Evaluation of Unexplained Lymphadenopathy and Suspected Lymphoma in a Lymphoma Rapid Diagnostic Clinic
Bibliographic record
Abstract
Abstract Background: Lymphomas often present a diagnostic challenge, leading to delays in obtaining a definitive diagnosis. For some lymphomas, a delay in diagnosis can negatively influence outcomes of therapy and result in a shorter duration of remission. Patients are often referred for evaluation with lymphoma diagnosis based on cytology from fine needle aspiration (FNA), whereas excisional or core lymph node biopsy is required for definitive diagnosis. We established a nurse practitioner-led Lymphoma Rapid Diagnostic Clinic (LRDC) with the goal of reducing wait times to diagnosis and initiation of treatment. Program development included surgical and interventional radiology engagement, and dedicated operating room time for excisional lymph node biopsies. We conducted a retrospective chart review to describe the initial 30-month experience of the LRDC. Results were compared to time periods before implementation of the clinic to determine program impact, and identify areas for quality improvement. Methods: All patients referred to LRDC at Princess Margaret Cancer Centre (PM), part of University Health Network (UHN), with suspicion of lymphoma from June 1, 2015 to Nov 30, 2017 were evaluated. Patients were excluded if they arrived in clinic with confirmed diagnosis of lymphoma or declined further LRDC evaluation. Patient symptoms and relevant laboratory/imaging findings were collected to identify patterns of presentation and predictive factors for benign diagnoses. Time from initial consultation to diagnosis and treatment were compared to patients diagnosed with lymphoma by surgical, medical, or oncology services at UHN in 2008 and 2012. Statistical significance was investigated using Fisher Exact test for categorical variables, and non-parametric Wilcoxon rank sum test for continuous data. Statistical significance level was chosen at a 2-sided p-value of 0.05 or less. Results: Of 129 patients referred to LRDC with suspected lymphoma, 126 were included in the analysis. Median age was 55yrs (range 18- 95yrs), and 67 patients (53%) were female. Thirty-nine patients had non-diagnostic FNA and/or core biopsies at initial assessment. Twenty-five had a prior cancer diagnosis, 30 presented with B symptoms and 57 had palpable enlarged lymph nodes (Table 1). To obtain a definitive diagnosis, 93 patients had biopsies (46 image-guided cores, 37 excisional, 6 FNA, 4 bone marrow); 13 patients received a diagnosis after pathology review, 4 via peripheral blood flow cytometry, and 16 following other clinical or serologic/imaging investigations. Following evaluation, 66 patients (52%) had confirmation of a diagnosis of lymphoma (34 indolent, 18 aggressive, 14 Hodgkin lymphoma (HL)), 2 had acute leukemia, 14 had metastatic cancer, and 44 had non-malignant diagnoses (Table 2). Median time from initial assessment to lymphoma diagnosis was 16 days (interquartile range 9-24 days) for the patients assessed in LRDC and 28 days (interquartile range 19-48 days) for historical controls (p <0.001). Median time from initial LRDC assessment to treatment for aggressive lymphomas and HL was 29 days (interquartile range 21-43 days) compared to 48 days (interquartile range 28-78 days) for historical controls (p= <0.001). The total number of biopsies obtained before diagnosis was significantly fewer for patients assessed in LRDC compared to historical controls (p<0.001, Fisher's exact test). By univariable analysis, lymph node size >3.4 cm and presence of mediastinal or abdominal adenopathy increased the likelihood of a diagnosis of malignancy, while younger age, being a non-smoker, and prior rheumatologic condition were associated with a non-malignant diagnosis. In multivariable analysis, lymph node size, age and prior rheumatologic diagnosis remained significant. Presence of B symptoms was not predictive. Conclusion: Establishing the nurse practitioner-led LRDC was effective in shortening time to diagnosis and treatment, and reduced the number of biopsies required for definitive diagnosis of lymphoma. Younger age, smaller lymph node size and prior rheumatologic disorder reduced the likelihood of a cancer diagnosis in our patient population. Disclosures Kuruvilla: Princess Margaret Cancer Foundation: Research Funding; Janssen: Consultancy, Honoraria, Research Funding; Abbvie: Consultancy; Lundbeck: Honoraria; Seattle Genetics: Consultancy, Honoraria; Gilead: Consultancy, Honoraria; Karyopharm: Honoraria; Roche: Consultancy, Honoraria, Research Funding; Leukemia and Lymphoma Society Canada: Research Funding; BMS: Consultancy, Honoraria; Celgene: Honoraria; Merck: Consultancy, Honoraria; Amgen: Honoraria.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".