Radiomics to predict outcomes and abscopal response of patients with cancer treated with immunotherapy combined with radiotherapy using a validated signature of CD8 cells

Defining clinically useful biomarkers of immune checkpoint inhibitors in solid tumours

Although more than a decade has passed since the approval of immune checkpoint inhibitors (ICIs) for the treatment of melanoma and non-small-cell lung, breast and gastrointestinal cancers, many patients still show limited response. US Food and Drug Administration (FDA)-approved biomarkers include programmed cell death 1 ligand 1 (PDL1) expression, microsatellite status (that is, microsatellite instability-high (MSI-H)) and tumour mutational burden (TMB), but these have limited utility and/or lack standardized testing approaches for pan-cancer applications. Tissue-based analytes (such as tumour gene signatures, tumour antigen presentation or tumour microenvironment profiles) show a correlation with immune response, but equally, these demonstrate limited efficacy, as they represent a single time point and a single spatial assessment. Patient heterogeneity as well as inter- and intra-tumoural differences across different tissue sites and time points represent substantial challenges for static biomarkers. However, dynamic biomarkers such as longitudinal biopsies or novel, less-invasive markers such as blood-based biomarkers, radiomics and the gut microbiome show increasing potential for the dynamic identification of ICI response, and patient-tailored predictors identified through neoadjuvant trials or novel ex vivo tumour models can help to personalize treatment. In this Perspective, we critically assess the multiple new static, dynamic and patient-specific biomarkers, highlight the newest consortia and trial efforts, and provide recommendations for future clinical trials to make meaningful steps forwards in the field.

Reconciling the discrepancies in randomized data of combining immunotherapy and radiation therapy: Not all radiotherapy is created equal

It remains highly unclear and debatable whether combining radiotherapy (RT) and immune checkpoint blocker (ICB) therapy yields improved outcomes compared to either modality alone. Whereas some randomized data have shown improved outcomes, others have not. As a result of these conflicting data, it is essential to reconcile differences in the data and postulate reasons thereof. This work seeks to address these discrepancies, and uses the lessons learned from both positive and negative trials, including the most cutting-edge data available, in order to guide future clinical trial design and clarify the ideal/expected role of combinatorial therapy going forward. Because RT offers two distinct contributions (cytoreductive (local) effects & immune-stimulating (systemic) effects), RT should complement immunotherapy by addressing immunotherapy-resistant clones, and immunotherapy should complement RT by addressing RT-resistant or out-of-field clones. RT is not merely a single "drug", but rather a constellation of diverse "drugs" that can be varied based on dose regimens, previous systemic therapy regimens, number of irradiated sites, treatment intent/location/timing, tumor biology, and individual patient immunological circumstances. These factors are discussed as an important explanation for the discrepancies in results of various randomized trials in heterogeneous populations and clinical settings, and these discrepancies may continue until trials of more uniform circumstances are designed to use particular RT paradigms that meaningfully add value to systemic therapy.

Robustness of radiomic features in healthy abdominal parenchyma of patients with repeated examinations on dual-layer dual-energy CT

OBJECTIVES

Robustness of radiomic features in physiological tissue is an important prerequisite for quantitative analysis of tumor biology and response assessment. In contrast to previous studies which focused on different tumors with mostly short scan-re-scan intervals, this study aimed to evaluate the robustness of radiomic features in cancer-free patients and over a clinically encountered inter-scan interval.

MATERIALS AND METHODS

Patients without visible tumor burden who underwent at least two portal-venous phase dual energy CT examinations of the abdomen between May 2016 and January 2020 were included, while macroscopic tumor burden was excluded based upon follow-up imaging for all patients (≥3 months). Further, patients were excluded if no follow-up imaging was available, or if the CT protocol showed deviations between repeated examinations. Circular regions of interest were placed and proofread by two board-certified radiologists (4 years and 5 years experience) within the liver (segments 3 and 6), the psoas muscle (left and right), the pancreatic head, and the spleen to obtain radiomic features from normal-appearing organ parenchyma using PyRadiomics. Radiomic feature robustness was tested using the concordance correlation coefficient with a threshold of 0.75 considered indicative for deeming a feature robust.

RESULTS

In total, 160 patients with 480 repeated abdominal CT examinations (range: 2-4 per patient) were retrospectively included in this single-center, IRB-approved study. Considering all organs and feature categories, only 4.58 % (25/546) of all features were robust with the highest rate being found in the first order feature category (20.37 %, 22/108). Other feature categories (grey level co-occurrence matrix, grey level dependence matrix, grey level run length matrix, grey level size zone matrix, and neighborhood gray-tone difference matrix) yielded an overall low percentage of robust features (range: 0.00 %-1.19 %). A subgroup analysis revealed the reconstructed field of view and the X-ray tube current as determinants of feature robustness (significant differences in subgroups for all organs, p < 0.001) as well as the size of the region of interest (no significant difference for the pancreatic head with p = 0.135, significant difference with p < 0.001 for all other organs).

CONCLUSION

Radiomic feature robustness obtained from cancer-free subjects with repeated examinations using a consistent protocol and CT scanner was limited, with first order features yielding the highest proportion of robust features.

Artificial Intelligence-Based Treatment Decisions: A New Era for NSCLC

Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related mortality among women and men, in developed countries, despite the public health interventions including tobacco-free campaigns, screening and early detection methods, recent therapeutic advances, and ongoing intense research on novel antineoplastic modalities. Targeting oncogenic driver mutations and immune checkpoint inhibition has indeed revolutionized NSCLC treatment, yet there still remains the unmet need for robust and standardized predictive biomarkers to accurately inform clinical decisions. Artificial intelligence (AI) represents the computer-based science concerned with large datasets for complex problem-solving. Its concept has brought a paradigm shift in oncology considering its immense potential for improved diagnosis, treatment guidance, and prognosis. In this review, we present the current state of AI-driven applications on NSCLC management, with a particular focus on radiomics and pathomics, and critically discuss both the existing limitations and future directions in this field. The thoracic oncology community should not be discouraged by the likely long road of AI implementation into daily clinical practice, as its transformative impact on personalized treatment approaches is undeniable.

Research on automatic recognition radiomics algorithm for early sacroiliac arthritis based on sacroiliac MRI imaging

OBJECTIVE

To create an automated machine learning model using sacroiliac joint MRI imaging for early sacroiliac arthritis detection, aiming to enhance diagnostic accuracy.

METHODS

We conducted a retrospective analysis involving 71 patients with early sacroiliac arthritis and 85 patients with normal sacroiliac joint MRI scans. Transverse T1WI and T2WI sequences were collected and subjected to radiomics analysis by two physicians. Patients were randomly divided into training and test groups at a 7:3 ratio. Initially, we extracted the region of interest on the sacroiliac joint surface using ITK-SNAP 3.6.0 software and extracted radiomic features. We retained features with an Intraclass Correlation Coefficient > 0.80, followed by filtering using max-relevance and min-redundancy (mRMR) and LASSO algorithms to establish an automatic identification model for sacroiliac joint surface injury. Receiver operating characteristic (ROC) curves were plotted, and the area under the ROC curve (AUC) was calculated. Model performance was assessed by accuracy, sensitivity, and specificity.

RESULTS

We evaluated model performance, achieving an AUC of 0.943 for the SVM-T1WI training group, with accuracy, sensitivity, and specificity values of 0.878, 0.836, and 0.943, respectively. The SVM-T1WI test group exhibited an AUC of 0.875, with corresponding accuracy, sensitivity, and specificity values of 0.909, 0.929, and 0.875, respectively. For the SVM-T2WI training group, the AUC was 0.975, with accuracy, sensitivity, and specificity values of 0.933, 0.889, and 0.750. The SVM-T2WI test group produced an AUC of 0.902, with accuracy, sensitivity, and specificity values of 0.864, 0.889, and 0.800. In the SVM-bimodal training group, we achieved an AUC of 0.974, with accuracy, sensitivity, and specificity values of 0.921, 0.889, and 0.971, respectively. The SVM-bimodal test group exhibited an AUC of 0.964, with accuracy, sensitivity, and specificity values of 0.955, 1.000, and 0.875, respectively.

CONCLUSION

The radiomics-based detection model demonstrates excellent automatic identification performance for early sacroiliitis.

Radiological artificial intelligence - predicting personalized immunotherapy outcomes in lung cancer

Personalized medicine has revolutionized approaches to treatment in the field of lung cancer by enabling therapies to be specific to each patient. However, physicians encounter an immense number of challenges in providing the optimal treatment regimen for the individual given the sheer complexity of clinical aspects such as tumor molecular profile, tumor microenvironment, expected adverse events, acquired or inherent resistance mechanisms, the development of brain metastases, the limited availability of biomarkers and the choice of combination therapy. The integration of innovative next-generation technologies such as deep learning-a subset of machine learning-and radiomics has the potential to transform the field by supporting clinical decision making in cancer treatment and the delivery of precision therapies while integrating numerous clinical considerations. In this review, we present a brief explanation of the available technologies, the benefits of using these technologies in predicting immunotherapy response in lung cancer, and the expected future challenges in the context of precision medicine.

Artificial Intelligence and Radiomics: Clinical Applications for Patients with Advanced Melanoma Treated with Immunotherapy

Immunotherapy has greatly improved the outcomes of patients with metastatic melanoma. However, it has also led to new patterns of response and progression, creating an unmet need for better biomarkers to identify patients likely to achieve a lasting clinical benefit or experience immune-related adverse events. In this study, we performed a focused literature survey covering the application of artificial intelligence (AI; in the form of radiomics, machine learning, and deep learning) to patients diagnosed with melanoma and treated with immunotherapy, reviewing 12 studies relevant to the topic published up to early 2022. The most commonly investigated imaging modality was CT imaging in isolation (n = 9, 75.0%), while patient cohorts were most frequently recruited retrospectively and from single institutions (n = 7, 58.3%). Most studies concerned the development of AI tools to assist in prognostication (n = 5, 41.7%) or the prediction of treatment response (n = 6, 50.0%). Validation methods were disparate, with two studies (16.7%) performing no validation and equal numbers using cross-validation (n = 3, 25%), a validation set (n = 3, 25%), or a test set (n = 3, 25%). Only one study used both validation and test sets (n = 1, 8.3%). Overall, promising results have been observed for the application of AI to immunotherapy-treated melanoma. Further improvement and eventual integration into clinical practice may be achieved through the implementation of rigorous validation using heterogeneous, prospective patient cohorts.

Application of radiomics-based multiomics combinations in the tumor microenvironment and cancer prognosis

The advent of immunotherapy, a groundbreaking advancement in cancer treatment, has given rise to the prominence of the tumor microenvironment (TME) as a critical area of research. The clinical implications of an improved understanding of the TME are significant and far-reaching. Radiomics has been increasingly utilized in the comprehensive assessment of the TME and cancer prognosis. Similarly, the advancement of pathomics, which is based on pathological images, can offer additional insights into the panoramic view and microscopic information of tumors. The combination of pathomics and radiomics has revolutionized the concept of a "digital biopsy". As genomics and transcriptomics continue to evolve, integrating radiomics with genomic and transcriptomic datasets can offer further insights into tumor and microenvironment heterogeneity and establish correlations with biological significance. Therefore, the synergistic analysis of digital image features (radiomics, pathomics) and genetic phenotypes (genomics) can comprehensively decode and characterize the heterogeneity of the TME as well as predict cancer prognosis. This review presents a comprehensive summary of the research on important radiomics biomarkers for predicting the TME, emphasizing the interplay between radiomics, genomics, transcriptomics, and pathomics, as well as the application of multiomics in decoding the TME and predicting cancer prognosis. Finally, we discuss the challenges and opportunities in multiomics research. In conclusion, this review highlights the crucial role of radiomics and multiomics associations in the assessment of the TME and cancer prognosis. The combined analysis of radiomics, pathomics, genomics, and transcriptomics is a promising research direction with substantial research significance and value for comprehensive TME evaluation and cancer prognosis assessment.

Radiomics and artificial intelligence for precision medicine in lung cancer treatment

Lung cancer is the leading cause of cancer-related deaths worldwide. It exhibits, at the mesoscopic scale, phenotypic characteristics that are generally indiscernible to the human eye but can be captured non-invasively on medical imaging as radiomic features, which can form a high dimensional data space amenable to machine learning. Radiomic features can be harnessed and used in an artificial intelligence paradigm to risk stratify patients, and predict for histological and molecular findings, and clinical outcome measures, thereby facilitating precision medicine for improving patient care. Compared to tissue sampling-driven approaches, radiomics-based methods are superior for being non-invasive, reproducible, cheaper, and less susceptible to intra-tumoral heterogeneity. This review focuses on the application of radiomics, combined with artificial intelligence, for delivering precision medicine in lung cancer treatment, with discussion centered on pioneering and groundbreaking works, and future research directions in the area.

Dissociated response to PD-1 inhibitors combined with radiotherapy in patients with advanced metastatic solid tumors: a single-center experience

BACKGROUND

Anti-programmed death 1/anti-programmed death ligand 1 (PD-1/PD-L1) combined with radiotherapy (RT) has a synergistic effect on systemic tumor control. A dissociated response (DR), characterized by some lesions shrinking and others growing, has been recognized with immune checkpoint inhibitor (ICI) monotherapy or combination therapy. The objective of this study was to assess the frequency and clinical benefit of DR in patients with advanced metastatic solid tumors receiving PD-1 inhibitors in combination with RT.

METHODS

We conducted a single-center retrospective analysis of patients with advanced metastatic solid tumors receiving PD-1 inhibitor combined with RT at the Department of Radiotherapy & Oncology, The Second People's Hospital Affiliated with Soochow University. Treatment response was assessed for each measurable lesion according to the Response Evaluation Criteria in Solid Tumours ( RECIST) v 1.1 guidelines. Patterns of response are divided into four groups: (1) DR, (2) uniform response, (3) uniform progression, and (4) only stable lesions. The overall survival (OS) of different groups was compared using Kaplan-Meier methods and log-rank tests.

RESULTS

Between March 2019 and July 2022, 93 patients were included. The median follow-up was 10.5 months (95% CI 8.8-12.1). The most common tumor types were lung cancer (19.8%), colorectal adenocarcinoma (17.2%), and esophageal cancer (10.8%). DR was observed in 22 (23.7%) patients. The uniform progression and DR are two different patterns of progression. After confirming progression, the overall survival of patients with DR was significantly longer than that of patients with uniform progression (9.9 months (95%CI 5.7-14.1) vs. 4.2 months (95%CI 1.9-6.5), P = 0.028). Compared with DR patients who did not continue PD-1 inhibitor combined with RT or PD-1 inhibitor monotherapy (n = 12), DR patients who continued treatment (n = 10) had significantly longer OS (15.7 (95%CI 3.5-27.9) vs 8.2 (95%CI 5.6-10.8) months, P = 0.035).

CONCLUSIONS

DR is not uncommon (23.7%) in patients with advanced metastatic solid tumors treated with PD-1 inhibitors combined with RT and shows a relatively favorable prognosis. Some patients with DR may benefit from continued PD-1 inhibitor therapy in combination with RT or PD-1 inhibitor monotherapy and may have longer OS.

Immune modulation during anti-cancer radio(immuno)therapy

Cancer can affect all human organs and tissues and ranks as a prominent cause of death as well as an obstruction to increasing life expectancy. A notable breakthrough in oncology has been the inclusion of the immune system in fighting cancer, potentially prolonging life and providing long-term benefits. The concept of "immunotherapy" has been discussed from the 19th and early 20th centuries by Wilhelm Busch, William B. Coley and Paul Ehrlich. This involves distinct approaches, including vaccines, non-specific cytokines and adoptive cell therapies. However, despite the advances made in recent years, questions on how to select the best therapeutic options or how to select the best combinations to improve clinical outcomes are still relevant for scientists and clinicians. More than half of cancer patients receive radiotherapy (RT) as part of their treatment. With the advances in RT and immunotherapy approaches, it is reasonable to consider how to enhance immunotherapy with radiation and vice versa, and to investigate whether combinations of these therapies would be beneficial. In this chapter, we will discuss how the immune system responds to cancer cells and different cancer therapies with a focus on combination of RT and immunotherapy (radioimmunotherapy, RIT).

Updates on radiotherapy-immunotherapy combinations: Proceedings of 6th annual ImmunoRad conference

Focal radiation therapy (RT) has attracted considerable attention as a combinatorial partner for immunotherapy (IT), largely reflecting a well-defined, predictable safety profile and at least some potential for immunostimulation. However, only a few RT-IT combinations have been tested successfully in patients with cancer, highlighting the urgent need for an improved understanding of the interaction between RT and IT in both preclinical and clinical scenarios. Every year since 2016, ImmunoRad gathers experts working at the interface between RT and IT to provide a forum for education and discussion, with the ultimate goal of fostering progress in the field at both preclinical and clinical levels. Here, we summarize the key concepts and findings presented at the Sixth Annual ImmunoRad conference.

Potential non-invasive biomarkers in tumor immune checkpoint inhibitor therapy: response and prognosis prediction

Immune checkpoint inhibitors (ICIs) have dramatically enhanced the treatment outcomes for diverse malignancies. Yet, only 15-60% of patients respond significantly. Therefore, accurate responder identification and timely ICI administration are critical issues in tumor ICI therapy. Recent rapid developments at the intersection of oncology, immunology, biology, and computer science have provided an abundance of predictive biomarkers for ICI efficacy. These biomarkers can be invasive or non-invasive, depending on the specific sample collection method. Compared with invasive markers, a host of non-invasive markers have been confirmed to have superior availability and accuracy in ICI efficacy prediction. Considering the outstanding advantages of dynamic monitoring of the immunotherapy response and the potential for widespread clinical application, we review the recent research in this field with the aim of contributing to the identification of patients who may derive the greatest benefit from ICI therapy.

Partial tumor irradiation plus pembrolizumab in treating large advanced solid tumor metastases

BACKGROUNDWe previously demonstrated the safety of stereotactic body radiotherapy followed by pembrolizumab (SBRT+P) in patients with advanced solid tumors. This phase I clinical trial was expanded to study the safety of partial tumor irradiation (partial-Rx). We assessed irradiated local failure (LF) and clinical outcomes with correlations to biomarkers including CD8+ T cell radiomics score (RS) and circulating cytokines.METHODSPatients received SBRT to 2-4 metastases and pembrolizumab for up to 7 days after SBRT. Tumors measuring up to 65 cc received the full radiation dose (complete-Rx), whereas tumors measuring more than 65 cc received partial-Rx. Landmark analysis was used to assess the relationship between tumor response and overall survival (OS). Multivariable analysis was performed for RS and circulating cytokines.RESULTSIn the combined (expansion plus original) cohort, 97 patients (219 metastases) were analyzed and received SBRT+P. Forty-six (47%) patients received at least 1 partial-Rx treatment. There were 7 (7.2%)dose-limiting toxicities (DLTs). 1-year LF was 7.6% overall, and 13.3% and 5.4% for partial-Rx and complete-Rx tumors, respectively (HR 2.32, 95% CI 0.90-5.97, P = 0.08). The overall, unirradiated, and irradiated objective response rates were 22%, 12%, and 34%, respectively. Irradiated tumor response to SBRT+P was associated with prolonged OS; 1-year OS was 71% (responders), 42% (mixed-responders), and 0% (nonresponders) (P < 0.01). High-RS was significantly associated with improved LF, progression-free survival (PFS), and OS. Elevated circulating IL-8 was independently associated with inferior PFS and OS.CONCLUSIONSBRT+P is safe in patients with large, advanced solid tumors. Additional studies are warranted to assess noninferiority of complete versus partial irradiation of tumors in the setting of immunotherapy.TRIAL REGISTRATIONClinicaltrials.gov NCT02608385FUNDINGMerck Investigator Studies Program; Hillman Fellows for Innovative Cancer Research Program; NIH grants UM1CA186690-06, P50CA254865-01A1, P30CA047904-32, and R01DE031729-01A1.

The abscopal effect in patients with cancer receiving immunotherapy

Interest in the abscopal effect has been rekindled over the past decade with the advent of immunotherapy. Although purportedly elusive, this phenomenon is being increasingly reported. Venturing further using a multimodality approach with an array of systemic agents and unconventional modalities is direly needed. In this perspective, we describe the fundamentals of abscopal responses (ARs), explore combinations with systemic therapies that hold promise in eliciting ARs, and reconnoiter unconventional modalities that may induce ARs. Finally, we scrutinize prospective agents and modalities that exhibit preclinical ability to elicit ARs and discuss prognostic biomarkers, their limitations, and pathways of abscopal resistance for reproducibility.

Can radiomics replace the SPARCC scoring system in evaluating bone marrow edema of sacroiliac joints in patients with axial spondyloarthritis?

OBJECTIVES

To develop an objective and efficient method based on radiomics to evaluate bone marrow edema (BMO) of sacroiliac joints (SIJs) by magnetic resonance imaging (MRI) in patients with axial spondyloarthritis (axSpA) and to compare with the Spondyloarthritis Research Consortium of Canada (SPARCC) scoring system.

METHODS

From September 2013 to March 2022, patients with axSpA who underwent 3.0T SIJ-MRI were included and were randomly divided into training and validation cohorts at a ratio of 7:3. The optimal radiomics features selected from the SIJ-MRI in the training cohort were included to generate the radiomics model. The performance of the model was evaluated by ROC analysis and decision curve analysis (DCA). Rad scores were calculated using the radiomics model. The responsiveness was compared for Rad scores and SPARCC scores. We also assessed the correlation between the Rad score and SPARCC score.

RESULTS

A total of 558 patients were finally included. The radiomics model showed favorable discrimination of a SPARCC score <2 or ≥2 both in the training (AUC, 0.90; 95% CI: 0.87-0.93) and validation cohorts (AUC, 0.90; 95% CI, 0.86-0.95). DCA confirmed that the model was clinically useful. Rad score showed higher responsiveness to treatment-related change than SPARCC score. Furthermore, a significant correlation was noted between the Rad score and SPARCC score when scoring the status of BMO (r_s=0.80, P < 0.001), and a strong correlation was noted when scoring the change in BMO (r=0.70, P < 0.001).

CONCLUSION

The study proposed a radiomics model to accurately quantify the BMO of SIJs in patients with axSpA, providing an alternative to the SPARCC scoring system. Key Points • The Rad score is an index with high validity for the objective and quantitative evaluation of bone marrow edema (BMO) of the sacroiliac joints in axial spondyloarthritis. • The Rad score is a promising tool to monitor the change of BMO upon treatment.

PET-based radiomics visualizes tumor-infiltrating CD8 T cell exhaustion to optimize radiotherapy/immunotherapy combination in mouse models of lung cancer

BACKGROUND

Cumulative preclinical and clinical evidences showed radiotherapy might augment systemic antitumoral responses to immunotherapy for metastatic non-small cell lung cancer, but the optimal timing of combination is still unclear. The overall infiltration and exhausted subpopulations of tumor-infiltrating CD8⁺ T cells might be a potential biomarker indicating the response to immune checkpoint inhibitors (ICI), the alteration of which is previously uncharacterized during peri-irradiation period, while dynamic monitoring is unavailable via repeated biopsies in clinical practice.

METHODS

Basing on tumor-bearing mice model, we investigated the dynamics of overall infiltration and exhausted subpopulations of CD8⁺ T cells after ablative irradiation. With the understanding of distinct metabolic characteristics accompanied with T cell exhaustion, we developed a PET radiomics approach to identify and visualize T cell exhaustion status.

RESULTS

CD8⁺ T cell infiltration increased from 3 to 14 days after ablative irradiation while terminally exhausted populations significantly predominated CD8⁺ T cells during late course of this infiltrating period, indicating that 3-7 days post-irradiation might be a potential appropriate window for delivering ICI treatment. A PET radiomics approach was established to differentiate T cell exhaustion status, which fitted well in both ICI and irradiation settings. We also visualized the underlying association of more heterogeneous texture on PET images with progressed T cell exhaustion.

CONCLUSIONS

We proposed a non-invasive imaging predictor which accurately assessed heterogeneous T cell exhaustion status relevant to ICI treatment and irradiation, and might serve as a promising solution to timely estimate immune-responsiveness of tumor microenvironment and the optimal timing of combined therapy.

Translating Data Science Results into Precision Oncology Decisions: A Mini Review

While reviewing and discussing the potential of data science in oncology, we emphasize medical imaging and radiomics as the leading contextual frameworks to measure the impacts of Artificial Intelligence (AI) and Machine Learning (ML) developments. We envision some domains and research directions in which radiomics should become more significant in view of current barriers and limitations.

Emerging and Evolving Concepts in Cancer Immunotherapy Imaging

Criteria based on measurements of lesion diameter at CT have guided treatment with historical therapies due to the strong association between tumor size and survival. Clinical experience with immune checkpoint modulators shows that editing immune system function can be effective in various solid tumors. Equally, novel immune-related phenomena accompany this novel therapeutic paradigm. These effects of immunotherapy challenge the association of tumor size with response or progression and include risks and adverse events that present new demands for imaging to guide treatment decisions. Emerging and evolving approaches to immunotherapy highlight further key issues for imaging evaluation, such as dissociated response following local administration of immune checkpoint modulators, pseudoprogression due to immune infiltration in the tumor environment, and premature death due to hyperprogression. Research that may offer tools for radiologists to meet these challenges is reviewed. Different modalities are discussed, including immuno-PET, as well as new applications of CT, MRI, and fluorodeoxyglucose PET, such as radiomics and imaging of hematopoietic tissues or anthropometric characteristics. Multilevel integration of imaging and other biomarkers may improve clinical guidance for immunotherapies and provide theranostic opportunities.

Radiotherapy as a means to increase the efficacy of T-cell therapy in solid tumors

Chimeric antigen receptor (CAR)-T cells have demonstrated significant improvements in the treatment of refractory B-cell malignancies that previously showed limited survival. In contrast, early-phase clinical studies targeting solid tumors have been disappointing. This may be due to both a lack of specific and homogeneously expressed targets at the surface of tumor cells, as well as intrinsic properties of the solid tumor microenvironment that limit homing and activation of adoptive T cells. Faced with these antagonistic conditions, radiotherapy (RT) has the potential to change the overall tumor landscape, from depleting tumor cells to reshaping the tumor microenvironment. In this article, we describe the current landscape and discuss how RT may play a pivotal role for enhancing the efficacy of adoptive T-cell therapies in solid tumors. Indeed, by improving homing, expansion and activation of infused T cells while reducing tumor volume and heterogeneity, the use of RT could help the implementation of engineered T cells in the treatment of solid tumors.

Radiomics to evaluate interlesion heterogeneity and to predict lesion response and patient outcomes using a validated signature of CD8 cells in advanced melanoma patients treated with anti-PD1 immunotherapy

PURPOSE

While there is still a significant need to identify potential biomarkers that can predict which patients are most likely to respond to immunotherapy treatments, radiomic approaches have shown promising results. The objectives of this study were to evaluate whether a previously validated radiomics signature of CD8 T-cells could predict progressions at a lesion level and whether the spatial heterogeneity of this radiomics score could be used at a patient level to assess the clinical response and survival of melanoma patients.

METHODS

Clinical data from patients with advanced melanoma treated in our center with immunotherapy were retrieved. Radiomic features were extracted and the CD8 radiomics signature was applied. A progressive lesion was defined by an increase in lesion size of 20% or more. Dispersion metrics of the radiomics signature were estimated to evaluate the impact of interlesion heterogeneity on patient's response. Fine-tuned cut-offs for predicting overall survival were evaluated after splitting data into training and test sets.

RESULTS

A total of 136 patients were included in this study, with 1120 segmented lesions at baseline, and 1052 lesions at first evaluation. A low CD8 radiomics score at baseline was associated with a significantly higher risk of lesion progression (AUC=0.55, p=0.0091), especially for lesions larger than >1 mL (AUC=0.59 overall, p=0.0035, with AUC=0.75, p=0.002 for subcutaneous lesions, AUC=0.68, p=0.01, for liver lesions and AUC=0.62, p=0.03 for nodes). The least infiltrated lesion according to the radiomics score of CD8 T-cells was positively associated with overall survival (training set HR=0.31, p=0.00062, test set HR=0.28, p=0.016), which remained significant in a multivariate analysis including clinical and biological variables.

CONCLUSIONS

These results confirm the predictive value at a lesion level of the biologically inspired CD8 radiomics score in melanoma patients treated with anti-PD1-based immunotherapy and may be interesting to assess the disease spatial heterogeneity to evaluate the patient prognosis with potential clinical implication such as tumor selection for focal ablative therapies.

CT-based radiomics signature to predict CD8+ tumor infiltrating lymphocytes in non-small-cell lung cancer

BACKGROUND

An abundance of CD8+ tumor infiltrating lymphocytes (TILs) in the center of solid tumors is a reliable predictive biomarker for patients eligible for immunotherapy.

PURPOSE

To develop a computed tomography (CT)-based radiomics signature for a preoperative prediction of an abundance of CD8+ TILs in non-small-cell lung cancer (NSCLC).

MATERIAL AND METHODS

In this retrospective study, 117 consecutive patients with pathologically confirmed NSCLC were included and randomly divided into training (n = 77) and test sets (n = 40). A total of 107 radiomics features were extracted from the three-dimensional volumes of interest of each patient. Least absolute shrinkage and selection operator (LASSO) regression was used to select the strongest features for abundance of CD8+ TILs in NSCLC, and the radiomics score was constructed through a linear combination of these selected features. Receiver operating characteristic (ROC) curve analysis was used to evaluate the predictive performance of the radiomics score.

RESULTS

The radiomics score was associated with an abundance of CD8+ TILs in NSCLC, which achieved an area under the curve (AUC) of 0.83 (95% CI=0.73-0.92) and 0.68 (95% CI=0.54-0.87) in the training and test sets, respectively. The difference was not statistically significant (P = 0.20). The tumors with high CD8+ TILs tended to have heterogeneous dependences (high value of Dependence Non-Uniformity Normalized) and complicated texture (high value of Informational Measure of Correlation 1).

CONCLUSION

CT-based radiomics features have the ability to predict CD8+ TILs expression levels of an abundance of CD8+ TILs in NSCLC, which was shown to be a potential imaging biomarker for stratifying patients who may benefit from immunotherapy.

Imaging to predict checkpoint inhibitor outcomes in cancer. A systematic review

BACKGROUND

Checkpoint inhibition has radically improved the perspective for patients with metastatic cancer, but predicting who will not respond with high certainty remains difficult. Imaging-derived biomarkers may be able to provide additional insights into the heterogeneity in tumour response between patients. In this systematic review, we aimed to summarise and qualitatively assess the current evidence on imaging biomarkers that predict response and survival in patients treated with checkpoint inhibitors in all cancer types.

METHODS

PubMed and Embase were searched from database inception to 29th November 2021. Articles eligible for inclusion described baseline imaging predictive factors, radiomics and/or imaging machine learning models for predicting response and survival in patients with any kind of malignancy treated with checkpoint inhibitors. Risk of bias was assessed using the QUIPS and PROBAST tools and data was extracted.

RESULTS

In total, 119 studies including 15,580 patients were selected. Of these studies, 73 investigated simple imaging factors. 45 studies investigated radiomic features or deep learning models. Predictors of worse survival were (i) higher tumour burden, (ii) presence of liver metastases, (iii) less subcutaneous adipose tissue, (iv) less dense muscle and (v) presence of symptomatic brain metastases. Hazard rate ratios did not exceed 2.00 for any predictor in the larger and higher quality studies. The added value of baseline fluorodeoxyglucose positron emission tomography parameters in predicting response to treatment was limited. Pilot studies of radioactive drug tracer imaging showed promising results. Reports on radiomics were almost unanimously positive, but numerous methodological concerns exist.

CONCLUSIONS

There is well-supported evidence for several imaging biomarkers that can be used in clinical decision making. Further research, however, is needed into biomarkers that can more accurately identify which patients who will not benefit from checkpoint inhibition. Radiomics and radioactive drug labelling appear to be promising approaches for this purpose.

Artificial intelligence and radiomics: fundamentals, applications, and challenges in immunotherapy

Immunotherapy offers the potential for durable clinical benefit but calls into question the association between tumor size and outcome that currently forms the basis for imaging-guided treatment. Artificial intelligence (AI) and radiomics allow for discovery of novel patterns in medical images that can increase radiology’s role in management of patients with cancer, although methodological issues in the literature limit its clinical application. Using keywords related to immunotherapy and radiomics, we performed a literature review of MEDLINE, CENTRAL, and Embase from database inception through February 2022. We removed all duplicates, non-English language reports, abstracts, reviews, editorials, perspectives, case reports, book chapters, and non-relevant studies. From the remaining articles, the following information was extracted: publication information, sample size, primary tumor site, imaging modality, primary and secondary study objectives, data collection strategy (retrospective vs prospective, single center vs multicenter), radiomic signature validation strategy, signature performance, and metrics for calculation of a Radiomics Quality Score (RQS). We identified 351 studies, of which 87 were unique reports relevant to our research question. The median (IQR) of cohort sizes was 101 (57–180). Primary stated goals for radiomics model development were prognostication (n=29, 33.3%), treatment response prediction (n=24, 27.6%), and characterization of tumor phenotype (n=14, 16.1%) or immune environment (n=13, 14.9%). Most studies were retrospective (n=75, 86.2%) and recruited patients from a single center (n=57, 65.5%). For studies with available information on model testing, most (n=54, 65.9%) used a validation set or better. Performance metrics were generally highest for radiomics signatures predicting treatment response or tumor phenotype, as opposed to immune environment and overall prognosis. Out of a possible maximum of 36 points, the median (IQR) of RQS was 12 (10–16). While a rapidly increasing number of promising results offer proof of concept that AI and radiomics could drive precision medicine approaches for a wide range of indications, standardizing the data collection as well as optimizing the methodological quality and rigor are necessary before these results can be translated into clinical practice.

Imaging approaches and radiomics: toward a new era of ultraprecision radioimmunotherapy?

Strong rationale and a growing number of preclinical and clinical studies support combining radiotherapy and immunotherapy to improve patient outcomes. However, several critical questions remain, such as the identification of patients who will benefit from immunotherapy and the identification of the best modalities of treatment to optimize patient response. Imaging biomarkers and radiomics have recently emerged as promising tools for the non-invasive assessment of the whole disease of the patient, allowing comprehensive analysis of the tumor microenvironment, the spatial heterogeneity of the disease and its temporal changes. This review presents the potential applications of medical imaging and the challenges to address, in order to help clinicians choose the optimal modalities of both radiotherapy and immunotherapy, to predict patient’s outcomes and to assess response to these promising combinations.

Role of radiomics in predicting immunotherapy response

Immunotherapies have revolutionised cancer management. Despite their success, durable responses are limited to a subset of patients. Prediction of immunotherapy response in patients has proven to be difficult due to a lack of robust biomarkers. Routinely collected imaging may offer an additional information source to personalise patient treatment, with advantages over tissue-based biomarkers. Quantitative image analysis or radiomics, which involves the high-throughput extraction of imaging features, has the potential to non-invasively predict cancer histology, outcomes and prognosis. This review evaluates the value of radiomics in patients undergoing immunotherapy, with a summary provided of the performance of radiomics models in predicting immunotherapy response and toxicity, as well as immune correlates. Much of the literature focussed on clinical endpoints and correlates to tissue biomarkers, particularly in lung cancer, while few studies investigated association with immune-related adverse events. Strengths of the studies included more frequent use of clinical trial datasets, homogenous patient cohorts and high-quality diagnostic scans. Limitations of the studies include heterogeneity in study methodology, lack of well-defined homogenous imaging datasets, limited open publishing of imaging datasets, coding and parameters used for radiomics signature development and limited use of external validation datasets. Future research should address the above limitations, as well as further explore the relationship between radiomics and immune-related adverse effects and less well-studied biological correlates such tumour mutational burden, and incorporate known clinical prognostic scores into radiomics models.

Evaluation of radio-immunotherapy sequence on immunological responses and clinical outcomes in patients with melanoma brain metastases (ELEKTRA)

In patients with melanoma brain metastases (MBM), a combination of radiotherapy (RT) with immune checkpoint inhibitors (ICI) is routinely used. However, the best sequence of radio-immunotherapy (RIT) remains unclear. In an exploratory phase 2 trial, MBM patients received RT (stereotactic or whole-brain radiotherapy depending on the number of MBM) combined with ipilimumab (ipi) ± nivolumab (nivo) in different sequencing (Rad-ICI or ICI-Rad). Comparators arms included patients treated with ipi-free systemic treatment or without RT (in MBM-free patients). The primary endpoints were radiological and immunological responses in the peripheral blood. Secondary endpoints were progression-free survival (PFS) and overall survival (OS). Of 106 screened, 92 patients were included in the study. Multivariate analysis revealed an advantage for patients starting with RT (Rad-ICI) for overall response rate (RR: p = .007; HR: 7.88 (95%CI: 1.76–35.27)) and disease control rate (DCR: p = .036; HR: 6.26 (95%CI: 1.13–34.71)) with a trend for a better PFS (p = .162; HR: 1.64 (95%CI: 0.8–3.3)). After RT plus two cycles of ipi-based ICI in both RIT sequences, increased frequencies of activated CD4, CD8 T cells and an increase in melanoma-specific T cell responses were observed in the peripheral blood. Lasso regression analysis revealed a significant clinical benefit for patients treated with Rad-ICI sequence and immunological features, including high frequencies of memory T cells and activated CD8 T cells in the blood. This study supports increasing evidence that sequencing RT followed by ICI treatment may have better effects on the immunological responses and clinical outcomes in MBM patients.

[Highlights from the 2021 ASCO and ESMO annual meetings on radiotherapy of head and neck cancer]

At this year's annual meetings of the American Society of Clinical Oncology (ASCO) and the European Society for Medical Oncology (ESMO), several studies on radiotherapy of locally advanced head and neck cancer were presented. For the indication of definitive radiochemotherapy, particularly the administration of immune checkpoint inhibitors concomitant to radiotherapy was investigated. In the phase III GORTEC-REACH trial, combined inhibition of epidermal growth factor receptor (EGFR) and programmed death-ligand (PD-L1) concomitant to radiotherapy of locally advanced head and neck cancer was inferior to platinum-based chemoradiotherapy. However, this therapeutic approach may be more efficient than radiotherapy with simultaneous EGFR inhibition alone. The concept of the phase II CheckRad-CD8 trial with induction chemoimmunotherapy followed by chemotherapy-free radioimmunotherapy after appropriate patient selection also proved to be highly efficient. In initial phase II trials, dose de-escalation of radiotherapy seems feasible for HPV-positive oropharyngeal cancer after appropriate patient selection both postoperatively (ECOG-ACRIN E3311 trial) and after induction therapy (Optima II trial). However, dose de-escalation should currently not be performed outside of clinical trials. In addition, first studies indicate a benefit of functional imaging (diffusion-weighted magnetic resonance imaging [MRI] or F‑fluoromisonidazole positron-emission tomography [FMISO-PET]) to establish personalized dose concepts in radiotherapy.

Novel imaging biomarkers predict outcomes in stage III unresectable non-small cell lung cancer treated with chemoradiation and durvalumab

Background

The landmark study of durvalumab as consolidation therapy in NSCLC patients (PACIFIC trial) demonstrated significantly longer progression-free survival (PFS) in patients with locally advanced, unresectable non-small cell lung cancer (NSCLC) treated with durvalumab (immunotherapy, IO) therapy after chemoradiotherapy (CRT). In clinical practice in the USA, durvalumab continues to be used in patients across all levels of programmed cell death ligand-1 (PD-L1) expression. While immune therapies have shown promise in several cancers, some patients either do not respond to the therapy or have cancer recurrence after an initial response. It is not clear so far who will benefit of this therapy or what the mechanisms behind treatment failure are.

Methods

A total of 133 patients with unresectable stage III NSCLC who underwent durvalumab after CRT or CRT alone were included. Patients treated with durvalumab IO after CRT were randomly split into training (D1=59) and test (D2=59) sets and the remaining 15 patients treated with CRT alone were grouped in D3. Radiomic textural patterns from within and around the target nodules were extracted. A radiomic risk score (RRS) was built and was used to predict PFS and overall survival (OS). Patients were divided into high-risk and low-risk groups based on median RRS.

Results

RRS was found to be significantly associated with PFS in D1 (HR=2.67, 95% CI 1.85 to 4.13, p<0.05, C-index=0.78) and D2 (HR=2.56, 95% CI 1.63 to 4, p<0.05, C-index=0.73). Similarly, RRS was associated with OS in D1 (HR=1.89, 95% CI 1.3 to 2.75, p<0.05, C-index=0.67) and D2 (HR=2.14, 95% CI 1.28 to 3.6, p<0.05, C-index=0.69), respectively. RRS was found to be significantly associated with PFS in high PD-L1 (HR=3.01, 95% CI 1.41 to 6.45, p=0.0044) and low PD-L1 (HR=2.74, 95% CI 1.8 to 4.14, p=1.77e-06) groups. Moreover, RRS was not significantly associated with OS in the high PD-L1 group (HR=2.08, 95% CI 0.98 to 4.4, p=0.054) but was significantly associated with OS in the low PD-L1 group (HR=1.61, 95% CI 1.14 to 2.28, p=0.0062). In addition, RRS was significantly associated with PFS (HR=2.77, 95% CI 1.17 to 6.52, p=0.019, C-index=0.77) and OS (HR=2.62, 95% CI 1.25 to 5.51, p=0.01, C-index=0.77) in D3, respectively.

Conclusions

Tumor radiomics of pretreatment CT images from patients with stage III unresectable NSCLC were prognostic of PFS and OS to CRT followed by durvalumab IO and CRT alone.

Magnetic resonance imaging-based radiomics signature for preoperative prediction of Ki67 expression in bladder cancer

PURPOSE

The Ki67 expression is associated with the advanced clinicopathological features and poor prognosis in bladder cancer (BCa). We aimed to develop and validate magnetic resonance imaging (MRI)-based radiomics signatures to preoperatively predict the Ki67 expression status in BCa.

METHODS AND MATERIALS

We retrospectively collected 179 BCa patients with Ki67 expression and preoperative MRI. Radiomics features were extracted from T2-weighted (T2WI) and dynamic contrast-enhancement (DCE) images. The synthetic minority over-sampling technique (SMOTE) was used to balance the minority group (low Ki67 expression group) in the training set. Minimum redundancy maximum relevance was used to identify the best features associated with Ki67 expression. Support vector machine and Least Absolute Shrinkage and Selection Operator algorithms (LASSO) were used to construct radiomics signatures in training and SMOTE-training sets, and diagnostic performance was assessed by the area under the curve (AUC) and accuracy. The decision curve analyses (DCA) and calibration curve and were used to investigate the clinical usefulness and calibration of radiomics signatures, respectively. The Kaplan-Meier test was performed to investigate the prognostic value of radiomics-predicted Ki67 expression status.

RESULTS

1218 radiomics features were extracted from T2WI and DCE images, respectively. The SMOTE-LASSO model based on nine features achieved the best predictive performance in the SMOTE-training (AUC, 0.859; accuracy, 80.3%) and validation sets (AUC, 0.819; accuracy, 81.5%) with a good calibration performance and clinical usefulness. Immunohistochemistry-based high Ki67 expression and radiomics-predicted high Ki67 expression based on the SMOTE-LASSO model were significantly associated with poor disease-free survival in training and validation sets (all P < 0.05).

CONCLUSIONS

The SMOTE-LASSO model could predict the Ki67 expression status and was associated with survival outcomes of the BCa patients, thereby may aid in clinical decision-making.

[Artificial intelligence and medical imaging]

The use of artificial intelligence methods for image recognition is one of the most developed branches of the AI field and these technologies are now commonly used in our daily lives. In the field of medical imaging, approaches based on artificial intelligence are particularly promising, with numerous applications and a strong interest in the search for new biomarkers. Here, we will present the general methods used in these approaches as well as the potential areas of application.

Predictive Value of Multiparametric MRI for Response to Single-Cycle Induction Chemo-Immunotherapy in Locally Advanced Head and Neck Squamous Cell Carcinoma

Objectives

To assess the predictive value of multiparametric MRI for treatment response evaluation of induction chemo-immunotherapy in locally advanced head and neck squamous cell carcinoma.

Methods

Twenty-two patients with locally advanced, histologically confirmed head and neck squamous cell carcinoma who were enrolled in the prospective multicenter phase II CheckRad-CD8 trial were included in the current analysis. In this unplanned secondary single-center analysis, all patients who received contrast-enhanced MRI at baseline and in week 4 after single-cycle induction therapy with cisplatin/docetaxel combined with the immune checkpoint inhibitors tremelimumab and durvalumab were included. In week 4, endoscopy with representative re-biopsy was performed to assess tumor response. All lesions were segmented in the baseline and restaging multiparametric MRI, including the primary tumor and lymph node metastases. The volume of interest of the respective lesions was volumetrically measured, and time-resolved mean intensities of the golden-angle radial sparse parallel-volume-interpolated gradient-echo perfusion (GRASP-VIBE) sequence were extracted. Additional quantitative parameters including the T1 ratio, short-TI inversion recovery ratio, apparent diffusion coefficient, and dynamic contrast-enhanced (DCE) values were measured. A model based on parallel random forests incorporating the MRI parameters from the baseline MRI was used to predict tumor response to therapy. Receiver operating characteristic (ROC) curves were used to evaluate the prognostic performance.

Results

Fifteen patients (68.2%) showed pathologic complete response in the re-biopsy, while seven patients had a residual tumor (31.8%). In all patients, the MRI-based primary tumor volume was significantly lower after treatment. The baseline DCE parameters of time to peak and wash-out were significantly different between the pathologic complete response group and the residual tumor group (p < 0.05). The developed model, based on parallel random forests and DCE parameters, was able to predict therapy response with a sensitivity of 78.7% (95% CI 71.24–84.93) and a specificity of 78.6% (95% CI 67.13–87.48). The model had an area under the ROC curve of 0.866 (95% CI 0.819–0.914).

Conclusions

DCE parameters indicated treatment response at follow-up, and a random forest machine learning algorithm based on DCE parameters was able to predict treatment response to induction chemo-immunotherapy.

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.

Robustness of dual-energy CT-derived radiomic features across three different scanner types

OBJECTIVES

To investigate the robustness of radiomic features between three dual-energy CT (DECT) systems.

METHODS

An anthropomorphic body phantom was scanned on three different DECT scanners, a dual-source (dsDECT), a rapid kV-switching (rsDECT), and a dual-layer detector DECT (dlDECT). Twenty-four patients who underwent abdominal DECT examinations on each of the scanner types during clinical follow-up were retrospectively included (n = 72 examinations). Radiomic features were extracted after standardized image processing, following ROI placement in phantom tissues and healthy appearing hepatic, splenic and muscular tissue of patients using virtual monoenergetic images at 65 keV (VMI_65keV) and virtual unenhanced images (VUE). In total, 774 radiomic features were extracted including 86 original features and 8 wavelet transformations hereof. Concordance correlation coefficients (CCC) and analysis of variances (ANOVA) were calculated to determine inter-scanner robustness of radiomic features with a CCC of ≥ 0.9 deeming a feature robust.

RESULTS

None of the phantom-derived features attained the threshold for high feature robustness for any inter-scanner comparison. The proportion of robust features obtained from patients scanned on all three scanners was low both in VMI_65keV (dsDECT vs. rsDECT:16.1% (125/774), dlDECT vs. rsDECT:2.5% (19/774), dsDECT vs. dlDECT:2.6% (20/774)) and VUE (dsDECT vs. rsDECT:11.1% (86/774), dlDECT vs. rsDECT:2.8% (22/774), dsDECT vs. dlDECT:2.7% (21/774)). The proportion of features without significant differences as per ANOVA was higher both in patients (51.4-71.1%) and in the phantom (60.6-73.4%).

CONCLUSIONS

The robustness of radiomic features across different DECT scanners in patients was low and the few robust patient-derived features were not reflected in the phantom experiment. Future efforts should aim to improve the cross-platform generalizability of DECT-derived radiomics.

KEY POINTS

• Inter-scanner robustness of dual-energy CT-derived radiomic features was on a low level in patients who underwent clinical examinations on three DECT platforms. • The few robust patient-derived features were not confirmed in our phantom experiment. • Limited inter-scanner robustness of dual-energy CT derived radiomic features may impact the generalizability of models built with features from one particular dual-energy CT scanner type.

[Precision medicine and immuno-radiotherapy]

Numerous clinical studies aim to integrate immunotherapy in radiotherapy oncology, either for generating abscopal responses in metastatic patients in combination with radiotherapy, or in the treatment of a locally advanced tumor. The search for biomarkers of response to treatment is a major axis in the development of these therapeutic combinations, to allow the early identification of patients who will benefit from the treatment, in the context of an increasingly personalized approach. We review some of the strategies that can be applied for personalization to combined radiotherapy and immunotherapy treatments.

[Artificial intelligence, radiomics and pathomics to predict response and survival of patients treated with radiations]

Artificial intelligence approaches in medicine are more and more used and are extremely promising due to the growing number of data produced and the variety of data they allow to exploit. Thus, the computational analysis of medical images in particular, radiological (radiomics), or anatomopathological (pathomics), has shown many very interesting results for the prediction of the prognosis and the response of cancer patients. Radiotherapy is a discipline that particularly benefits from these new approaches based on computer science and imaging. This review will present the main principles of an artificial intelligence approach and in particular machine learning, the principles of a radiomic and pathomic approach and the potential of their use for the prediction of the prognosis of patients treated with radiotherapy.

Radiographical assessment of tumour stroma and treatment outcomes using deep learning: a retrospective, multicohort study

BACKGROUND

The tumour stroma microenvironment plays an important part in disease progression and its composition can influence treatment response and outcomes. Histological evaluation of tumour stroma is limited by access to tissue, spatial heterogeneity, and temporal evolution. We aimed to develop a radiological signature for non-invasive assessment of tumour stroma and treatment outcomes.

METHODS

In this multicentre, retrospective study, we analysed CT images and outcome data of 2209 patients with resected gastric cancer from five independent cohorts recruited from two centres (Nanfang Hospital of Southern Medical University [Guangzhou, China] and Sun Yat-sen University Cancer Center [Guangzhou, China]). Patients with histologically confirmed gastric cancer, at least 15 lymph nodes harvested, preoperative abdominal CT available, and complete clinicopathological and follow-up data were eligible for inclusion. Tumour tissue was collected for patients in the training cohort (321 patients), internal validation cohort one (246 patients), and external validation cohort one (128 patients). Four stroma classes were defined according to the protein expression of α-smooth muscle actin and periostin assessed by immunohistochemistry. The primary objective was to predict the histologically based stroma classes by using preoperative CT images. We trained a deep convolutional neural network model using the training cohort and tested the model in the internal and external validation cohort one. We evaluated the model's association with prognosis in the training cohort, two internal, and two external validation cohorts and compared outcomes of patients who received or did not receive adjuvant chemotherapy.

FINDINGS

The deep-learning model achieved a high diagnostic accuracy for assessing tumour stroma in both internal validation cohort one (area under the receiver operating characteristic curve [AUC] 0·96-0·98]) and external validation cohort one (AUC 0·89-0·94). The stromal imaging signature was significantly associated with disease-free survival and overall survival in all cohorts (p<0·0001). The predicted stroma classes remained an independent prognostic factor adjusting for clinicopathological variables including tumour size, stage, differentiation, and Lauren histology. In patients with stage II or III disease in predicted stroma classes one and two subgroups, patients who received adjuvant chemotherapy had improved survival compared with those who did not (in those with stage II disease hazard ratio [HR] 0·48 [95% CI 0·29-0·77], p=0·0021; and in those with stage III disease HR 0·70 [0·57-0·85], p=0·00042). However, in the other two subgroups adjuvant chemotherapy was not associated with survival and might even be detrimental in the predicted stroma class 4 subgroup (HR 1·48 [1·08-2·03], p=0·013).

INTERPRETATION

The deep-learning model could allow for accurate and non-invasive evaluation of tumour stroma from CT images in gastric cancer. The radiographical model predicted chemotherapy outcomes and could be used in combination with clinicopathological criteria to refine prognosis and inform treatment decisions of patients with gastric cancer.

FUNDING

None.

Radiotherapy in the Era of Immunotherapy With a Focus on Non-Small-Cell Lung Cancer: Time to Revisit Ancient Dogmas?

Radiation-induced immune effects have been extensively deciphered over the last few years, leading to the concept of the dual immune effect of radiotherapy with both immunostimulatory and immunosuppressive effects. This explains why radiotherapy alone is not able to drive a strong anti-tumor immune response in most cases, hence underlining the rationale for combining both radiotherapy and immunotherapy. This association has generated considerable interest and hundreds of trials are currently ongoing to assess such an association in oncology. However, while some trials have provided unprecedented results or shown much promise, many hopes have been dashed. Questions remain, therefore, as to how to optimize the combination of these treatment modalities. This narrative review aims at revisiting the old, well-established concepts of radiotherapy relating to dose, fractionation, target volumes and organs at risk in the era of immunotherapy. We then propose potential innovative approaches to be further assessed when considering a radio-immunotherapy association, especially in the field of non-small-cell lung cancer (NSCLC). We finally propose a framework to optimize the association, with pragmatic approaches depending on the stage of the disease.

Hyperprogressive Disease: Main Features and Key Controversies

Along with the positioning of immunotherapy as a preferential treatment for a wide variety of neoplasms, a new pattern of response consisting in a sudden acceleration of tumor growth has been described. This phenomenon has received the name of "hyperprogressive disease", and several definitions have been proposed for its identification, most of them relying on radiological criteria. However, due to the fact that the cellular and molecular mechanisms have not been elucidated yet, there is still some debate regarding whether this fast progression is induced by immunotherapy or only reflects the natural course of some highly aggressive neoplasms. Moreover, contradictory results of trials including patients with different cancer types suggest that both the incidence, the associated factors and the implications regarding prognosis might differ depending on tumor histology. This article intends to review the main publications regarding this matter and critically approach the most controversial aspects.

Validation of a radiomic approach to decipher NSCLC immune microenvironment in surgically resected patients

BACKGROUND

Radiomics has emerged as a noninvasive tool endowed with the potential to intercept tumor characteristics thereby predicting clinical outcome. In a recent study on resected non-small cell lung cancer (NSCLC), we identified highly prognostic computed tomography (CT)-derived radiomic features (RFs), which in turn were able to discriminate hot from cold tumor immune microenvironment (TIME). We aimed at validating a radiomic model capable of dissecting specific TIME profiles bearing prognostic power in resected NSCLC.

METHODS

The validation cohort included 31 radically resected NSCLCs clinicopathologically matched with the training set (n = 69). TIME was classified in hot and cold according to a multiparametric immunohistochemical analysis involving PD-L1 score and incidence of immune effector phenotypes among tumor infiltrating lymphocytes (TILs). High-throughput radiomic features (n = 841) extracted from CT images were correlated to TIME parameters to ultimately define prognostic classes.

RESULTS

We confirmed PD-1 to CD8 ratio as best predictor of clinical outcome among TIME characteristics. Significantly prolonged overall survival (OS) was observed in patients carrying hot (median OS not reached) vs cold (median OS 22 months; hazard ratio 0.28, 95% confidence interval 0.09-0.82; p = 0.015) immune background, thus validating the prognostic impact of these two TIME categories in resected NSCLC. Importantly, in the validation setting, three out of eight previously identified RFs sharply distinguishing hot from cold TIME were endorsed. Among signature-related RFs, Wavelet-HHH_gldm_HighGrayLevelEmphasis highly performed as descriptor of hot immune contexture (area under the receiver operating characteristic curve 0.94, 95% confidence interval 0.81-1.00; p = 0.01).

CONCLUSION

Radiomics may decipher specific TIME profiles providing a noninvasive prognostic approach in resected NSCLC and an exploitable predictive strategy in advanced cases.