Blood Test for Tumor DNA in Lung Cancer Immunotherapy: A Promising Tool That Is Not Yet Ready to Guide Treatment

Table of Contents

• Key Points
• Background: Why Doctors Need Better Ways to Track Lung Cancer Treatment
• The Biology Behind ctDNA Testing in Lung Cancer
• How ctDNA Is Detected and Measured
• ctDNA in Neoadjuvant Immunotherapy (Before Surgery)
• ctDNA in Adjuvant Immunotherapy (After Surgery)
• ctDNA in Consolidation Immunotherapy After Chemoradiotherapy
• What This Means for Patients
• Limitations: What the Evidence Cannot Yet Prove
• Recommendations and Future Directions
• Frequently Asked Questions
• Source Information

Key Points

• ctDNA is a blood test that detects tiny fragments of DNA shed by lung tumors and can be measured repeatedly during treatment.
• ctDNA reliably identifies patients at higher risk of recurrence or death, making it a strong prognostic marker across all stages of lung cancer.
• Early declines in ctDNA often track with how well immunotherapy is working, and rising ctDNA may precede visible tumor growth on imaging by about four months.
• Evidence that ctDNA can predict which specific patients will benefit from immunotherapy, or guide treatment decisions, remains limited and investigational.
• Clonal hematopoiesis, variable tumor shedding, and limited sensitivity at low disease burden can affect ctDNA results and require careful interpretation.

Background: Why Doctors Need Better Ways to Track Lung Cancer Treatment

Immune checkpoint inhibitors (ICIs) are medicines that help the immune system attack cancer cells. These drugs are now a standard part of lung cancer treatment. Doctors use them alone or combined with chemotherapy and/or radiation therapy. They are used across the entire course of the disease, from treating curable tumors to controlling advanced cancer long-term.

However, the benefit is not the same for every patient. Some patients achieve lasting remission. Others do not respond at all, or their cancer comes back soon after treatment. Some patients experience serious side effects without gaining much benefit. This variation creates a major challenge for doctors: how do you identify which patients will benefit most from immunotherapy? And how do you adjust treatment intensity and duration for each person?

Current tissue-based biomarkers only partly solve this problem. PD-L1 (programmed death-ligand 1) is a protein measured on tumor tissue. Its levels correlate imperfectly with treatment benefit. PD-L1 levels can vary across different areas of the same tumor. They can also change over time or after treatment. TMB (tumor mutational burden) measures how many genetic mutations a tumor has. TMB captures one aspect of how visible the tumor is to the immune system. But TMB rarely works well as a standalone predictor. It is also complicated by differences between testing methods and by limitations in how tumor samples are collected.

More fundamentally, a single biopsy is a static snapshot. It shows one moment in time from one small area of a tumor that is spatially heterogeneous (genetically different in different areas) and constantly evolving. A biopsy cannot reliably answer whether the current treatment is working right now, or whether it is likely to keep working.

Radiographic assessment (imaging with CT scans) has related limitations. Size-based response criteria often lag behind molecular and immune changes. Immunotherapy introduces additional interpretive challenges. These include pseudoprogression (tumors appear to grow on scans because immune cells are flooding in, even though the cancer is actually responding) and delayed response. As a result, ineffective therapy may be continued unnecessarily. Meanwhile, patients with lasting benefit may receive prolonged treatment with avoidable toxicity and cost, because no validated dynamic biomarker defines response or optimal treatment duration.

In this context, ctDNA offers a complementary approach. Because ctDNA reflects tumor burden and genomic heterogeneity, serial measurement can provide a real-time view of how tumor clones are changing under treatment pressure. The concept makes sense: a rapid decline or clearance of ctDNA could serve as an early molecular signal of response, often before radiographic change. Rising ctDNA or re-emergent variants may indicate molecular progression or relapse before it becomes clinically obvious.

In the minimal residual disease (MRD) setting after surgery or definitive chemoradiotherapy, ctDNA positivity can flag patients at high recurrence risk even when imaging remains negative. These potential advantages must be interpreted in light of variable tumor shedding, limited sensitivity at low disease burden, and false-positive signals from clonal hematopoiesis (discussed below).

The authors use three specific terms throughout this review, each with different evidence requirements:

• Prognostic biomarker: Associated with an outcome regardless of which treatment the patient received.
• Predictive biomarker: Identifies differential benefit from a specific therapy. This is most convincingly supported by a statistically tested treatment-by-biomarker interaction in a randomized comparison.
• Response-associated biomarker: Correlates with response or outcome within a treated group. Without an appropriate comparison group and interaction analysis, it does not establish treatment-specific benefit.

These distinctions define both the scope and the evidence framework of this review. Most available data establish ctDNA as a prognostic marker: baseline or post-treatment detectability consistently stratifies risk. The more clinically actionable but less established objective is true prediction and decision support. This means using ctDNA to select patients for immunotherapy or to guide initiation, intensification, de-escalation, or discontinuation in ways that improve outcomes. Early ctDNA dynamics are often associated with response and survival, but association alone does not demonstrate treatment-specific benefit or clinical utility.

The Biology Behind ctDNA Testing in Lung Cancer

In lung cancer, plasma ctDNA levels are influenced by the burden, turnover, and vascular accessibility of tumor deposits. This means ctDNA can serve as a molecular surrogate of disease burden. Serial profiling can also capture changes in clonal composition. Under the selective pressure of immunotherapy, treatment-sensitive clones contract, and ctDNA carrying their characteristic variants declines. Conversely, resistant subclones may expand. This increases the variant allele frequency of specific alterations and signals the outgrowth of resistant populations. Longitudinal ctDNA profiling can therefore complement imaging by tracking molecular tumor evolution during lung cancer immunotherapy.

In patients with lung cancer who respond to immunotherapy, tumor regression is commonly accompanied by a marked decrease, and sometimes clearance, of ctDNA. This pattern is often called molecular response. Early ctDNA decline is frequently associated with subsequent radiographic tumor shrinkage and may precede visible imaging changes. This supports its use as a response-associated readout. Conversely, persistent or rising ctDNA during treatment is associated with primary resistance or progressive disease.

Importantly, ctDNA dynamics offer a temporal advantage over conventional radiographic assessment. For example, in locally advanced NSCLC, a rise in ctDNA may precede radiographic progression by approximately four months. Thus, a decrease or clearance of ctDNA is regarded as a molecular hallmark of effective immunotherapy. Persistently elevated or rising ctDNA indicates a poor response. This pattern of molecular response is closely associated with both progression-free survival (PFS, the time before the cancer worsens) and overall survival (OS, the time until death from any cause). This supports the use of ctDNA as a valuable complementary biomarker for evaluating the efficacy of immunotherapy.

How ctDNA Is Detected and Measured

Mutation-based ctDNA assays use two main architectures: tumor-informed or tumor-agnostic.

• Tumor-informed assays begin with somatic variants (mutations) identified in an individual patient's tumor. They then track a personalized set of variants. This generally improves sensitivity and specificity at low disease burden.
• Tumor-agnostic (plasma-only) assays do not require tissue genotyping. Instead, they interrogate predefined genes or broader genomic features in plasma.

Both architectures may be implemented using targeted sequencing. CAPP-Seq (Cancer Personalized Profiling by Deep Sequencing) uses cancer-specific hybrid-capture panels with integrated error suppression. Phased-variant methods can further improve sensitivity by requiring co-occurring variants on the same DNA molecule. TEC-Seq (Targeted Error Correction Sequencing) combines ultra-deep sequencing with molecular error correction. It is useful when tissue is unavailable or the tumor genotype is unknown.

Analytically, these architectures can be implemented using allele-specific quantitative polymerase chain reaction (qPCR), droplet digital PCR (ddPCR), or targeted next-generation sequencing (NGS). PCR-based methods provide rapid, highly sensitive quantification of a small number of predefined variants. They are therefore well suited to serial tracking when the target is known. Targeted NGS uses amplicon or hybrid-capture panels. It may incorporate unique molecular identifiers (UMIs), duplex consensus sequencing, integrated error suppression, or phased-variant tracking to suppress background noise and improve sensitivity at low tumor fraction. NGS provides greater breadth for monitoring multiple clones, but requires deeper sequencing, more complex bioinformatics, and assay-specific threshold calibration.

Beyond mutation counting, emerging plasma assays assess genome-wide copy-number alterations, methylation patterns, fragment lengths and end motifs, nucleosome footprints, or combinations of genomic and epigenomic signals. Low-pass whole-genome sequencing (WGS) can support copy-number and fragmentomic analyses without predefined variants. Methylation and fragmentomic approaches may improve detection in tumors with few trackable mutations. However, evidence linking these modalities to serial response assessment or treatment-by-biomarker interactions in lung cancer immunotherapy remains limited.

For the clinical use cases reviewed here, there is no single universally superior platform. Tumor-informed sequencing or ddPCR is advantageous for high-sensitivity MRD and known-variant tracking. Broader plasma-only NGS is useful when tissue is unavailable or clonal evolution is of interest. Genome-wide or epigenomic assays remain investigational for immunotherapy-guided decisions. In all cases, analytical sensitivity, blood volume, turnaround time, false-positive control, and reproducibility at prespecified timepoints must match the intended clinical use.

For lung cancer immunotherapy, ctDNA readouts can be organized as static baseline measures, dynamic kinetic parameters, and composite metrics.

• Baseline detectability, mutant-copy level, and variant allele frequency (VAF), including maximum VAF (maxVAF), primarily reflect tumor burden and overall risk. They are therefore generally prognostic rather than predictive of immunotherapy benefit.
• Dynamic indicators capture change over time. These include ctDNA clearance (conversion from detectable to undetectable or below a prespecified threshold) and VAF kinetics, such as the relative change in mean mutant VAF (mmVAF).
• Composite indices summarize these shifts across variants. MinerVa-Delta, for example, integrates the on-treatment change in mutation burden into a single quantitative score.

Among these, molecular response — typically defined as ctDNA clearance or a marked decline (often greater than 50%) after a set number of cycles — appears most clinically informative. It is repeatedly associated with longer PFS and OS. The flip side is molecular progression: rising ctDNA can precede radiographic progression, likely reflecting emerging resistance, and it generally portends poor outcomes.

Within the same monitoring framework, mmVAF has been used to probe clonal structure. In locally advanced NSCLC treated with chemoradiotherapy plus immunotherapy, a higher baseline mmVAF has been interpreted as a signal of lower heterogeneity and higher clonality. This potentially enriches for shared clonal neoantigens that are more readily targeted by activated immune cells. Consistent with that idea, patients with high baseline mmVAF often show rapid ctDNA declines and better outcomes. In contrast, low baseline mmVAF (suggesting greater heterogeneity) and absent early ctDNA decline tend to align with immune escape and reduced benefit.

MinerVa-Delta offers another angle. In first-line immunotherapy for advanced lung squamous cell carcinoma, residual ctDNA of 30% or greater after two cycles was used to define molecular non-response. Notably, this outperformed RECIST-based imaging for predicting efficacy and survival, especially among patients labeled as having stable disease (SD).

It is also increasingly clear that ctDNA and imaging can complement each other. Among radiographic SD cases, molecular clearance by ctDNA often corresponds to meaningful clinical benefit, with outcomes closer to partial response. SD without ctDNA decline tends to resemble progressive disease. In that sense, integrating ctDNA kinetics with radiology may help correct the blind spots of imaging alone and sharpen response stratification under immunotherapy.

Reliable longitudinal ctDNA assessment in lung cancer depends on standardized pre-analytical handling and quality control. Plasma is preferred over serum because clotting can release background DNA from blood cells, dilute the tumor signal, and reduce mutation-detection specificity. Samples should be processed promptly to separate plasma and stored at low temperature to limit leukocyte lysis and genomic DNA contamination.

Timing also matters. In neoadjuvant and adjuvant settings, serial sampling at baseline (before surgery), after surgery, and at defined treatment timepoints is particularly useful for MRD assessment and for gauging adjuvant efficacy. In advanced disease, early kinetics — often assessed about 2 to 6 weeks after treatment initiation — appear especially informative for response prediction.

A key confounder is clonal hematopoiesis (CH). Age-associated mutations in genes such as DNMT3A, TET2, and JAK2 can arise from expanded hematopoietic clones. These clones shed DNA into circulation independent of tumor, creating false-positive ctDNA signals. A practical mitigation is parallel sequencing of matched peripheral blood leukocyte DNA, followed by bioinformatic filtering to remove CH-derived variants. With standardized processing, appropriate sampling schedules, and CH-aware interpretation, ctDNA testing becomes substantially more robust for response monitoring and exploratory treatment-selection research in lung cancer.

ctDNA in Neoadjuvant Immunotherapy (Before Surgery)

In resectable NSCLC (non-small cell lung cancer that can be removed by surgery), neoadjuvant immunotherapy combined with chemotherapy has become a major step forward. In this setting, dynamic ctDNA changes can provide a timelier and often more sensitive signal of treatment response than conventional radiographic imaging.

In a prospective study by Yue et al., the relative change in mean VAF (RDmeanVAF) during treatment was associated with MPR (major pathological response). It classified MPR status with 100% sensitivity and 91.7% accuracy, substantially outperforming CT-based assessment. In practical terms, ctDNA kinetics may allow early identification of pathological responders. Persistent ctDNA positivity tends to be associated with limited pathological response.

Exploratory biomarker data from the CheckMate 816 trial align with this concept. Patients receiving neoadjuvant nivolumab plus chemotherapy showed a higher ctDNA clearance rate (56%) than those treated with chemotherapy alone (34%). Importantly, across treatment arms, ctDNA clearance was strongly associated with a higher pathological complete response (pCR) rate (46% vs. 13%). This supports ctDNA clearance as a response-associated pharmacodynamic readout. Because clearance was evaluated after treatment and the response association was assessed across arms, these findings do not establish that ctDNA predicts incremental benefit from nivolumab.

Similar patterns are reported in the NADIM I/II and LCMC3 trials. In NADIM I/II, most patients who cleared ctDNA after neoadjuvant immunochemotherapy achieved MPR. In LCMC3, which evaluated neoadjuvant atezolizumab monotherapy, patients with MPR experienced a significantly greater ctDNA reduction than non-responders. The magnitude of decline correlated moderately with both pathological response and radiographic tumor shrinkage. This suggests ctDNA kinetics track genuine biological activity even when immunotherapy is given without chemotherapy.

Pooled analyses, including CTONG1804, further reinforce that conversion from ctDNA positivity to negativity is a robust marker of deep pathological response to immunochemotherapy. Patients maintaining ctDNA negativity across serial timepoints consistently achieved pCR or MPR. Those with any persistent ctDNA were largely non-responders.

ctDNA dynamics could also support prospective studies of neoadjuvant treatment duration. In the NCT05157776 trial, which compared different cycles of neoadjuvant immunochemotherapy, MRD dynamics based on ctDNA helped distinguish patients more likely to benefit from extended therapy. Those who cleared ctDNA early and sustained negativity achieved a pCR rate of 75%, compared with 0% among patients who never cleared ctDNA.

Larger biomarker datasets, including AEGEAN, show similar trends. Higher preoperative ctDNA clearance and improved pCR/MPR rates occurred in patients receiving durvalumab plus chemotherapy. Conversely, failure to clear ctDNA was rarely followed by deep pathological response. These data support ctDNA kinetics as a response-associated pharmacodynamic readout, but they do not yet validate ctDNA-guided modification of neoadjuvant duration.

In summary, baseline ctDNA detectability in the neoadjuvant setting is principally prognostic. On-treatment decline or clearance is response-associated with the depth of pathological response. These measures can identify molecular and pathological response before long-term survival endpoints mature. However, current data do not establish a treatment-selective predictive biomarker without a treatment-by-ctDNA interaction.

ctDNA in Adjuvant Immunotherapy (After Surgery)

The introduction of adjuvant immunotherapy for resectable NSCLC has raised a central question: which patients truly need it, and who is most likely to benefit? ctDNA, as a sensitive marker of minimal residual disease (MRD), has been studied primarily for prognostic stratification. More recently, it has been used as an enrichment variable in trials testing whether immunotherapy benefit differs by ctDNA status.

The phase III IMpower010 trial prospectively included ctDNA analysis in resected stage IB-IIIA NSCLC after adjuvant chemotherapy. Detectable ctDNA after surgery and chemotherapy was associated with significantly shorter disease-free survival (DFS, the time before the cancer returns). This finding confirms the prognostic value of postoperative ctDNA testing in this population.

In the adjuvant setting, postoperative ctDNA primarily stratifies recurrence risk. Whether immunotherapy provides differential benefit based on ctDNA status remains exploratory and has not been validated prospectively. The KEYNOTE-091 trial also used postoperative ctDNA primarily to stratify recurrence risk. Treatment-selective utility remains unvalidated in this setting.

ctDNA in Consolidation Immunotherapy After Chemoradiotherapy

For patients with unresectable stage III NSCLC, the standard treatment approach is chemoradiotherapy (CRT) followed by consolidation immunotherapy. The PACIFIC regimen combines chemoradiotherapy with consolidation immunotherapy and is a standard of care.

In this setting, post-chemoradiotherapy ctDNA is prognostic. It identifies patients at higher risk of recurrence or death. Comparisons of consolidation immune checkpoint inhibitor benefit across ctDNA groups provide exploratory predictive signals. These signals require prospective validation before they can guide treatment decisions.

Studies including Moding et al., CHOICE-01, CameL-sq, and BR.36 showed that post-treatment detectability or early ctDNA kinetics stratified risk or response. However, none of these studies prospectively used ctDNA to assign immunotherapy. This means the predictive role of ctDNA in this setting remains investigational.

What This Means for Patients

If you or a loved one is being treated for lung cancer with immunotherapy, ctDNA testing may already be part of a clinical trial or research study. Here is what the current evidence means in practical terms:

• ctDNA can tell your doctor about risk. If ctDNA is detectable in your blood after surgery or after definitive treatment, you have a higher risk of the cancer returning. This is prognostic information that can help your care team plan monitoring.
• ctDNA can show whether treatment is working. If ctDNA levels drop or become undetectable during immunotherapy, this is a good sign. It often means the treatment is having an effect at the molecular level, even before scans show changes.
• ctDNA cannot yet tell your doctor which treatment to choose. The evidence does not yet prove that patients with certain ctDNA patterns benefit more from one immunotherapy drug versus another, or from immunotherapy versus a different approach. This is still being studied.
• ctDNA may detect problems earlier than scans. In some cases, rising ctDNA can precede visible tumor growth on imaging by approximately four months. This early warning could allow doctors to adjust treatment sooner, though this approach has not yet been proven in randomized trials.
• ctDNA results must be interpreted carefully. False-positive results can occur due to clonal hematopoiesis, a common age-related condition where blood cells carry mutations that are not from the tumor. Your doctor should use a testing approach that accounts for this possibility.

The authors emphasize that ctDNA currently functions mainly as a prognostic and response-associated biomarker in lung cancer immunotherapy. Its predictive, decision-defining role remains an important but investigational objective.

Limitations: What the Evidence Cannot Yet Prove

The review identifies several important limitations in the current evidence for ctDNA as a predictive tool in lung cancer immunotherapy.

• Most analyses are single-arm and/or retrospective. This means they look at outcomes within one treatment group rather than comparing two groups. Without a comparison group, researchers cannot determine whether ctDNA status predicts differential benefit from a specific treatment.
• Formal treatment-by-biomarker interaction tests are uncommon. These statistical tests are needed to prove that a biomarker predicts different responses to different treatments. Most studies have not performed these tests.
• Prospective ctDNA-guided trials have not yet demonstrated clinical utility. No randomized trial has shown that using ctDNA to guide treatment decisions improves patient outcomes compared with standard approaches.
• Sensitivity at low disease burden is limited. When only a small amount of tumor DNA is present in the blood, current tests may not detect it reliably.
• Variable tumor shedding affects results. Different tumors shed different amounts of DNA into the bloodstream. This variability can affect ctDNA measurements.
• Clonal hematopoiesis can cause false-positive results. Age-related mutations in blood cells can be mistaken for tumor DNA without proper filtering methods.
• Assays and sampling timepoints are not harmonized. Different testing platforms and different schedules for blood collection make it difficult to compare results across studies.

These limitations mean that while ctDNA shows great promise, it is not yet ready for routine use in guiding immunotherapy decisions for lung cancer patients outside of clinical trials.

Recommendations and Future Directions

The authors outline several priorities for translating ctDNA into routine clinical care for lung cancer immunotherapy.

1. Harmonize assays and sampling timepoints. Standardizing how ctDNA is measured and when blood samples are collected is essential for comparing results across studies and clinical settings.
2. Improve sensitivity at low disease burden. Better detection methods are needed to reliably identify minimal residual disease when tumor DNA levels are very low.
3. Control for clonal hematopoiesis. Testing approaches must account for age-related blood cell mutations that can create false-positive results.
4. Conduct randomized interventional validation. Prospective randomized trials are needed to prove that using ctDNA to guide treatment decisions actually improves patient outcomes.
5. Test treatment escalation, de-escalation, and duration strategies. Future trials should evaluate whether adjusting treatment based on ctDNA results can improve outcomes or reduce unnecessary toxicity.
6. Enrich for molecular residual disease-positive patients. Trials should specifically enroll patients with detectable ctDNA after treatment, since these patients are at highest risk and may benefit most from treatment intensification.
7. Identify resistance early. Rising ctDNA may signal emerging resistance before it becomes clinically apparent, potentially allowing earlier intervention.

ctDNA dynamics provide a biologically and clinically coherent framework for future risk-adapted strategies. These include enrichment of molecular residual disease-positive patients, early identification of resistance, and prospective testing of treatment escalation, de-escalation, or duration. Translation into routine care will require harmonized assays and sampling timepoints, improved sensitivity at low disease burden, control of clonal hematopoiesis, and randomized interventional validation.

Thus, ctDNA currently functions mainly as a prognostic and response-associated biomarker in lung cancer immunotherapy. Its predictive, decision-defining role remains an important but investigational objective.

Frequently Asked Questions

What is a ctDNA blood test for lung cancer?

ctDNA testing is a blood test that detects tiny fragments of DNA shed by lung tumors into the bloodstream. Because ctDNA reflects tumor burden and genetic changes, measuring it repeatedly can give a real-time view of how tumor clones are changing during treatment, often before changes appear on scans.

Can ctDNA testing tell my doctor whether immunotherapy is working?

If ctDNA levels drop or become undetectable during immunotherapy, that is a good sign and often means the treatment is having an effect at the molecular level, sometimes before scans show change. However, ctDNA cannot yet tell your doctor which treatment to choose; that remains investigational.

Can ctDNA find a recurrence earlier than a CT scan?

In some cases, rising ctDNA can precede visible tumor growth on imaging by approximately four months. This early warning could allow doctors to adjust treatment sooner, though this approach has not yet been proven in randomized trials to improve outcomes compared with standard monitoring.

What is clonal hematopoiesis and why does it matter for ctDNA results?

Clonal hematopoiesis is a common age-related condition in which blood cells carry mutations that are not from the tumor. These can create false-positive ctDNA signals. Your doctor should use a testing approach that accounts for this, such as sequencing matched blood cells and filtering out those variants.

Is ctDNA testing ready to guide immunotherapy decisions outside of clinical trials?

No. The evidence does not yet prove that patients with certain ctDNA patterns benefit more from one immunotherapy drug versus another, or from immunotherapy versus a different approach. Most analyses are single-arm or retrospective, and no randomized trial has shown that using ctDNA to guide treatment improves patient outcomes.

What are the limitations of ctDNA testing for lung cancer immunotherapy?

Sensitivity is limited when only a small amount of tumor DNA is present, and different tumors shed different amounts of DNA, which affects measurements. Assays and blood-sampling timepoints are not harmonized across studies, and clonal hematopoiesis can cause false-positive results without proper filtering methods.

If I'm being treated with immunotherapy for lung cancer and my doctor wants to use a ctDNA blood test to decide my treatment, should I get a second opinion?

Yes, this is a reasonable moment to seek one. ctDNA currently functions mainly as a prognostic and response-associated marker: detectable ctDNA after surgery or definitive treatment signals higher recurrence risk, and falling levels often track with treatment effect. However, no randomized trial has shown that using ctDNA to guide immunotherapy decisions improves outcomes, and its predictive, decision-defining role remains investigational. A second opinion can clarify whether a proposed ctDNA-guided change rests on validated evidence or on exploratory findings. Diagnostic Detectives Network provides independent expert second opinions.

Source Information

Original article title: Circulating tumor DNA in lung cancer immunotherapy: prognostic marker today, predictive tool tomorrow?

Authors: Li Y, Lu G, Song W, Lu J, Liu X, Cui J.

Affiliation: Cancer Center, First Hospital of Jilin University, Changchun, Jilin, China

Journal: Frontiers in Immunology, Volume 17, Article 1911583

DOI: 10.3389/fimmu.2026.1911583

Publication dates: Received 17 June 2026; Revised 26 July 2026; Accepted 06 August 2026; Published 20 August 2026

Article type: Review

This patient-friendly article is based on peer-reviewed research.