial, and lung, SDI = sociodemographic index. Figure S11. Global ASIR, ASMR and ASDR for TBL cancer among older patients for both sexes by SDI, 1990-2021. Abbreviations: ASIR = age-standardized incidence rate, ASMR = age-standardized mortality rate, ASDR = age-standardized rate of DALYs, DALYs = disability-adjusted life years, TBL = tracheal, bronchial, and lung, SDI = sociodemographic index. Figure S12. Association between SDI and the proportions of DALYs attributable to 16 risk factors for TBL cancer among older patients for both sexes in 21 GBD regions, 2021. Abbreviations: DALYs = disability-adjusted life years, TBL = tracheal, bronchial, and lung, SDI = sociodemographic index, GBD = global burden of disease. Figure S13. Global ASIR, ASMR and ASDR for TBL cancer among older patients for female by SDI, 1990-2021. Abbreviations: ASIR = age-standardized incidence rate, ASMR = age-standardized mortality rate, ASDR = age-standardized rate of DALYs, DALYs = disability-adjusted life years, TBL = tracheal, bronchial, and lung, SDI = sociodemographic index. Figure S14. Global ASIR, ASMR and ASDR for TBL cancer among older patients for male by SDI, 1990-2021. Abbreviations: ASIR = age-standardized incidence rate, ASMR = age-standardized mortality rate, ASDR = age-standardized rate of DALYs, DALYs = disability-adjusted life years, TBL = tracheal, bronchial, and lung, SDI = sociodemographic index. Figure S15. Association between SDI and the proportions of DALYs attributable to 16 risk factors for TBL cancer among older patients for male in 21 GBD regions, 2021. Abbreviations: DALYs = disability-adjusted life years, TBL = tracheal, bronchial, and lung, SDI = sociodemographic index, GBD = global burden of disease. Figure S16. Association between SDI and the proportions of DALYs attributable to 16 risk factors for TBL cancer among older patients for female in 21 GBD regions, 2021. Abbreviations: DALYs = disability-adjusted life years, TBL = tracheal, bronchial, and lung, SDI = sociodemographic index, GBD = global burden of disease.

Female and male population and their respective standardized rates and gross death value_Colon_rectum_2010_2019.

This multi-scale map shows life expectancy - a widely-used measure of health and mortality. From the County Health Rankings page about Life Expectancy:"Life Expectancy is an AverageLife Expectancy measures the average number of years from birth a person can expect to live, according to the current mortality experience (age-specific death rates) of the population. Life Expectancy takes into account the number of deaths in a given time period and the average number of people at risk of dying during that period, allowing us to compare data across counties with different population sizes.Life Expectancy is Age-AdjustedAge is a non-modifiable risk factor, and as age increases, poor health outcomes are more likely. Life Expectancy is age-adjusted in order to fairly compare counties with differing age structures.What Deaths Count Toward Life Expectancy?Deaths are counted in the county where the individual lived. So, even if an individual dies in a car crash on the other side of the state, that death is attributed to his/her home county.Some Data are SuppressedA missing value is reported for counties with fewer than 5,000 population-years-at-risk in the time frame.Measure LimitationsLife Expectancy includes mortality of all age groups in a population instead of focusing just on premature deaths and thus can be dominated by deaths of the elderly.[1] This could draw attention to areas with higher mortality rates among the oldest segment of the population, where there may be little that can be done to change chronic health problems that have developed over many years. However, this captures the burden of chronic disease in a population better than premature death measures.[2]Furthermore, the calculation of life expectancy is complex and not easy to communicate. Methodologically, it can produce misleading results caused by hidden differences in age structure, is sensitive to infant and child mortality, and tends to be overestimated in small populations."Breakdown by race/ethnicity in pop-up: (This map has been updated with new data, so figures may vary from those in this image.)There are many factors that play into life expectancy: rates of noncommunicable diseases such as cancer, diabetes, and obesity, prevalence of tobacco use, prevalence of domestic violence, and many more.Proven strategies to improve life expectancy and health in general A database of dozens of strategies can be found at County Health Rankings' What Works for Health site, sorted by Health Behaviors, Clinical Care, Social & Economic Factors, and Physical Environment. Policies and Programs listed here have been evaluated as to their effectiveness. For example, consumer-directed health plans received an evidence rating of "mixed evidence" whereas cultural competence training for health care professionals received a rating of "scientifically supported." Data from County Health Rankings (layer referenced below), available for nation, state, and county, and available in ArcGIS Living Atlas of the World.

This multi-scale map shows life expectancy - a widely-used measure of health and mortality. From the 2020 County Health Rankings page about Life Expectancy:"Life Expectancy is an AverageLife Expectancy measures the average number of years from birth a person can expect to live, according to the current mortality experience (age-specific death rates) of the population. Life Expectancy takes into account the number of deaths in a given time period and the average number of people at risk of dying during that period, allowing us to compare data across counties with different population sizes.Life Expectancy is Age-AdjustedAge is a non-modifiable risk factor, and as age increases, poor health outcomes are more likely. Life Expectancy is age-adjusted in order to fairly compare counties with differing age structures.What Deaths Count Toward Life Expectancy?Deaths are counted in the county where the individual lived. So, even if an individual dies in a car crash on the other side of the state, that death is attributed to his/her home county.Some Data are SuppressedA missing value is reported for counties with fewer than 5,000 population-years-at-risk in the time frame.Measure LimitationsLife Expectancy includes mortality of all age groups in a population instead of focusing just on premature deaths and thus can be dominated by deaths of the elderly.[1] This could draw attention to areas with higher mortality rates among the oldest segment of the population, where there may be little that can be done to change chronic health problems that have developed over many years. However, this captures the burden of chronic disease in a population better than premature death measures.[2]Furthermore, the calculation of life expectancy is complex and not easy to communicate. Methodologically, it can produce misleading results caused by hidden differences in age structure, is sensitive to infant and child mortality, and tends to be overestimated in small populations."Click on the map to see a breakdown by race/ethnicity in the pop-up: Full details about this measureThere are many factors that play into life expectancy: rates of noncommunicable diseases such as cancer, diabetes, and obesity, prevalence of tobacco use, prevalence of domestic violence, and many more.Data from County Health Rankings 2020 (in this layer and referenced below), available for nation, state, and county, and available in ArcGIS Living Atlas of the World

Female and male population and their respective standardized rates and gross death value_Colon_rectum_2000_2009.

Sergipe CRC Mortality_Absolute values, specific rate, crude rate and standardized rate, per 100,000 men and age group, 1990–2019 year by year.

Cervical cancer is a prevalent disease among women, especially in low- and middle-income countries (LMICs), where most deaths occur. Integrating cervical cancer screening services into healthcare facilities is essential in combating the disease. Thus, this review aims to map evidence related to integrating cervical cancer screening into existing primary care services and identify associated barriers and facilitators in LMICs. The scoping review employed a five-step framework as proposed by Arksey and O’Malley. Five databases (MEDLINE, Maternity Infant Care, Scopus, Cumulative Index to Nursing and Allied Health Literature (CINAHL), and Web of Science) were systematically searched. Data were extracted, charted, synthesized, and summarised. A total of 28 original articles conducted in LMICs from 2000 to 2023 were included. Thirty-nine percent of the reviewed studies showed that cervical cancer screening (CCS) was integrated into HIV clinics. The rest of the papers revealed that CCS was integrated into existing reproductive and sexual health clinics, maternal and child health, family planning, well-baby clinics, maternal health clinics, gynecology outpatient departments, and sexually transmitted infections clinics. The cost-effectiveness of integrated services, promotion, and international initiatives were identified as facilitators while resource scarcity, lack of skilled staff, high client loads, lack of preventive oncology policy, territorial disputes, and lack of national guidelines were identified as barriers to the services. The evidence suggests that CCS can be integrated into healthcare facilities in LMICs, in various primary care services, including HIV clinics, reproductive and sexual health clinics, well-baby clinics, maternal health clinics, and gynecology OPDs. However, barriers include limited health system capacity, workload, waiting times, and lack of coordination. Addressing these gaps could strengthen the successful integration of CCS into primary care services and improve cervical cancer prevention and treatment outcomes.