Second primary malignancies (SPM) in patients with gastrointestinal stromal tumors (GIST): 10-year experience from the Ottawa Hospital (TOH).
Bibliographic record
Abstract
11536 Background: The secondary malignancies in patients with GIST are relatively high. We present our 10-year experience of SPM in patients with GIST from a regional Cancer Centre in Canada. Methods: A retrospective cohort study was performed in all GIST patients treated at TOH between January 2011 and December 2021. Patients were identified using ICD-10 codes and electronic medical records reviewed. Clinicopathological data were analyzed. Logistic regression analysis was used to evaluate associated factors with SPM. Survival analysis was estimated with the use of the Kaplan-Meir method and compared by log-rank test. Results: In total, 248 patients with GIST were identified. Of these patients, 61 (25%) had SPM; synchronous (9, 15%) and metachronous (52, 85%). Nine patients had two additional primary cancers other than GIST, and four patients had three additional primary cancers. The median age at diagnosis was 70 (range 44 – 90) years, and males were (59%). The most common SPMs were skin cancer (14, 17%), melanoma (5), and non-melanoma (9), followed by prostate cancer (13, 16%) and breast cancer (12, 15%). Colorectal cancer and hematological malignancies were found in (5, 11%) patients each, while RCC was found in (4, 6%). Thyroid cancer, lung cancer, neuroendocrine tumor, bladder cancer, thymoma, and other types of cancers were collectively found in 15 patients (24%). The majority (57%) of SPM diagnosed before GIST, 30% after GIST. The most common primary GIST locations were in the stomach (62%), followed by the small bowel (30%), and the most common histology was spindle cell (69%), followed by mixed histology (13%). The majority of GIST were localized (46 patients, 75%). Based on Miettinen risk classes for non-metastatic GIST, 74% had zero to low-risk disease, while 26% had moderate or high-risk disease. There was no association between SPM and GIST primary site (p = 0.4), TNM stage (p = 0.8), histology (p = 0.2), Mitosis (p = 0.5), and Miettinen risk class (p = 0.6). The median follow-up time was 53 months (range 1 – 132), and four patients lost follow-ups. Five years overall survival of SPM group vs. non SPM, 79.8% vs. 94.1%, (p = 0.03). Cox regression did not reveal a significant association with the covariants Conclusions: We observed that one out of four GIST patients have SPM. Skin, prostate, breast cancers were the most common SPM associated with GIST. Molecular studies are needed to explore the association/underlying mechanisms of GIST with these malignancies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".