Intraluminal Small Bowel Metastasis From Primary Lung Cancer
Bibliographic record
Abstract
Lung cancer is the leading cause of cancer-related death worldwide, with frequent metastases to the brain, liver, adrenal glands, and bone. The incidence of intraluminal small bowel metastases of the lung is extremely rare and poorly documented within the literature. Few case studies have been published since the late 1980s and early 1990s. However, little is known about this rare form of metastasis. Small bowel metastatic disease has atypical symptoms that mimic a variety of other diseases; as a result, signs and symptoms may be overlooked until the disease has progressed to a late stage. Signs of small bowel obstruction, symptomatic anemia, abdominal pain, and peritonitis are commonly reported signs and symptoms. Various modalities can be utilized for the workup of suspected small bowel metastasis, including positron emission tomography, computed tomography, and various forms of endoscopy. The prognosis for lung cancer patients with intestinal metastases is poor, with many only surviving months to a few years after diagnosis. Therefore, it is critical to consider small bowel masses as a differential diagnosis in a patient with primary lung cancer who demonstrates clinical signs consistent with symptomatic anemia secondary to gastrointestinal (GI) bleeding, peritonitis, or small bowel obstruction. We report an unusual case of intraluminal and fungating small bowel masses in a patient who had previously undergone lung resections and chemo-immunotherapy. She was diagnosed with non-small undifferentiated carcinoma with tumor necrosis over 12 years before disease recurrence in the bilateral lungs, right adrenal gland, bone, and small bowel. The discovery of the small bowel metastases occurred while undergoing treatment for advanced-stage disease. At this time, she completed chemo-immunotherapy and remained on maintenance immunotherapy. The patient also underwent a partial right adrenalectomy and radiotherapy to the right adrenal gland. Given that she was experiencing symptomatic anemia and further workup indicated that the GI masses were causing her anemia, she underwent palliative small bowel resection of the masses. The pathology results demonstrated that the masses originated from her primary lung cancer, confirming metastatic disease to the small bowel.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".