IgG4-Related Disease as Mimicker of Malignancy
Bibliographic record
Abstract
Abstract Background IgG4-related disease (IgG4-RD) is an immune-mediated disease that may present as a tumefactive lesion in nearly any organ. These mass lesions often resemble malignancy both clinically and radiologically, and some patients undergo surgical resection which could possibly be avoided with early recognition of IgG4-RD. We performed a retrospective single-center study examining how many patients with IgG4-RD were initially believed to have malignancy, with particular attention to those who underwent potentially avoidable surgical procedures. Methods Sixty-three patients with biopsy confirmed IgG4-related disease were included. Clinical, laboratory, radiological, and histological data were collected and analyzed. Results Over 60% of patients (38/63) were initially thought to have a malignancy when they initially presented with symptomatic IgG4-RD. The most common types of malignancy suspected were lymphoma (18/38) and pancreatic cancer (11/38). Of the 38 patients with suspected malignancy, 14 underwent an invasive intervention either to alleviate the severity of their symptoms or as treatment for their presumed malignancy. These included Whipple resection/attempted Whipple (3), nephrectomy (3), bile duct resection and reconstruction (1), removal of other abdominal/retroperitoneal masses (3), and stenting of obstructed organs (4). Conclusion IgG4-RD should be on the differential diagnosis of patients with mass lesions, in particular those with pancreatic masses and obstructive jaundice, extensive lymphadenopathy, or retroperitoneal masses. Oncologists and other physicians involved in cancer care should be aware of the various manifestations and diagnostic approach to IgG4-RD in order to provide accurate diagnosis and minimize unnecessary invasive procedures. While some procedures in this study, such as stenting of obstructed organs, were required regardless of diagnosis, others could have potentially been avoided or attenuated with early recognition of IgG4-RD. Patients with mass lesions suspicious for IgG4-RD should have serum protein electrophoresis, IgG subclass measurement, and, where possible, tissue biopsy before undergoing major surgical resection. Consultation with a physician experienced in IgG4-RD is recommended.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".