Introduction: The Evolving Landscape of Immunotherapy Monitoring

Immunotherapy has fundamentally reshaped oncology by harnessing the patient’s own immune system to recognize and eliminate cancer cells. Unlike chemotherapy or radiation, which directly target tumor cells, immunotherapies—such as immune checkpoint inhibitors, CAR‑T cell therapy, and bispecific antibodies—can induce durable remissions even in advanced or metastatic disease. However, this paradigm shift introduces a critical challenge: how to accurately and early assess whether a tumor is responding to treatment. Conventional imaging methods, originally designed for cytotoxic therapies, often fall short in distinguishing true tumor progression from the transient inflammation or immune cell infiltration that immunotherapy can provoke. Advanced imaging technologies are now stepping into this gap, offering functional, metabolic, and molecular insights that enable clinicians to differentiate responders from non‑responders with greater confidence, adapt treatment strategies promptly, and ultimately improve patient outcomes.

Limitations of Conventional Imaging in the Immunotherapy Era

Traditional tumor response criteria, such as RECIST (Response Evaluation Criteria in Solid Tumors), rely primarily on changes in tumor size measured by computed tomography (CT) or magnetic resonance imaging (MRI). These structural metrics work reasonably well for cytotoxic agents, where a decrease in size generally correlates with treatment effect. However, immunotherapy often triggers an initial increase in lesion size due to immune cell infiltration and edema—a phenomenon known as pseudoprogression. Studies report pseudoprogression in up to 10–15% of patients treated with checkpoint inhibitors, and rates can be higher in certain tumor types such as melanoma and renal cell carcinoma. Mistaking pseudoprogression for true progression could lead to premature discontinuation of an effective therapy.

Similarly, conventional imaging cannot reliably capture the immune‑related response patterns described by the immune‑RECIST (iRECIST) and immune‑modified RECIST criteria. These adapted frameworks account for atypical response kinetics, including delayed responses that appear after an initial period of stability or even slight growth. Yet even iRECIST relies on serial size measurements, missing the underlying biological changes that precede structural alterations. There is a clear need for imaging techniques that visualize the tumor microenvironment, immune cell activity, and metabolic reprogramming—capabilities that advanced modalities uniquely provide.

Advanced Imaging Modalities for Immunotherapy Monitoring

Positron Emission Tomography (PET) – Beyond Glucose Metabolism

18F‑FDG PET/CT remains the most widely used molecular imaging tool in oncology. 18F‑fluorodeoxyglucose (FDG) accumulates in metabolically active cells, including both tumor cells and activated immune cells. Following immunotherapy, a transient increase in FDG uptake can signal immune activation (the “flare” phenomenon), which may herald a subsequent response. However, the nonspecific uptake of FDG in inflammatory cells makes it challenging to differentiate pseudoprogression from true progression. To address this, several novel PET tracers are under investigation:

  • 89Zr‑immuno‑PET – Uses radiolabeled antibodies (e.g., against PD‑L1, CTLA‑4, or CD8) to directly visualize target expression and immune cell trafficking. Early clinical studies show that high baseline PD‑L1 uptake correlates with response to checkpoint inhibitors.
  • 18F‑FACBC and 18F‑DCFPyL – Amino acid and PSMA‑targeted tracers that are less affected by inflammation and may provide clearer discrimination in prostate cancer and other solid tumors.
  • 18F‑FLT – Measures cellular proliferation and can help distinguish tumor regrowth from immune‑mediated expansion.

Combining PET with MRI (PET/MRI) further enhances soft‑tissue contrast and reduces radiation exposure, making it particularly attractive for repeated assessments in younger patients or for monitoring central nervous system metastases.

Multiparametric MRI – Functional Signatures of Response

MRI delivers excellent anatomical detail, but its true power for immunotherapy monitoring lies in multiparametric sequences that probe tissue physiology. Diffusion‑weighted imaging (DWI) measures the random motion of water molecules, which becomes restricted in hypercellular tumors. A rise in the apparent diffusion coefficient (ADC) after treatment often indicates necrosis or reduced cellularity—a sign of response. Conversely, an early drop in ADC may reflect immune cell infiltration and edema. Combining DWI with dynamic contrast‑enhanced (DCE) MRI quantifies perfusion and vascular permeability. Immunotherapy can induce vascular normalization or, in some cases, hyperpermeability due to cytokine release. These changes can be detected before any size reduction occurs.

Recent studies have also explored whole‑body DWI (WB‑DWI) as a non‑contrast tool for assessing tumor burden across multiple sites. Known as “diffusion MRI of the body,” WB‑DWI is increasingly used in clinical trials to provide a volumetric, functional readout that correlates with overall survival in melanoma and non‑small cell lung cancer patients treated with checkpoint inhibitors.

Computed Tomography with Texture Analysis (Radiomics)

Standard CT already provides high‑resolution anatomical images. With advanced computational analysis, subtle texture patterns—collectively termed radiomics—can be extracted to reveal intratumoral heterogeneity that is invisible to the human eye. Features such as entropy, uniformity, and kurtosis derived from CT texture have been shown to predict response to immunotherapy in patients with advanced melanoma and lung cancer. These imaging biomarkers can be integrated with clinical and genomic data to build robust predictive models. The main advantage of CT radiomics is its availability: CT is performed routinely, and existing scans can be retrospectively analyzed without additional patient burden or cost.

Emerging Imaging Biomarkers of Immune Activity

Visualizing PD‑L1 Expression

Programmed death‑ligand 1 (PD‑L1) expression on tumor cells and immune cells is a key determinant of checkpoint inhibitor sensitivity. While immunohistochemistry on biopsy tissue remains the clinical standard, it is limited by spatial and temporal heterogeneity. 89Zr‑atezolizumab‑PET (a radiolabeled anti‑PD‑L1 antibody) can non‑invasively map PD‑L1 distribution throughout the whole body. Phase I trials have demonstrated that high tracer uptake in tumor lesions correlates with response, and changes in uptake after one cycle of therapy predict eventual outcome. This approach is now being refined with smaller antibody fragments (e.g., nanobodies) that clear faster and enable same‑day imaging.

Mapping Immune Cell Infiltration

Beyond PD‑L1, imaging probes targeting CD8+ T cells, CD3, and other immune cell markers are in development. 89Zr‑labeled anti‑CD8 minibodies have shown promise in preclinical models and early human studies, allowing visualization of tumor‑infiltrating lymphocytes (TILs). A lack of CD8 signal after therapy may indicate poor immune activation, prompting a change in strategy. Similarly, 18F‑FAC (fluoro‑arectine) and other probes for granzyme B (a marker of cytotoxic T‑cell activity) could provide a real‑time snapshot of effector function within the tumor microenvironment.

Metabolic Changes in the Tumor Microenvironment

Immunotherapy reshapes tumor metabolism in ways that can be captured by hyperpolarized 13C MRI or magnetic resonance spectroscopy (MRS). For instance, a shift from glycolysis toward oxidative phosphorylation or a decrease in lactate‑to‑alanine ratios may signal effective immune attack. While these techniques remain largely research tools, their ability to detect early metabolic reprogramming offers a future avenue for rapid response assessment without radiation.

Quantitative Imaging and Artificial Intelligence

The sheer volume and complexity of data generated by advanced imaging demand computational tools for analysis. Artificial intelligence (AI), particularly deep learning, is transforming how images are interpreted. AI algorithms can automatically segment tumors, extract radiomic features, and integrate them with clinical variables to produce predictive scores. For example, a convolutional neural network trained on pre‑treatment CT images of non‑small cell lung cancer patients was able to predict which patients would benefit from pembrolizumab with an accuracy exceeding 80%, outperforming PD‑L1 immunohistochemistry alone.

Radiomics‑based models are also being used to classify pseudoprogression versus true progression on follow‑up scans. By analyzing changes in texture, vessel density, and peritumoral edema, these models reduce the need for repeat biopsies and unnecessary treatment switches. Large‑scale initiatives such as the Quantitative Imaging Network (QIN) and the Imaging Data Commons are standardizing radiomic workflows to enable multi‑institutional validation and clinical translation.

Challenges and Future Directions

Despite its promise, the integration of advanced imaging into routine immunotherapy monitoring faces several hurdles. Cost and accessibility remain significant barriers: dedicated PET tracers require cyclotron infrastructure, and multiparametric MRI protocols demand longer acquisition times and specialized radiologist training. Standardization of acquisition parameters, reconstruction algorithms, and interpretation criteria is essential for consistent results across centers. Regulatory approval for novel tracers and AI‑based software also lags behind scientific development.

Furthermore, the dynamic nature of the immune response means that timing of scans matters. A single time point may miss transient flares or late responses. Future clinical trials will need to incorporate serial imaging schedules optimized for each therapy class. Artificial intelligence will play a key role in harmonizing multi‑center data and generating adaptive imaging schedules based on early response patterns.

Emerging techniques such as photoacoustic imaging, intravital microscopy, and liquid biopsy combined with imaging are also on the horizon. Photoacoustic imaging can non‑invasively measure oxygen saturation and hemoglobin content, providing a readout of tumor hypoxia—a known resistance factor to immunotherapy. Meanwhile, pairing advanced imaging with circulating tumor DNA (ctDNA) dynamics could offer complementary information: ctDNA levels drop sharply in responders, while imaging reveals the anatomical and functional correlates of that molecular change.

Conclusion

Advanced imaging has moved beyond its traditional role of measuring tumor size and has become a critical tool for understanding how tumors interact with the immune system. By capturing metabolic activity, immune cell infiltration, target expression, and tissue heterogeneity, modalities such as PET, multiparametric MRI, and CT radiomics enable earlier and more accurate assessment of immunotherapy response. While challenges of cost, standardization, and validation remain, ongoing research and technological innovation are steadily bringing these capabilities into routine clinical practice. The future of oncologic imaging lies in a multi‑modal, quantitative, and AI‑augmented approach that empowers clinicians to personalize treatment, avoid ineffective therapies, and maximize the promise of immunotherapy for every patient.