The Efficacy of PET-CT in Detecting Bone Marrow Involvement in Patients with Newly Diagnosed Lymphoma: A Retrospective Study
Bibliographic record
Abstract
Abstract Background: Patients diagnosed with Hodgkin lymphoma (HL) or non-Hodgkin lymphoma (NHL) undergo various investigations to determine disease stage. This includes Positron Emission Tomography with Computed Tomography (PET-CT) scan and bone marrow biopsy (BMB), which has historically been considered the gold standard for detecting involvement of the bone marrow (BM) by lymphoma. Recent studies have suggested that PET-CT scans can detect BM involvement in most patients with lymphoma. As such, there is growing interest into whether or not PET-CT scans may replace the need for BMB in staging patients with lymphoma. Objective: We conducted a retrospective chart review of patients newly diagnosed with lymphoma at our local cancer centre to evaluate the diagnostic performance of PET-CT scans in detecting BM involvement. Methods: Patients were eligible for inclusion if they were age 18 or older, had previously untreated HL or NHL diagnosed from January 2012 to August 2016, and underwent both BMB and PET-CT scan at time of staging. We collected the results of BM aspirate, flow cytometry, cytogenetics and biopsy. Patients were considered to have BM involvement if they had a positive BMB. PET-CT scans were retrospectively re-analyzed by two readers from the Division of Nuclear Medicine. Both were blinded to the patients' clinical information, including lymphoma diagnosis and BMB results. BM positivity by PET-CT scan was characterized as being either diffuse (relatively uniform increase in tracer uptake in the BM) or focal (localized increase in tracer uptake to one or more demarcated regions of BM). We performed sensitivity and specificity analyses of PET-CT scans. Results: The study included 110 patients: 102 patients with NHL and 8 with HL. The most common NHL subtype was diffuse large B-cell lymphoma (DLBCL) (n=40). For the entire cohort of patients with NHL, the sensitivity and specificity of PET-CT scan for diagnosing BM involvement were 78% (95% CI: 52-93%) and 86% (95% CI: 76-92%), respectively (Table 1). For patients with DLBCL, the sensitivity and specificity of PET-CT scan were 100% (95% CI: 52-100%) and 79% (95% CI: 63-91%), respectively. In this group, none of the patients with a negative PET-CT scan had a positive BMB. Five of the 7 patients with positive PET-CT scans but negative BMB had focal BM involvement on PET-CT scan. Conclusions: Although limited by small sample size, our data suggests that PET-CT scan is particularly adept at detecting BM involvement in patients with DLBCL and may detect focal BM involvement missed by BMB. The high sensitivity of PET-CT scan may lend support to clinicians foregoing BMB in patients with newly diagnosed lymphoma if imaging with PET-CT scan does not show involvement of the BM by lymphoma. Download : Download high-res image (120KB) Download : Download full-size image Disclosures No relevant conflicts of interest to declare.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".