Noninvasive imaging in cancer immunotherapy: The way to precision medicine

Gastrointestinal tumor personalized immunotherapy: an integrated analysis from molecular genetics to imaging biomarkers

The immunotherapy landscape for gastrointestinal (GI) tumors is rapidly evolving. There is an urgent need for reliable biomarkers capable of predicting treatment outcomes to optimize therapeutic strategies and enhance patient prognosis. This review presents a comprehensive overview of biomarkers associated with the immunotherapy response of GI tumors, covering advances in molecular genetics, histopathological markers, and imaging. Key molecular biomarkers, such as microsatellite instability, tumor mutational burden, and programmed death-ligand 1 expression, remain critical for identifying patients likely to benefit from immune checkpoint inhibitors. The significance of tumor-infiltrating lymphocytes, notably the CD8+ T cell to regulatory T cell ratio, as a predictor of immunotherapy response is explored. In addition, advanced imaging techniques, including computed tomography (CT), magnetic resonance imaging, and positron emission tomography-CT, facilitate the noninvasive evaluation of tumor biology and therapeutic response. By bridging molecular and imaging data, this integrated strategy enhances precision in patient selection, treatment monitoring, and adaptive therapy design. Future studies should aim to validate these biomarkers in larger, multicenter cohorts and focus on clinical translation to advance precision medicine in GI oncology.

Immunogenic Cell Death and Metabolic Reprogramming in Cancer: Mechanisms, Synergies, and Innovative Therapeutic Strategies

Immunogenic cell death (ICD) is a promising cancer therapy where dying tumor cells release damage-associated molecular patterns (DAMPs) to activate immune responses. Recent research highlights the critical role of metabolic reprogramming in tumor cells, including the Warburg effect, oxidative stress, and lipid metabolism, in modulating ICD and shaping the immune microenvironment. These metabolic changes enhance immune activation, making tumors more susceptible to immune surveillance. This review explores the molecular mechanisms linking ICD and metabolism, including mitochondrial oxidative stress, endoplasmic reticulum (ER) stress, and ferroptosis. It also discusses innovative therapeutic strategies, such as personalized combination therapies, metabolic inhibitors, and targeted delivery systems, to improve ICD efficacy. The future of cancer immunotherapy lies in integrating metabolic reprogramming and immune activation to overcome tumor immune evasion, with multi-omics approaches and microbiome modulation offering new avenues for enhanced treatment outcomes.

A predictive radiotranscriptomics model based on DCE-MRI for tumor immune landscape and immunotherapy in cholangiocarcinoma

This study aims to determine the predictive role of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) derived radiomic model in tumor immune profiling and immunotherapy for cholangiocarcinoma. To perform radiomic analysis, immune related subgroup clustering was first performed by single sample gene set enrichment analysis (ssGSEA). Second, a total of 806 radiomic features for each phase of DCE-MRI were extracted by utilizing the Python package Pyradiomics. Then, a predictive radiomic signature model was constructed after a three-step features reduction and selection, and receiver operating characteristic (ROC) curve was employed to evaluate the performance of this model. In the end, an independent testing cohort involving cholangiocarcinoma patients with anti-PD-1 Sintilimab treatment after surgery was used to verify the potential application of the established radiomic model in immunotherapy for cholangiocarcinoma. Two distinct immune related subgroups were classified using ssGSEA based on transcriptome sequencing. For radiomic analysis, a total of 10 predictive radiomic features were finally identified to establish a radiomic signature model for immune landscape classification. Regarding to the predictive performance, the mean AUC of ROC curves was 0.80 in the training/validation cohort. For the independent testing cohort, the individual predictive probability by radiomic model and the corresponding immune score derived from ssGSEA was significantly correlated. In conclusion, radiomic signature model based on DCE-MRI was capable of predicting the immune landscape of chalangiocarcinoma. Consequently, a potentially clinical application of this developed radiomic model to guide immunotherapy for cholangiocarcinoma was suggested.

NIR-II light in clinical oncology: opportunities and challenges

Novel strategies utilizing light in the second near-infrared region (NIR-II; 900-1,880 nm wavelengths) offer the potential to visualize and treat solid tumours with enhanced precision. Over the past few decades, numerous techniques leveraging NIR-II light have been developed with the aim of precisely eliminating tumours while maximally preserving organ function. During cancer surgery, NIR-II optical imaging enables the visualization of clinically occult lesions and surrounding vital structures with increased sensitivity and resolution, thereby enhancing surgical quality and improving patient prognosis. Furthermore, the use of NIR-II light promises to improve cancer phototherapy by enabling the selective delivery of increased therapeutic energy to tissues at greater depths. Initial clinical studies of NIR-II-based imaging and phototherapy have indicated impressive potential to decrease cancer recurrence, reduce complications and prolong survival. Despite the encouraging results achieved, clinical translation of innovative NIR-II techniques remains challenging and inefficient; multidisciplinary cooperation is necessary to bridge the gap between preclinical research and clinical practice, and thus accelerate the translation of technical advances into clinical benefits. In this Review, we summarize the available clinical data on NIR-II-based imaging and phototherapy, demonstrating the feasibility and utility of integrating these technologies into the treatment of cancer. We also introduce emerging NIR-II-based approaches with substantial potential to further enhance patient outcomes, while also highlighting the challenges associated with imminent clinical studies of these modalities.

RGD targeted magnetic ferrite nanoparticles enhance antitumor immunotherapeutic efficacy by activating STING signaling pathway

Manganese has been used in tumor imaging for their ability to provide T1-weighted MRI signal. Recent research find Mn2+ can induce activation of the stimulator of interferon gene (STING) pathway to create an active and favorable tumor immune microenvironment. However, the direct injection of Mn2+ often results in toxicity. In this study, we developed an RGD targeted magnetic ferrite nanoparticle (RGD-MnFe2O4) to facilitate tumor targeted imaging and improve tumor immunotherapy. Magnetic resonance imaging and fluorescence molecular imaging were performed to monitor its in vivo biodistribution. We found that RGD-MnFe2O4 showed active tumor targeting and longer accumulation at tumor sites. Moreover, RGD-MnFe2O4 can activate STING pathway with low toxicity to enhance the PD-L1 expression. Furthermore, combining RGD-MnFe2O4 and anti-PD-L1 antibody (aPD-L1) can treat several types of immunogenic tumors through promoting the accumulation of tumor-infiltrating cytotoxic T cells. In general, our study provides a promising new strategy for the targeted and multifunctional theranostic nanoparticle for the improvement of tumor immunotherapy.

Noninvasively Deciphering the Immunosuppressive Tumor Microenvironment Using Galectin-1 PET to Inform Immunotherapy Responses

Immune checkpoint blockade (ICB) has achieved groundbreaking results in clinical cancer therapy; however, only a subset of patients experience durable benefits. The aim of this study was to explore strategies for predicting tumor responses to optimize the intervention approach using ICB therapy. Methods: We used a bilateral mouse model for proteomics analysis to identify new imaging biomarkers for tumor responses to ICB therapy. A PET radiotracer was synthesized by radiolabeling the identified biomarker-targeting antibody with 124I. The radiotracer was then tested for PET prediction of tumor responses to ICB therapy. Results: We identified galectin-1 (Gal-1), a member of the carbohydrate-binding lectin family, as a potential negative biomarker for ICB efficacy. We established that Gal-1 inhibition promotes a sensitive immune phenotype within the tumor microenvironment (TME) for ICB therapy. To assess the pre-ICB treatment status of the TME, a Gal-1-targeted PET radiotracer, 124I-αGal-1, was developed. PET imaging with 124I-αGal-1 showed the pretreatment immunosuppressive status of the TME before the initiation of therapy, thus enabling the prediction of ICB resistance in advance. Moreover, the use of hydrogel scaffolds loaded with a Gal-1 inhibitor, thiodigalactoside, demonstrated that a single dose of thiodigalactoside-hydrogel significantly potentiated ICB and adoptive cell transfer immunotherapies by remodeling the immunosuppressive TME. Conclusion: Our study underscores the potential of Gal-1-targeted PET imaging as a valuable strategy for early-stage monitoring of tumor responses to ICB therapy. Additionally, Gal-1 inhibition effectively counteracts the immunosuppressive TME, resulting in enhanced immunotherapy efficacy.

[68Ga]Ga-AUNP-12 PET imaging to assess the PD-L1 status in preclinical and first-in-human study

PURPOSE

PD-L1 PET imaging, as a non-invasive procedure, can perform a real-time, dynamic and quantitative analysis of PD-L1 expression at tumor sites. In this study, we developed a novel peptide-based PET tracer, [68 Ga]Ga-AUNP-12, for preclinical and first-of-its-kind imaging of PD-L1 expression in patients.

METHODS

Radiosynthesis of [68 Ga]Ga-AUNP-12 was conducted. Assays for cellular uptake and binding were conducted on the PANC02, CT26, and B16F10 cell lines. Preclinical models were used to investigate its biodistribution, imaging capacity, and pharmacokinetics. Furthermore, interferon-γ (IFN-γ) was used for development of an animal model with high PD-L1 expression for targeted PET imaging and efficacy evaluation of PD-L1 blocking therapy. In healthy volunteers and cancer patients, the PD-L1 imaging, radiation dosimetry, safety, and biodistribution were further evaluated.

RESULTS

In vitro and in vivo animal studies showed that [68 Ga]Ga-AUNP-12 PET imaging displayed a high specificity in evaluating PD-L1 expression. The radiochemical yield of [68 Ga]Ga-AUNP-12 was 71.7 ± 8.2%. Additionally, its molar activity and radiochemical purity were satisfactory. The B16F10 tumor was visualized with the tumor uptake of 6.86 ± 0.71% ID/g and tumor-to-muscle ratio of 6.83 ± 0.36 at 60 min after [68 Ga]Ga-AUNP-12 injection. Furthermore, [68 Ga]Ga-AUNP-12 PET imaging could sensitively detect the PD-L1 dynamic changes in CT26 tumor xenograft models regulated by IFN-γ treatment, and correspondingly can effectively guide immunotherapy. Regarding radiation dosimetry, [68 Ga]Ga-AUNP-12 is safe for human use. The first human study found that [68 Ga]Ga-AUNP-12 can be rapidly cleared from blood and other nonspecific organs through the kidney excretion, leading to form a clear imaging contrast in the clinical framework. The specificity of [68 Ga]Ga-AUNP-12 was validated and tumor uptake strongly correlated with the high PD-L1 expression in patients with lung adenocarcinoma and oesophageal squamous cell carcinoma (OSCC).

CONCLUSION

[68 Ga]Ga-AUNP-12 was successfully developed as a PD-L1-specific PET imaging tracer in preclinical and first-in-human studies.

Reconstruction based on adaptive group least angle regression for fluorescence molecular tomography

Fluorescence molecular tomography can combine two-dimensional fluorescence imaging with anatomical information to reconstruct three-dimensional images of tumors. Reconstruction based on traditional regularization with tumor sparsity priors does not take into account that tumor cells form clusters, so it performs poorly when multiple light sources are used. Here we describe reconstruction based on an "adaptive group least angle regression elastic net" (AGLEN) method, in which local spatial structure correlation and group sparsity are integrated with elastic net regularization, followed by least angle regression. The AGLEN method works iteratively using the residual vector and a median smoothing strategy in order to adaptively obtain a robust local optimum. The method was verified using numerical simulations as well as imaging of mice bearing liver or melanoma tumors. AGLEN reconstruction performed better than state-of-the-art methods with different sizes of light sources at different distances from the sample and in the presence of Gaussian noise at 5-25%. In addition, AGLEN-based reconstruction accurately imaged tumor expression of cell death ligand-1, which can guide immunotherapy.

CEACAM1 Is a Prognostic Biomarker and Correlated with Immune Cell Infiltration in Clear Cell Renal Cell Carcinoma

Background

CEACAM1 has been shown to be aberrantly expressed in a variety of tumors, and modulation of CEACAM1-related signaling pathways has been suggested as a novel approach for cancer immunotherapy in recent years. However, its role in clear cell renal cell carcinoma (ccRCC) is unclear.

Methods

The relationship between CEACAM1 and ccRCC was demonstrated based on data from TCGA, GEO, and HPA databases. And the relationship between clinicopathological features and CEACAM1 expression was also assessed. Survival curve analysis was performed to analyze the prognostic relationship between CEACAM1 expression and ccRCC. Protein interaction network analysis was used to analyze the relationship between CEACAM1 and microenvironment-related proteins. In addition, the immunomodulatory role of CEACAM1 in ccRCC was assessed by analyzing CEACAM1 and immune cell infiltration.

Results

The expression of CEACAM1 was lower in ccRCC tissues than in adjacent normal tissues, and its expression level was negatively correlated with tumor size status (P < 0.001), metastasis status (P = 0.009), pathological stage (P = 0.002), gender (P < 0.001), histological grade (P < 0.001), and primary therapy outcome (P = 0.045) of ccRCC. Survival curve analysis showed that ccRCC patients with lower CEACAM1 expression exhibited shorter overall survival (P < 0.001), and CEACAM1 interacted with microenvironmental molecules such as fibronectin and integrins. Furthermore, immune infiltration analysis showed that CEACAM1 expression correlated with CD8⁺ and CD4⁺ T cells, macrophage, neutrophil, and dendritic cell infiltration in ccRCC.

Conclusions

CEACAM1 expression correlates with progression, prognosis, and immune cell infiltration in ccRCC patients, and it may be a promising prognostic biomarker and therapeutic target for ccRCC.

3D-CEUS tracking of injectable chemo-sonodynamic therapy-enabled mop-up of residual renal cell carcinoma after thermal ablation

Thermal ablation (TA), as a minimally invasive therapeutic technique, has been extensively used to the treatment of solid tumors, such as renal cell carcinoma (RCC), which, unfortunately, still fails to overcome the high risk of local recurrence and distant metastasis since the incomplete ablation cannot be ignored due to various factors such as the indistinguishable tumor margins and limited ablation zone. Herein, we report the injectable thermosensitive hydrogel by confining curcumin (Cur)-loaded hollow mesoporous organosilica nanoparticles (Cur@HMON@gel) which can locate in tumor site more than half a month and mop up the residual RCC under ultrasound (US) irradiation after transforming from colloidal sol status to elastic gel matrix at physiological temperature. Based on the US-triggered accelerated diffusion of the model chemotherapy drug with multi-pharmacologic functions, the sustained and controlled release of Cur has been demonstrated in vitro. Significantly, US is employed as an external energy to trigger Cur, as a sonosensitizer also, to generate reactive oxygen species (ROS) for sonodynamic tumor therapy (SDT) in parallel. Tracking by the three-dimensional contrast-enhanced ultrasound (3D-CEUS) imaging, the typical decreased blood perfusions have been observed since the residual xenograft tumor after incomplete TA were effectively suppressed during the chemo-sonodynamic therapy process. The high in vivo biocompatibility and biodegradability of the multifunctional nanoplatform confined by thermogel provide the potential of their further clinical translation for the solid tumor eradication under the guidance and monitoring of 3D-CEUS.

Emerging Biomaterials Imaging Antitumor Immune Response

Immunotherapy is one of the most promising clinical modalities for the treatment of malignant tumors and has shown excellent therapeutic outcomes in clinical settings. However, it continues to face several challenges, including long treatment cycles, high costs, immune-related adverse events, and low response rates. Thus, it is critical to predict the response rate to immunotherapy by using imaging technology in the preoperative and intraoperative. Here, the latest advances in nanosystem-based biomaterials used for predicting responses to immunotherapy via the imaging of immune cells and signaling molecules in the immune microenvironment are comprehensively summarized. Several imaging methods, such as fluorescence imaging, magnetic resonance imaging, positron emission tomography imaging, ultrasound imaging, and photoacoustic imaging, used in immune predictive imaging, are discussed to show the potential of nanosystems for distinguishing immunotherapy responders from nonresponders. Nanosystem-based biomaterials aided by various imaging technologies are expected to enable the effective prediction and diagnosis in cases of tumors, inflammation, and other public diseases.

Glucose–Thymidine Ratio as a Metabolism Index Using 18F-FDG and 18F-FLT PET Uptake as a Potential Imaging Biomarker for Evaluating Immune Checkpoint Inhibitor Therapy

Immune checkpoint inhibitors (ICIs) are widely used in cancer immunotherapy, requiring effective methods for response monitoring. This study evaluated changes in 18F-2-fluoro-2-deoxy-D-glucose (FDG) and 18F-fluorothymidine (FLT) uptake by tumors following ICI treatment as potential imaging biomarkers in mice. Tumor uptakes of 18F-FDG and 18F-FLT were measured and compared between the ICI treatment and control groups. A combined imaging index of glucose–thymidine uptake ratio (GTR) was defined and compared between groups. In the ICI treatment group, tumor growth was effectively inhibited, and higher proportions of immune cells were observed. In the early phase, 18F-FDG uptake was higher in the treatment group, whereas 18F-FLT uptake was not different. There was no difference in 18F-FDG uptake between the two groups in the late phase. However, 18F-FLT uptake of the control group was markedly increased compared with the ICI treatment group. GTR was consistently higher in the ICI treatment group in the early and late phases. After ICI treatment, changes in tumor cell proliferation were observed with 18F-FLT, whereas 18F-FDG showed altered metabolism in both tumor and immune cells. A combination of 18F-FLT and 18F-FDG PET, such as GTR, is expected to serve as a potentially effective imaging biomarker for monitoring ICI treatment.

A Fast and Automated FMT/XCT Reconstruction Strategy Based on Standardized Imaging Space

The traditional finite element method-based fluorescence molecular tomography (FMT)/ X-ray computed tomography (XCT) imaging reconstruction suffers from complicated mesh generation and dual-modality image data fusion, which limits the application of in vivo imaging. To solve this problem, a novel standardized imaging space reconstruction (SISR) method for the quantitative determination of fluorescent probe distributions inside small animals was developed. In conjunction with a standardized dual-modality image data fusion technology, and novel reconstruction strategy based on Laplace regularization and L1-fused Lasso method, the in vivo distribution can be calculated rapidly and accurately, which enables standardized and algorithm-driven data process. We demonstrated the method's feasibility through numerical simulations and quantitatively monitored in vivo programmed death ligand 1 (PD-L1) expression in mouse tumor xenografts, and the results demonstrate that our proposed SISR can increase data throughput and reproducibility, which helps to realize the dynamically and accurately in vivo imaging.

Predicting response to immunotherapy plus chemotherapy in patients with esophageal squamous cell carcinoma using non-invasive Radiomic biomarkers

Objectives

To develop and validate a radiomics model for evaluating treatment response to immune-checkpoint inhibitor plus chemotherapy (ICI + CT) in patients with advanced esophageal squamous cell carcinoma (ESCC).

Methods

A total of 64 patients with advance ESCC receiving first-line ICI + CT at two centers between January 2019 and June 2020 were enrolled in this study. Both 2D ROIs and 3D ROIs were segmented. ComBat correction was applied to minimize the potential bias on the results due to different scan protocols. A total of 788 features were extracted and radiomics models were built on corrected/uncorrected 2D and 3D features by using 5-fold cross-validation. The performance of the radiomics models was assessed by its discrimination, calibration and clinical usefulness with independent validation.

Results

Five features and support vector machine algorithm were selected to build the 2D uncorrected, 2D corrected, 3D uncorrected and 3D corrected radiomics models. The 2D radiomics models significantly outperformed the 3D radiomics models in both primary and validation cohorts. When ComBat correction was used, the performance of 2D models was better (p = 0.0059) in the training cohort, and significantly better (p < 0.0001) in the validation cohort. The 2D corrected radiomics model yielded the optimal performance and was used to build the nomogram. The calibration curve of the radiomics model demonstrated good agreement between prediction and observation and the decision curve analysis confirmed the clinical utility.

Conclusions

The easy-to-use 2D corrected radiomics model could facilitate noninvasive preselection of ESCC patients who would benefit from ICI + CT.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08899-x.

Monitoring Immunotherapy With Optical Molecular Imaging

Immunotherapy is an effective way to mobilize the body's own immune system to confront tumor cells. However, the efficacy of immunotherapy is affected by tumor heterogeneity, and the low therapeutic response to immunotherapy may lead to negative outcomes, which reinforces the urgency for early benefit predictors. Evaluating the infiltration of immune cells in solid tumors and metabolism changes of tumors provide potential response targets for monitoring immune response. Non-invasive imaging identifying prognostic biomarkers can select the beneficiaries of targeted immunotherapy from non-responses. Quantitative biomarkers may eventually improve the cancer management, help customize individual treatment plans and predict the treatment outcomes. In this review, we summarize the non-invasive optical molecular imaging methods for monitoring immunotherapy. With the combination of imaging and immunotherapy, the prediction of immunotherapy response may promote the development of precision medicine.

ImmunoAIzer: A Deep Learning-Based Computational Framework to Characterize Cell Distribution and Gene Mutation in Tumor Microenvironment

Spatial distribution of tumor infiltrating lymphocytes (TILs) and cancer cells in the tumor microenvironment (TME) along with tumor gene mutation status are of vital importance to the guidance of cancer immunotherapy and prognoses. In this work, we developed a deep learning-based computational framework, termed ImmunoAIzer, which involves: (1) the implementation of a semi-supervised strategy to train a cellular biomarker distribution prediction network (CBDPN) to make predictions of spatial distributions of CD3, CD20, PanCK, and DAPI biomarkers in the tumor microenvironment with an accuracy of 90.4%; (2) using CBDPN to select tumor areas on hematoxylin and eosin (H&E) staining tissue slides and training a multilabel tumor gene mutation detection network (TGMDN), which can detect APC, KRAS, and TP53 mutations with area-under-the-curve (AUC) values of 0.76, 0.77, and 0.79. These findings suggest that ImmunoAIzer could provide comprehensive information of cell distribution and tumor gene mutation status of colon cancer patients efficiently and less costly; hence, it could serve as an effective auxiliary tool for the guidance of immunotherapy and prognoses. The method is also generalizable and has the potential to be extended for application to other types of cancers other than colon cancer.

Medical imaging and nuclear medicine: a Lancet Oncology Commission

The diagnosis and treatment of patients with cancer requires access to imaging to ensure accurate management decisions and optimal outcomes. Our global assessment of imaging and nuclear medicine resources identified substantial shortages in equipment and workforce, particularly in low-income and middle-income countries (LMICs). A microsimulation model of 11 cancers showed that the scale-up of imaging would avert 3·2% (2·46 million) of all 76·0 million deaths caused by the modelled cancers worldwide between 2020 and 2030, saving 54·92 million life-years. A comprehensive scale-up of imaging, treatment, and care quality would avert 9·55 million (12·5%) of all cancer deaths caused by the modelled cancers worldwide, saving 232·30 million life-years. Scale-up of imaging would cost US$6·84 billion in 2020-30 but yield lifetime productivity gains of $1·23 trillion worldwide, a net return of $179·19 per $1 invested. Combining the scale-up of imaging, treatment, and quality of care would provide a net benefit of $2·66 trillion and a net return of $12·43 per $1 invested. With the use of a conservative approach regarding human capital, the scale-up of imaging alone would provide a net benefit of $209·46 billion and net return of $31·61 per $1 invested. With comprehensive scale-up, the worldwide net benefit using the human capital approach is $340·42 billion and the return per dollar invested is $2·46. These improved health and economic outcomes hold true across all geographical regions. We propose actions and investments that would enhance access to imaging equipment, workforce capacity, digital technology, radiopharmaceuticals, and research and training programmes in LMICs, to produce massive health and economic benefits and reduce the burden of cancer globally.

A radiomics model of predicting tumor volume change of patients with stage III non-small cell lung cancer after radiotherapy

To predict the volume change of stage III NSCLC after radiotherapy with 60 Gy.This retrospective study included two independent cohorts, a train cohort of 192 patients, and a test cohort of 31 patients. We developed a radiomics model based on radiomics features and clinical variables. LIFEx package was used to extract radiomics texture features from CT images. The classification method was logistic regression analysis and feature selection was performed by correlation coefficients. Performance metrics of logistic regression include accuracy, precision, the receiver operating characteristic curves, and recall.The combination features of clinical variables and radiomics can predict the tumor volume change after radiotherapy with 88.7% accuracy (88.6% precision, 88.7% recall, and 88.7% ROC area).Radiomics features combined with medical knowledge have a great potential to predict accurately tumor volume change of stage III NSCLC after radiotherapy with 60 Gy.

Assessing Immunotherapy with Functional and Molecular Imaging and Radiomics

Immunotherapy is changing the treatment paradigm for cancer and has introduced new challenges in medical imaging. Because not all patients benefit from immunotherapy, pretreatment imaging should be performed to identify not only prognostic factors but also factors that allow prediction of response to immunotherapy. Follow-up studies must allow detection of nonresponders, without confusion of pseudoprogression with real progression to prevent premature discontinuation of treatment that can benefit the patient. Conventional imaging techniques and classic tumor response criteria are limited for the evaluation of the unusual patterns of response that arise from the specific mechanisms of action of immunotherapy, so advanced imaging methods must be developed to overcome these shortcomings. The authors present the fundamentals of the tumor immune microenvironment and immunotherapy and how they influence imaging findings. They also discuss advances in functional and molecular imaging techniques for the assessment of immunotherapy in clinical practice, including their use to characterize immune phenotypes, assess patient prognosis and response to therapy, and evaluate immune-related adverse events. Finally, the development of radiomics and radiogenomics in these therapies and the future role of imaging biomarkers for immunotherapy are discussed. Online supplemental material is available for this article. ^©RSNA, 2020.

Radiomics and "radi-…omics" in cancer immunotherapy: a guide for clinicians

In recent years the concept of precision medicine has become a popular topic particularly in medical oncology. Besides the identification of new molecular prognostic and predictive biomarkers and the development of new targeted and immunotherapeutic drugs, imaging has started to play a central role in this new era. Terms such as "radiomics", "radiogenomics" or "radi…-omics" are becoming increasingly common in the literature and soon they will represent an integral part of clinical practice. The use of artificial intelligence, imaging and "-omics" data can be used to develop models able to predict, for example, the features of the tumor immune microenvironment through imaging, and to monitor the therapeutic response beyond the standard radiological criteria. The aims of this narrative review are to provide a simplified guide for clinicians to these concepts, and to summarize the existing evidence on radiomics and "radi…-omics" in cancer immunotherapy.