Front. Immunol. Frontiers in Immunology Front. Immunol. 1664-3224 Frontiers Media S.A. 10.3389/fimmu.2023.1088874 Immunology Review Radiomics-guided checkpoint inhibitor immunotherapy for precision medicine in cancer: A review for clinicians Zhou Huijie 1 † Luo Qian 2 † Wu Wanchun 1 Li Na 1 Yang Chunli 1 Zou Liqun 1 * 1 Division of Medical Oncology, Cancer Center and State Key Laboratory of Biotherapy, Sichuan University West China Hospital, Chengdu, China 2 Department of Hematology, the Second Affiliated Hospital Zhejiang University School of Medicine, Zhejiang, China

Edited by: Qiang Tong, Renmin Hospital of Wuhan University, China

Reviewed by: Guozhu Hou, Chinese Academy of Medical Sciences and Peking Union Medical College, China; Fei Yu, Tongji University School of Medicine, China

*Correspondence: Liqun Zou, zouliqun1971@163.com

†These authors have contributed equally to this work and share first authorship

This article was submitted to Cancer Immunity and Immunotherapy, a section of the journal Frontiers in Immunology

 01 03 2023 2023 14 1088874 03 11 2022 16 02 2023 Copyright © 2023 Zhou, Luo, Wu, Li, Yang and Zou 2023 Zhou, Luo, Wu, Li, Yang and Zou

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

Immunotherapy using immune checkpoint inhibitors (ICIs) is a breakthrough in oncology development and has been applied to multiple solid tumors. However, unlike traditional cancer treatment approaches, immune checkpoint inhibitors (ICIs) initiate indirect cytotoxicity by generating inflammation, which causes enlargement of the lesion in some cases. Therefore, rather than declaring progressive disease (PD) immediately, confirmation upon follow-up radiological evaluation after four–eight weeks is suggested according to immune-related Response Evaluation Criteria in Solid Tumors (ir-RECIST). Given the difficulty for clinicians to immediately distinguish pseudoprogression from true disease progression, we need novel tools to assist in this field. Radiomics, an innovative data analysis technique that quantifies tumor characteristics through high-throughput extraction of quantitative features from images, can enable the detection of additional information from early imaging. This review will summarize the recent advances in radiomics concerning immunotherapy. Notably, we will discuss the potential of applying radiomics to differentiate pseudoprogression from PD to avoid condition exacerbation during confirmatory periods. We also review the applications of radiomics in hyperprogression, immune-related biomarkers, efficacy, and immune-related adverse events (irAEs). We found that radiomics has shown promising results in precision cancer immunotherapy with early detection in noninvasive ways.

 radiomics immunotherapy precision medicine pseudoprogression hyperprogression Introduction

Immunotherapy using ICIs has been revolutionary in cancer treatment owing to its significant impact on the reactivation of the immune system (1, 2). Unlike traditional cancer treatment approaches, which kill tumor cells directly, ICIs initiate indirect cytotoxicity by generating inflammation and may cause enlargement of the lesion in some cases. Hence, there may be different interpretations of medical imaging for patients undergoing immunotherapy (3).

Medical images contain many quantitative biomedical features based on intensity, shape, size or volume, and texture, which can offer information on the tumor microenvironment and phenotype. These features are difficult to identify by human vision alone.

Radiomics is an emerging field that extracts quantitative features from medical images and converts digital medical images into mineable, high-dimensional data with new high-throughput approaches.

Features extracted in radiomics can be divided into two categories: “semantic” and “agnostic” (4). Semantic features include shape, location, vascularity, speculation, necrosis, attachments, and lepidics, commonly used in imaging reports. However, radiomics can quantify these features with computer assistance. Agnostic features include histograms (skewness, kurtosis), haralick textures, laws textures, wavelets, Laplacian transforms, Minkowski functionals, and fractal dimensions. These features can provide intratumoral heterogeneity information through quantitative descriptors (4).

The process of radiomics involves the following discrete steps:

Image acquisition (i.e., CT, MR, and PET/CT)

Volume of interest (VOI) identification and segmentation: identifying tumors and their surroundings as VOIs and delineating the borders of the volume

Feature extraction and qualification: extracting and qualifying high-dimensional features from the VOI

Modeling: mining extracted features with artificial intelligence to develop classifier models that aid detection, diagnosis, prognosis assessment, and treatment response prediction

This approach could be applied to any aspect of medical imaging analysis, including immunotherapy, thereby providing a novel noninvasive approach to precision cancer treatment.

Genomic and microenvironment heterogeneity within the tumor volume is displayed on the imaging, while these fine distinctions cannot be recognizable by the naked eye, even for experienced radiologists. Nevertheless, these subtle differences can be recognized by radiomics using quantitative assays, allowing for microscopic analyses of medical imaging to establish predictive, diagnostic, and prognostic models ( Figure 1 ).

Radiomics analysis can obtain more information from medical images. (A) There is genomic and microenvironment heterogeneity within the tumor volume displayed on the imaging, while these fine distinctions cannot be recognizable by the naked eye, even for experienced radiologists. (B) These subtle differences can be recognized by radiomics using quantitative assays, allowing microscopic analyses of medical imaging to establish predictive, diagnostic, and prognostic models.

This review summarizes radiomics concerning immunotherapy from a clinical perspective. It discusses its potential to predict outcomes, molecular biomarkers, atypical responses, and immune-related adverse events (irAEs) of immunotherapy.

 Prediction of immune-related biomarkers

To date, predictive biomarkers of immune responses are mainly driven by invasive tissue biopsy, while limited biopsy samples may be difficult to provide a holistic picture of the heterogeneity within the tumor and its microenvironment. Radiomics is a powerful auxiliary to conventional invasive biopsies, overcoming the intratumoral heterogeneity within the same patient. The section below describes current progress in immune-related biomarkers using radiomics and how radiomics can overcome these limitations ( Table 1 ).

Summary of Key Studies on the Role of radiomics in predicting the expression of immune-related biomarkers.

Reference Tumor type Application NO. of cases Imaging modality Segmention Segmention software Machine learning algorithm Prediction model Machine learning software Results Type of validation
Giulia Mazzaschi (5) NSCLC PD-L1 100 CT Semi-automatically 3d-Slicer Regression Multiparametric prognostic model SPSS, STATA AUC: 0.91 Independent validation with single-center data
Hectors (6) HCC PD-L1 48 MRI Manual OsiriX Logistic regression Radiomics features MA TLAB, SPSS R=0. 41–0.47, p<0. 029 Independent validation with single-center data
Yoon (7) Advanced lung adenocarcinoma PD-L1 153 Pretreatment thin section CT Semi-automatically Aview Research Logistic regression Clinical-Radiomics model SPSS, MedCalc, R C-statistic, 0.667 Independent validation with single-center data
JIANG M (8) NSCLC PD-L1 399 PET/CT Semiautomatically ITK-Snap Logistic regression and random forest classifier Radiomics model Python The highest AUC, 0.97 Independent validation with single-center data
CHEN (9) Squamous Cell Carcinoma of the Head and Neck PD-L1 53 PET/CT Manual SPSS Regression Textural index of correlation SPSS OR, 0.009 (p = 0.015) Independent validation with single-center data
MU (10) NSCLC PD-L1 616 PET/CT Semiautomatically ITK-Snap Deep learning Deep learning score MATLAB AUC, 0.81 Independent validation with multi-center data
Polverari (11) NSCLC PD-L1 57 PET/CT Semiautomatically LIFEx Regression Radiomics features (coarseness and GLZLM_ZLNU) R P=0.025,0.035 Independent validation with single-center data
Sun (12) Advanced solid tumours Tumour-infiltrating CD8 cells and response to immunotherapy 491 CT Semiautomatically LIFEx Regression Radiomics signature R AUC, 0.67 Independent validation with multi-center data
Tang (13) NSCLC PD-L1 and CD3 infiltration 290 CT Semiautomatically 3D Slicer, IBEX Hierarchical clustering algorithm Radiomics model R AUC, 0.853 Independent validation with single-center data
LIAO (14) HCC Tumor-Infiltrating CD8+T Cells 142 CT Manual ITK-SNAP Elastic net regression Radiomics model R AUC, 0.751 Independent validation with single-center data
CHEN (15) HCC Immunoscore 207 MRI Manual A.K. software Regression Clinical -radiomics nomogram Python, R AUC, 0.926 Independent validation with single-center data
Khorrami (16) NSCLC TILs 139 CT Manual 3D SLICER Linear discriminant analysis (LDA) classifier Delta-radiomics model R AUC=0.88 ± 0.08 Independent validation with multi-center data
HE (17) NSCLC TMB 327 CT Semiautomatically 3D SLICER Deep learning Deep learning model Python, R AUC= 0.85 Independent validation with single-center data
Veeraraghavan (18) Endometrial cancers MMR, TMB 150 CT Manual ITK-SNAP Random forest classifier Clinical-radiomic model R AUC(MMR)=0.78;AUC(TMB)=0.87 Independent validation with single-center data
Golia (19) Colorectal cancer MSI 198 CT Manual ITK-SNAP Random forest classifier Clinical -Radiomics model MATLAB AUC=0.80 Independent validation with single-center data
Kather (20) Gastrointestinal cancer MSI 81 H&E histology Manual Macenko method Deep learning Deep learning model MATLAB, R AUC=0.84 Independent validation with multi-center data
Wang (21) Lung adenocarcinoma TMB 51 CT Manual 3D-Slicer Support vector machine (SVM) Clinical-radiomic model R AUC= 0.671 Independent validation with single-center data
Tang (13) Bladder cancer TMB 75 CT Manual 3D-Slicer Regression Radiomics model R AUC=0.853 Independent validation with single-center data
WEN (22) NSCLC TMB; PD-L1 120 CT Manual 3D Slicer Regression Clinicopathological radiomics model R AUC(PD-L1) = 0.839AUC(TMB)= Independent validation with single-center data
Li (23) Colorectal cancer MSS 173 PET/CT Manual ITP-SNAP Regression Radiomics model Python AUC=0.828 Independent validation with single-center data
CAO (24) Colorectal cancer MSI 502 CT Manual ITP-SNAP Regression Clinical Radiomics nomogram R AUC=0.898 Independent validation with multi-center data
Pei (25) Colorectal cancer MSI 702 CT Manual – Regression Clinical Radiomics nomogram R AUC=0.74 Independent validation with single-center data
Zhang (26) Rectal cancer MSI 491 MRI Manual ITK-SNAP Regression Deep learning model Python AUC= 0.868 Independent validation with single-center data
Jiang (27) Gastric cancer Immunescore based on immunohistochemistry 1778 CT Manual ITK-SNAP Regression Radiomics model R AUC= 0.766 Independent validation with multi-center data
Xue (28) Rectal cancer Immunescore based on immunohistochemistry 133 MR Manual ITK-SNAP Logistic regression Radiomics model R AUC= 0.768 Independent validation with single-center data
Radiomics and programmed cell death ligand 1

As a promising treatment for cancer patients, immunotherapy is not effective for all patients (3, 29, 30). Thus, recognizing the appropriate candidate for immunotherapy is of vital importance. PD-L1 expression examined via immunohistochemistry (IHC) is associated with the clinical efficacy of anti-PD-1/PD-L1 therapy. It has been widely applied as a reference to immunotherapy decision-making in most cancer types during clinical practice (29, 31). The expression levels of PD-L1 may change during therapy (32, 33). Due to the existence of intratumoral heterogeneity, the IHC test results of a small number of biopsy samples could not be representative of the whole (34–36).

As the artificial intelligence (AI) field progresses, PD-L1 prediction with radiomics has received increased attention in recent years ( Table 1 ). Giulia Mazzaschi et al. established a noninvasive model with computed tomography (CT)-extracted features to predict the level of PD-L1 expression and tumor infiltrating lymphocytes (TILs). They found that texture, effect, and margins were directly associated with PD-L1 expression and TILs. These features also correlated with the prognosis of patients with non-small cell lung cancer (NSCLC) (5). Similarly, researchers have tried to explore the potential of radiomics biomarkers for predicting the immuno-oncologic characteristics of hepatocellular carcinomas (HCC) based on magnetic resonance imaging (MRI). They found that radiomics features, specifically texture feature variance and enhancement ratios, were strongly associated with PD-L1 expression and were predictive diagnostic biomarkers for assessing early HCC recurrence (6).

PET/CT was also applied to predict PD-L1 expression levels. The researchers extracted imaging histology features from PET/CT images of 399 lung cancer patients, of which 24 features were closely related to PD-L1 expression levels. The researchers further developed prediction models based on these features. PET/CT-based prediction model achieves 88% AUC in predicting patients with >50% PD-L1 expression (8).

Radiomics features, in combination with clinical characteristics, may present a better predictive performance. Yoon et al. built a PD-L1 prediction model using both the Rad-score and clinical variables, which turned out to be more accurate than the clinical-variable-only derived model (7).

To date, radiomics remains insufficient to replace IHC testing for the detection of PD-L1. Despite this, the repeatable and noninvasive nature of radiomics analysis may offer additional information for difficult-to-repeat invasive PD-L1 analyses.

 Radiomics and TILs

TILs in the tumor microenvironment play an essential part in the immune response against cancer and appear to be associated with the outcome of immunotherapy. Previous studies have indicated that tumor-infiltrating regulatory T cells (Treg) and tumor-associated macrophages (TAMs) induce an immunosuppressive microenvironment that is directly responsible for the failure of immunotherapy, while CD8+ T tumor infiltration is associated with better outcomes to cancer immunotherapy (37).

One of the early attempts to create radiomics signatures aimed at predicting the presence of CD8+ T cells and the clinical efficacy of immunotherapy was conducted by Sun et al. The signature combining eight radiomics features was developed from CT images, genomic data, and ribonucleic acid (RNA) sequencing. The signature was validated on external cohorts to discriminate CD8+ cells and immune phenotypes. Researchers also found that a higher baseline radiomics score is associated with a better response to immunotherapy (12). This study used a radiomics-based biomarker to correlate pathology with prognosis, while the area under the ROC curve (AUC) score for this prediction model was relatively low. In addition, the parameters of image acquisition were not uniform. Therefore, the credibility of the radiomics signature would be affected (12). Another retrospective study revealed that low CT image intensity and high heterogeneity were associated with lower PD-L1 expression and higher CD3 cell infiltration, which was an immune-activated state strongly correlated with favorable overall survival (OS) (13).

Many studies extract radiomics features from pretreatment or posttreatment medical images to predict the TILs associated with the response to immunotherapy (14, 15). “Delta radiomics” can explore changes in the tumor microenvironment before and after immunotherapy. Therefore, it may provide an earlier and more accurate prediction of the efficacy of immunotherapy before visible changes to the naked eye. Khorrami and colleagues explored the potential of radiomics using pretreatment and subsequent CT images from 135 NSCLC patients treated with ICIs. The concordance of the radiomics features with TIL infiltration was confirmed by comparison with TIL infiltration in diagnostic biopsy samples. They reported that delta-radiomics is associated with response and OS in NSCLC patients undergoing ICIs (16).

 Radiomics and tumor mutational burden

Previous studies have suggested that TMB is another predictive biomarker for immunotherapy across multiple cancer types, as high TMB is correlated with greater neoantigen and immune infiltration (38).

Researchers have investigated the potential of applying radiomics to predict the TMB status in patients with advanced NSCLC and endometrial and bladder cancers (17, 18, 21, 22) ( Table 1 ). He et al. constructed the TMB radiomics biomarker (TMBRB) to predict the pretreatment TMB status. They observed that TMRRB could accurately divide patients into high TMB and low TMB, thus predicting the OS and progression-free survival (PFS) of NSCLC patients treated with ICIs. The predicted treatment efficacy improves when combined with the Eastern Cooperative Oncology Group (ECOG) performance status (17).

Radiomics has the potential value of being a powerful aid in classifying TMB status. Specifically, combining clinical and pathological features may improve prediction performance (14–16). Radiomics may provide sufficient information despite intratumor heterogeneity to assist clinical decisions on immunotherapy.

 Radiomics and other predictive biomarkers of immunotherapy

Studies have also explored the prediction of other predictive biomarkers of immunotherapy. Recent studies have evaluated whether radiomics can identify mismatch repair (MMR)/microsatellite instability (MSI) status. These explorations focused on gastrointestinal malignancies based on CT, MRI, and positron emission tomography-computed tomography (PET/CT) images (19, 23–25, 39–43) ( Table 1 ).

CAO et al. evaluated whether CT-based radiomics can predict MSI status in 502 patients with colorectal cancer (CRC) based on preoperative contrast-enhanced CT images. They further combined the clinical characteristics with radiomics and then developed a nomogram to predict the MSI status. The predictive performance of the radiomics-clinical nomogram was superior to radiomics only and clinical only (24). Such findings are consistent with another study (25). Combining clinical and pathological features with radiomics may add specificity to the prediction model and contribute to personalized clinical decision-making. The MRI-based MSI prediction model was similarly developed, with an AUC of 0.868 (26). Jiang et al. immunoscored gastric cancer patients based on the immunohistochemical expression of CD3, CD8, CD45 and CD66b and classified the immunoscores using CT-based radiomics model. The radiomics model accurately classified patients with high immunoscore and low immunoscore with an AUC of 0.786 and had the potential to select patients who would benefit from chemotherapy (27).

 Radiomics and response to immunotherapy

The efficacy of immunotherapy has been proven in large-scale, randomized clinical trials and clinical practice (29, 44–51). However, immunotherapy is only partially effective, emphasizing the need for finding noninvasive and accurate predictive biomarkers to target immunotherapy to the appropriate patients (52, 53).

Predictive biomarkers of immunotherapy, including PD-L1 and TMB status, are acquired via biopsy, which is invasive, difficult to perform dynamically, and restricted to a small sample of pathological specimens.

Radiomics offers a noninvasive whole-body evaluation of tissue biomarkers. Heterogeneity within the tumor may harbor prognostic information that can be captured and transferred into radiomics features by radiomics analysis. Considerable evidence suggests that radiomics could predict immunotherapy efficacy by recognizing radiomics features associated with response ( Table 2 ).

Summary of Key Studies on the Role of radiomics in predicting the response to immunotherapy.

Reference Tumor type ICI Application NO. of cases Imaging modality Machine learning algorithm Imaging feature type Results(response) Result(survival) Type of validation
Khorrami (16) NSCLC Nivolumab/pembrolizumab/atezolizumab Predict TILs, response and OS 139 CT LDA classifier Delta-radiomics model AUC=0.88 ± 0.08 HR(OS) :1.64, P = 0.0011, C-Index = 0.72 Independent validation with multi-center data
Trebeschi 2019 (54) NSCLC, melanoma Anti-PD1 Predict response and OS 465 CT Random forest classifier Radiomics model AUC=0.76 1-year survival difference: 24% Independent validation with single-center data
Durot (55) Metastatic melanoma Pembrolizumab Predict OS and PFS 31 CT Regression Radiomics features (Skewness at coarse texture scale) – HR(PFS)=4.55, p= 0.0089;HR(OS)=6.017, p= 0.016 Independent validation with single-center data
VELCHETI (56) locally advanced NSCLC Nivolumab Predict response 50 CT SVM classifier Delta-radiomics (vessel tortuosity metrics) AUC=0.79 – Independent validation with single-center data
Mu (10) NSCLC ICIs(Mixed) Predict response 161 PET/CT Deep learning (small-residual-convolutional-network model) Deep learning score AUC=0.81 – Independent validation with multi-center data
MU (57) NSCLC ICIs Predict durable clinical benefit, PFS and OS 194 PET/CT LASSO regression Radiomics nomogram AUC=0.86 C-indices (PFS)=0.74C-indices (OS)=0.83 Independent validation with single-center data
BHATIA (58) Melanoma brain metastases ICIs Predict survival 88 MRI Regression Radiomics features (Laplacian of Gaussian edge features) – HR(OS)=0.68, P= 0.001 Independent validation with single-center data
Ji (59) Malignant Tumors of the Digestive System ICIs Predict response and OS 87 CT LASSO Regression Radiomics model AUC, 0.806 OS: 6.2 vs.13.8 month; p<0.001 Independent validation with single-center data
Khene ZE (60) Metastatic renal cell carcinoma Nivolumab Predict response 48 CT Logistic regression Radiomics model AUC=0.91 – Independent validation with single-center data
Korpics MC (61) treatment-refractory adult solid tumors Multi-site SBRT and Pembrolizumab Predict response, OS, and PFS 68 CT Linear elastic-net model (regression) Radiomics model OR =10.2, p=0.012 HR(OS)=0.39, P= 0.005; HR(PFS)=0.47, P= 0.013 Phase I clinical trial
Park KJ (62) Metastatic urothelial carcinoma ICIs Predict response 62 CT Multivariate logistic regression Radiomics model (heterogeneous distribution and a peripheral enhancing rim pattern) AUC, 0.87 PFS (p= 0.044); OS (p=0.035) Independent validation with single-center data
Polverari (11) NSCLC ICIs Predict response 57 18F-FDG PET/CT Regression MTV, TLG and radiomics features (volume and heterogeneity) MTV (p = 0.027); TLG (p = 0.022) PFS(p=0.002); OS(p=0.049) Independent validation with single-center data
Valentinuzzi (63) NSCLC Pembrolizumab Predict survival 30 PET/CT Regression Radiomics model (Small Run Emphasis is the most dominant feature) – AUC= 0.90 Independent validation with single-center data
Wang (64) Metastatic melanoma Pembrolizumab or ipilimumab Predict Response 50 CT SVM Delta-radiomics model AUC= 0.882 – Independent validation with single-center data
Nardone (65) NSCLC Nivolumab Predict Response 59 CT Kaplan Meier analysis Delta-radiomics model – OS (26:5months, P=0.002) Independent validation with multi-center data
Dercle (66) Melanoma ICIs OS 1374 CT Random forests Radiomics model – AUC= 0.92 Independent validation with multi-center data
Liang (67) Gastric cancer PD-1 inhibitors Predict Response 87 CT SVM and logistic regression Radiomics nomogram AUC= 0.778 – Independent validation with single-center data
Barabino (68) NSCLC ICIs Predict Response 33 CT Relative Variation method Delta-Radiomics P value <0.05 – –
Yu (69) Solid cancers ICIs Predict Response 152 Pretreatment CT LASSO Regression Radiomics-based nomogram AUC=0.847 – Independent validation with multi-center data

Trebeschi et al. developed a model able to predict the response to immunotherapy. They suggested that greater heterogeneous and nonuniform lesions were associated with a better response in NSCLC, possibly with infiltration and inflammation of the tumor, while the sample size of the melanoma cohort was too small to identify optimal imaging biomarkers (54).

Skewness, representing the heterogeneity of a segmented lesion, was a significant independent predictor of OS and PFS, with a higher skewness value linked with poorer survival (55). Similarly, Velcheti analyzed the CT features of 50 NSCLC patients who underwent nivolumab, and they found that vessel tortuosity was an independent predictor of nivolumab’s efficacy (56).

Radiomics features from 18F-FDG PET/CT scans also showed the ability to predict the response to immunotherapy. Several texture features (PET_SRLGE, KLD_SZE) were associated with durable clinical benefits, demonstrating that patients with more heterogeneous tumors might benefit more from immunotherapy. Notably, the prospective cohort validated the model with an AUC of 0.81 (57). However, these results are somewhat inconsistent with the results from prior studies, which indicated that more heterogeneous tumors with CT textures are associated with worse response rates to chemotherapy or radiation.

Other retrospective studies have explored the potential of radiomics in evaluating survival and responses to immunotherapy in different cancer types, including melanoma (58, 66), gastrointestinal malignancies (59, 67), metastatic renal cell carcinoma (mRCC) (60), treatment-refractory adult solid tumors (61), metastatic urothelial carcinoma (62), and NSCLC (11, 65, 68).

The predictive performance of the radiomics model (consisting of small run emphasis and difference entropy) developed by Valentinuzzi et al. was superior to that of PD-L1 and iRECIST, with an AUC of 0.90. Specifically, small run emphasis has the highest predictive performance to discriminate survival, with higher small run emphasis possibly having OS survival from pembrolizumab treatment (63). These results reflected that patients with more homogeneous tumors might benefit from immunotherapy, consistent with a previous study conducted by Polverari et al., where nonresponders exhibited higher tumor heterogeneity at pretreatment CT images (reflected by higher kurtosis and skewness) than responders (11). A study conducted by Mu et al. showed the opposite result. They found that heterogeneous tumors might be more likely to achieve durable clinical responses (57).

These studies have demonstrated that radiomics has the potential to predict the response to immunotherapy and could facilitate clinical decision-making. Despite its great potential, the application of radiomics in clinical immunotherapy is still in its infancy. Reproducibility and standardization are major problems. Studies have already explored the standardized workflow of radiomics (70–74).

 Atypical response

In the widespread use of immunotherapy in cancer treatment, unconventional characteristics of response, so-called atypical response, have been observed through imaging ( Figure 2 ) (75–77). Atypical patterns of response, including pseudoprogression and hyperprogression, have been demonstrated in clinical trials of ICIs and have prompted the development of immune-related response criteria (78–80). In this part, we summarize how radiomics can support clinical decision-making in light of atypical responses ( Table 3 ).

Atypical patterns in patients undergoing immunotherapy. (A) Hyperprogression is an atypical response to ICIs with a paradoxical acceleration of tumor growth soon after the initiation of immunotherapy. (B) Dissociated Response is defined as a reduction at baseline or increase < 20% in target lesions compared with a nadir in the presence of the new lesion. (C) Pseudoprogression is defined as an initial radiographic increase in tumor size or the appearance of new lesions, followed by a response.

Summary of Key Studies on the Role of radiomics in predicting atypical responses and irAEs.

Reference Tumor type ICI Application NO. of cases NO. of atypical responses/ irAEs Imaging modality Machine learning algorithm Prediction model Results Type of validation
pseudoprogression
JI (59) Malignant Tumors of the Digestive System ICIs Predict Response 87 3 CT LASSO algorithm Radiomics model AUC= 0.76 Independent validation with single-center data
Basler (81) Metastatic melanoma ICIs Differentiate pseudoprogression from true progression 112 patients with 716 metastases 30 lesions FDG-PET/CT, lesion and routine blood markers Regression Blood/radiomics-model AUC= 0.82 Independent validation with single-center data
He (82) Solid tumors ICIs Discriminating pseudoprogression from true progression 135 34 Pretreatment CT LASSO regression Radiomics model AUC= 0.919 Independent validation with muli-center data
Hyperprogression
JI (59) Malignant Tumors of the Digestive System ICIs Predict hyperprogression 87 7 CT LASSO algorithm Radiomics model (maximum gray has the great significance) AUC= 0.877 Independent validation with single-center data
MU (10) NSCLC ICIs Predict hyperprogression 616 – PET/CT Deep learning EGFR-DLS p= 0.037 Independent validation with muli-center data
Vaidya (83) NSCLC ICIs Predict hyperprogression 109 19 CT Random forest classifier Peritumoral texture and vasculature patterns AUC= 0.96 Independent validation with single-center data
Tunali (84) NSCLC ICIs Predicate rapid disease progression 228 15 CT Logistic regression Clinical-radiomic model AUC=0.865 Independent validation with single-center data
He (82) Solid tumors ICIs Discriminating hyperprogression from true progression 135 43 Pretreatment CT LASSO regression Radiomics model AUC=0.933 Independent validation with muli-center data
Gabryś (85) Metastatic melanoma ICIs Predict hyperprogression 56 8 PET/CT Logistic regression model Radiomics model AUC=0.704 Independent validation with single-center data
irAEs
Colen (86) Advanced cancers ICIs Predicate immune-related pneumonitis 32 2 CT Unsupervised anomaly detection algorithm Radiomic model Accuracy, 100% (p=0.0033) Independent validation with single-center data
Pseudoprogression

Pseudoprogression is defined as an initial radiographic increase in tumor size or the appearance of new lesions, followed by a response (76, 87, 88). This radiologic effect is due to inflammatory cell infiltration around tumor cells, with an apparent increase in size, and can be confused with tumor cell proliferation (79, 87, 89). To discern pseudoprogression, immune-related response criteria were developed, which suggest further radiological evaluation after four to eight weeks rather than declaring PD immediately (90). A switch to next-line treatment might be delayed while waiting for the confirmatory follow-up evaluation. It is crucial to distinguish pseudoprogression from true disease progression in a timely manner, as it is highly relevant in daily clinical decision-making processes. Therefore, early detection of pseudoprogression is of vital importance.

Texture features extracted from radiological images have been identified to distinguish inflammatory infiltration from tumor cell proliferation. Basler et al. evaluated the capability of PET/CT-based radiomics features, lesion volume, and routine blood markers to differentiate pseudoprogression from true progression at the third month. Of the seven models constructed based on blood, volume, and radiomics, the blood-radiomics model has the best predictive performance, achieving the highest AUC (0.82), and it is a promising biomarker for the early differentiation of pseudoprogression in the third month (81). In a single-center retrospective study, Ji et al. used four radiomics models constructed using contrast-enhanced CT of 87 patients with lung cancer treated with ICIs; of these, model three and model four had AUCs of 0.736 and 0.760, respectively, and were both accurate in predicting responses in two of three pseudoprogression patients (59). Similarly, He et al. used a radiomics approach to identify pseudoprogression from true progression. They extracted intratumoral and peritumoral radiomics features from baseline chest CT scans of 135 patients and built a predictive model. The model had an AUC of 0.96 in the validation set (82). These findings suggest that radiomics can predict pseudoprogression in the course of immunotherapy and may supplement immune-related response evaluation criteria.

 Hyperprogression

Hyperprogression is an atypical response to ICIs with paradoxical tumor growth acceleration soon after immunotherapy initiation (91–94). It has been described in numerous cancer types with an incidence of 6%–29% (95, 96). It has been associated with high metastatic burden, significantly shortened survival, and poorer performance status (95, 97–100), thus limiting the potential for administration of other therapies. Identifying high-risk groups is vital, yet there are no predictive biomarkers with apparent effects to identify the risk of hyperprogression (96, 98). As a noninvasive method, radiomics has been explored for the risk stratification of hyperprogression in patients undergoing immunotherapy.

To evaluate the accuracy of a pretreatment CT-based radiomics model in predicting hyperprogression in NSCLC patients treated with ICIs, Vaidya et al. found that peritumoural texture and vasculature patterns in the baseline CT scans positively correlate with hyperprogression. These features had higher expression in hyperprogression than in responders or nonresponders, suggesting that patients with more heterogeneous tumors are more likely to derive durable clinical benefits (83). Consistent with a previous study, a complex model including three radiomics features extracted from the tumor border and several clinical variables was able to predict hyperprogression with an accuracy of 82.28% (84). Other radiomics features, such as the maximum gray value, are intimately linked to the determination of hyperprogression (59). Deep learning models built by Mu et al. have also been reported as possible predictive biomarkers of hyperprogression in NSCLC patients undergoing ICIs. A total of 33.33% of patients with higher EGFR-deep learning scores developed hyperprogression, and deep learning scores were associated with shorter PFS among patients undergoing ICIs (10). Similarly, He et al. developed a prediction model using a CT-based radiomics approach which had an accuracy of 0.933 in identifying hyperprogression from true progression (82). PET/CT-based radiomics has also been attempted for predicting hyperprogression. Gabryś et al. developed a predictive model using PET/CT of patients with metastatic melanoma. CT-based radiological features were shown to be better predictors of hyperprogression than PET-based features (85).

Given the poor prognosis of hyperprogression, it is of great importance for high-risk populations to be screened before initiating immunotherapy. The remarkably accelerated development of radiomics in immunotherapy suggests that radiomics could be used to stratify the risk of hyperprogression. These findings warrant further exploration.

 Radiomics and irAEs

IrAEs associated with immunotherapy, resulting from activating an immune response against healthy tissues, may involve almost every organ and system (101, 102). Timely diagnosis and prompt management depending on its severity, with a proper suspension of ICIs or corticosteroid treatment, is vital. If left untreated, irAEs could develop into life-threatening complications. Therefore, early diagnosis and monitoring of irAEs are crucial for radiologists.

Medical imaging, including CT, ultrasonography, magnetic resonance imaging, X-rays, and PET/CT, was used to detect irAEs. In a retrospective study with a small sample size, Mekki et al. found that 74% (19) of irAE patients (55) showed abnormalities on medical imaging and could be diagnosed by radiologists. The rates of enterocolitis, hypophysitis, thyroiditis, hepatitis, arthralgia or arthritis, lung/mediastinum side effects, and pancreas range from 28% to 100% (103).

The detection of irAEs generally depends on blood test indicators, clinical manifestations, and imaging characteristics. However, radiomics can help in the identification of early invisible signs of irAEs in medical imaging. Colen et al. utilized a radiomics approach to predict the risk for immune-related pneumonitis. They extracted 1860 radiomics features from baseline chest CT scans of 32 patients treated with ICIs, of whom two developed immune-related pneumonitis. Selective radiomics features were utilized to develop the predictive model of the subsequent development of pneumonitis. This model correctly identified the two patients who developed immune-related pneumonitis (86). Despite the noninvasive and impersonal nature of radiomics, studies relevant to the early detection of irAEs are rarely reported ( Table 3 ).

 Discussion

With the rapid development and application of artificial intelligence in medicine, radiomics may become a valuable tool in clinical decision-making. Here, we focus on the exciting and innovative space of radiomics to solve the problems in immunotherapy and discuss how radiomics serves as a means to support the precision design of immunotherapy, especially in pseudoprogression and hyperprogression.

Radiomics represents a potential noninvasive and feasible strategy in clinical decision-making that can ensure timely access to results and minimize the bias caused by localized tissue sampling from heterogeneous tumors. In addition, radiomics can potentially be applied to daily clinical practice to monitor responses and side reactions to ICIs.

These advantages provide convenience for clinical diagnosis and treatment, such as noninvasive biopsy, differentiating pseudoprogression from true progression, risk stratification for hyperprogression, immune-related response assessment, and so on. Current studies have indicated that the potential of radiomics in immunotherapy is substantial.

While the results of recent radiomics research are promising, they remain insufficient for its widespread application in daily clinical practice, and radiomics cannot replace biopsy or iRECIST in clinical application at this stage. Limitations and challenges in terms of practical application are not neglectable, and reproducibility presents the most significant challenge.

The heterogeneity of articles exploring radiomics features is an important issue that limits the generalization of the role of radiomics in daily clinical practice, as different imaging modalities are studied (CT, MRI, PET/CT) in different clinical settings (several different neoplasms at various stages of disease) with different “a priori” expected responsivities to immunotherapy. Due to the complexity of radiomics, few studies can be wholly reproduced, thus inhibiting the widespread use of radiomics in clinical practice (104).

Reproducibility remains a huge obstacle in the pace of clinical application Guidelines were established to standardize protocol and analysis of radiomics research (70, 105). The radiomics quality score (RQS) (106) and Individual Prognosis or Diagnosis (TRIPOD) (107) were developed to bridge this gap. Moreover, ongoing single and multicenter prospective randomized clinical trials are needed to improve and validate reliability and reproducibility ( Table 4 ). Integration and analysis of radiomics features with genomics, proteomics, and other omics data would provide additional information in precision medicine by uncovering microlevel features (105, 108).

Clinical trials of radiomics in immunotherapy.

NCT Number Diseases Prediction Enrollment Study Designs Start Date Status
NCT04364776 Lung Cancer Response to Chemoradiation and Durvalumab 70 Prospective 2020/4/15 Recruiting
NCT04452058 Lung Cancer Assisting Surgery Decision and Predicting Immunotherapy Response 500 Retrospective 2019/8/1 Recruiting
NCT04792437 Glioma TMB, OS, PFS 120 Other 2021/3/10 Recruiting
NCT04984148 NSCLC Predicting the Efficacy of Immunotherapy 70 Prospective 2019/2/1 Recruiting
NCT04994795 NSCLC Response to Chemotherapy and/or pembrolizumab 4000 Other 2022/8/21 Not yet recruiting
NCT03305380 NSCLC Identify Patients at Risk for Developing immune-related Pneumonitis 637 Other 2017/9/1 Completed
NCT03712566 Squamous cell cancer of the head & neck, esophagus, or anal canal Identify genomics and response to immunotherapy 39 Prospective 2018/11/6 Active, not recruiting
NCT04007068 NSCLC Immunotherapeutics Response and survival 31 Prospective 2017/1/1 Unknown status
NCT04079283 Solid Tumor Immunotherapeutics Response 285 Retrospective 2017/1/1 Completed
NCT04193956 Melanoma NSCLC Immunotherapeutics Response 3500 Prospective 2018/8/1 Recruiting
NCT04243720 Solid Tumor Immunotherapeutics Response 100 Prospective 2020/8/26 Recruiting
NCT04314349 Aerodigestive tract cancers Identify genomics and pathological features, response to immunotherapy 100 Prospective 2020/8/1 Recruiting

Radiomics provides a window of opportunity for precision medicine of immunotherapy by analyzing microscopic medical imaging in a noninvasive, efficient, economical, and rapid fashion. In the foreseeable future, we envision that radiomics will be widely applied to clinical decision-making and will serve as the impetus for the next major breakthroughs in precision medicine. However, at this stage, there are still significant challenges in the process of clinical translation and application, and further refinements are warranted.

 Author contributions

HZ, QL, WW, NL, CY and LZ: Literature search, concepts development, manuscript drafts. All authors contributed to the article and approved the submitted version. All figures created with BioRender.com.

 Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

 Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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ICIs Immune checkpoint inhibitors
PD-L1 Programmed cell death ligand 1
ir-RECIST Immune-related Response Evaluation Criteria in Solid Tumors
TILs Tumor infiltrating lymphocytes
AI Artificial intelligence
NSCLC Non-small cell lung cancer
HCC Hepato-cellular carcinomas
MRI Magnetic resonance imaging
IHC Immunohistochemistry
RNA Ribonucleic acid
AUC Area under ROC curve
OS Overall survival
Treg Regulatory T cells
TAMs Tumor associated macrophages
TMB Tumor mutational burden
TMBRB TMB radiomics biomarker
PFS Progression free survival
ECOG Eastern Cooperative Oncology Group
MMR Mismatch repair
MSI Microsatellite instability
PET/CT Positron emission tomography computed tomography
CRC Colorectal cancer
mRCC Metastatic Renal-Cell Carcinoma
PD Progressive disease
irAEs Immune-related adverse events