Radiomics to predict immunotherapy-induced pneumonitis: proof of concept

Computational modelling of modern cancer immunotherapy

Modern cancer immunotherapy has revolutionised oncology and carries the potential to radically change the approach to cancer treatment. However, numerous questions remain to be answered to understand immunotherapy response better and further improve the benefit for future cancer patients. Computational models are promising tools that can contribute to accelerated immunotherapy research by providing new clues and hypotheses that could be tested in future trials, based on preceding simulations in addition to the empirical rationale. In this topical review, we briefly summarise the history of cancer immunotherapy, including computational modelling of traditional cancer immunotherapy, and comprehensively review computational models of modern cancer immunotherapy, such as immune checkpoint inhibitors (as monotherapy and combination treatment), co-stimulatory agonistic antibodies, bispecific antibodies, and chimeric antigen receptor T cells. The modelling approaches are classified into one of the following categories: data-driven top-down vs mechanistic bottom-up, simplistic vs detailed, continuous vs discrete, and hybrid. Several common modelling approaches are summarised, such as pharmacokinetic/pharmacodynamic models, Lotka-Volterra models, evolutionary game theory models, quantitative systems pharmacology models, spatio-temporal models, agent-based models, and logic-based models. Pros and cons of each modelling approach are critically discussed, particularly with the focus on the potential for successful translation into immuno-oncology research and routine clinical practice. Specific attention is paid to calibration and validation of each model, which is a necessary prerequisite for any successful model, and at the same time, one of the main obstacles. Lastly, we provide guidelines and suggestions for the future development of the field.

Strategies for improving the management of immune-related adverse events

With the advent of immunotherapeutic agents, durable and dramatic responses have been observed in several hard-to-treat malignancies, outlining a roadmap to conquering cancer. Immune checkpoint inhibitors (ICPi) are a class of immunotherapeutic agents that attack the tumor cells by reinvigorating the suppressed immune system. However, the unbridled T-cell activity disrupts the immune homeostasis and induces a unique spectrum of side effects called immune-related adverse events (irAEs) in a significant proportion of patients. These irAEs are distinct from the side effects produced by traditional chemotherapeutic agents. Although majority of irAEs are manageable with corticosteroids and other immunosuppressive agents, life-threatening and fatal events have been reported. In the absence of predictive biomarkers to identify patients at risk for irAEs and standardized approach to detect, report, and treat irAEs, management of irAEs has been challenging to the patients, caregivers and the healthcare providers alike. With increasing use of ICPis for treatment of various cancers, the incidence of irAEs will undoubtedly increase. There is a compelling need to develop measures to effectively manage irAEs, both in the community settings and in cancer centers alike. To this end, in this paper, we propose several strategies, such as providing patient education, harmonizing irAE management guidelines, standardizing reporting of irAEs, optimizing the choice of immunosuppressive agents, conducting preclinical, clinical and translational studies to better understand irAEs, including high-risk patients, incorporating diagnostic tools to personalize irAE management using wireless technology and digital health, providing a platform to hear the missing patient's voice, and sharing evolving data to improve the management of irAEs.

Immune Checkpoint Inhibitor-Related Pneumonitis

Immune checkpoint inhibitors are novel agents that have been proved efficacious in a variety of cancer types, but they are associated with a unique set of organ-specific, immune-related adverse events. Among them, immune-related pneumonitis requires special attention because it is difficult to diagnose and potentially lethal. Accumulating real-world epidemiological data suggest that immune-related pneumonitis is more frequent than previously reported. Its diagnosis requires exclusion of other causes and assessment of radiographic features on high-resolution CT of the chest. Management of immune-related pneumonitis is based on the use of immunosuppressants. Future research should be focused on finding predictive biomarkers for immune-related pneumonitis as well as optimizing its management.

Systematic review of radiomic biomarkers for predicting immune checkpoint inhibitor treatment outcomes

BACKGROUND

Systemic therapy agents targeting immune checkpoint inhibitors have been approved for use since 2011. This type of therapy aims to trigger a patient's immune response to attack tumor cells, rather than acting against the tumor directly. Radiomics is an automated method of medical image analysis that is now being actively investigated for predictive markers of treatment response in immunotherapy.

OBJECTIVE

To conduct an early systematic review determining the current status of radiomic features as potential predictive markers of immunotherapy response. Provide a detailed critical appraisal of methodological quality of models, as this informs the degree of confidence about current reports of model performance. In addition, to offer some recommendations for future studies that could establish robust evidence for radiomic features as immunotherapy response markers.

METHOD

A PubMed citation search was conducted for publications up to and including April 2020, followed by full-text screening. A total of seven articles meeting the eligibility criteria were examined in detail for study characteristics, model information and methodological quality. The review was conducted in the Cochrane style but has not been prospectively registered. Results are reported following Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA) guidelines.

RESULTS

A total of seven studies were examined in detail, comprising non-small cell lung cancer, metastatic melanoma and a diverse assortment of solid tumors. Methodological robustness of reviewed studies varied greatly. Principal shortcomings were lack of prospective registration, and deficiencies in feature selection and dimensionality reduction, model calibration, clinical utility and external validation. A few studies with overall moderate to good methodological quality were identified. These results suggest that current state-of-the-art performance of radiomics in regards to discrimination (area under the curve or concordance index) is in the vicinity of 0.7, but the very small number of studies to date prevents any conclusive remarks to be made. We recommended future improvements in regards to prospective study registration, clinical utility, methodological procedure and data sharing.

CONCLUSIONS

Radiomics has a potentially significant role for predicting immunotherapy response. Additional multi-institutional studies with robust methodological underpinning and repeated external validations are required to establish the (added) value of radiomics within the pantheon of clinical tools for decision-making in immunotherapy.

[Research Advances and Obstacles of CT-based Radiomics in Diagnosis and Treatment of Lung Cancer]

影像组学是一种基于多模态医学影像处理分析的技术，该技术能够基于高性能计算机及算法从目前普遍使用的计算机断层扫描（computed tomography, CT）、磁共振图像（magnetic resonance imaging, MRI）和正电子发射/断层图像（positron emission tomography/computed tomography, PET/CT）中自动提取海量数据进行分析，对疾病的早期诊断、良恶性肿瘤鉴别、疾病治疗全程管理，个体化精准治疗等需求提供更多有价值信息。近年来，许多研究表明基于CT的影像组学技术在肺癌的早期诊断、基因表型预测、疗效预测及预后评估均有良好的应用价值，且影像学检查具有无创、经济、可重复等优势。其对临床的指导价值已有所展露，在肺癌的个体化、精准化治疗和研究方面具有较大价值，但是，影像组学特征的重复性和一致性问题以及在肺部肿瘤图像提取中的特征筛选还需进一步研究。

Use of Radiomics to Predict Response to Immunotherapy of Malignant Tumors of the Digestive System

BACKGROUND Despite the promising results of immunotherapy in cancer treatment, new response patterns, including pseudoprogression and hyperprogression, have been observed. Radiomics is the automated extraction of high-fidelity, high-dimensional imaging features from standard medical images, allowing comprehensive visualization and characterization of the tissue of interest and corresponding microenvironment. This study assessed whether radiomics can predict response to immunotherapy in patients with malignant tumors of the digestive system. MATERIAL AND METHODS Computed tomography (CT) images of patients with malignant tumors of the digestive system obtained at baseline and after immunotherapy were subjected to radiomics analyses. Radiomics features were extracted from each image. The formula of the screened features and the final predictive model were obtained using the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm. RESULTS Imaging analysis was feasible in 87 patients, including 3 with pseudoprogression and 7 with hyperprogression. One hundred ten radiomics features were obtained before and after treatment, including 109 features of the target lesions and 1 of the aorta. Four models were constructed, with the model constructed from baseline and post-treatment CT features having the best classification performance, with a sensitivity, specificity, and AUC of 83.3%, 88.9%, and 0.806, respectively. CONCLUSIONS Radiomics can predict the response of patients with malignant tumors of the digestive system to immunotherapy and can supplement conventional evaluations of response.

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.

Understanding Response to Immunotherapy Using Standard of Care and Experimental Imaging Approaches

Immunotherapy has emerged as a standard of care in the treatment of a wide variety of malignancies, and it may be used in combination with other treatments including surgery, radiation, and chemotherapy. However, a patient's imaging response to immunotherapy can be confounded by a variety of factors, including the appearance of pseudoprogression or the development of immune-related adverse events. In these situations, the immune response itself can mimic disease progression, potentially causing confusion in assessment and determination of further treatment. To address these challenges, a variety of approaches have been proposed to improve response assessment. First, revised definitions of response criteria, accounting for the appearance of pseudoprogression, can improve specificity of assessment. Second, advanced image processing including radiomics and machine learning analysis can be used to further analyze standard of care imaging data. In addition, new molecular imaging techniques can be used to directly interrogate immune cell activity or study aspects of the tumor microenvironment. These approaches have promise for improving the understanding of the response to immunotherapy and improving patient care.

The mechanism and risk factors for immune checkpoint inhibitor pneumonitis in non-small cell lung cancer patients

Immune checkpoint inhibitors (ICIs) are new and promising therapeutic agents for non-small cell lung cancer (NSCLC). However, along with demonstrating remarkable efficacy, ICIs can also trigger immune-related adverse events. Checkpoint inhibitor pneumonitis (CIP) has been reported to have a morbidity rate of 3% to 5% and a mortality rate of 10% to 17%. Moreover, the incidence of CIP in NSCLC is higher than that in other tumor types, reaching 7% to 13%. With the increased use of ICIs in NSCLC, CIP has drawn extensive attention from oncologists and cancer researchers. Identifying high risk factors for CIP and the potential mechanism of CIP are key points in preventing and monitoring serious adverse events. In this review, the results of our analysis and summary of previous studies suggested that the risk factors for CIP may include previous lung disease, prior thoracic irradiation, and combinations with other drugs. Our review also explored potential mechanisms closely related to CIP, including increased T cell activity against associated antigens in tumor and normal tissues, preexisting autoantibodies, and inflammatory cytokines.

A multidisciplinary consensus on the morphological and functional responses to immunotherapy treatment

The implementation of immunotherapy has radically changed the treatment of oncological patients. Currently, immunotherapy is indicated in the treatment of patients with head and neck tumors, melanoma, lung cancer, bladder tumors, colon cancer, cervical cancer, breast cancer, Merkel cell carcinoma, liver cancer, leukemia and lymphomas. However, its efficacy is restricted to a limited number of cases. The challenge is, therefore, to identify which subset of patients would benefit from immunotherapy. To this end, the establishment of immunotherapy response criteria and predictive and prognostic biomarkers is of paramount interest. In this report, a group of experts of the Spanish Society of Medical Oncology (SEOM), the Spanish Society of Medical Radiology (SERAM), and Spanish Society of Nuclear Medicine and Molecular Imaging (SEMNIM) provide an up-to-date review and a consensus guide on these issues.

Investigational Biomarkers for Checkpoint Inhibitor Immune-Related Adverse Event Prediction and Diagnosis

BACKGROUND

Immune checkpoint inhibitors (ICIs) have revolutionized the treatment of multiple cancers. However, these promising therapies may also cause immune-related adverse events (irAEs) in a substantial proportion of patients. These autoimmune phenomena may affect almost any organ system and may occur at almost any point in therapy. In some instances, these toxicities are life-threatening and potentially permanent. Diverse clinical presentation and unpredictable timing further complicate their anticipation and diagnosis.

CONTENT

To improve patient safety and selection for ICI use, biomarkers for irAE diagnosis and prediction are under development. Clinicians may use traditional laboratory markers such as routine chemistries, creatinine clearance, thyroid function tests, and serum cortisol/adrenocorticotrophic hormone to monitor for specific irAEs, but noted aberrations may not necessarily represent an immune-mediated etiology. Novel biomarkers have the potential to be more specific to assist in the diagnosis of irAEs. The prediction of irAEs is more challenging. Apart from a history of autoimmune disease, no other clinical parameters are routinely used to project risk. Biomarker candidates under investigation for irAE diagnosis and prediction include blood cell analysis, chemokines/cytokines, autoantibodies, and genetic predisposition, such as human leukocyte antigen haplotype. Among other emerging candidates are immune-cell subsets, T-cell repertoire, fecal microbiome, tumor genomics, and radiomic characterization.

SUMMARY

Several conventional laboratory indexes of end-organ dysfunction are currently in routine clinical use for irAE monitoring and diagnosis. Novel biomarkers for the prediction and diagnosis of these irAEs, which primarily characterize patient immune function, represent an area of active investigation.

Checkpoint Inhibitor Pneumonitis: Mechanisms, Characteristics, Management Strategies, and Beyond

PURPOSE OF REVIEW

Checkpoint inhibitor pneumonitis (CIP) is a toxicity of immune checkpoint blockade (ICB) that can be highly morbid and at times fatal. Here, we review the proposed biologic mechanisms of CIP, epidemiology and risk factors for CIP development, diagnostic work-up and management strategies for CIP, and future directions of CIP research.

RECENT FINDINGS

CIP incidence appears to be greater in real-world populations and may continue to rise as FDA approvals for ICB continue to expand to multiple malignancies. Multiple retrospective studies and case series have identified potential risk factors for CIP. Several society guidelines have helped to unify the classification of CIP severity and standardize treatment approaches but significant gaps remain, including formal validated diagnostic criteria for CIP. While significant strides have been made in enhancing the knowledge and management of CIP, ongoing research is needed to continue to advance our understanding of the biologic underpinnings of CIP, as well as optimize diagnostic and management strategies for this potentially devastating toxicity.

Radiomics and deep learning in lung cancer

Lung malignancies have been extensively characterized through radiomics and deep learning. By providing a three-dimensional characterization of the lesion, models based on radiomic features from computed tomography (CT) and positron-emission tomography (PET) have been developed to detect nodules, distinguish malignant from benign lesions, characterize their histology, stage, and genotype. Deep learning models have been applied to automatically segment organs at risk in lung cancer radiotherapy, stratify patients according to the risk for local and distant recurrence, and identify patients candidate for molecular targeted therapy and immunotherapy. Moreover, radiomics has also been applied successfully to predict side effects such as radiation- and immunotherapy-induced pneumonitis and differentiate lung injury from recurrence. Radiomics could also untap the potential for further use of the cone beam CT acquired for treatment image guidance, four-dimensional CT, and dose-volume data from radiotherapy treatment plans. Radiomics is expected to increasingly affect the clinical practice of treatment of lung tumors, optimizing the end-to-end diagnosis-treatment-follow-up chain. The main goal of this article is to provide an update on the current status of lung cancer radiomics.

Combination Strategies for Immune-Checkpoint Blockade and Response Prediction by Artificial Intelligence

The therapeutic concept of unleashing a pre-existing immune response against the tumor by the application of immune-checkpoint inhibitors (ICI) has resulted in long-term survival in advanced cancer patient subgroups. However, the majority of patients do not benefit from single-agent ICI and therefore new combination strategies are eagerly necessitated. In addition to conventional chemotherapy, kinase inhibitors as well as tumor-specific vaccinations are extensively investigated in combination with ICI to augment therapy responses. An unprecedented clinical outcome with chimeric antigen receptor (CAR-)T cell therapy has led to the approval for relapsed/refractory diffuse large B cell lymphoma and B cell acute lymphoblastic leukemia whereas response rates in solid tumors are unsatisfactory. Immune-checkpoints negatively impact CAR-T cell therapy in hematologic and solid malignancies and as a consequence provide a therapeutic target to overcome resistance. Established biomarkers such as programmed death ligand 1 (PD-L1) and tumor mutational burden (TMB) help to select patients who will benefit most from ICI, however, biomarker negativity does not exclude responses. Investigating alterations in the antigen presenting pathway as well as radiomics have the potential to determine tumor immunogenicity and response to ICI. Within this review we summarize the literature about specific combination partners for ICI and the applicability of artificial intelligence to predict ICI therapy responses.

Imaging of Adverse Events Related to Checkpoint Inhibitor Therapy

Immunotherapy with checkpoint inhibitors (ICIs) is becoming standard of practice for an increasing number of cancer types. ICIs enhance T-cell action against the cancer cells. By unbalancing the immune system ICIs may cause dysimmune toxicities, a series of disorders broadly defined immune-related adverse events (irAEs). IrAEs may affect any organ or apparatus and most frequently involve skin, colon, endocrine organs, liver, and lungs. Early identification and appropriate treatment of irAEs can improve patient outcome. The paper aims at reviewing mechanisms of the occurrence of irAEs, the importance of a proper diagnosis and the main pillars of therapy. To provide effective guidance to the comprehension of major irAEs imaging findings will be reviewed.

Immune-Related Adverse Events: Pneumonitis

Checkpoint inhibitors are part of the family of immunotherapies and are increasingly being used in a wide variety of cancers. Immune-related adverse events pose a major challenge in the treatment of cancer patients. Pneumonitis is a rare immune-related adverse event that presents in distinct patterns. The goal of this chapter is to instruct readers on the incidence and clinical manifestations of pneumonitis and to offer guidance in the evaluation and treatment of patients with pneumonitis.

Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics

PURPOSE

The aim of this systematic review was to analyse literature on artificial intelligence (AI) and radiomics, including all medical imaging modalities, for oncological and non-oncological applications, in order to assess how far the image mining research stands from routine medical application. To do this, we applied a trial phases classification inspired from the drug development process.

METHODS

Among the articles we considered for inclusion from PubMed were multimodality AI and radiomics investigations, with a validation analysis aimed at relevant clinical objectives. Quality assessment of selected papers was performed according to the QUADAS-2 criteria. We developed the phases classification criteria for image mining studies.

RESULTS

Overall 34,626 articles were retrieved, 300 were selected applying the inclusion/exclusion criteria, and 171 high-quality papers (QUADAS-2 ≥ 7) were identified and analysed. In 27/171 (16%), 141/171 (82%), and 3/171 (2%) studies the development of an AI-based algorithm, radiomics model, and a combined radiomics/AI approach, respectively, was described. A total of 26/27(96%) and 1/27 (4%) AI studies were classified as phase II and III, respectively. Consequently, 13/141 (9%), 10/141 (7%), 111/141 (79%), and 7/141 (5%) radiomics studies were classified as phase 0, I, II, and III, respectively. All three radiomics/AI studies were categorised as phase II trials.

CONCLUSIONS

The results of the studies are promising but still not mature enough for image mining tools to be implemented in the clinical setting and be widely used. The transfer learning from the well-known drug development process, with some specific adaptations to the image mining discipline could represent the most effective way for radiomics and AI algorithms to become the standard of care tools.

Imaging-based Biomarkers for Predicting and Evaluating Cancer Immunotherapy Response

Proper patient selection for immunotherapy is critical, as certain tumor microenvironments are more permissible to therapy than others. Currently, the use of programmed cell death ligand-1 (PD-L1) and microsatellite instability high and/or mismatch repair deficiency are used as biomarkers for immunotherapy response. To improve tumor characterization, methodologies are being developed to combine imaging with tumor immune environment characterization. Imaging of tumors from immunotherapy responders and nonresponders with various imaging modalities has led to the development of criteria that could predict patient response to immunotherapy. Additionally, radiomics-based artificial intelligence methods are being used to characterize tumor microenvironments to predict and evaluate immunotherapy responses, as well as to predict risk of immune-related adverse events. Molecular imaging techniques are also being developed for various modalities to observe tumor expression of immunotherapy targets, such as PD-L1 and, to confirm the target is being expressed on resident tumors. In all, the advancements of imaging techniques to define tumor immunologic characteristics will help to stratify patients who are more likely to respond to immunotherapies. Keywords: Computer Aided Diagnosis (CAD), Computer Applications-Virtual Imaging, Efficacy Studies, MR-Imaging, Molecular Imaging-Cancer, Molecular Imaging-Immunotherapy, Molecular Imaging-Nanoparticles, Molecular Imaging-Probe Development, Molecular Imaging-Target Development, SPECT/CT © RSNA, 2019.

Imaging for Response Assessment in Radiation Oncology: Current and Emerging Techniques

Imaging in radiation oncology is essential for the evaluation of treatment response in tumors and organs at risk. This influences further treatment decisions and could possibly be used to adapt therapy. This review article focuses on the currently used imaging modalities for response assessment in radiation oncology and gives an overview of new and promising techniques within this field.

Immune Checkpoint Inhibitor Toxicity in Head and Neck Cancer: From Identification to Management

Benefiting from the continuously clarifying underlying biology of immune checkpoints and ligand–receptor interactions, the emergence of new anticancer treatment strategy, immunotherapy has shown substantial benefits on several liquid and solid tumors. Immune checkpoint inhibitors (ICIs) can block the negative regulatory components and enhance the T cell function, thus leading to prominent anticancer activity. On account of their promising effect on various malignancies shown in clinical trials, ICIs have been considered to be the most potent anticancer agents in the near future. Head and neck cancer is the seventh most common neoplasm worldwide, and the gross 5-year survival rate was only 60%. Managing locoregionally advanced, recurrent, or metastatic head and neck tumors is still a challenging problem for both oncologists and surgeons. Recent clinical trials employing the immune-modulating antibodies that target cytotoxic T-lymphocyte-associated antigen-4 (CTLA-4) and programmed cell death 1 (PD-1) herald a new era of anticancer therapy. However, like all other anticancer drugs, ICIs also have side effects while upregulating the immune system to enhance antitumor response, which were known as immune-related adverse events (irAEs). Generally, most irAEs were transient, but sometimes they can cause serious organ dysfunction, even fatal. In addition, due to the distinct anatomical feature, advanced head and neck tumors often affect the upper aerodigestive tract and cause serious dyspnea or dysphagia. Toxicities of ICIs may be more lethal for such patients. Thus, with the increasing application of anti-checkpoint agents in head and neck cancer, there is urgent need to ascertain the safety of this novel treatment strategy. Here, we compile this review of existing clinical trials on the toxicity of ICIs during cancer treatment. The particular clinical manifestation, characteristics of complication development in fatal cases, and the management strategies were discussed. This may provide vital information for future oncology trials and clinical practice.

The Promise of Digital Biopsy for the Prediction of Tumor Molecular Features and Clinical Outcomes Associated With Immunotherapy

Immunotherapy by immune checkpoint inhibitors has emerged as an effective treatment for a slight proportion of patients with aggressive tumors. Currently, some molecular determinants, such as the expression of the programmed cell death ligand-1 (PD-L1) or the tumor mutational burden (TMB) have been used in the clinical practice as predictive biomarkers, although they fail in consistency, applicability, or reliability to precisely identify the responding patients mainly because of their spatial intratumoral heterogeneity. Therefore, new biomarkers for early prediction of patient response to immunotherapy, that could integrate several approaches, are eagerly sought. Novel methods of quantitative image analysis (such as radiomics or pathomics) might offer a comprehensive approach providing spatial and temporal information from macroscopic imaging features potentially predictive of underlying molecular drivers, tumor-immune microenvironment, tumor-related prognosis, and clinical outcome (in terms of response or toxicity) following immunotherapy. Preliminary results from radiomics and pathomics analysis have demonstrated their ability to correlate image features with PD-L1 tumor expression, high CD3 cell infiltration or CD8 cell expression, or to produce an image signature concordant with gene expression. Furthermore, the predictive power of radiomics and pathomics can be improved by combining information from other modalities, such as blood values or molecular features, leading to increase the accuracy of these models. Thus, “digital biopsy,” which could be defined by non-invasive and non-consuming digital techniques provided by radiomics and pathomics, may have the potential to allow for personalized approach for cancer patients treated with immunotherapy.

Multicenter study demonstrates radiomic features derived from magnetic resonance perfusion images identify pseudoprogression in glioblastoma

Pseudoprogression (PsP) is a diagnostic clinical dilemma in cancer. In this study, we retrospectively analyse glioblastoma patients, and using their dynamic susceptibility contrast and dynamic contrast-enhanced perfusion MRI images we build a classifier using radiomic features obtained from both Ktrans and rCBV maps coupled with support vector machines. We achieve an accuracy of 90.82% (area under the curve (AUC) = 89.10%, sensitivity = 91.36%, 67 specificity = 88.24%, p = 0.017) in differentiating between pseudoprogression (PsP) and progressive disease (PD). The diagnostic performances of the models built using radiomic features from Ktrans and rCBV separately were equally high (Ktrans: AUC = 94%, 69 p = 0.012; rCBV: AUC = 89.8%, p = 0.004). Thus, this MR perfusion-based radiomic model demonstrates high accuracy, sensitivity and specificity in discriminating PsP from PD, thus provides a reliable alternative for noninvasive identification of PsP versus PD at the time of clinical/radiologic question. This study also illustrates the successful application of radiomic analysis as an advanced processing step on different MR perfusion maps.

MRI scans of glioblastoma patients can be misleading and some patients appear to show features of progressive disease although they respond to treatment. Here, the authors use MRI images of progressive disease or pseudoprogression and build a classifier using machine learning to distinguish the two.

Integrating molecular nuclear imaging in clinical research to improve anticancer therapy

Effective patient selection before or early during treatment is important to increasing the therapeutic benefits of anticancer treatments. This selection process is often predicated on biomarkers, predominantly biospecimen biomarkers derived from blood or tumour tissue; however, such biomarkers provide limited information about the true extent of disease or about the characteristics of different, potentially heterogeneous tumours present in an individual patient. Molecular imaging can also produce quantitative outputs; such imaging biomarkers can help to fill these knowledge gaps by providing complementary information on tumour characteristics, including heterogeneity and the microenvironment, as well as on pharmacokinetic parameters, drug-target engagement and responses to treatment. This integrative approach could therefore streamline biomarker and drug development, although a range of issues need to be overcome in order to enable a broader use of molecular imaging in clinical trials. In this Perspective article, we outline the multistage process of developing novel molecular imaging biomarkers. We discuss the challenges that have restricted the use of molecular imaging in clinical oncology research to date and outline future opportunities in this area.

Assessing the Clinical Utility of Computed Tomography-Based Radiomics

This commentary provides an overview of the evolving field of radiomics, which aims to noninvasively augment clinical prognostic nomograms, correlate imaging phenotypes, and support clinical decision‐making.

Risk factors for pneumonitis in patients treated with anti-programmed death-1 therapy: A case-control study

Immune checkpoint blockade‐related pneumonitis is a rare but potentially life‐threatening adverse effect, but its risk factors are not completely understood. This case‐control study was conducted to identify pneumonitis risk factors in patients treated with anti‐PD1 monoclonal antibodies (mAbs), including all the patients who developed pneumonitis after anti‐PD‐1 mAbs treatment in the Cancer Center of the Chinese People's Liberation Army from September 2015 to September 2017. Two controls per case were matched according to a propensity‐score matching algorithm to account for confounding effects caused by individual baseline variables. Demographic and clinical information was obtained from medical records. In total, 55 cases and 110 controls were included in the study. No association was observed between smoking status or primary lung cancer and risk of pneumonitis. Significant risk factors for pneumonitis related to anti‐PD‐1 mAbs were prior thoracic radiotherapy, prior lung disease and combination therapy with odds ratios of 3.34 (1.51‐7.39), 2.86 (1.45‐5.64) and 2.73 (1.40‐5.31), respectively. The associations remained significant in the multivariable logistic regression model. The risk of pneumonitis induced by anti‐PD‐1 mAbs is associated with prior thoracic radiotherapy, prior lung disease, and combination therapy. Clinicians should monitor these features in patients receiving anti‐PD‐1 therapy to optimize clinical safety and efficacy.

The role of radiology in the evaluation of the immunotherapy efficacy

In the last years, a great interest has arisen on immunotherapy for the treatment of advanced non-small cell lung cancer (NSCLC). Check-point inhibitor drugs are now considered clinical practice standard in different settings and their use is expected to increase significantly in the near future. As treatment options for lung cancer advance and vary, the different patterns of radiological response increase in number and heterogeneity. To correctly evaluate the radiological findings after and during these treatments is of paramount importance, both in the clinical and sperimental setting. In consideration of their peculiar mechanism, immunotherapies can determine unusual response patterns on imaging, that cannot be correctly evaluated with the traditional response criteria such as World Health Organization (WHO) and Response Evaluation Criteria in Solid Tumours (RECIST). Therefore, during these years, several response criteria [immune-related response criteria (irRC), irRECIST and iRECIST] were proposed and applied in clinical trials on immunotherapies. The aim of this review is to describe the radiological findings after immunotherapy, to critically discuss the different response criteria and the imaging of immune-related adverse events.

Immunotherapy and the role of imaging

Significant advances in the genetic and molecular characterization of cancer have led to the development of effective immunotherapies. These therapeutics help the host immune system recognize cancer as foreign, promote the immune system, and relieve the inhibition that allows growth and spread of tumors. Experience with various immunotherapies, particularly the immunomodulatory monoclonal antibody ipilimumab, has demonstrated that unique patterns of response may be encountered that cannot be adequately captured by traditional response criteria, such as the World Health Organization (WHO) criteria and Response Evaluation Criteria in Solid Tumors (RECIST), which have been used primarily with cytotoxic chemotherapies. In response to these observations, several novel response criteria have been developed to evaluate patients who receive immunotherapy, including immune-related response criteria (irRC), immune-related RECIST (irRECIST), and immune RECIST (iRECIST). These criteria are typically used in conjunction with RECIST version 1.1 in the clinical trial setting, because approval of new therapeutics by the US Food and Drug Administration relies on the responses derived from RECIST version 1.1. Finally, a wide variety of immune-related adverse events may affect patients who receive immunotherapy, many of which can be identified on imaging studies such as computed tomography, magnetic resonance imaging, and 2-deoxy-2-(fluorine-18)fluoro-D-glucose-positron emission tomography/computed tomography. In this review, the authors present the role of imaging in the evaluation of patients treated with immunotherapy, including the background and application of irRC, irRECIST, and iRECIST; the imaging of immune-related adverse events; and future directions in advanced imaging of immunotherapy. Cancer 2018;124:2906-22. © 2018 American Cancer Society.

Immune-Related Adverse Events: Pneumonitis

Checkpoint inhibitors are part of the family of immunotherapies and are increasingly being used in a wide variety of cancers. Immune-related adverse events pose a major challenge in the treatment of cancer patients. Pneumonitis is a rare immune-related adverse event that presents in distinct patterns. The goal of this chapter is to instruct readers on the incidence and clinical manifestations of pneumonitis and to offer guidance in the evaluation and treatment of patients with pneumonitis.