Abstract 1832: Associations between ovarian cancer and other malignant neoplasms in an international population-based study
Bibliographic record
Abstract
Abstract Recent studies have suggested that different histological types of ovarian cancer develop via different pathways. Large epidemiological studies of first and second malignant neoplasms associated with ovarian cancer can quantify such risk and may provide etiologic clues in understanding these complex pathways. We analyzed ovarian cancer data from 13 different cancer registries in Europe, Australia, Canada and Singapore from 1943-2000. Cumulative risks were calculated using a competing risk model, and standardized incidence ratios (SIR) for 34 cancers were estimated based on the observed numbers of second malignancies and the expected numbers obtained from population-specific incidence rates. We observed 5,052 second malignant neoplasms in 107,038 ovarian cancer patients and 5,804 second ovarian cancers in 3,722,434 patients with cancer of other sites. Within 20 years from diagnosis of ovarian cancer, the cumulative risk of second malignant neoplasms was 4.8% (95% CI 4.6%, 5.0%) before 1980 and 7.1% (6.1, 8.0) after 1980. After sex cord-stromal, mucinous or endometrioid tumors of the ovary, the cumulative risk of second malignant neoplasms was higher [12.7% (11.2, 14.3), 10.0% (9.0, 11.0), 9.3% (7.9, 10.7), respectively] than after germ-cell tumors, serous tumors or adenocarcinoma NOS [6.2% (3.7, 8.7), 5.9% (5.5, 6.3), 5.1% (4.9, 5.4), respectively] within 20 years from diagnosis at age 45+. We observed increased SIRs for cancer of stomach [SIR=1.5 (95% CI 1.1, 2.0)], small intestine [3.2 (1.3, 6.5)], colon [1.5 (1.2, 1.8)], rectum [1.9 (1.4, 2.4)], soft tissue sarcoma [3.2 (1.5, 5.8)], non-melanoma of skin [1.4 (1.0, 1.7)], breast [1.3 (1.2, 1.5)], other female genital organ [2.2 (1.3, 3.5)], bladder [2.3 (1.7, 3.1)], kidney [1.8 (1.3, 2.6)], myeloid leukemia [3.5 (2.2, 5.4)] and other leukemia [6.4 (3.7, 10.2)] after serous tumors of the ovary. There were increased SIRs for colon [2.2 (1.7, 2.7)], rectum [1.9 (1.3, 2.6)], lung [2.1 (1.6, 2.7)] and bladder cancer [1.8 (1.0, 2.8)] after mucinous tumors. After endometrioid tumors, we observed an excess risk for cancer of colon [2.1 (1.5, 2.9)], bone [12.1 (1.5, 43.6)], soft tissue sarcoma [5.2 (1.1, 15.3)], corpus uteri [3.3 (2.2, 4.7)] and kidney [2.3 (1.1, 4.4)]. There was an association with cancer of soft tissue sarcoma [3.2 (1.0, 7.4)], bladder [2.2 (1.2, 3.4)], kidney [2.1 (1.2, 3.3)], thyroid gland [3.1 (1.5, 5.6)] and myeloid leukemia [4.2 (1.5, 9.1)] after sex cord-stromal tumors. SIRs of second ovarian cancer were increased after stomach, small intestine, colon, gallbladder, pancreas, peritoneum, non-melanoma skin and breast cancer. Women who survive ovarian cancer are at long-term risk of developing a second malignant neoplasm. The associations between specific types of ovarian cancer and other cancers may be attributed to late effects of radio- and chemotherapy, as well as to common genetic and environmental risk factors and pathways. Note: This abstract was not presented at the AACR 101st Annual Meeting 2010 because the presenter was unable to attend. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 1832.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".