Table 1.
Summary of radiomics study evaluating response to radioimmunotherapy and immunotherapy
| Reference | Cancer type | Patients | Multilesion analysis | Modality | Endpoints | Features | Machine learning | Findings |
| Prediction of response to radiotherapy and immunotherapy | ||||||||
| Korpics et al 202074 | Solid tumor | 68 pts with solid tumors SBRT (30–50 Gy, 3–5 fractions)+pembrolizumab combination. 139 irradiated lesions |
Y | CT | RT+ICI lesion and patient response | Published radiomic signature of CD8 cells (Sun et al, Lancet Oncol 201869) – average score. Cut-off=1st and 3rd quartiles. No machine learning. |
Association with tumor response: OR=10.2; 95% CI 1.76 to 59.17; p=0.012. PFS (HR 0.47, 95% CI 0.26 to 0.85; p=0.013) and OS (HR 0.39, 95% CI 0.20 to 0.75; p=0.005). |
|
| Sun et al 202073 | Solid tumor | 94 pts with RT (8 Gy × 3 mostly)+immunotherapy. 100 irradiated+189 non-irradiated lesions. |
Y | CT | RT+ICI lesion and patient response | Published radiomic signature of CD8 cells (Sun et al, Lancet Oncol 201869). No machine learning. |
Association with lesion response AUC=0.63, p=0.0020. Spatial heterogeneity assessment using MinCD8RS and entropy was associated with OS and PFS. |
|
| Biologically driven radiomic biomarker for immunotherapy response prediction | ||||||||
| Sun et al 201869 | Solid tumors | Training: n=135 for CD8 cells prediction. Validation: n=119 (CD8 cells validation), n=100 (immunophenotype) and n=137 (ICI response). |
N | CT |
|
78 radiomic features from tumor+border, five location variables, and one technical variable. | Elastic net regularized regression. Eight variables retained |
Validation of CD8 cells prediction: AUC=0.67; 95% CI 0.57 to 0.77; p=0.0019. Association with immune inflamed tumors: AUC=0.76; 95% CI 0.66 to 0.86; p<0.0001. Association with IO response and OS (HR 0.58, 95% CI 0.39 to 0.87; p=0.0081). |
| He et al 202091 | NSCLC | n=327 pts with complete resection of lung ADK for TMBRB (TMB radiomic biomarker) development (Tr/V/Te : 236/26/65 pts). n=123 NSCLC: ICI response. |
N | CT | TMB ICI response |
1020 deep learning features | Feature extraction: 3D-densenet. Classification: fully connected network. |
TMB prediction: AUC=0.81, 95% CI 0.77 to 0.85 in test cohort. TMBRB was associated with ICI-treated patients. OS: HR=0.54, 95% CI 0.31 to 0.95; p=0.030, and PFS=HR: 1.78, 95% CI 1.07 to 2.95; p=0.023. |
| Mu et al 202199 | NSCLC | Tr=284, V: 116, test: 85. | N | PET | PD-L1 | Deep learning | PD-L1: AUC ≥0.82 in all the cohorts PFS, OS |
|
| Immunotherapy response prediction | ||||||||
| Tunali et al 201957 | NSLCC | n=228 NSCLC patients treated with single agent or double agent immunotherapy. No validation set. |
N | CT | Rapid progression phenotypes | 600 features from the largest tumor+border. Logistic regression. No validation |
AUC 0.804 to 0.865 to predict rapid disease progression phenotypes (TTP <2 months or hyperprogressive disease). | |
| Trebeschi et al 201956 | NSCLC and melanoma | n=203 patients with advanced melanoma and NSCLC undergoing anti-PD-1 therapy. Accounting for 1055 target lesions. Training, tuning and test sets. |
Y | CT | Lesion progression | Features extracted from original CT and image transformations, with different scales. | Comparisons of different feature selection methods and eight trained classifiers. | Prediction of NSCLC lesions progression (AUC up to 0.83; p<0.001) and melanoma lymph nodes progression (0.64 AUC, p=0.05). Patient response prediction based on lesion progression probability: AUC of up to 0.76 for both cancer types (p<0.001). |
| Alessandrino et al 201965 | Urothelial | n=31 pts with metastatic urothelial cancer treated with anti-PD-1/PD-L1. 65 lesions ≥1 cm analyzed at baseline, 72 at the first evaluation. |
Y | CT 2D |
PFS <12 months | Histogram features from single slice of each lesion at different spatial scale filters (TexRad). Aggregation by mean value. Logistic stepwise regression – no validation. |
Entropy and mean were associated with patients with PFS <12 months. | |
| Khorrami et al 2019(58 | NSCLC | n=139 patients with NSCLC treated with ICI. Discovery set (D1=50) and two validation sets (D2=62, D3=27). 36 pts for TILs evaluation. |
N | CT |
|
495 delta texture features 2D+49 shape features (DelRADx) (intranodular and perinodular). | Linear discriminant analysis (LDA) classifier was trained with eight DelRADx features. | Responders AUC of 0.88, 0.85 and 0.81 in D1, D2 and D3 OS: HR=1.64; 95% CI 1.22 to 2.21; p=0.0011; deltaradiomics. Peritumoral Gabor features were associated with the density of TILs on diagnostic biopsy samples. |
| Mu et al 202059 | NSCLC | n=194 stage IIIB–IV NSCLC pts treated with ICI. Tr: 99 retrospective patients. V: retrospective (n=47) and prospective test cohorts (n=48). |
N | PET | Durable clinical benefit (DCB) (6 months PFS) |
790 features from PET, CT and PET+CT fusion images. | Feature selection Pearson LASSO with 100 times fivefold CV eight features retained. |
Prediction of DCB=AUC 0.86 (95% CI 0.79 to 0.94), 0.83 (95% CI 0.71 to 0.94), and 0.81 (95% CI 0.68 to 0.92). Association with OS and PFS. |
| Khatua et al 2020104 | Medulloblastoma and ependymoma. | n=12 pediatric pts treated with intraventricular infusions of ex vivo expanded autologous NK cells (7 pts for the radiomic study). |
N | MRI | Responders | Features not detailed LASSO. No validation. |
Exploratory results: accuracy and specificity 100% but not significant (only five patients were analyzed). |
|
| Polverari et al 202060 | NSCLC | n=57 NSLSC pts (stage IIIb/c or IV). Treated with ICI. |
N | PET | Progression | PET parameters and radiomic features (shape, histogram, texture). Univariate analysis (Fisher, Wilcoxon). No validation. |
Metabolic tumor volume (MTV) (p=0.028) and total lesion glycolysis (TLG) (p=0.035) were associated with progression. High tumor volume, TLG and heterogeneity (‘skewness’ and ‘kurtosis’) had a higher probability of failing immunotherapy. | |
| Park et al 202066 | Urothelial carcinoma | n=62 pts with metastatic urothelial carcinoma treated with ICI. Tr: n = 41/V: n=21. 224 lesions analyzed. |
Y | CT | Objective response and disease control | 49 RFs (histogram, GLCM, GLRLM): 26 RFs were reliable. Feature selection by LASSO (progressive lesions). |
Five features and the presence of visceral organ involved. | A radiomics signature for each lesion was built to predict patient response (objective response and disease control). The median signature of each lesion was used at the patient level for patients with multiple lesions. Optimum cut-off (Youden index) for disease control. Objective response: AUC 0.87 (95% CI 0.65 to 0.97) disease control: AUC 0.88 (95% CI 0.67 to 0.98). Association with OS and PFS. |
| Khene et al 202068 | mRCC | n=48 mRCC pts treated with nivolumab. 1–5 lesions per pt (aggregation method not described). Random split for Tr and V. |
Y | CT 2D |
PD versus SD/PR/CR | 279 RFs histogram, GLCM, GLRLM, autoregressive model features, Haar wavelet. | Feature selection: LASSO: 5 RFs. Four models tested. |
Prediction of PD: accuracy of 0.82, 0.71, 0.91 and 0.81 (KNN, random forest tree, logistic regression and SVM, respectively) AUC of 0.79, 0.67, 0.92 and 0.71, respectively. |
| Valentinuzzi et al 202061 | NSCLC | n=30 pts with NSCLC treated with pembrolizumab. | N | PET | Responders (OS>median) | Five preselected features at baseline, months 1 and 4. Logistic regression analyses and fivefold cross-validation. No test set. | Association between features and OS. | |
| Colen et al 2021134 | Advanced rare cancers | n=57 pts in pembrolizumab phase II trials. | N | CT | Controlled disease versus progression | 610 features | Feature selection: LASSO. ML: XGBoost+LOOCV. |
Progressive disease (RECIST): accuracy, SE, and Sp of 94.7%, 97.3%, and 90%, respectively; p<0.001. |
| Tunali et al 202192 | NSCLC | Advanced NSCLC treated with IO. Tr=180, V1=90, V2=62. |
N | CT | OS | 213 Intra+peritumoral features, reduced to 67 stability and reproducibility (segm. algorithms, image parameters, RIDER). | Univariate analysis of RF and OS, then ML: CART 1RF+2 clinical variables (dependency ?). |
Radioclinical model: OS (four risk groups). The RF (GLCM inverse): association with CAIX (hypoxia) using retrospective radiogenomics cohort of 103 surgically resected adenocarcinomas. Validation by IHC on 16 patients. |
| Del Re et al 2021135 | NSCLC | Advanced NSCLC treated with anti-PD1 n=32;. Radmioc analysis for 11 pts. |
N | CT | PFS | 25 RFs, exosomal mRNA expression of PD-L1 and IFN-γ, PD-L1 polymorphisms, TML. |
LASSO. 11-fold CV. |
Association with PD-L1. |
| Granata et al 2021136 | NSCLC | n=38 IO and 50 with chemo- or targeted therapy. No validation set. |
N | CT | OS, PFS | 573 RFs | LASSO, SVM, Tree-based methods. | OS (AUC 0.89, accuracy 81%). RFs to predict OS or PFS time were different between the control group and the IO group |
| Yang et al 2021137 | NSCLC | n=92. Tr=64, V=28. |
N | CT | DCB, PFS | 88 RFs | Random forest. | DCB (model 1): AUC 0.848 in Tr and 0.795 in V. PFS (model 2): AUC 0.717 in Tr and 0.760 in V. |
| Rundo et al 202167 | Urothelial | n=42 metastatic urothelial cancer. Tr 70%, V 30%. |
N | CT | OS | 3D deep radiomics. | 3D deep radiomics. | Acuracy 82.5%, SE 96%, Sp 60%. |
| Liu et al 202182 | NSCLC | n=197. 322 RECIST target lesions. Tr=137, V=60. |
Y | CT | Responders at 6 months. | Largest lesion (LL) model. Target lesion (TL) model: average RF of all lesions. |
mRMR (feature selection) and LASSO (model). | LL model and TL models performance where comparable. Baseline signatures performance were not significant Best model: TL-delta radiomics with clinical factor of distant metastasis, AUC=0.81 (95% CI 0.68 to 0.95). |
| Trebeschi et al 2021102 | NSCLC | 152 stage IV patients treated with nivolumab. 73 discovery, 79 test, 903 CTs. |
N | CT | 1 year OS from the last acquisition. | Chest CT morphological changes. | Deep learning. | Using CTs from the first 3–5 months of treatment: AUC of 0.69–0.75. Independent of clinical, radiological, PDL1, and histopathological factors. |
| Shen et al 2021138 | NSCLC | 63 patients. 72 lesions. No validation set. |
Y | CT | Lesion Progression | Texture features 3 Feature selection methods (Fisher, MI, POE+ACC) |
three classifiers evaluated (PCA, LDA, NDA) | Lesion-wise model of lesion progression Best model performance: AUC=0.812) |
| Yang et al 2021139 | NSCLC | n=200 patients. 1633 CTs. No independent validation set (cross-validation). |
Y | CT | 90-day responders. | Deep radiomics±clinical and biological features. | Deep learning model with simple temporal attention. | AUC for response prediction=0.80. The model was associated with OS and PFS. |
| Aoude et al 64 | Melanoma | 52 III/IV treated with BRAF inhibitors and/or immunotherapy. WES+RNAseq+immune signature. No validation set. |
N | PET | OS and PFS | Histogram features+MTV, SUV, TLG, extracted from largest lesion (node or metastasis). | Univariate analysis+optimal cut-offs analyses for survival. | High SD or high mean of MPP associated with PFS (p=0.00047 and p=0.0014) OS (0.0223, p=0.0389) CD8 expression p=0.0028. |
| Liu et al 2021140 | NSCLC | 46 IIB/IV NSCLC treated with nivolumab. No validation set (performance estimated by LOOCV). |
N | CT | OS and PFS | 1106 RFs from the largest tumor. | SVM, logistic regression, Gaussian Naïve Bayes. | AUC of the model 0.73 and 0.61 for PFS and OS. |
| Zerunian et al 141 | NSCLC | 21 pts treated with pembrolizumab. No validation. |
Y | CT | OS and PFS | TexRad features extracted from aggregation of VOIs. | Univariate analysis AUC and log-rank tests. |
Association of MPP and OS (HR=0.89). |
| Corino et al 202162 | HNSCC | 85 recurrent or metastatic pts treated with nivolumab. Tr=68, V=17 pts. |
N | CT | 10-month OS | 536 RFs from the largest tumor. | LASSO+SVM | AUC in validation set=0.67. Performance of radiomic score was higher than the one obtainable with clinical variables. |
| Chen et al 202163 | Melanoma | 50 patients. | N | CT | PD | Automated multi-objective delta-radiomics (Auto-MODR) – 2D largest lesion. |
497 RFs × 3 (pre+post +deltaRFs) from largest lesion. | AUC 86 in cross-validation and 0.73 in independent study. |
| Brendlin et al 2021142 | Melanoma | 140 stage IV pts. 776 lesions. Tr=70 pts V=70 pts. 1291 follow-up examinations (6533 lesions). |
Y | DECT. SECT. |
Lesion reponse. Patient response (PD vs CRPRSD). |
Pyradiomics features. Aggregation of lesion features. |
Feature selection. Multiple logistic regression. Random Forest. |
Patient response: AUC SECT=0.5, DECT=0.75; lesion response AUROC SECT=0.61, DECT=0.85; p<0.001. |
| Barabino et al 2022132 | NSCLC | 33 patients. 43 lesions delineated. No test – aggregation not described. |
Y | CT | PD, PR and SD. | 93 features extracted at baseline and first evaluation. | ANOVA | 27 delta radiomics features were associated with response (univariate). Nine features correlated with pseudoprogression. |
| Dercle et al 202281 | Melanoma | 575 patients. Tr=252, V=287. |
Y | CT | OS at the month 6 post-treatment. | Features extracted from the aggregation of lesions volumes into tumor burden. | 50 best features at baseline and 50 month 3 delta features. Random forest. |
Radiomics signature performed better than RECIST 1.1 with AUC for estimation of OS of 0.92 (95% CI 0.89 to 0.95) versus AUC=0.80 (95% CI 0.75 to 0.84). |
| Preclinical studies | ||||||||
| Mihaylov et al 202176 | Mice | 15 mice treated with RT (8 Gy × 3)+IO, 4 for control. Tr=6 mice, V=9 mice. 1 irradiated lesion and 1 non-irradiated. |
Y | CT MRI |
Response of a non-irradiated lesion (occurred in four mice). | 92 CT and 92 MRI radiomics features from both lesion. Lesion-level analysis to predict abscopal response occurence. |
ANOVA for feature selection, logistic regression for training. | Imaging model (either CT or MRI) combined with NLR achieved good performance to predict abscopal response (AUC close to 1, to be interpreted with caution due to the limited sample size). |
| Eresen et al 2021143 | Mice pancreatic cancer | 8 mice with dendritic cell vaccine+8 mice for control. | N | MRI | OS | 264 delta features. Feature selection using SVM (LOOCV) for identification of the treatment relative changes. |
Regression for OS prediction | Association of RFs with OS and histological tumor markers (fibrosis percentage, CK19+area, Ki67+cells). |
| Devkota et al 202098 | Mice | Xenograft tumors with or without MDSC and some mice treated with MDSC-targeting immunotherapy. | N | Nanoparticle contrast-enhanced CT, CT angiograms and T2w-MR. | Immunotherapy-treated group. | 107 RFs. | Univariate analysis (Kruskal-Wallis test) and Bonferroni correction. | Nano-radiomics revealed texture-based features capable of differentiating immune-treated tumors and untreated tumors. |
ANOVA, analysis of variance; AUC, area under the curve; CART, classification and regression trees; CI, confidence interval; CK19, cytokeratin 19; CR, complete response; DCB, durable clinical benefit; DECT, dual energy CT; GLCM, gray-level co-occurrence matrix; GLRLM, gray-level run-length matrix; HR, hazard ratio; ICI, immune checkpoint inhibitors; IFN, interferon; IHC, immunohistochemistry; IO, immuno-oncology; KNN, k nearest neighbors; LASSO, least absolute shrinkage and selection operator logistic regression model; LDA, linear discriminant analysis; LOOCV, leave-one-out cross validation; MDSC, myeloid-derived suppressor cells; MI, mutual information; MinCD8RS, Minimal value of the CD8 radiomic score; ML, machine learning; MPP, mean value of positive pixels; mRMR, minimum redundancy maximum relevance; mRNA, messenger ribonucleic acid; N, no; NDA, non-linear discriminant analysis; NK, natural killer cells; NLR, neutrophil-to-lymphocyte ratio; NSCLC, non-small cell lung cancer; OR, odds ratio; OS, overall survival; PCA, principal component analysis; PD-1, programmed death 1; PD, progressive disease; PD-L1, programmed death ligand 1; PFS, progression-free survival; POE+ACC, minimization of classification error probability combined average correlation coefficients; PR, partial response; RECIST, response evaluation criteria in solid tumours; RFs, radiomic features; RT, radiotherapy; SBRT, stereotactic body radiation therapy; SD, stable disease; SECT, single energy CT; SUVmax, maximum standardized uptake value; SVM, support vector machine; Te, test set; TIL, tumor-infiltrating lymphocyte; TLG, total lesion glycolysis; TMB, tumor mutational burden; TML, tumor mutational load; Tr, training set; TTP, time-to-progression; V, validation set; WES, whole exome sequencing; Y, yes.