The paper identifies challenges associated

The gap

Evidence profile

Sourced from the stated challenges and future work and conclusions of the source papers, classified as general, spanning 5 journals.

Supporting evidence — 7 representative gaps

• Integrating Artificial Intelligence, Circulating Tumor DNA, and Real-World Evidence to Optimize Hematologic Clinical Trials: Toward Adaptive and Learning Trial Designs (2026) · Cancers · doi

  The paper identifies challenges associated with the implementation of AI, ctDNA, and RWE, including data standardization and regulatory considerations. It also highlights the limitations of ctDNA, such as insufficient sensitivity for low tumor burden detection and inconsistencies between circulating and tissue-based genotyping. The paper discusses the need for more research on the integration of these emerging technologies to address the challenges and limitations associated with their implementation.

  generalstated challenges
  Keywords: paper identifies challenges associated implementation ctdna rwe including
• The multi-omic transformation of breast cancer diagnostics: a comprehensive narrative of the transition from immunohistochemistry to liquid biopsy and next-generation sequencing (2026) · Egyptian Journal of Medical Human Genetics · doi

  The transformation of breast cancer diagnostics from a single IHC-based assessment to a multi-omics, lon- gitudinal pipeline is the hallmark of modern precision Gholipour Maralan Egyptian Journal of Medical Human Genetics (2026) 27:32 medicine. While IHC remains essential for initial screen- ing and morphological context, it is no longer sufficient for the management of complex, evolving disease. Future directions: Interventional genomics: Clinical trials must now prioritize treating patients based on MRD status rather than waiting for radiological recurrence. Multi-modal AI integration: The future lies in combining "spatial transcriptomics" with serial liquid biopsy to create "digital twins" of patient tumors. Realizing this requires addressing significant technical challenges in data integration and demonstrating clinical utility. Equitable precision medicine: Standardizing bioinformatics and reducing sequencing costs is vital to bridge the "genomic divide" and ensure global access. Expanding the multi-omic lens: Future research must integrate epigenomics, metabolomics, and spatial transcriptomics to fully map the breast cancer landscape beyond genomics and transcriptomics.

  generalfuture workevidence 5/5
  Keywords: multi future transcriptomics breast cancer based precision medicine genomics clinical must integration spatial transformation diagnostics
• Evaluating the Accuracy of Artificial Intelligence Models for Early Lung Cancer Detection: Evidence From a Systematic Review (2026) · Cureus · doi

  Therefore, future research should focus on prospective study designs, standardized reporting in line with established guidelines, and rigorous external validation across diverse populations to strengthen clinical applicability. In addition, further work is needed to assess the real-world clinical impact of AI systems, including their effect on diagnostic pathways and patient outcomes, while also expanding applications toward biomarker quantification and multimodal data integration to advance precision lung cancer care.

  generalconclusionsevidence 5/5
  Keywords: clinical future focus prospective designs standardized reporting line established guidelines rigorous external validation across diverse
• Multimodal data fusion: integrating PET/MRI and liquid biopsy for a holistic view of cancer biology (2026) · Frontiers in Oncology · doi

  Based on the aforementioned multidimensional complementary mechanisms, the multimodal data fusion of PET/MRI and liquid biopsy demonstrates significant value throughout the entire clinical diagnosis and treatment process (Figure 3). From early diagnosis and risk stratification to treatment efficacy evaluation, drug resistance monitoring, and even long-term follow-up and recurrence early warning, the two modalities can contribute with differential decision weights at different stages, collectively establishing a dynamic and precise tumor management system. 4.1 Early diagnosis and risk stratification In early cancer screening, liquid biopsy (such as multi-cancer detection based on ctDNA methylation signatures) can serve as efficient preliminary screening tools. However, a key challenge in general screening settings is that the low prevalence of cancer can limit the positive predictive value (PPV) of these tests, which, despite high specificity, leads to a risk of false-positive results (59). Whole-body PET/MRI technology provides crucial secondary verification and stratification capabilities. By integrating metabolic imaging (such as 18F-FDG PET) with high-resolution anatomical and functional MRI, this technology enables systemic evaluation of individuals with positive liquid biopsy results: accurately localizing suspicious lesions, distinguishing between benign and malignant conditions through multi-parameter analysis (e.g., SUVmax, ADC values), and offering anatomical guidance for subsequent interventions (74). This sequential “liquid biopsy-based initial screening followed by imaging-based precise screening” model has shown promise in early-phase and prospective cohort studies (e.g., the PATHFINDER study (59)) to improve the specificity and efficiency of screening. Recent large-scale prospective studies have further advanced this field: the K-DETEK study validated a multimodal ctDNA-based MCED test in 9, 057 asymptomatic individuals, demonstrating 70.8% sensitivity and 99.7% specificity (75).

  generalfuture workevidence 5/5
  Keywords: screening based early liquid biopsy cation diagnosis risk strati cancer positive speci city multimodal value
• Multimodal data fusion: integrating PET/MRI and liquid biopsy for a holistic view of cancer biology (2026) · Frontiers in Oncology · doi

  Conflict of interest The deep multimodal data fusion of PET/MRI and liquid biopsy is driving the evolution of cancer diagnosis and treatment toward a multimodal approach. This strategy synergizes macroscopic imag- ing information with microscopic molecular data, demonstrating clear value in early tumor detection, heterogeneity analysis, dy- namic treatment monitoring, and precise prognostic stratification. However, its clinical translation still faces core challenges, including a lack of standardization, algorithmic bottlenecks, and insufficient high-level evidence. Moving forward, leveraging artificial intelli- gence and multi-omics technologies to build standardized data analysis platforms and validate clinical utility through prospective trials will be essential. The advancement of this integrated paradigm will provide critical technical support for the transition from population-based treatments to individualized precision medicine, ultimately enhancing the systematic and effective management of cancer. Future directions include: developing AI-driven, multimodal data fusion platforms to achieve end-to-end optimization from raw data to clinical decision-making; exploring the integration of multi-dimensional liquid biopsies beyond blood (such as cerebro- spinal fluid and urine) with site-specific imaging for specialized types like central nervous system tumors; building personalized dynamic monitoring networks based on regular liquid biopsies and key time-point PET/MRI scans to enable predictive healthcare; and ultimately forming a closed-loop, integrated diagnostic and therapeutic system. For instance, by leveraging The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

  generalfuture workevidence 5/5
  Keywords: multimodal liquid clinical interest fusion cancer treatment monitoring leveraging multi platforms integrated based ultimately biopsies
• A visual analysis of the research dynamics of biomarkers for lung cancer screening (2026) · Clinical Epigenetics · doi

  Emerging diagnostic technologies, especially liquid biopsy and AI-assisted diagnosis, are profoundly chang- ing the landscape of early screening markers for lung cancer. Liquid biopsy enables non-invasive detection of biomarkers, theoretically overcoming tumour heteroge- neity and facilitating dynamic monitoring; meanwhile, AI leverages machine learning to analyse medical imaging and multi-omics data, significantly enhancing screening efficiency and the precision of risk stratification. Never- theless, the transition of these technologies from proof- of-concept to routine clinical implementation confronts multiple translational bottlenecks, including technical sensitivity, ethical compliance, and data privacy, which necessitate systematic resolution through large-scale multi-centre validation and the establishment of indus- try standards. Variations in technical sensitivity and the absence of standardisation constitute pivotal barriers to the clinical deployment of liquid biopsy. As a previ- ous review suggested, a successful screening NGS-based blood test would have to test up to 1000 genes, with the ctDNA detection limit improving ten-fold from the current 0.1% to less than 0.01% [52]. The lack of unified SOPs across the entire analytical workflow—from speci- men acquisition and exosome isolation to methylation quantification—results in significant systematic dispari- ties between platforms regarding exosome distribution, ctDNA detection sensitivity, and methylation quantifica- tion, severely compromising inter-laboratory data com- parability and reproducibility. More critically, existing evidence derives predominantly from small-scale, single- centre retrospective studies; the distinct lack of rigorous validation through large-scale prospective cohort stud- ies precludes confirmation of the clinical utility, screen- ing benefits, and cost-effectiveness of these biomarkers, thereby impeding their rapid integration into precision oncology frameworks. On the other hand, technological limitations of AI-assisted diagnostics include the opacity of decision-making processes and insufficient algorithmic generalisability. These technologies create severe ethi- cal challenges linked to patient informed authorisation and data privacy abuses. Clinical translation depends not Zhu et al. Clinical Epigenetics (2026) 18:90 merely on technological optimisation but critically upon the establishment of transparent algorithmic auditing mechanisms, stringent data security standards, and cor- responding ethical governance frameworks to ensure the fairness and accountability of AI-driven decisions. Systematic resolution requires synergistic advance- ment across three dimensions: first, the establishment of ISO-compliant shared pre-validation platforms, facili- tated by interdisciplinary research funding, to promote end-to-end standardisation from biospecimen process- ing to data analysis, with stringent SOPs ensuring inter- platform coherence; second, the execution of large-scale, multi-centre prospective clinical trials to empirically validate the diagnostic performance, screening benefits, and health economic value of biomarkers and AI models, thereby addressing the current deficit in clinical valida- tion; and third, the construction of clear and harmon- ised regulatory frameworks that unambiguously define approval pathways for liquid biopsy and AI as novel in vitro diagnostics or medical devices, coupled with policy interventions to reduce technology costs and enhance accessibility in resource-limited settings. Achieving technical standardisation, validation, and ethical governance is critical for integrating liquid biopsy and AI—potentially through comprehensive screening models that combine various molecular markers—to enhance intervention strategies against lung cancer. This integrated approach aims to decrease lung cancer mortal- ity through precision prevention and targeted therapies.

  generalfuture workevidence 5/5
  Keywords: clinical liquid biopsy screening scale validation technologies lung cancer detection biomarkers multi precision technical sensitivity
• Liquid biopsy in pediatric acute lymphoblastic leukemia (2026) · Frontiers in Oncology · doi

  The future of liquid biopsy in pediatric ALL is bright. We anticipate increasingly multimodal approaches – combining ctDNA, circulating RNA, and EV analyses to capture complemen- tary information about the leukemia. Machine learning models might integrate these data with clinical variables to improve prediction of outcomes (for example, AI algorithms to stratify risk based on longitudinal ctDNA trends). With targeted and immunotherapies now central to pediatric ALL precision care (47, 48), liquid biopsy serves as a cornerstone for real-time monitoring, MRD detection, and personalized therapy guidance, aiming to boost survival, minimize toxicity, and enhance children’s quality of life (49). Building on this, liquid biopsy could also guide truly personalized therapy: one can monitor in real-time which clones are expanding and tailor drugs accordingly, or detect emerging resis- tance mutations and switch therapy before relapse fully develops. Moreover, liquid biopsy might enable new endpoints in clinical trials (such as molecular response rate, instead of waiting for morphological response). As assays become cheaper and faster, even point-of-care testing is conceivable – e.g., a rapid cartridge- based PCR test for an ALL-specific DNA sequence from a fingerstick blood sample. Finally, lessons from adult oncology will continue to inform pediatric use. The field acknowledges that pediatric liquid biopsy efforts are “still behind adult oncology”, but publications and innovations in pediatrics have sharply in- creased in the last few years. With ongoing validation in pediatric trials and growing clinician familiarity, liquid biopsy is moving from bench to bedside. In conclusion, liquid biopsy in pediatric ALL is a transformative approach that complements traditional methods of disease assess- ment. By enabling non-invasive, sensitive detection of tumor- derived biomarkers like ctDNA, miRNAs, and exosomes, it holds the potential to improve diagnostic accuracy, refine prognostica- tion, and allow earlier intervention for relapse – all while reducing the burden on young patients. Challenges of sensitivity, standard- ization, and interpretation are actively being addressed through research. As evidence mounts, it is likely that the coming years will see liquid biopsy assays integrated into routine pediatric ALL management, heralding a new era of precision monitoring and truly individualized therapy for children with leukemia. curation, Investigation, Methodology, Project administration, Supervision, Validation, Writing – review & editing. CY: Investigation, Methodology, Project administration, Supervision, Validation, Writing – review & editing. XX: Investigation, Methodology, Project administration, Supervision, Validation, Writing – review & editing. MY: Conceptualization, Investigation, Project administration, Resources, Supervision, Validation, Writing – review & editing. XZ: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing.

  generalfuture workevidence 5/5
  Keywords: liquid biopsy pediatric validation writing investigation project administration supervision review editing therapy methodology ctdna leukemia

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