Evidence map›Paper›PMID 39921787›Full record

ReviewDiscover oncology2025

Innovative laboratory techniques shaping cancer diagnosis and treatment in developing countries.

Azeez Okikiola Lawal, Tolutope Joseph Ogunniyi, Oriire Idunnuoluwa Oludele, Oluwaloseyi Ayomipo Olorunfemi, Olalekan John Okesanya, Jerico Bautista Ogaya, Emery Manirambona, Mohamed Mustaf Ahmed, Don Eliseo Lucero-Prisno

Abstract readReview
In one paragraph

Review in Discover oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Article
  4. Review
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

9 authors.

Azeez Okikiola LawalDepartment of Medical Laboratory Science, Kwara State University, Malete, Nigeria.ORCID http://orcid.org/0000-0002-0085-5362
Tolutope Joseph OgunniyiDepartment of Medical Laboratory Science, Kwara State University, Malete, Nigeria.ORCID http://orcid.org/0000-0003-2582-4420
Oriire Idunnuoluwa OludeleDepartment of Medical Laboratory Science, University Teaching Hospital, Ibadan, Nigeria.ORCID http://orcid.org/0009-0007-2152-5665
Oluwaloseyi Ayomipo OlorunfemiDepartment of Medical Laboratory Science, University of Ilorin Teaching Hospital, Ilorin, Kwara, Nigeria.ORCID http://orcid.org/0009-0000-3589-0427
Olalekan John OkesanyaDepartment of Public Health and Maritime Transport, University of Thessaly, Volos, Greece.ORCID http://orcid.org/0000-0002-3809-4271
Jerico Bautista OgayaDepartment of Medical Technology, Institute of Health Sciences and Nursing, Far Eastern University, Manila, Philippines.ORCID http://orcid.org/0009-0005-3595-8643
Emery ManirambonaDepartment of Medicine, University of Rwanda, Kigali, Rwanda.ORCID http://orcid.org/0000-0002-0579-3607
Mohamed Mustaf AhmedFaculty of Medicine and Health Sciences, SIMAD University, Mogadishu, Somalia. momustafahmed@simad.edu.so.ORCID http://orcid.org/0009-0006-5991-4052
Don Eliseo Lucero-PrisnoDepartment of Global Health and Development, London School of Hygiene and Tropical Medicine, London, UK.ORCID http://orcid.org/0000-0002-2179-6365

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cancer is a major global health challenge, with approximately 19.3 million new cases and 10 million deaths estimated by 2020. Laboratory advancements in cancer detection have transformed diagnostic capabilities, particularly through the use of biomarkers that play crucial roles in risk assessment, therapy selection, and disease monitoring. Tumor histology, single-cell technology, flow cytometry, molecular imaging, liquid biopsy, immunoassays, and molecular diagnostics have emerged as pivotal tools for cancer detection. The integration of artificial intelligence, particularly deep learning and convolutional neural networks, has enhanced the diagnostic accuracy and data analysis capabilities. However, developing countries face significant challenges including financial constraints, inadequate healthcare infrastructure, and limited access to advanced diagnostic technologies. The impact of COVID-19 has further complicated cancer management in resource-limited settings. Future research should focus on precision medicine and early cancer diagnosis through sophisticated laboratory techniques to improve prognosis and health outcomes. This review examines the evolving landscape of cancer detection, focusing on laboratory research breakthroughs and limitations in developing countries, while providing recommendations for advancing tumor diagnostics in resource-constrained environments.

Indexed as

BiomarkersCancerCancer diagnosisLaboratory challengesLaboratory investigationTumor research

Identifiers

PMID39921787
PMCPMC11807038

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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 Socratic graph.