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.

INTRODUCTION

Cancer is a multifocal and heterogeneous disease responsible for substantial global mortality, with low- and middle-income countries (LMICs) bearing a disproportionate share of the mortality. Nearly two thirds of global cancer deaths occur in these regions, where mortality rates are particularly high among individuals younger than the global healthy life expectancy of 65 years (Fig 1).^1-3 This increased mortality is largely driven by delayed diagnoses, insufficient infrastructure, and limited access to early treatment.^3-5 As cancer cases are projected to increase by 47% globally^1,3 with the most significant increases in LMICs, developing innovative diagnostic solutions tailored to resource-limited settings has become imperative.

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.¹ ASR, age-standardized rate; IARC, International Agency for Research on Cancer.

Point-of-care technologies (POCTs) offer a paradigm shift by decentralizing cancer diagnostics, potentially providing rapid, cost-effective testing directly at or near the site of initial patient evaluation.⁶ POCT technological advancements enable the detection of complex biomarker profiles necessary for effective cancer subtyping to guide appropriate treatments (Fig 2A).¹⁰ Nucleic acid–based diagnostics, such as loop-mediated isothermal amplification (LAMP),^11,12 provide a practical alternative to traditional polymerase chain reaction (PCR) methods, enabling fast, specific, and sensitive biomarker detection without the need for sophisticated laboratory infrastructure. Similarly, multiplexed lateral flow immunoassays (LFIAs) facilitate simultaneous detection of multiple cancer biomarkers in a low-cost, portable format, overcoming logistical challenges in LMICs.¹³

FIG 2.

(A) Multiplexed point-of-care testing. Reprinted with permission from Dincer et al.⁷ (B) Key differences between LAMP assay and PCR. Reprinted with permission from New England BioLabs.⁸ (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.⁹ LAMP, loop-mediated isothermal amplification; LED, light-emitting diode; PCR, polymerase chain reaction; UV, ultraviolet.

Complementing molecular tests, portable imaging systems provide noninvasive, high-resolution visualizations of malignancies, especially for epithelial and GI cancers. Emerging imaging modalities, including optical coherence tomography (OCT) and fluorescence-guided microscopy, enhance surgical precision and intraoperative decision making in resource-limited environments.^14-19 These technologies reduce dependency on centralized pathology services and allow for earlier interventions. Furthermore, the integration of artificial intelligence (AI) strengthens diagnostic performance by automating data interpretation, supporting minimally trained personnel and addressing human resource constraints.²⁰

To fully realize the potential of POCTs in global oncology, the tests must address limitations in sensitivity and specificity, infrastructure deficits, and logistical barriers related to supply chains and regulatory approvals.^21,22 The WHO's REASSURED criteria provide a framework for developers, emphasizing affordability, robustness, and user-friendliness.²³ However, widespread adoption of POCTs remains contingent on regulatory approvals and sustainable integration into health care systems. To bridge the gaps, collaborative efforts among health care providers, governments, regulators, industry leaders, and nongovernmental organizations have been instrumental in advancing POCT implementation. The Point-of-Care Technology Research Network (POCTRN) has played a critical role in supporting the development of clinically validated and scalable diagnostic technologies, fostering multidisciplinary and international partnerships.²⁴ Several centers within POCTRN focus on different aspects of POCT innovation, including the Center for Innovation in POC Technologies for HIV/AIDS and Emerging Infectious Diseases at Northwestern University (C-THAN), which emphasizes diagnostic solutions for infectious diseases,; the Center for Innovation and Translation of POC Technologies for Expanded Cancer Care Access at Rice University, which focuses on oncology applications; and the Center for Point of Care Technologies for Nutrition, Infection and Cancer for Global Health, dedicated to advancing point-of-care (POC) diagnostics for nutrition, infection, and cancer in global health. These initiatives collectively provide a structured pathway for technology validation, regulatory navigation, and large-scale implementation.

In this review, we examine emerging POCT innovations in molecular diagnostics, imaging systems, and AI-driven platforms, focusing on their applications in oncology within LMICs. By synthesizing current evidence, addressing technical and operational challenges, and exploring scalable implementation strategies, this review highlights advancements in POCTs and their role in improving oncology care in LMICs.

TECHNOLOGICAL ADVANCEMENTS IN POCT FOR ONCOLOGY

Molecular Diagnostics: LAMP and Real-Time PCR in Cancer Detection

The widespread application of nucleic acid amplification tests (NAATs) has been a critical tool for detecting cancer biomarkers, supporting tumor characterization, and guiding therapeutic decisions. Of NAATs, real-time PCR remains the gold standard due to its high sensitivity, reproducibility, and multiplexing capabilities for the simultaneous detection of multiple genetic targets.^25,26 However, in LMICs where access to centralized laboratory infrastructure is limited, expanding the reach of molecular diagnostics is essential to address the high burden of cancer driven by infectious diseases. Hepatitis B and C infections contribute to nearly 80% of hepatocellular carcinoma cases globally,²⁷ and HPV is the primary cause of cervical cancer.²⁸ Although WHO-prequalified platforms, such as the GeneXpert hepatitis C virus test, offer some capacity for molecular POC testing (the only molecular POC test under WHO prequalification²⁹), PCR's reliance on thermal cycling and complex equipment remains a barrier in decentralized settings. Overcoming these challenges is critical to improving cancer diagnostics and treatment access in LMICs.

LAMP serves as a practical alternative to PCR in decentralized settings (Fig 2B) and can effectively address these logistical limitations. LAMP operates at a constant, moderate temperature (60°C-70°C) using strand-displacing DNA polymerase to facilitate amplification.³⁰ This eliminates the need for thermal cycling, making it compatible with low-cost, portable devices suited for POC applications. Additionally, LAMP's robustness against inhibitors allows for crude sample preparations with minimal nucleic acid purification, reducing processing time and cost.¹² Its accessibility is further enhanced by the availability of reagents from multiple suppliers and publicly available protocols, promoting widespread adoption in diagnostic development.¹¹

The recent success of LAMP-based diagnostics during the COVID-19 pandemic highlights its scalability and clinical potential. As global demand for rapid and decentralized testing grew, LAMP gained significant traction, with 12 tests receiving US Food and Drug Administration (FDA) Emergency Use Authorization and 23 securing Conformité Européenne Mark status.³¹ Demonstrating LAMP's utility in global health, Tuberculosis (TB) LAMP, developed by Eiken Chemical Company, was included in the WHO's list of essential diagnostics.³²

Integrating LAMP with liquid biopsy technologies could further expand minimally invasive cancer detection. Liquid biopsy methods detect tumor-derived biomarkers, such as circulating cell-free DNA in blood or other bodily fluids. These biomarkers, released through processes like apoptosis and necrosis, provide valuable information for early cancer detection, disease monitoring, and treatment guidance.³³ Although several FDA-approved liquid biopsy tests are available,³⁴ none are currently waived for POC use by the Clinical Laboratory Improvement Amendments (CLIA). Thus, combining the speed and robustness of LAMP assays with liquid biopsies could enable rapid, on-site biomarker detection in settings where conventional diagnostics are not feasible.

Multiplexed LFIAs for Cancer Subtyping

Building on advances in nucleic acid–based diagnostics, multiplexed LFIAs offer a complementary approach to detect a broad spectrum of cancer biomarkers. Cancer is a highly heterogeneous disease, characterized by diverse genetic, proteomic, and metabolic alterations that vary across patients, cancer types, and even within individual tumors over time.³⁵ This complexity poses significant diagnostic challenges, as effective clinical decision making requires comprehensive biomarker detection to guide personalized treatments.³⁶ Traditional diagnostic methods, such as immunohistochemistry and PCR-based assays, are often costly, time-consuming, and reliant on centralized laboratories, limiting their scalability in LMICs.³⁷ Multiplexed LFIAs offer a practical alternative, allowing simultaneous detection of multiple biomarkers in a portable, user-friendly format. Their rapid, decentralized capabilities make them ideal for early cancer detection and intervention in resource-limited settings.³⁸

The application of multiplexed LFIAs spans various cancer biomarkers, including tumor-associated antigens, circulating tumor DNA (ctDNA), exosomes, and cytokines, which collectively provide holistic insights into tumor progression and patient stratification.¹⁰ For instance, carcinoembryonic antigen, alpha-fetoprotein, and cancer antigen 125 have been incorporated into multiplexed formats for detecting colorectal, liver, and ovarian cancers, respectively.³⁹ Recent innovations in LFIA technology, such as the integration of nanomaterials (eg, quantum dots and lanthanide-doped nanoparticles), have significantly enhanced their sensitivity and specificity, expanding their versatility in oncology.^40-42

Despite these advancements, several technical challenges hinder the full potential of multiplexed LFIAs. A major obstacle is maintaining high diagnostic accuracy when detecting multiple biomarkers within a single assay. Cross-reactivity between antibodies can cause false positives or signal interference, compromising diagnostic accuracy.¹³ Advanced bioconjugation techniques, dual-zone test strip designs, and optimized reagent concentrations have been used to address these issues and improve assay precision.^36,38 Additionally, the varying concentrations of biomarkers within clinical samples introduce further complexity. Low-abundance targets, such as ctDNA, require enhanced signal amplification, whereas high-abundance targets risk triggering the hook effect, leading to false-negative results.⁴³ Fluorescence-based detection platforms have been developed to overcome these challenges, providing quantitative and high-precision outputs that surpass traditional qualitative readouts.⁴⁴

Stability and usability issues also limit the widespread deployment of LFIAs in decentralized settings. Environmental factors such as temperature and humidity can degrade reagents and compromise assay reliability. To address this, lyophilized reagents and robust membrane materials have been developed to ensure consistent assay performance under diverse conditions.⁴⁵ Additionally, smartphone-based diagnostic systems with automated result interpretation and real-time data sharing have been integrated into LFIA workflows, enabling remote monitoring and centralized oversight through telemedicine networks.^44,46,47 By addressing these logistical challenges, multiplexed LFIAs are poised to deliver timely, accurate cancer diagnostics and improve patient outcomes in underserved regions.

Integrated Imaging Strategies for Cancer Diagnostics and Pathologic Assessment at POC

Although molecular diagnostics provide essential biomarker detection, effective cancer management also relies on precise spatial mapping of cellular changes and tumor margins. Integrated imaging technologies address this need by offering noninvasive visualization of cellular and tissue-level changes linked to tumor progression. By combining optical imaging with computational microscopy, these systems facilitate early cancer detection and detailed pathological assessment. For POC applications, low-cost, durable imaging technologies can help overcome infrastructure constraints to deliver accurate, timely diagnostic support.^9,48

One of the key areas of advancement is the development of low-cost, wide-field, and high-resolution optical imaging systems for detecting epithelial precancers and ensuring complete tumor resection with negative margins (Fig 2C). For instance, digital white-light imaging using low-power microscopes or endoscopes can reveal premalignant lesions in accessible epithelial surfaces, such as the uterine cervix.⁴⁹ Narrow-band illumination further improves detection by highlighting vascular abnormalities associated with precancers in the upper aerodigestive tract.⁵⁰ In parallel, fluorescence and white-light imaging systems enhance lesion triage in the oral cavity, aiding clinicians in identifying patients who require closer monitoring or further diagnostic follow-up.^51,52 Notably, fluorescence-guided surgical resection has demonstrated reduced local recurrence rates for high-grade and early-stage oral cancers,⁵³ minimizing the need for repeated surgeries and intraoperative pathology.

To complement wide-field imaging, high-resolution imaging techniques provide detailed visualization of cellular and tissue-level changes associated with cancer progression. Techniques such as confocal microscopy, two-photon fluorescence microscopy, and OCT enable the assessment of cellular morphology, metabolic activity, and early angiogenesis.^14-19 For example, low-cost microendoscopy has shown success in imaging cervical precancers by detecting morphological changes in superficial epithelial cells in studies conducted in resource-limited settings.^14,19 OCT has been particularly effective in detecting skin cancers and, when adapted for endoscopic use, has improved early detection of GI cancers by identifying light-scattering changes associated with neoplasia.¹⁸ In addition, photoacoustic imaging, which combines light and sound, provides deeper tissue penetration and can detect angiogenesis in tumors within the breast, skin, and prostate.¹⁷ Additionally, studies in oral dysplasia demonstrated that optical markers correlated with gene expression profiles of more advanced lesions, offering a noninvasive alternative for clinical risk assessment.⁵⁴

The utility of imaging technologies extends beyond in vivo applications, with innovations in intraoperative pathology designed to overcome resource limitations during tumor resection. In high-resource settings, frozen section pathology is used during surgery to assess tumor margins. However, in low-resource settings, this process is often unavailable, leading to delayed assessment and the need for secondary surgeries when cancerous tissue is discovered later. To address this challenge, freshly resected tumors can be rapidly imaged using fluorescence microscopy after staining with inexpensive vital dyes. Ultraviolet excitation, confined to the tissue surface, allows for high-resolution imaging without the need for thin tissue sections prepared by a cryostat.^55,56 Computational imaging methods further enhance depth of focus and imaging accuracy in fresh tissue samples, providing an efficient alternative for real-time margin assessment.^57,58

Collectively, the portability, low cost, and high performance of optical imaging systems make them ideal for use in LMICs. Many of these systems are battery-operated, require minimal infrastructure, and support automated image interpretation, reducing the dependency on highly trained personnel. When integrated with molecular and other diagnostic methods, optical imaging technologies can contribute to a comprehensive diagnostic toolkit, with the potential to significantly support early cancer detection, diagnosis, and treatment in under-resourced health care environments.

AI-Driven Diagnostics for Cancer

Advancements in imaging and molecular diagnostics have expanded cancer detection by producing vast, multidimensional data sets. However, the sheer scale and complexity of this information often exceeds the capabilities of manual interpretation, with critical diagnostic patterns frequently overlooked. AI-powered diagnostic platforms address these challenges by synthesizing diverse inputs—including imaging, genomic profiles, and pathology—into integrated diagnostic assessments that improve accuracy^20,59 (Fig 3A).

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.⁵⁹ (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.⁶⁰ 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.

One notable example of AI's diagnostic potential is its success in distinguishing CNS tumors based on genomic data. Differentiating glioblastoma from medulloblastoma via biopsy can be difficult due to overlapping histopathological features. However, an AI platform has demonstrated the ability to differentiate the tumors with high accuracy by identifying key epigenetic differences in the DNA methylation profiles.²⁰ This precision is critical as glioblastomas typically require targeted therapies, whereas medulloblastomas require intensive multimodal treatments including craniospinal irradiation. Similarly, in breast cancer diagnostics, AI-based models analyzing mammographic imaging have demonstrated the ability to predict the location of malignant tumors up to 4 years before clinical presentation.⁶¹ In ophthalmology, an AI platform predicting the progression of exudative age-related macular degeneration in the second eye using three dimensional OCT images and automated tissue segmentation outperformed retinal specialists and optometrists (Fig 3B).⁶⁰ By identifying early tissue abnormalities, these systems enable timely interventions which significantly reduce the risk of advanced disease progression.

Several AI-driven diagnostic platforms have already shown substantial impact in LMICs. In Ghana, the MinoHealth AI platform is designed to analyze digital mammograms and chest x-rays for breast cancer and respiratory conditions.⁶² The platform integrates deep learning algorithms that identify abnormal tissue patterns with minimal human oversight, providing diagnostic insights in remote areas with limited radiologists. In Uganda, an AI-powered platform improves cervical cancer screening by detecting cellular abnormalities in Pap smear slides with 99.28% sensitivity. Using advanced clustering algorithms, it reduces human error and enhances diagnostic accuracy.⁶³ Egypt's national cancer program has integrated AI into its lung cancer diagnostics by leveraging routine chest x-rays. Using convolutional neural networks, the system achieves 93.6% accuracy in classifying lung nodules according to the Lung-Reporting and Data System framework, aiding early-stage lung cancer diagnosis without the need for computed tomography scans or advanced imaging infrastructure.⁶⁴ Another impactful technology is the iBreastExam, a portable AI-enabled device that uses tactile sensor arrays to detect abnormal breast tissue on the basis of differences in stiffness. The device maps these measurements into a color-coded output to indicate regions of concern. Clinical trials conducted in India and Nigeria have shown that the system achieves 97% sensitivity and 94% specificity, making it particularly effective for large-scale screening in settings without mammographic equipment.⁶⁵

Despite these advancements, barriers to scaling AI diagnostics globally remain. Data poverty in LMICs, characterized by limited representation of diverse populations in training data sets, can reduce diagnostic accuracy when applied across heterogeneous populations.⁶⁶ Additionally, cultural skepticism toward AI-based decision making and concerns about cost-effectiveness hinder large-scale adoption.^67,68 Addressing these challenges will require strategic efforts in localized data curation, capacity building, and the development of affordable, context-specific AI models.⁶⁸

CHALLENGES AND LIMITATIONS OF POCT IN ONCOLOGY

Sensitivity and Specificity Issues in POCT

By supporting screening, diagnosis, staging, and treatment monitoring, POCTs are integral to the cancer care continuum. However, cancer is a complex disease, and the accuracy of POCTs must strike a balance between high sensitivity (capturing all true positives) and high specificity (avoiding false positives). Inaccurate diagnoses can result in devastating outcomes, including unnecessary treatments, psychological distress, and delays in initiating appropriate interventions, potentially leading to preventable deaths.^69,70 For definitive cancer diagnoses, sensitivity and specificity should ideally approach 100%, although achieving this remains challenging due to the multifaceted nature of the disease. Traditional diagnostic workflows, which may include a combination of histopathology, imaging, biochemical assays, and molecular analyses, highlight the limitations of relying solely on a single POCT.⁷¹

To enhance the diagnostic performance of POCTs, understanding biomarker variability is critical. Biomarkers commonly targeted by POCTs include nucleic acids (eg, cell-free DNA and RNA), proteins (antigens, enzymes, autoantibodies), circulating tumor cells, and exosomes.⁷² However, biomarker accuracy can vary depending on sample type (eg, blood, urine, or saliva), cancer stage, and environmental factors.^72,73 Additionally, population-specific variables—such as the high prevalence of Kaposi sarcoma–associated herpesvirus in sub-Saharan Africa—can further complicate the interpretation of results, as biomarkers may exhibit nonspecific increases in certain settings.⁷⁴

Innovative approaches can address these challenges by integrating multiple biomarkers and analytical processes within a single POCT. Combining complementary biomarkers enhances sensitivity and specificity, whereas quantitative thresholds help reduce false results and ensure test calibration across different populations.⁶ Multistage POCT systems capable of streamlining sample preparation and minimizing variability can further improve accuracy. Importantly, extensive validation in diverse LMIC settings is necessary to establish performance characteristics that are representative of real-world conditions.⁶ By addressing these limitations, novel POCTs can complement conventional methods, reducing reliance on time-consuming diagnostic procedures without compromising accuracy.

Infrastructure and Logistical Barriers in LMICs

Although optimizing sensitivity and specificity enhances diagnostic precision, real-world implementation of POCTs requires overcoming systemic barriers beyond the technology itself. Unlike well-supported infectious disease diagnostics, such as those for malaria, TB, and HIV, cancer-related POCTs often lack sustained funding and robust distribution networks needed to ensure reliable access and performance in LMICs.^21,22

A key limitation is the inadequate laboratory infrastructure needed to support POCT deployment. Many LMICs lack well-equipped laboratories, essential ancillary equipment, and reliable cold-chain systems for storing and transporting diagnostic reagents.⁷⁵ Additionally, shortages of trained personnel, including laboratory specialists and bioengineers, restrict both the development and maintenance of diagnostic tools. Furthermore, the scarcity of well-characterized, high-quality biopsy samples and inconsistencies in histopathological review hinder the validation of tissue-based POCTs.^76,77

Navigating regulatory and logistical barriers exacerbates delays in POCT testing and deployment. Regulatory approval processes in LMICs vary significantly, and intercountry differences create additional hurdles when devices need to be tested across borders.⁷⁸ Importing diagnostic equipment from high-income countries (HICs) is often delayed due to customs complications, particularly when novel devices are not well understood by officials. Conversely, the export of biological specimens to HICs for validation requires material transfer agreements which are time-consuming and administratively complex in some settings. Furthermore, co-development raises concerns about intellectual property ownership and equitable access to resulting technologies, requiring clear agreements among stakeholders.⁷⁹

Overcoming these logistical barriers requires targeted, scalable solutions tailored to LMIC conditions. Solar-powered diagnostic devices can alleviate power limitations in rural areas, enabling uninterrupted off-grid testing.⁸⁰ AI-driven diagnostic algorithms can be integrated with POCTs to assist minimally trained health care workers by automating result interpretation, improving accuracy without requiring specialized expertise.⁸¹ Furthermore, telehealth networks equipped with cloud-based platforms can enable remote data sharing and real-time expert consultation, bridging the gap between rural clinics and central laboratories. Portable, cold-chain–independent reagent formulations, such as lyophilized diagnostic kits, can enhance logistical flexibility by maintaining assay stability in diverse environmental conditions.⁷⁵ By ensuring POCT designs are cost effective, easy to operate, and validated in real-world LMIC settings, these innovations can help address the persistent diagnostic inequities in cancer care.⁶

IMPLEMENTATION AND FUTURE DIRECTIONS

Scalability and Sustainability of POCT in LMICs

Addressing infrastructural, financial, and operational challenges ensures sustainable scaling of POCTs in LMICs. Despite the critical role of diagnostics, approximately 47% of the global population has limited access to diagnostic services, a disparity exacerbated by supply-chain disruptions during the COVID-19 pandemic.⁸² Limited local manufacturing and dependence on external suppliers further reduce affordability and accessibility in LMICs.

To guide sustainable development, the WHO established the REASSURED criteria (Real-time connectivity, Ease of specimen collection, Affordable, Sensitive, Specific, User-friendly, Rapid and Robust, Equipment-free, and Deliverable to end users), updating its earlier ASSURED framework.²³ These criteria emphasize the importance of designing diagnostics for environments lacking reliable power, refrigeration, and water infrastructure. Ideal tests should require minimal local training and maintain functionality under extreme conditions. Key design features include internal self-calibration and built-in error diagnostics, ensuring operational reliability in the absence of technical support. Devices should minimize energy consumption, making them compatible with battery or solar power, and test cartridges should incorporate lyophilized reagents capable of maintaining stability for extended periods at high temperatures.

In addition to technical specifications, developers must address the impact of POCTs on existing clinical workflows. CLIA-waived diagnostics, for example, eliminate the need for precise measurements or user interpretation, making them suitable for minimally trained personnel.⁸³ Incorporating input from local health care workers during the early stages of design ensures that diagnostics fit seamlessly into under-resourced health systems.⁸⁴ When aligned with local infrastructure and clinical needs, these innovations can be scaled to meet the demands of LMIC populations, improving diagnostic equity globally.

Case Studies: Successes in Infectious Diseases to Guide POCTs for Global Oncology

Efforts to advance POCT integration in LMICs have been significantly supported by the National Institutes of Health, which funds several initiatives focused on global health. Among these, two key programs—the Affordable Cancer Technologies (ACT) Program and POCTRN—have played complementary roles in expanding the development and implementation of point-of-care solutions. The ACT Program has specifically driven key milestones in developing and deploying cost-effective cancer diagnostics and treatment solutions for resource-limited settings (Fig 4). Meanwhile, POCTRN, which funds several centers focused on advancing POCTs, fosters innovations in POC diagnostics across various health domains (Fig 5). Within POCTRN, C-THAN plays a crucial role in strengthening partnerships with local investigators and institutions, particularly in addressing infectious diseases that are closely linked to cancer. These include TB, HIV, and hepatitis, which are leading comorbidities in oncology. Over 7 years, C-THAN has supported 51 projects, 30% of which were led by African principal investigators, leading to 56 peer-reviewed publications and two market-ready diagnostic products.^24,93-95

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.^85-88 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,⁸⁹ AIM-HPV for cervical cancer screening,⁹⁰ Lucia for instructional training on cervical cancer,⁹¹ and COPHAS to assess POC diagnostics for sexually transmitted infections.⁹² 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.

A notable success is the Diagnostic Analyzer for Selective Hybridization (DASH) for rapid PCR, a NAAT platform designed for POCT applications. Developed to meet the accuracy standards of laboratory-based PCR while addressing the speed, cost, and simplicity required for decentralized use, DASH offers an alternative for detecting infectious diseases in LMICs.^89,93,96 Unlike conventional NAAT systems, DASH is portable, CLIA-waived, and delivers results in 15 minutes, significantly reducing the turnaround time compared with centralized laboratory testing. Clinical trials demonstrated high concordance with the FDA-approved Cepheid Xpert Xpress SARS-CoV-2 test, resulting in its Emergency Use Authorization for COVID-19 detection in 2022.⁹⁷ Beyond COVID-19, ongoing development within the C-THAN network aims to expand DASH's applications to TB, hepatitis B, and HIV, addressing the broader diagnostic needs of LMICs.

Another example is the Sinapi Specimen Cup, a product designed to address persistent issues in TB diagnostics. Inadequate or contaminated sputum specimens, often due to insufficient volume or improperly sealed specimen cups, account for up to 8% of rejected samples during TB testing.⁹⁸ These rejected samples result in diagnostic delays, repeat visits, and increased patient burden, especially in regions with limited access to diagnostic centers. The Sinapi Specimen Cup mitigates this problem with a dual-cup design, featuring clear volume indicators and a watertight, twist-sealing mechanism that minimizes leakage and cross-contamination.^97-99 By ensuring accurate sample collection and reducing rejections, the Sinapi cup enhances the reliability and efficiency of TB diagnostics, demonstrating how localized solutions can address specific health care challenges in LMICs.

These examples underscore the importance of developing diagnostic technologies through collaborations that integrate local needs with scalable designs. Programs like C-THAN provide a framework for sustained innovation by fostering partnerships between LMIC investigators and global experts, ensuring that solutions are not only effective but contextually relevant. Continued investment in such models is essential to scaling successful POCTs and fostering long-term self-sufficiency in LMIC health care systems.

In conclusion, POCTs offer a potentially transformative pathway to advancing cancer diagnostics in LMICs by enabling rapid, affordable testing that overcomes logistical and infrastructural barriers. As highlighted in this review, innovations in molecular diagnostics, imaging-based assessments, and AI-driven analytics are driving improvements in diagnostic accuracy and timeliness, with the potential to enhance clinical outcomes. However, realizing the full potential of POCTs requires addressing persistent obstacles, including regulatory bottlenecks, operational challenges, and financial constraints. Successful deployment depends on strategic collaborations among stakeholders, adaptive regulatory pathways, and the development of local capacity to ensure reliable, long-term use. By aligning technological advancements with health care system needs and fostering innovation suited to resource-limited settings, future efforts can focus on integrating POCTs into existing infrastructure while addressing gaps in accessibility and scalability. Through targeted investments, cross-sector partnerships, and inclusive design, POCTs have the potential to reduce diagnostic disparities and deliver sustainable, impactful improvements in global oncology.

AUTHOR CONTRIBUTIONS

Conception and design: Wenting Gao, Rebecca R. Richards-Kortum, Sally M. McFall, Robert L. Murphy, Aggrey Semeere, David Erickson

Financial support: Aggrey Semeere, David Erickson

Administrative support: Aggrey Semeere

Collection and assembly of data: Wenting Gao, Jason C. Manning, Sally M. McFall

Data analysis and interpretation: Wenting Gao, Kirtana Devaraj, Robert L. Murphy

Manuscript writing: All authors

Final approval of manuscript: All authors

Accountable for all aspects of the work: All authors

AUTHORS' DISCLOSURES OF POTENTIAL CONFLICTS OF INTEREST

The following represents disclosure information provided by authors of this manuscript. All relationships are considered compensated unless otherwise noted. Relationships are self-held unless noted. I = Immediate Family Member, Inst = My Institution. Relationships may not relate to the subject matter of this manuscript. For more information about ASCO's conflict of interest policy, please refer to www.asco.org/rwc or ascopubs.org/go/authors/author-center.

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