Comparative study of cancer profiles between 2020 and 2022 using global cancer statistics (GLOBOCAN)
Bibliographic record
Abstract
Background: The International Agency for Research on Cancer (IARC) released the latest estimates of the global burden of cancer. We present a comparison of cancer profiles between 2020 and 2022, leveraging data from the Global Cancer Statistics (GLOBOCAN). Methods: Cancer incidence and mortality data were sourced from two different years, 2020 and 2022, in the GLOBOCAN database. We tracked changes in age-standardized incidence and mortality rates, as well as estimated numbers of new cancer cases and deaths of the 15 most common cancer types globally and in China between 2020 and 2022. Additionally, we conducted comparisons to assess alterations in the cancer burden and variations in mortality-to-incidence ratio (MIR) across different regions and countries for both 2020 and 2022. Results: Lung cancer remained the most common cancer and the leading cause of cancer death worldwide. The new cases of thyroid cancer witnessed a sharp increase in 2022. Conversely, the numbers of new cancer cases and deaths from stomach and esophageal cancer decreased significantly in 2022. The geographic distribution of cancer incidence and mortality across six continents in 2022 largely mirrored that of 2020. Higher Human Development Index (HDI) levels in countries corresponded with elevated rates of cancer incidence and mortality, consistent with the previous year. Among 185 countries or territories, China's age-standardized incidence rate (ASIR) ranked 64th and its age-standardized mortality rate (ASMR) ranked 68th, aligning with global averages. Lung cancer continued to impose the greatest burden of incidence and mortality. Stomach, breast, and esophageal cancers showed declines in both case counts and ASIR. Noteworthy reductions in both ASMR and absolute mortality numbers were observed in liver, stomach, and esophageal cancers. The global MIR decreased from 0.516 in 2020 to 0.488 in 2022. MIR trends indicated an upward trajectory with decreasing HDI levels in both 2022 and 2020. While Canada, Germany, India, Italy, Japan, and the United Kingdom demonstrated increasing MIRs, China exhibited the most significant decrease, followed by Russia and the United States. Conclusions: The global landscape of cancer incidence and mortality in 2022 reflects ongoing trends observed in 2020. Cancer burdens vary notably across countries with differing socioeconomic statuses. Decreases in stomach, liver, and esophageal cancer cases and deaths signify progress in cancer control efforts. The decrease in the global MIRs highlights potential improvements in cancer management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".