Emerging Trends in Point-of-Care Technology Development for Oncology in Low- and Middle-Income Countries

Abstract

The growing cancer burden and suboptimal diagnostic capacity in low- and middle-income countries calls for urgent innovation in diagnostic solutions. Point-of-care technologies (POCTs) offer a transformative approach to decentralizing cancer diagnostics by providing rapid, affordable, and scalable testing in resource-constrained settings. Recent advancements, including loop-mediated isothermal amplification and multiplexed lateral flow immunoassay, enable high-sensitivity detection of cancer biomarkers without the need for complex laboratory infrastructure. Additionally, noninvasive imaging tools, such as optical coherence tomography and fluorescence-guided microscopy, offer portable and cost-effective solutions for early cancer detection in settings with limited health care services. These innovations are complemented by the integration of artificial intelligence, which improves diagnostic accuracy and reduces reliance on highly trained personnel. However, significant infrastructure and logistical challenges persist, including resource constraints, unreliable electricity, and insufficient cold-chain logistics, which limit diagnostic precision and accessibility. This review discusses recent advances in POCTs for oncology and examines how public-private partnerships and multisector collaborations can address key implementation barriers. By prioritizing inclusivity, cross-sector collaboration, and targeted investments, POCTs can sustainably narrow global disparities in cancer diagnosis and treatment.

FIG 1

ASR for all types of cancer among individuals younger than the global healthy life expectancy of 65 years. (A) Cancer incidence rates. (B) Cancer mortality rates. Adapted from the IARC. ASR, age-standardized rate; IARC, International Agency for Research on Cancer.

FIG 2

(A) Multiplexed point-of-care testing. Reprinted with permission from Dincer et al. (B) Key differences between LAMP assay and PCR. Reprinted with permission from New England BioLabs. (C) A proposed clinical workflow for how wide-field imaging, high-resolution imaging, biopsy, and slide-free histological diagnoses could be integrated into current screening and diagnosis workflows. Reprinted with permission from Richards-Kortum et al. LAMP, loop-mediated isothermal amplification; LED, light-emitting diode; PCR, polymerase chain reaction; UV, ultraviolet.

FIG 3

(A) An AI-driven framework for detecting Kaposi sarcoma through the classification of anti–LANA-positive WSIs. The model used an MIL approach, ranking image tiles based on their probability of being LANA-positive to enhance AI-assisted pathology for more accurate diagnosis. Reprinted with permission from Hussain et al. (B) An ROC curve comparing the performance of an AI-based diagnostic system against clinical experts in predicting the progression to exAMD within a clinically actionable 6-month window. Filled and open circles represent individual expert performance on single-scan and sequential-scan tasks, whereas larger monochrome squares and circles indicate consensus-based human predictions requiring agreement among multiple specialists. Shaded regions indicate 95% CIs, and the gray diagonal line represents chance-level performance. The AI system outperforms five of six clinical experts, demonstrating improved sensitivity, specificity, and consistency in forecasting disease progression. Reprinted with permission from Yim et al. AI, artificial intelligence; exAMD, exudative age-related macular degeneration; LANA, latency-associated nuclear antigen; MIL, multiple-instance learning; ROC, receiver operating characteristic; WSIs, whole-slide images.

FIG 4

Key milestones of the ACT program's global impact. This timeline presents the major milestones of the ACT Program, initiated by the NCI, a division of the NIH, to enhance cancer diagnostics and treatment in LMICs.^- ACT, Affordable Cancer Technologies; CGH, Center for Global Health; FOA, Funding Opportunity Announcement; LMICs, low- and middle-income countries; NCI, National Cancer Institute; NGOs, nongovernmental organizations; NIH, National Institutes of Health.

FIG 5

Several POCTRN centers include clinical partnerships in LMICs to facilitate the research and development of POC technologies and initiatives to improve global oncology. These centers include C-THAN at Northwestern University, PORTENT at Cornell University, CITEC at Rice University, and CIDID at Johns Hopkins University. Initiatives led by these respective POCTRN centers include DASH for rapid PCR, AIM-HPV for cervical cancer screening, Lucia for instructional training on cervical cancer, and COPHAS to assess POC diagnostics for sexually transmitted infections. AI, artificial intelligence; AIM-HPV, AI-Powered Platform for Cervical Cancer Screening; CIDID, Center for Innovation in Diagnostics for Infectious Diseases; CITEC, Center for Innovation and Translation of POC Technologies for Expanded Cancer Care Access; COPHAS, Community Pharmacies for Assessing Sexually Transmitted Infections Using POC Diagnostics; C-THAN, Center for Innovation in POC Technologies for HIV/AIDS and Emerging Infectious Diseases at Northwestern University; DASH, Diagnostic Analyzer for Selective Hybridization; LMICs, low- and middle-income countries; PCR, polymerase chain reaction; POC, point-of-care; POCTRN, Point-of-Care Technology Research Network; PORTENT, Center for Point of Care Technologies for Nutrition, Infection and Cancer for Global Health.