Diagnostic Accuracy of Positron Emission Tomography-Computed Tomography (PET-CT Scan) In Detecting Bone Marrow Involvement in Patients with Diffuse Large B cell Lymphoma
Bibliographic record
Abstract
Objective: To evaluate the diagnostic accuracy of positron emission tomography combined with CT scan (PET-CT Scan) in detecting bone marrow involvement in patients with diffuse large B-cell lymphoma, keeping bone marrow biopsy as gold standard. Methodology: From November 2017 to May 2018, a cross sectional validation study was carried out at the Aga Khan University in Karachi Department of Oncology's Section of Clinical Hematology. The study comprised a total of 112 patients who were identified as having diffuse large B cell lymphoma after a lymph node was implicated was histopathologically examined. All patients had a PET-CT scan and bone marrow biopsy technique as part of the staging workup. With bone marrow biopsy acting as the gold standard, the diagnostic efficacy of a PET-CT scan for identifying bone marrow involvement was evaluated. Results: Of 112 patients, there were 71(63.39%) males and 41(36.61%) females. The mean age was 45.09±17.36 years. The mean duration of diagnosis was 17.19±6.02 days. Through biopsy, bone marrow involvement was identified in 40 (35.7%) cases. Through a PET-CT scan, bone marrow involvement was identified in 47 (41.9%) cases. The PET- CT scan in comparison with bone marrow biopsy for detecting bone marrow involvement in patients with DLBCL had a sensitivity, specificity, positive predictive value, negative predictive value and diagnostic accuracy of 95%, 87.7%, 80.85%, 96.92% and 90.18% respectively. Conclusion: PET-CT scan can accurately detect bone marrow involvement in patients with DLBCL so it can be used in most patients instead of invasive bone marrow biopsy procedure for staging of DLBCL patients.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".