Incidental findings on staging rectal MRI: clinical significance and outcomes
Bibliographic record
Abstract
BACKGROUND: Incidental findings (IFs) are commonly seen in staging rectal magnetic resonance imaging (MRI) scans. Their prevalence and clinical significance have not been previously documented. PURPOSE: To assess the prevalence, clinical significance, and outcomes of incidental findings in MRI scans performed for the staging of rectal cancer. MATERIAL AND METHODS: A retrospective study was performed at a tertiary colorectal imaging institution. Consecutive MRI rectal staging scans with correlative pathology confirmed primary rectal cancer between March 2014 and March 2021 were identified. The respective imaging reports were reviewed for IFs, which were classified as high, moderate, and low, according to their clinical significance. Medical records were reviewed to assess the outcomes of the highly significant IFs. RESULTS: There were 266 eligible patients (97 women; mean age = 64.2 years) during the study period. A total of 120 (45%) patients did not have any IFs. A total of 238 IFs in 146 (55%) patients were found. There were 21 (9%) IFs of high clinical significance, 122 (51%) of moderate clinical significance, and 95 (40%) of low clinical significance. The prostate and uterus had the most IFs of high clinical significance, two of which were subsequently pathology confirmed as prostate adenocarcinomas. CONCLUSION: IFs were seen in more than half of the staging MRI scans in rectal cancer but less than 10% of these were of high clinical significance. The results of this study highlight the range of potential IFs and can guide future research assessing the potential impact of these IFs on patients and the healthcare system.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".