Radiological Imaging Indicators of Survival in Trachea and Lung Cancers: A Global Perspective

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Ibraheem, Nathier A. Ibrahim, Qais Lateef Atea This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6558920/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Respiratory system cancer has long been a global health issue, with tracheal, bronchial, and lung cancers ranking second in incidence and first in mortality worldwide. This study aims to analyse recent data to understand the global burden, trends, and risk factors for respiratory system cancer, facilitating Improved prevention and treatment strategies. Methods We conducted a study on respiratory system cancer using data from the Global Cancer Observatory and the Cancer Incidence in Five Continents databases. We collected information on the incidence of cancers, risk factors, the final estimates of incidence and mortality rates were multiplied with population data from the United Nations Development Program. The age standardized rates were generated by GLOBOCAN using the world standard population. Results with 2,481,000 cases and 1,817,000 related deaths, trachea and lung cancer had the highest incidence. Asia had the highest incidence and mortality rates of cancer in 2023, with 1,566,000 new cases and 1,142,000 deaths. According to UN regions, Eastern Asia had the highest age-standardized incidence and mortality rates (per 100,000 people) in 2022, at 39.4 and 25.1, respectively. However, there were notable regional differences in the cancer burden, with the lowest rates observed in Western Africa (2.1 and 2.0), Middle Africa (1.3 and 2.2), and Eastern Africa (3.2 and 3.0). In crude incidence and mortality rates Eastern Asia had the highest rates, with 76.9 incidence and 52.7 mortality while the lowest crude rates occurring in Eastern Africa (1.6 and 1.5), Middle Africa (1.0 and 1.0), and Western Africa (1.0 and 0.9). China reported 1,610,000 incidence cases and 733,000 deaths from trachea and lung cancer in 2022, the most of any country. Conclusion In summary, this study supports the role of lung lesions as useful imaging biomarkers by highlighting their prognostic significance in lung disease. The results emphasize that in order to enhance patient outcomes, clinical workflows must integrate cutting-edge imaging methods and AI-driven analysis. Lung cancer bronchial Risk factor computed tomography Artificial Intelligence Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 1. Introduction Respiratory system cancer has long been a global health issue, with tracheal, bronchial, and lung cancers (TBL cancers) ranking second in incidence and first in mortality worldwide [ 1 ]. Despite extensive research into its epidemiology, the global burden of TBL cancers continued to increase, exacerbated by factors such as smoking prevalence, aging population, and environmental pollutants [ 2 – 6 ]. While studies have delineated the overall global impact of TBL cancers, regional disparities in incidence, prevalence, and mortality rates are evident [ 7 – 9 ], underscoring the need for localized data to inform healthcare strategies. According to GLOBOCAN 2020 (global cancer statistics), it is estimated that 2.2 million new cases of lung cancer were diagnosed worldwide in the year 2020. It is the most common cancer in men (14.3% of all cancers), and the third most common cancer in women (after breast cancer and colorectal cancer) with an incidence of about 8.4% of all cancers [ 10 ]. Moreover, the age-standardized incidence rates per 100 000 vary by sex and by region of the world, ranging from 2.8 in West Africa to 41.7 in Western Europe and 51.6 in Micronesia/Polynesia for men, and from 1.8 in West Africa to 25 in Western Europe and 30.1 in North America for women [ 10 ]. While high-income countries report 5-year survival rates of 20–25%, low-resource regions like West Africa struggle with rates below 10%. These gaps primarily stem from late diagnoses and limited access to advanced imaging. Although the majority of patients present at an advanced stage, those with early-stage lung cancer may be treated with potentially curative intent. Therefore, the importance of early diagnosis and an appropriate radiological staging cannot be underestimated. Moreover, imaging plays a fundamental role in the diagnosis and staging of patients with lung cancer. In everyday practice, combination of several imaging studies, namely conventional chest radiography, chest computed tomography (CT), magnetic resonance imaging (MRI) and nuclear medicine techniques, mainly positron emission tomography (PET), is needed to ensure the detection, characterization, staging and follow-up of lung cancer [ 11 ]. Ultrasound- and CT-guided interventions are minimally invasive and established methods for the diagnosis of pulmonary lesions. Now a day, Artificial intelligence uses the pathological anatomy of lung and bronchial cancer and can detect some vital signs to help monitor cancer and detect subtle changes that are difficult for the Attending Physician to see. The accuracy of the diagnosis made using the artificial intelligence system has increased compared to the diagnosis made by the video reading expert alone, and the time spent reading has been significantly reduced [ 12 ]. In this review, the use of these imaging techniques in lung cancer management will be discussed and also to better understand the TBL cancer burden among geographical locations, age groups, and sexes, we conducted various subgroup analyses to assess the burden and variation trends of TBL cancer on the basis of data from the GLOBOCAN 2022 study. 1.1 Baseline Radiological Examination: Chest X-Ray and Computed Tomography Role of Chest X-Ray in Lung Cancer Evaluation (CXR) The first imaging modality used in the diagnostic process for suspected lung cancer is a chest X-ray (CXR). Its historical prominence in early lung cancer detection can be attributed to its cost-effectiveness, low radiation exposure, technical feasibility, and widespread availability. Although CXR is still a useful screening tool, it has a low sensitivity for identifying early-stage cancers, frequently requiring additional imaging modalities for a thorough evaluation. On CXR, lung tumors may appear as central or peripheral masses. Radiographic presentation can mimic chronic airspace disease, even in cases of in situ adenocarcinoma [ 13 ]. Mediastinal invasion, bronchial blockage, or enlarged hilar lymph nodes are common symptoms of central neoplasms, which can cause partial or total lung collapse. Furthermore, tumor visibility may be obscured by parenchymal consolidation or superimposed infections, which occasionally act as the first radiological sign of an underlying malignancy [ 14 ]. The limited sensitivity of CXR in early lung cancer detection in comparison to low-dose computed tomography (LDCT) has been confirmed by recent studies. The need for more sensitive imaging methods in high-risk populations was highlighted by the National Lung Screening Trial (NLST), which showed that LDCT screening dramatically decreased lung cancer mortality when compared to CXR [ 15 ]. Additionally, CXR analysis aided by artificial intelligence (AI) has demonstrated promise in improving the detection of subtle lung abnormalities, which could lead to higher rates of early diagnosis [ 16 ]. Notwithstanding these developments, CXR is still used as a screening tool to direct additional diagnostic tests when anomalies are found. Computed Tomography for Lung Cancer Staging and Management The next step for thorough staging when CXR raises suspicions of malignancy is contrast-enhanced CT. CT plays a crucial role in identifying anomalies, describing lung lesions, and determining the severity of the disease based on symptoms like fever, coughing, or chest pain. Furthermore, CT is essential for radiation therapy planning and treatment response monitoring [ 17 ]. According to recent studies, CT plays a major role in determining the invasiveness of local tumors; its sensitivity ranges from 62–93%, while CXR's is only 1-2.7% [ 18 ]. But when it comes to assessing mediastinal and thoracic pleural invasion, CT has limitations [ 19 ]. According to longitudinal nodule assessment, volumetric growth—a crucial sign of malignancy—significantly correlates with even slight increases in diameter. The volume doubles with a 26% increase in lesion diameter, and the volume doubles with a doubling of diameter. On the other hand, the lesion is probably benign and may have an inflammatory or infectious origin if the nodule volume doubles in less than seven days [ 20 ]. Evaluation of Lymph Nodes and Distant Metastases with CT Traditionally, the evaluation of lymph node involvement in CT is based on size criteria, with nodes deemed suspicious when their short axis surpasses 10 mm [ 15 – 17 ]. However, CT sensitivity is only about 57% reduced by lymphadenopathy smaller than 10 mm. According to a recent meta-analysis, CT has predictive values, sensitivity, specificity, and accuracy of 33–75%, 66–90%, 65–79%, 46–55%, and 68–85%, respectively, for staging mediastinal lymph node involvement in lung cancer [ 21 ]. Staging CT also involves examining the upper abdomen to determine whether there are any distant metastases, especially in the liver and adrenal glands. 2. Methods 2.1 Data resources Data for this study were drawn from GLOBOCAN 2022 estimates of IARC that use data from cancer registries across the world [ 22 ]. We utilized estimates pertaining to code C33-34 of the International Classification of Disease (10th version). IARC produced GLOBOCAN estimates by first generating incidence and mortality rates using cancer registry data- population-based, local, and neighboring countries—as per data availability in different countries and by applying short-term prediction models and the mortality-to incidence ratio [ 23 ]. The final estimates of incidence and mortality rates were multiplied with population data from the United Nations Development Program (UNDP). The age standardized rates were generated by GLOBOCAN using the world standard population proposed by Segi and Doll [ 23 ]. 2.2 Statistical analyses We computed age-standardized rates (ASRs) and their estimated annual percentage change (EAPCs). The ASR trends over a given time period are described by the EAPC. It is assumed that the natural logarithm of ASR increases linearly with time [ 24 ], $$\:Y\:=\:\alpha\:\:+\:\beta\:X\:+\:\epsilon\:$$ 1 Where X is the year, ε is the error term, and Y is ln (ASR). The positive or negative ASR trends are represented by β in this formula. The EAPC was computed as follows: \(\:\:EAPC\:=\:100\:\times\:\:\left(exp\right(\beta\:)-1)\) (2) The linear model could be used to determine its 95% CI. When the EAPC and lower CI limit are positive, the ASR exhibits an upward trend. On the other hand, ASR exhibits a declining trend when the EAPC and upper CI limit are negative. To identify the possible factors influencing ASRs, we also assessed the correlation between SDI and ASRs in the various regions. Updated estimates of worldwide cancer from UN publications serve as the basis for the Global Cancers Indicators. Mortality and Incidence by Continent, by dividing the number of deaths for a particular cancer type and for a given year by the number of newly diagnosed cases in that same year, it determines the ratio of deaths to incidence. The number of men and women for a particular cancer is represented by the number of men and women by continent. The Crude Rate is computed by dividing the total number of cases by the population of the chosen region during the middle of the year, then multiplying the result by a constant and then by 10. Estimated Cumulative Risk, represents the percentage of new cases during a period in which the denominator is the initial number of infected people, is calculated from the following equation [ 25 ]: $$\:{R}_{Cumulative}=\frac{{N}_{d+newcases}}{{N}_{all\:persons\:at\:risk}}$$ 3 Where; \(\:{N}_{d+newcases}\to\:\:\) The number of new cases of the disease under observation during a given period \(\:\:{N}_{all\:persons\:at\:risk}\to\:\:\) The number of all persons at risk for getting ill with the disease under observation at the beginning of Based on regional scientific and economic advancements, these countries have the highest rates of injuries and fatalities and are those that have not placed a high priority on cancer treatment. The most frequent cancer cases in Iraq are shown in Cancer Sites and Survival Rate, along with the survival rate for each type of cancer. It should be noted that the survival rate is calculated [ 24 ]: Survival Rate = 1 – Mortality Rate (4) 3. Results 3.1 Global Cancers Indicators of TBL The ten most prevalent cancer types globally in terms of incidence and mortality in 2023 are shown in Fig. 3. With 2,481,000 cases and 1,817,000 related deaths, trachea and lung cancer had the highest incidence. Stomach cancer came in third place with 968,000 cases and 661,000 deaths, followed by colorectal cancer with 1,926,000 cases and 903,000 deaths. With 322,000 cases and 249,000 fatalities, brain and central nervous system cancers rank tenth in the incidence pattern, which shows a downward trend. Notably, thyroid cancer had the lowest mortality rate (47,000 deaths) despite ranking fifth in incidence (821,000 cases). Thyroid cancer had the highest survival rate (94%), followed by colorectal cancer (53%), kidney cancer (64%), and NHL (55%). Leukaemia (37%), stomach (32%), trachea and lung (27%), and brain CNS (23%), had significantly lower survival rates; the lowest survival rates were for liver (12%) and pancreatic cancer (8%) Figure (4) [ 26 ]. 3.2 Number of Incidence and Mortality by Continents (000) Asia had the highest incidence and mortality rates of cancer in 2023, with 1,566,000 new cases and 1,142,000 deaths. Europe followed with 484,000 cases and 376,000 deaths. North America ranked third with 257,000 cases and 151,000 deaths. In contrast to Latin America and the Caribbean, where there were 90,000 deaths and 105,000 new cases, Africa had 50,000 cases and 46,000 deaths. Oceania had the lowest burden, with 12,000 fatalities and 18,000 incidence cases (Figs. 5&6) [ 26 ]. 3.3 Age Standardized Rate According to UN regions, Eastern Asia had the highest age-standardized incidence and mortality rates (per 100,000 people) in 2022, at 39.4 and 25.1, respectively. With incidence and mortality rates of 37.5 and 23.7, Polynesia came in second. With rates of 31.9 and 17.2, North America came in third, followed by Micronesia with rates of 31.6 and 30.5. With an incidence rate of 31.2 and a mortality rate of 22.1, Western Europe likewise displayed comparatively high rates. However, there were notable regional differences in the cancer burden, with the lowest rates observed in Western Africa (2.1 and 2.0), Middle Africa (1.3 and 2.2), and Eastern Africa (3.2 and 3.0) (Fig. 7. [ 26 ]. 3.4 Crude Rate In 2022, crude incidence and mortality rates (per 100,000) were relatively comparable across several UN regions (Fig. 8). Eastern Asia had the highest rates, with 76.9 incidence and 52.7 mortality. Next were Southern Europe (70.3 and 56.8) and Western Europe (74.1 and 55.7). North America reported somewhat lower rates (68.9 and 40.4), although Northern Europe (68.7 and 49.3) and Eastern Europe (54.2 and 43.5) also showed significant burden. The cancer burden clearly varies by region, with the lowest crude rates occurring in Eastern Africa (1.6 and 1.5), Middle Africa (1.0 and 1.0), and Western Africa (1.0 and 0.9) [ 26 ]. 3.5 Estimated Cumulative Risk In 2022, there were notable regional variations in the estimated cumulative risk (%) for cancer incidence and mortality (both sexes, ages 0–74) across the United Nations (Fig. 9). With an incidence rate of 4.8% and a mortality rate of 4.0%, Polynesia had the highest cumulative risk. With incidence and mortality rates of 4.7% and 2.9%, respectively, Eastern Asia came next. The cumulative risk was 4.0% for incidence and 2.0% for mortality in North America and 3.9% and 2.7%, respectively, in Western Europe. At 3.6%, Micronesia's incidence and mortality risks were equal. Other noteworthy regions were Northern Europe (3.5% and 2.2%), Southern Europe (3.5% and 2.6%), and Eastern Europe (3.6% and 2.8%). On the other hand, there were notable regional differences in cancer risk, with the lowest cumulative risks found in Western Africa (0.24% and 0.2%), Middle Africa (0.28% and 0.3%), and Eastern Africa (0.38% and 0.4%) [ 26 ]. 3.6 Highest Incidence & Mortality Countries China reported 1,610,000 incidence cases and 733,000 deaths from trachea and lung cancer in 2022, the most of any country. Japan had 137,000 cases and 83,000 fatalities, followed by the United States with 226,000 cases and 128,000 fatalities. Russia reported 70,000 cases and 52,000 deaths, while India reported 82,000 cases and 75,000 deaths. Italy reported 44,000 cases and 36,000 deaths, France had 50,000 cases and 37,000 deaths, and Germany had 62,000 cases and 48,000 deaths among European nations. Brazil also reported 38,000 fatalities and 44,000 cases. There were 1,817,000 fatalities and 2,481,000 cases of lung and trachea cancer worldwide (Fig. 10). Countries' survival rates differed significantly. The United States had the highest survival rate (43%), followed by China (31%), and Japan (39%). The survival rate was 26% for France and Russia, 23% for Germany, 18% for Italy, and 14% for Brazil. With a 9% survival rate, India had the lowest rate globally, indicating notable differences in results (Fig. 11) [ 26 ]. 3.7 Cancers Sites and Survival Rate in Iraq Over the past 50 years, Iraq has seen challenging living conditions as a result of wars and conflicts, which have had a significant impact on the environment and public health. Numerous places have seen a rise in cancer cases as a result of these illnesses, putting a heavy burden on the healthcare system. Breast cancer, one of the most prevalent cancers in Iraq, killed 3,372 people and had the highest incidence (8,626 cases). Lung and tracheal cancers came next, with 2,814 cases and 2,613 fatalities. Leukaemia had 2,258 cases and 1,694 deaths, while colorectal cancer came in third with 2,320 cases and 1,438 fatalities. NHL (1,930 cases and 1,013 deaths), brain and central nervous system (1,769 cases and 1,485 deaths), thyroid (1,618 cases and 231 deaths), stomach (1,267 cases and 1,060 deaths), and prostate (1,187 cases and 249 deaths) were among the other common cancers (Fig. 12). Thyroid cancer had the highest survival rate (86%), followed by prostate cancer (79%), according to Fig. 11. The survival rate for bladder cancer was 51%, whereas the survival rate for breast cancer was 61%. The survival rate was 48% for NHL and 38% for colorectal cancer. The survival rate for brain and stomach CNS cancers was 16%, whereas the survival rate for leukaemia was 25%. At 7%, the survival rate for lung and trachea cancer was the lowest and significantly below the global average (Fig. 13) [ 26 ]. 4. Discussion Significant regional disparities exist in the global landscape of cancer incidence and mortality, which are frequently influenced by variations in healthcare infrastructure, socioeconomic conditions, environmental exposures, and health behaviors. These disparities are evident not only in the overall burden of cancer but also in the types of cancer that are most common in various regions, such as Eastern Asia and Polynesia, where the incidence of certain cancers, such as lung and trachea cancer, is exceptionally high. These disparities are frequently attributed to environmental factors, such as smoking, air pollution, and industrial exposure. Research has also demonstrated that urbanization in developing nations, especially China, has increased the incidence of lung cancer due to increased exposure to both indoor and outdoor air pollutants [ 27 ]. On the other hand, lower cancer incidence rates are reported in places like Sub-Saharan Africa. This is probably because these areas have fewer known carcinogens and fewer tools for diagnosing cancer [ 28 ]. There are regional differences in cancer mortality rates as well. Because they have greater access to early detection, cutting-edge treatments, and comprehensive healthcare systems, high-income nations—such as those in North America and Western Europe—have comparatively lower mortality rates. On the other hand, a number of LMICs continue to experience greater mortality rates, primarily as a result of delayed diagnosis and restricted access to efficient treatment alternatives. This is particularly true for cancers where early detection is essential to improving survival outcomes, like colorectal and lung cancers [ 29 ]. Cancer survival rates differ significantly between geographical areas, reflecting variations in early diagnosis and treatment effectiveness in addition to access to healthcare. Generally speaking, nations with strong healthcare systems, cutting-edge diagnostic tools, and efficient treatment plans have high survival rates. Lower survival rates in under resourced areas, however, highlight serious deficiencies in the infrastructure supporting cancer care and access to healthcare. The survival rates for cancers like thyroid and breast cancer are higher in nations like the US and Japan. For example, the availability of targeted therapies and improvements in screening programs (like mammography) have led to a steady increase in the survival rate for breast cancer in the United States [ 30 ]. This is especially true for localized cancers, where the prognosis is greatly improved by early diagnosis. Similar to this, improved survival outcomes have been a result of Japan's extensive public health system, which includes nationwide screening programs for stomach and colorectal cancers [ 31 ]. On the other hand, for a variety of reasons, nations such as Iraq have significantly lower survival rates for some cancers, most notably lung cancer. Poor survival outcomes are caused by a lack of early detection systems, restricted access to oncology specialists, and a shortage of cutting-edge treatments like chemotherapy and immunotherapy. The survival rate for lung and trachea cancer is 7%, which is much lower than the global average of about 18%, as was noted in Iraq [ 32 ]. Because of its affordability and ease of use, X-ray imaging is still the most popular first diagnostic method for lung conditions. It is especially helpful in identifying masses, pleural effusions, and lung consolidations. But because of its shortcomings in identifying lung diseases in their early stages, CT scans are required for a more thorough assessment. A more thorough evaluation of lung abnormalities, such as nodules, fibrosis, and tumor staging, is made possible by CT imaging's superior spatial resolution. CT is a vital tool in the treatment of lung diseases because of its high sensitivity in identifying minute parenchymal alterations [ 33 , 34 ]. Furthermore, the predictive ability of imaging-based biomarkers may be improved by incorporating artificial intelligence (AI) into radiological analysis. More accurate risk stratification could be made possible by AI-driven models, which would further customize patient care plans. To guarantee the dependability and clinical utility of AI applications, future studies should concentrate on validating them in larger, multi-center cohorts. 5. Conclusion In summary, this study supports the role of lung lesions as useful imaging biomarkers by highlighting their prognostic significance in lung disease. The results emphasize that in order to enhance patient outcomes, clinical workflows must integrate cutting-edge imaging methods and AI-driven analysis. Furthermore, a key component of public health strategies continues to be addressing regional differences in disease burden. To further improve diagnostic and prognostic capabilities in the management of lung diseases, future research should focus on improving predictive models and investigating innovative imaging techniques. Declarations Acknowledgements Not applicable. Authors’ contributions SI and NI conceptualized and supervised this study. SI and QA are responsible for radiology images interpretation, NI data curation and formal analysis. SI, NI and QA drafted the manuscript. SI, and NI reviewed and revised the manuscript. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Data availability Datasets used for this study were publicly available from the Global Cancer Observatory and the Cancer Incidence in Five Continents Plus. Competing interests The authors declare no competing interests. References Global R, National Burden of Respiratory Tract Cancers and Associated Risk Factors From 1990. to 2019: A Systematic Analysis for the Global Burden of Disease Study 2019. Lancet Respiratory Med. 2021;9(9):1030–49. https://doi.org/10.1016/s2213 -2600 . Safiri S, Sohrabi MR, Carson-Chahhoud K, Bettampadi D, Taghizadieh A, Almasi-Hashiani A, Kolahi AA. Burden of tracheal, bronchus, and lung cancer and its attributable risk factors in 204 countries and territories, 1990 to 2019. J Thorac Oncol. 2021;16(6):945–59. Murray L, Aravkin CY, Zheng A, Khatab P, Cristiana K, Ashkan A, A., Stephen SL. 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Bi S, Yuan Q, Dai Z, Sun X, Wan Sohaimi WFB, Yusoff B, A. L. Advances in CT-based lung function imaging for thoracic radiotherapy. Front Oncol. 2024;14:1414337. Kim J, Kim KH. Role of chest radiographs in early lung cancer detection. Translational lung cancer Res. 2020;9(3):522–31. Obuchowicz R, Lasek J, Wodziński M, Piórkowski A, Strzelecki M, Nurzynska K. Artif Intelligence-Empowered Radiology—Current Status Crit Rev Diagnostics. 2025;15(3):282. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6558920","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":451986820,"identity":"a6c7f00e-3930-44de-8bb4-05e94f0783ef","order_by":0,"name":"Shahad A. Ibraheem","email":"data:image/png;base64,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","orcid":"","institution":"Al Turath University College","correspondingAuthor":true,"prefix":"","firstName":"Shahad","middleName":"A.","lastName":"Ibraheem","suffix":""},{"id":451986821,"identity":"02446157-11be-457c-9893-b7a3fc0c5823","order_by":1,"name":"Nathier A. Ibrahim","email":"","orcid":"","institution":"Al Turath University College","correspondingAuthor":false,"prefix":"","firstName":"Nathier","middleName":"A.","lastName":"Ibrahim","suffix":""},{"id":451986824,"identity":"7cef045d-15bd-4d6e-8164-86a085e6a881","order_by":2,"name":"Qais Lateef Atea","email":"","orcid":"","institution":"Al Turath University College","correspondingAuthor":false,"prefix":"","firstName":"Qais","middleName":"Lateef","lastName":"Atea","suffix":""}],"badges":[],"createdAt":"2025-04-29 18:38:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6558920/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6558920/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82353053,"identity":"1f1ccf4c-434d-49cd-a83a-dce54ee37f56","added_by":"auto","created_at":"2025-05-09 11:05:00","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":33225,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Chest X-ray shows a right hilar mass with atelectasis of the right upper lobe in noted with loss of volume of the right hemi thorax. (B-C) CT shows a bronchogenic carcinoma with atelectasis of the upper middle lobe. The mass encases the right main bronchus and hilar and mediastinal nodal masses are noted. A moderate pericardial effusion is noted. Bilateral adrenal metastases are present.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6558920/v1/33e5d93ec910014a60d13845.jpg"},{"id":82355074,"identity":"b41a47ba-7ed6-4d41-bff7-b85bc3963fb3","added_by":"auto","created_at":"2025-05-09 11:13:00","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":32236,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Chest X-ray shows a left upper lobe pulmonary nodule. (B-C) CT shows a lung mass centered on the superior segment of the left lower lobe, with invasion through the oblique fissure into upper lobe and also into the mediastinum. Centrilobular and paraseptal emphysema mass is suspicious for primary bronchogenic carcinoma.\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6558920/v1/852a926cce67f8773209a7a0.jpg"},{"id":82353054,"identity":"f908291a-e7b9-4af0-b40c-67a3466a8dac","added_by":"auto","created_at":"2025-05-09 11:05:00","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":116459,"visible":true,"origin":"","legend":"\u003cp\u003eTop ten global cancers incidence \u0026amp; mortality both sexes (000)[26]\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6558920/v1/bf207d24e45a6ad8b6a5ba05.jpg"},{"id":82360080,"identity":"9a16f776-4249-43b5-95b2-ca3ba976e63a","added_by":"auto","created_at":"2025-05-09 11:37:00","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":122297,"visible":true,"origin":"","legend":"\u003cp\u003eSurvival Rate\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6558920/v1/b04b9f20ea8de1734b330933.jpg"},{"id":82357034,"identity":"648c44ec-2b31-4958-b152-017b700172a2","added_by":"auto","created_at":"2025-05-09 11:21:00","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":168678,"visible":true,"origin":"","legend":"\u003cp\u003eNumber of Male \u0026amp; Female by Continents (000), 2022[26]\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6558920/v1/8977cfb520035646017b724f.jpg"},{"id":82355079,"identity":"4552763d-2789-4766-8c1d-955ce4e0544a","added_by":"auto","created_at":"2025-05-09 11:13:00","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":126802,"visible":true,"origin":"","legend":"\u003cp\u003eSurvival Rate\u003c/p\u003e","description":"","filename":"Picture6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6558920/v1/1779d5e12bc57f1c5550405e.jpg"},{"id":82359232,"identity":"6cb6a596-30dd-4295-9fe0-9493df5cf69d","added_by":"auto","created_at":"2025-05-09 11:29:00","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":272879,"visible":true,"origin":"","legend":"\u003cp\u003eAge Standardized Rate, 2022, UN Regions[26]\u003c/p\u003e","description":"","filename":"Picture7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6558920/v1/d0fd8dcdebb0f2899960fb18.jpg"},{"id":82353068,"identity":"1b7c23df-9465-44cc-8e16-c4986264026b","added_by":"auto","created_at":"2025-05-09 11:05:00","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":211737,"visible":true,"origin":"","legend":"\u003cp\u003eCrude Rate, 2022, UN Regions [26]\u003c/p\u003e","description":"","filename":"Picture8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6558920/v1/d7017eb962d31c71f815995c.jpg"},{"id":82360081,"identity":"00e973ae-b03e-483a-9618-91bd669b5b0e","added_by":"auto","created_at":"2025-05-09 11:37:00","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":229918,"visible":true,"origin":"","legend":"\u003cp\u003eCumulative Rate, 2022, UN Regions [26]\u003c/p\u003e","description":"","filename":"Picture9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6558920/v1/31bcfff22ecaf6d4ff6d2854.jpg"},{"id":82353065,"identity":"0c300514-e08b-4fa7-997c-b2483857bbe2","added_by":"auto","created_at":"2025-05-09 11:05:00","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":100011,"visible":true,"origin":"","legend":"\u003cp\u003eHighest Incidence \u0026amp; Mortality Countries [26]\u003c/p\u003e","description":"","filename":"Picture10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6558920/v1/9d56d520af895ffbc8098fc8.jpg"},{"id":82353069,"identity":"aa23944a-5798-4683-b41b-6eb54c3c060e","added_by":"auto","created_at":"2025-05-09 11:05:00","extension":"jpg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":102295,"visible":true,"origin":"","legend":"\u003cp\u003eSurvival Rate\u003c/p\u003e","description":"","filename":"Picture11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6558920/v1/0cd298a548ee99974a438cce.jpg"},{"id":82355086,"identity":"43e36ac3-88cb-4721-a8d0-ef9643aa10a8","added_by":"auto","created_at":"2025-05-09 11:13:00","extension":"jpg","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":161216,"visible":true,"origin":"","legend":"\u003cp\u003eIncidence \u0026amp; Mortality Cancers in Iraq [26]\u003c/p\u003e","description":"","filename":"Picture12.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6558920/v1/5d221bc475ba30e6c3d355f9.jpg"},{"id":82353088,"identity":"46244708-247e-488b-84b1-ad114aaa5f89","added_by":"auto","created_at":"2025-05-09 11:05:00","extension":"jpg","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":114245,"visible":true,"origin":"","legend":"\u003cp\u003eSurvival Rate in Iraq by cancers sites\u003c/p\u003e","description":"","filename":"Picture13.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6558920/v1/824fc0eae4b85641625d8c48.jpg"},{"id":83115212,"identity":"6a1b41f7-c2a9-4e63-8e14-1c20957f3e54","added_by":"auto","created_at":"2025-05-20 08:02:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2451507,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6558920/v1/9e0c9427-1f15-45dd-903c-e869d7b97c27.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Radiological Imaging Indicators of Survival in Trachea and Lung Cancers: A Global Perspective","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eRespiratory system cancer has long been a global health issue, with tracheal, bronchial, and lung cancers (TBL cancers) ranking second in incidence and first in mortality worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Despite extensive research into its epidemiology, the global burden of TBL cancers continued to increase, exacerbated by factors such as smoking prevalence, aging population, and environmental pollutants [\u003cspan additionalcitationids=\"CR3 CR4 CR5\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. While studies have delineated the overall global impact of TBL cancers, regional disparities in incidence, prevalence, and mortality rates are evident [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], underscoring the need for localized data to inform healthcare strategies.\u003c/p\u003e \u003cp\u003eAccording to GLOBOCAN 2020 (global cancer statistics), it is estimated that 2.2\u0026nbsp;million new cases of lung cancer were diagnosed worldwide in the year 2020. It is the most common cancer in men (14.3% of all cancers), and the third most common cancer in women (after breast cancer and colorectal cancer) with an incidence of about 8.4% of all cancers [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Moreover, the age-standardized incidence rates per 100 000 vary by sex and by region of the world, ranging from 2.8 in West Africa to 41.7 in Western Europe and 51.6 in Micronesia/Polynesia for men, and from 1.8 in West Africa to 25 in Western Europe and 30.1 in North America for women [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. While high-income countries report 5-year survival rates of 20\u0026ndash;25%, low-resource regions like West Africa struggle with rates below 10%. These gaps primarily stem from late diagnoses and limited access to advanced imaging.\u003c/p\u003e \u003cp\u003eAlthough the majority of patients present at an advanced stage, those with early-stage lung cancer may be treated with potentially curative intent. Therefore, the importance of early diagnosis and an appropriate radiological staging cannot be underestimated. Moreover, imaging plays a fundamental role in the diagnosis and staging of patients with lung cancer. In everyday practice, combination of several imaging studies, namely conventional chest radiography, chest computed tomography (CT), magnetic resonance imaging (MRI) and nuclear medicine techniques, mainly positron emission tomography (PET), is needed to ensure the detection, characterization, staging and follow-up of lung cancer [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Ultrasound- and CT-guided interventions are minimally invasive and established methods for the diagnosis of pulmonary lesions. Now a day, Artificial intelligence uses the pathological anatomy of lung and bronchial cancer and can detect some vital signs to help monitor cancer and detect subtle changes that are difficult for the Attending Physician to see. The accuracy of the diagnosis made using the artificial intelligence system has increased compared to the diagnosis made by the video reading expert alone, and the time spent reading has been significantly reduced [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In this review, the use of these imaging techniques in lung cancer management will be discussed and also to better understand the TBL cancer burden among geographical locations, age groups, and sexes, we conducted various subgroup analyses to assess the burden and variation trends of TBL cancer on the basis of data from the GLOBOCAN 2022 study.\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e1.1 Baseline Radiological Examination: Chest X-Ray and Computed Tomography\u003c/h2\u003e \u003cp\u003e \u003cb\u003eRole of Chest X-Ray in Lung Cancer Evaluation (CXR)\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe first imaging modality used in the diagnostic process for suspected lung cancer is a chest X-ray (CXR). Its historical prominence in early lung cancer detection can be attributed to its cost-effectiveness, low radiation exposure, technical feasibility, and widespread availability. Although CXR is still a useful screening tool, it has a low sensitivity for identifying early-stage cancers, frequently requiring additional imaging modalities for a thorough evaluation. On CXR, lung tumors may appear as central or peripheral masses. Radiographic presentation can mimic chronic airspace disease, even in cases of in situ adenocarcinoma [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Mediastinal invasion, bronchial blockage, or enlarged hilar lymph nodes are common symptoms of central neoplasms, which can cause partial or total lung collapse. Furthermore, tumor visibility may be obscured by parenchymal consolidation or superimposed infections, which occasionally act as the first radiological sign of an underlying malignancy [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The limited sensitivity of CXR in early lung cancer detection in comparison to low-dose computed tomography (LDCT) has been confirmed by recent studies. The need for more sensitive imaging methods in high-risk populations was highlighted by the National Lung Screening Trial (NLST), which showed that LDCT screening dramatically decreased lung cancer mortality when compared to CXR [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Additionally, CXR analysis aided by artificial intelligence (AI) has demonstrated promise in improving the detection of subtle lung abnormalities, which could lead to higher rates of early diagnosis [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Notwithstanding these developments, CXR is still used as a screening tool to direct additional diagnostic tests when anomalies are found.\u003c/p\u003e \u003cp\u003e \u003cb\u003eComputed Tomography for Lung Cancer Staging and Management\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe next step for thorough staging when CXR raises suspicions of malignancy is contrast-enhanced CT. CT plays a crucial role in identifying anomalies, describing lung lesions, and determining the severity of the disease based on symptoms like fever, coughing, or chest pain. Furthermore, CT is essential for radiation therapy planning and treatment response monitoring [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. According to recent studies, CT plays a major role in determining the invasiveness of local tumors; its sensitivity ranges from 62\u0026ndash;93%, while CXR's is only 1-2.7% [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. But when it comes to assessing mediastinal and thoracic pleural invasion, CT has limitations [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. According to longitudinal nodule assessment, volumetric growth\u0026mdash;a crucial sign of malignancy\u0026mdash;significantly correlates with even slight increases in diameter. The volume doubles with a 26% increase in lesion diameter, and the volume doubles with a doubling of diameter. On the other hand, the lesion is probably benign and may have an inflammatory or infectious origin if the nodule volume doubles in less than seven days [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Evaluation of Lymph Nodes and Distant Metastases with CT Traditionally, the evaluation of lymph node involvement in CT is based on size criteria, with nodes deemed suspicious when their short axis surpasses 10 mm [\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. However, CT sensitivity is only about 57% reduced by lymphadenopathy smaller than 10 mm. According to a recent meta-analysis, CT has predictive values, sensitivity, specificity, and accuracy of 33\u0026ndash;75%, 66\u0026ndash;90%, 65\u0026ndash;79%, 46\u0026ndash;55%, and 68\u0026ndash;85%, respectively, for staging mediastinal lymph node involvement in lung cancer [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Staging CT also involves examining the upper abdomen to determine whether there are any distant metastases, especially in the liver and adrenal glands.\u003c/p\u003e "},{"header":"2. Methods","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data resources\u003c/h2\u003e \u003cp\u003eData for this study were drawn from GLOBOCAN 2022 estimates of IARC that use data from cancer registries across the world [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. We utilized estimates pertaining to code C33-34 of the International Classification of Disease (10th version). IARC produced GLOBOCAN estimates by first generating incidence and mortality rates using cancer registry data- population-based, local, and neighboring countries\u0026mdash;as per data availability in different countries and by applying short-term prediction models and the mortality-to incidence ratio [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The final estimates of incidence and mortality rates were multiplied with population data from the United Nations Development Program (UNDP). The age standardized rates were generated by GLOBOCAN using the world standard population proposed by Segi and Doll [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Statistical analyses\u003c/h2\u003e \u003cp\u003eWe computed age-standardized rates (ASRs) and their estimated annual percentage change (EAPCs). The ASR trends over a given time period are described by the EAPC. It is assumed that the natural logarithm of ASR increases linearly with time [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e],\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:Y\\:=\\:\\alpha\\:\\:+\\:\\beta\\:X\\:+\\:\\epsilon\\:$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere X is the year, ε is the error term, and Y is ln (ASR). The positive or negative ASR trends are represented by β in this formula.\u003c/p\u003e \u003cp\u003eThe EAPC was computed as follows: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:EAPC\\:=\\:100\\:\\times\\:\\:\\left(exp\\right(\\beta\\:)-1)\\)\u003c/span\u003e\u003c/span\u003e(2)\u003c/p\u003e \u003cp\u003eThe linear model could be used to determine its 95% CI. When the EAPC and lower CI limit are positive, the ASR exhibits an upward trend. On the other hand, ASR exhibits a declining trend when the EAPC and upper CI limit are negative. To identify the possible factors influencing ASRs, we also assessed the correlation between SDI and ASRs in the various regions.\u003c/p\u003e \u003cp\u003eUpdated estimates of worldwide cancer from UN publications serve as the basis for the Global Cancers Indicators. Mortality and Incidence by Continent, by dividing the number of deaths for a particular cancer type and for a given year by the number of newly diagnosed cases in that same year, it determines the ratio of deaths to incidence. The number of men and women for a particular cancer is represented by the number of men and women by continent. The Crude Rate is computed by dividing the total number of cases by the population of the chosen region during the middle of the year, then multiplying the result by a constant and then by 10. Estimated Cumulative Risk, represents the percentage of new cases during a period in which the denominator is the initial number of infected people, is calculated from the following equation [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]:\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:{R}_{Cumulative}=\\frac{{N}_{d+newcases}}{{N}_{all\\:persons\\:at\\:risk}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere;\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{N}_{d+newcases}\\to\\:\\:\\)\u003c/span\u003e \u003c/span\u003eThe number of new cases of the disease under observation during a given period\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:\\:{N}_{all\\:persons\\:at\\:risk}\\to\\:\\:\\)\u003c/span\u003e \u003c/span\u003eThe number of all persons at risk for getting ill with the disease under observation at the beginning of\u003c/p\u003e \u003cp\u003eBased on regional scientific and economic advancements, these countries have the highest rates of injuries and fatalities and are those that have not placed a high priority on cancer treatment. The most frequent cancer cases in Iraq are shown in Cancer Sites and Survival Rate, along with the survival rate for each type of cancer. It should be noted that the survival rate is calculated [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]:\u003c/p\u003e \u003cp\u003eSurvival Rate\u0026thinsp;=\u0026thinsp;1 \u0026ndash; Mortality Rate (4)\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Global Cancers Indicators of TBL\u003c/h2\u003e\n \u003cp\u003eThe ten most prevalent cancer types globally in terms of incidence and mortality in 2023 are shown in Fig.\u0026nbsp;3. With 2,481,000 cases and 1,817,000 related deaths, trachea and lung cancer had the highest incidence. Stomach cancer came in third place with 968,000 cases and 661,000 deaths, followed by colorectal cancer with 1,926,000 cases and 903,000 deaths. With 322,000 cases and 249,000 fatalities, brain and central nervous system cancers rank tenth in the incidence pattern, which shows a downward trend. Notably, thyroid cancer had the lowest mortality rate (47,000 deaths) despite ranking fifth in incidence (821,000 cases). Thyroid cancer had the highest survival rate (94%), followed by colorectal cancer (53%), kidney cancer (64%), and NHL (55%). Leukaemia (37%), stomach (32%), trachea and lung (27%), and brain CNS (23%), had significantly lower survival rates; the lowest survival rates were for liver (12%) and pancreatic cancer (8%) Figure (4) [\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Number of Incidence and Mortality by Continents (000)\u003c/h2\u003e\n \u003cp\u003eAsia had the highest incidence and mortality rates of cancer in 2023, with 1,566,000 new cases and 1,142,000 deaths. Europe followed with 484,000 cases and 376,000 deaths. North America ranked third with 257,000 cases and 151,000 deaths. In contrast to Latin America and the Caribbean, where there were 90,000 deaths and 105,000 new cases, Africa had 50,000 cases and 46,000 deaths. Oceania had the lowest burden, with 12,000 fatalities and 18,000 incidence cases (Figs.\u0026nbsp;5\u0026amp;6) [\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Age Standardized Rate\u003c/h2\u003e\n \u003cp\u003eAccording to UN regions, Eastern Asia had the highest age-standardized incidence and mortality rates (per 100,000 people) in 2022, at 39.4 and 25.1, respectively. With incidence and mortality rates of 37.5 and 23.7, Polynesia came in second. With rates of 31.9 and 17.2, North America came in third, followed by Micronesia with rates of 31.6 and 30.5. With an incidence rate of 31.2 and a mortality rate of 22.1, Western Europe likewise displayed comparatively high rates. However, there were notable regional differences in the cancer burden, with the lowest rates observed in Western Africa (2.1 and 2.0), Middle Africa (1.3 and 2.2), and Eastern Africa (3.2 and 3.0) (Fig.\u0026nbsp;7. [\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 Crude Rate\u003c/h2\u003e\n \u003cp\u003eIn 2022, crude incidence and mortality rates (per 100,000) were relatively comparable across several UN regions (Fig.\u0026nbsp;8). Eastern Asia had the highest rates, with 76.9 incidence and 52.7 mortality. Next were Southern Europe (70.3 and 56.8) and Western Europe (74.1 and 55.7). North America reported somewhat lower rates (68.9 and 40.4), although Northern Europe (68.7 and 49.3) and Eastern Europe (54.2 and 43.5) also showed significant burden. The cancer burden clearly varies by region, with the lowest crude rates occurring in Eastern Africa (1.6 and 1.5), Middle Africa (1.0 and 1.0), and Western Africa (1.0 and 0.9) [\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5 Estimated Cumulative Risk\u003c/h2\u003e\n \u003cp\u003eIn 2022, there were notable regional variations in the estimated cumulative risk (%) for cancer incidence and mortality (both sexes, ages 0\u0026ndash;74) across the United Nations (Fig.\u0026nbsp;9). With an incidence rate of 4.8% and a mortality rate of 4.0%, Polynesia had the highest cumulative risk. With incidence and mortality rates of 4.7% and 2.9%, respectively, Eastern Asia came next. The cumulative risk was 4.0% for incidence and 2.0% for mortality in North America and 3.9% and 2.7%, respectively, in Western Europe. At 3.6%, Micronesia\u0026apos;s incidence and mortality risks were equal. Other noteworthy regions were Northern Europe (3.5% and 2.2%), Southern Europe (3.5% and 2.6%), and Eastern Europe (3.6% and 2.8%). On the other hand, there were notable regional differences in cancer risk, with the lowest cumulative risks found in Western Africa (0.24% and 0.2%), Middle Africa (0.28% and 0.3%), and Eastern Africa (0.38% and 0.4%) [\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.6 Highest Incidence \u0026amp; Mortality Countries\u003c/h2\u003e\n \u003cp\u003eChina reported 1,610,000 incidence cases and 733,000 deaths from trachea and lung cancer in 2022, the most of any country. Japan had 137,000 cases and 83,000 fatalities, followed by the United States with 226,000 cases and 128,000 fatalities. Russia reported 70,000 cases and 52,000 deaths, while India reported 82,000 cases and 75,000 deaths. Italy reported 44,000 cases and 36,000 deaths, France had 50,000 cases and 37,000 deaths, and Germany had 62,000 cases and 48,000 deaths among European nations. Brazil also reported 38,000 fatalities and 44,000 cases. There were 1,817,000 fatalities and 2,481,000 cases of lung and trachea cancer worldwide (Fig.\u0026nbsp;10). Countries\u0026apos; survival rates differed significantly. The United States had the highest survival rate (43%), followed by China (31%), and Japan (39%). The survival rate was 26% for France and Russia, 23% for Germany, 18% for Italy, and 14% for Brazil. With a 9% survival rate, India had the lowest rate globally, indicating notable differences in results (Fig.\u0026nbsp;11) [\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e3.7 Cancers Sites and Survival Rate in Iraq\u003c/h2\u003e\n \u003cp\u003eOver the past 50 years, Iraq has seen challenging living conditions as a result of wars and conflicts, which have had a significant impact on the environment and public health. Numerous places have seen a rise in cancer cases as a result of these illnesses, putting a heavy burden on the healthcare system. Breast cancer, one of the most prevalent cancers in Iraq, killed 3,372 people and had the highest incidence (8,626 cases). Lung and tracheal cancers came next, with 2,814 cases and 2,613 fatalities. Leukaemia had 2,258 cases and 1,694 deaths, while colorectal cancer came in third with 2,320 cases and 1,438 fatalities. NHL (1,930 cases and 1,013 deaths), brain and central nervous system (1,769 cases and 1,485 deaths), thyroid (1,618 cases and 231 deaths), stomach (1,267 cases and 1,060 deaths), and prostate (1,187 cases and 249 deaths) were among the other common cancers (Fig.\u0026nbsp;12). Thyroid cancer had the highest survival rate (86%), followed by prostate cancer (79%), according to Fig.\u0026nbsp;11. The survival rate for bladder cancer was 51%, whereas the survival rate for breast cancer was 61%. The survival rate was 48% for NHL and 38% for colorectal cancer. The survival rate for brain and stomach CNS cancers was 16%, whereas the survival rate for leukaemia was 25%. At 7%, the survival rate for lung and trachea cancer was the lowest and significantly below the global average (Fig.\u0026nbsp;13) [\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eSignificant regional disparities exist in the global landscape of cancer incidence and mortality, which are frequently influenced by variations in healthcare infrastructure, socioeconomic conditions, environmental exposures, and health behaviors. These disparities are evident not only in the overall burden of cancer but also in the types of cancer that are most common in various regions, such as Eastern Asia and Polynesia, where the incidence of certain cancers, such as lung and trachea cancer, is exceptionally high. These disparities are frequently attributed to environmental factors, such as smoking, air pollution, and industrial exposure. Research has also demonstrated that urbanization in developing nations, especially China, has increased the incidence of lung cancer due to increased exposure to both indoor and outdoor air pollutants [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOn the other hand, lower cancer incidence rates are reported in places like Sub-Saharan Africa. This is probably because these areas have fewer known carcinogens and fewer tools for diagnosing cancer [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. There are regional differences in cancer mortality rates as well. Because they have greater access to early detection, cutting-edge treatments, and comprehensive healthcare systems, high-income nations\u0026mdash;such as those in North America and Western Europe\u0026mdash;have comparatively lower mortality rates. On the other hand, a number of LMICs continue to experience greater mortality rates, primarily as a result of delayed diagnosis and restricted access to efficient treatment alternatives. This is particularly true for cancers where early detection is essential to improving survival outcomes, like colorectal and lung cancers [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Cancer survival rates differ significantly between geographical areas, reflecting variations in early diagnosis and treatment effectiveness in addition to access to healthcare. Generally speaking, nations with strong healthcare systems, cutting-edge diagnostic tools, and efficient treatment plans have high survival rates. Lower survival rates in under resourced areas, however, highlight serious deficiencies in the infrastructure supporting cancer care and access to healthcare. The survival rates for cancers like thyroid and breast cancer are higher in nations like the US and Japan. For example, the availability of targeted therapies and improvements in screening programs (like mammography) have led to a steady increase in the survival rate for breast cancer in the United States [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. This is especially true for localized cancers, where the prognosis is greatly improved by early diagnosis. Similar to this, improved survival outcomes have been a result of Japan's extensive public health system, which includes nationwide screening programs for stomach and colorectal cancers [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. On the other hand, for a variety of reasons, nations such as Iraq have significantly lower survival rates for some cancers, most notably lung cancer. Poor survival outcomes are caused by a lack of early detection systems, restricted access to oncology specialists, and a shortage of cutting-edge treatments like chemotherapy and immunotherapy. The survival rate for lung and trachea cancer is 7%, which is much lower than the global average of about 18%, as was noted in Iraq [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Because of its affordability and ease of use, X-ray imaging is still the most popular first diagnostic method for lung conditions. It is especially helpful in identifying masses, pleural effusions, and lung consolidations. But because of its shortcomings in identifying lung diseases in their early stages, CT scans are required for a more thorough assessment. A more thorough evaluation of lung abnormalities, such as nodules, fibrosis, and tumor staging, is made possible by CT imaging's superior spatial resolution. CT is a vital tool in the treatment of lung diseases because of its high sensitivity in identifying minute parenchymal alterations [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Furthermore, the predictive ability of imaging-based biomarkers may be improved by incorporating artificial intelligence (AI) into radiological analysis. More accurate risk stratification could be made possible by AI-driven models, which would further customize patient care plans. To guarantee the dependability and clinical utility of AI applications, future studies should concentrate on validating them in larger, multi-center cohorts.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn summary, this study supports the role of lung lesions as useful imaging biomarkers by highlighting their prognostic significance in lung disease. The results emphasize that in order to enhance patient outcomes, clinical workflows must integrate cutting-edge imaging methods and AI-driven analysis. Furthermore, a key component of public health strategies continues to be addressing regional differences in disease burden. To further improve diagnostic and prognostic capabilities in the management of lung diseases, future research should focus on improving predictive models and investigating innovative imaging techniques.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSI and NI conceptualized and supervised this study. SI and QA are responsible for radiology images interpretation, NI data curation and formal analysis. SI, NI and QA drafted the manuscript. SI, and NI reviewed and revised the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDatasets used for this study were publicly available from the Global Cancer Observatory and the Cancer Incidence in Five Continents Plus.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGlobal R, National Burden of Respiratory Tract Cancers and Associated Risk Factors From 1990. to 2019: A Systematic Analysis for the Global Burden of Disease Study 2019. 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Cancer J Clin. 2023;73(1):17\u0026ndash;48.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBi S, Yuan Q, Dai Z, Sun X, Wan Sohaimi WFB, Yusoff B, A. L. Advances in CT-based lung function imaging for thoracic radiotherapy. Front Oncol. 2024;14:1414337.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim J, Kim KH. Role of chest radiographs in early lung cancer detection. Translational lung cancer Res. 2020;9(3):522\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eObuchowicz R, Lasek J, Wodziński M, Pi\u0026oacute;rkowski A, Strzelecki M, Nurzynska K. Artif Intelligence-Empowered Radiology\u0026mdash;Current Status Crit Rev Diagnostics. 2025;15(3):282.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Lung cancer, bronchial, Risk factor, computed tomography, Artificial Intelligence","lastPublishedDoi":"10.21203/rs.3.rs-6558920/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6558920/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e Respiratory system cancer has long been a global health issue, with tracheal, bronchial, and lung cancers ranking second in incidence and first in mortality worldwide. This study aims to analyse recent data to understand the global burden, trends, and risk factors for respiratory system cancer, facilitating\u003c/p\u003e\n\u003cp\u003eImproved prevention and treatment strategies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e We conducted a study on respiratory system cancer using data from the Global Cancer Observatory and the Cancer Incidence in Five Continents databases. We collected information on the incidence of cancers, risk factors, the final estimates of incidence and mortality rates were multiplied with population data from the United Nations Development Program. The age standardized rates were generated by GLOBOCAN using the world standard population.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e with 2,481,000 cases and 1,817,000 related deaths, trachea and lung cancer had the highest incidence. Asia had the highest incidence and mortality rates of cancer in 2023, with 1,566,000 new cases and 1,142,000 deaths. According to UN regions, Eastern Asia had the highest age-standardized incidence and mortality rates (per 100,000 people) in 2022, at 39.4 and 25.1, respectively. \u0026nbsp;However, there were notable regional differences in the cancer burden, with the lowest rates observed in Western Africa (2.1 and 2.0), Middle Africa (1.3 and 2.2), and Eastern Africa (3.2 and 3.0). In crude incidence and mortality rates Eastern Asia had the highest rates, with 76.9 incidence and 52.7 mortality while the lowest crude rates occurring in Eastern Africa (1.6 and 1.5), Middle Africa (1.0 and 1.0), and Western Africa (1.0 and 0.9). China reported 1,610,000 incidence cases and 733,000 deaths from trachea and lung cancer in 2022, the most of any country.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion \u003c/strong\u003eIn summary, this study supports the role of lung lesions as useful imaging biomarkers by highlighting their prognostic significance in lung disease. The results emphasize that in order to enhance patient outcomes, clinical workflows must integrate cutting-edge imaging methods and AI-driven analysis.\u003c/p\u003e","manuscriptTitle":"Radiological Imaging Indicators of Survival in Trachea and Lung Cancers: A Global Perspective","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-09 11:04:55","doi":"10.21203/rs.3.rs-6558920/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ead24eef-c2af-4175-aa7b-ae315bebe4c7","owner":[],"postedDate":"May 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-05-20T07:54:02+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-09 11:04:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6558920","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6558920","identity":"rs-6558920","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}