Patterns and Signal Intensity Characteristics of Pelvic Recurrence of Rectal Cancer at MR Imaging
Bibliographic record
Abstract
Magnetic resonance (MR) imaging is becoming the cross-sectional imaging modality of choice for follow-up of patients with previous rectal cancer to diagnose pelvic recurrence and plan for surgery. The authors conducted a retrospective review of MR imaging examinations performed at their institution for evaluation of local recurrence of rectal cancer in 42 patients. Twenty-six patients had undergone rectal anastomosis and 16 had undergone abdominoperineal resection. The mean interval between initial surgery and recurrence was 2.5 years. Recurrence sites were axial (involving the anastomosis) (n = 19); lateral (sidewall) (n = 6); anterior (prostate or seminal vesicle [n = 2], bladder [n = 4], ureter [n = 3], vagina or uterus [n = 5]); or posterior (presacral fascia [n = 11], sacrum [n = 2]). Other recurrence sites included the pelvic floor (n = 7), sciatic nerve (n = 2), obturator nerve (n = 1), perineum (n = 1), abdominal wall (n = 1), or adnexa (n = 1). Recurrence was confirmed at surgery or by evidence of tumor growth at follow-up imaging. Recurrence patterns, signal intensity characteristics, findings of unresectability, potential MR imaging pitfalls, and the role of MR imaging versus other modalities in evaluating recurrent rectal carcinoma are discussed. Supplemental material available at http://radiographics.rsna.org/lookup/suppl/doi:10.1148/rg335115170/-/DC1.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.002 | 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".