Evidence map›Paper›PMID 41649702›Full record

ArticleHealth economics review2026

Point-of-care core needle biopsy pathway for early diagnosis of lymph node masses: comparative costing of a scalable pathway.

David Richardson, Waarisa Fareed-Brey, Katherine Antel, Karryn Brown, Jenna Bailey, Estelle Verburgh, Lucy Cunnama

Abstract read
In one paragraph

Article in Health economics review, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

David RichardsonDivision of Clinical Haematology, Department of Medicine, Faculty of Health Sciences, University of Cape Town and Groote Schuur Hospital, Cape Town, South Africa.
Waarisa Fareed-BreyHealth Economics Unit and Division, School of Public Health, University of Cape Town, Anzio Road, Cape Town, South Africa.
Katherine AntelDivision of Clinical Haematology, Department of Medicine, Faculty of Health Sciences, University of Cape Town and Groote Schuur Hospital, Cape Town, South Africa.
Karryn BrownDivision of Clinical Haematology, Department of Medicine, Faculty of Health Sciences, University of Cape Town and Groote Schuur Hospital, Cape Town, South Africa.
Jenna BaileyDivision of Clinical Haematology, Department of Medicine, Faculty of Health Sciences, University of Cape Town and Groote Schuur Hospital, Cape Town, South Africa.
Estelle VerburghDivision of Clinical Haematology, Department of Medicine, Faculty of Health Sciences, University of Cape Town and Groote Schuur Hospital, Cape Town, South Africa.
Lucy CunnamaHealth Economics Unit and Division, School of Public Health, University of Cape Town, Anzio Road, Cape Town, South Africa. Lucy.Cunnama@uct.ac.za.

Funding

Research training in HIV-related transfusion medicine and hematology in South AfricaD43TW010345 · FIC · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI UTE JENTSCH, EDWARD L MURPHY · 2017 to 2026
$3.0M
FIC NIH HHS D43 TW010345
6 · The paper itself

Abstract

backgroundThere are three common and significant causes of lymphadenopathy (LAP) in tuberculosis-and-HIV-endemic settings - tuberculosis, lymphoma, and metastatic solid organ cancers. Overlapping clinical symptoms of these diseases necessitates diagnostic procedural investigation. Fine needle aspiration (FNA) with Xpert TB/RIF Ultra has good sensitivity for tuberculosis diagnosis, while FNA cytology can detect solid cancer lymph node metastasis, although with lower sensitivity. Critically, lymphoma cannot be reliably detected or classified using FNA. Lymphoma diagnosis often is made after repeat testing and eventual surgical excision biopsy (SEB), contributing to treatment delays, inappropriate empiric anti-tuberculosis therapy, and increased strain on already limited healthcare resources.

methodsThis study presents a comparative provider-perspective cost analysis of three diagnostic procedures: FNA, SEB and core-needle biopsy (CNB). Costs were evaluated for individual procedures and within investigative pathways for unexplained LAP. Data were collected at Groote Schuur Hospital, a tertiary academic hospital in South Africa, during 2018 and 2025. Costing was conducted using a mixed-methods approach combining bottom-up ingredients-based costing with top-down allocation of overheads. Capital costs were annualised, and all costs were adjusted to 2025 values in United States dollars.

resultsHistological diagnosis and tuberculosis testing using CNB were less costly (US$ 145) than either SEB (US$ 244), or FNA cytology and tuberculosis testing (US$ 179). Total investigative costs increased substantially when repeat FNAs were required while awaiting access to SEB (US$ 518). CNB demonstrated high diagnostic accuracy and obviated the need for further procedures in most cases, improving diagnostic efficiency and offering significant potential for cost savings. Sensitivity analyses suggested further reductions in CNB costs are possible through task-shifting to junior clinicians and through the use of reusable biopsy equipment.

conclusionThe CNB-centred diagnostic approach to lymphadenopathy provides lower-cost, timeous, and accurate diagnosis of the three most significant causes of LAP in tuberculosis- and HIV-endemic settings. This approach enables early initiation of appropriate treatment, reduces unnecessary procedures, and optimises the use of limited healthcare resources. These findings support the integration of CNB into national diagnostic algorithms and highlight the value of investments in training and equipment to enable its decentralised implementation.

Indexed as

Comparative cost analysisCore needle biopsyDiagnostic pathwaysFine needle aspirateLymphadenopathyLymphomaSurgical excision biopsyTuberculosis

Identifiers

PMID41649702
PMCPMC13005345

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.