Skip to main content
. 2022 Dec 30;17:217. doi: 10.1186/s13014-022-02192-2

Table 1.

Characteristics of studies on the current applications of radiogenomics in cancer

Research Cancer type Application Sample size Images Methods Features in models Results

Chen et al.

[85]

ccRCC Diagnosis 197 CT LR Radiomics; genomics AUC: 0.864–0.900

Smedley et al.

[86]

NSCLC Diagnosis 351 CT NN Radiomics; genomics AUC: 0.86 (adenocarcinoma), 0.91 (squamous cell), and 0.71 (other)

Shim et al.

[87]

Glioblastoma Recurrence 192 MRI NN Radiomics; AUC: 0.969 for local recurrence and 0.864 for distant recurrence

Kirienko et al

[88]

NSCLC Recurrence 151 PET/CT

GLM;

ML

Radiomics; genomics AUC: 0.87

Yan et al.

[89]

MB Survival 166 MRI LASSO-COX Radiomics; clinicomolecular C-index: 0.762 for OS and 0.697 for PFS

Xie et al.

[81]

ESCC Survival 106 CT ML

Radiomics;

clinical factors

AUC: 0.852 for 5-year DFS;

Significant risk stratification for DFS (p < 0.001)

Huang et al

[90]

ccRCC Survival 205 Contrast-enhanced CT RF Radiomic; genomics AUC: 0.84, 0.81, and 0.75 for 1, 3, and 5-year OS, respectively

Liu et al.

[80]

CRC Metastasis 134 CT LR

Radiomics; genomics;

clinical factors

AUC: 0.752 (95% CI 0.608–0.896)

Kim et al

[92]

Paediatric osteosarcoma Chemotherapy response 73 PET/CT ML Radiomics; genomics; AUC: 0.89

Yi et al.

[93]

OC Platinum resistance 102 CT SVM

Radiomics; genomics;

clinical factors

AUC: 0.967 (95% CI 0.83–0.98)

Zeng et al.

[77]

ccRCC Molecular subtypes 382 Contrast-enhanced CT ML Radiomics; genomics; transcriptomics; proteomics AUC: 0.973 (m1), 0.968 (m2), 0.961 (m3), 0.953 (m4)

Park et al.

[94]

Glioblastoma Molecular characteristics 121 MRI ML

Radiomics;

clinical factors

AUC: 0.863

Li et al.

[95]

BC Molecular subtypes 91 MRI Linear classifier Radiomics AUC: 0.89 (ER + vs. ER −), 0.69 (PR + vs. PR −), 0.65 (HER2 + vs. HER2 −), and 0.67 (triple-negative vs. others)

ccRCC: clear cell renal cell carcinoma; NSCLC: non-small cell lung cancer; MB: medulloblastoma; ESCC: oesophageal squamous cell carcinoma; CRC: colorectal cancer; OC: ovarian cancer; BC: breast cancer; CT: computed tomography; MRI: magnetic resonance imaging; PET: positron emission tomography; NN: neural network; GLM: generalized linear model; ML: machine learning; LASSO-COX: least absolute shrinkage and selection operator penalized Cox proportional hazards regression; RF: random forest; LR: logistic regression; SVM: support vector machine; AUC: area under the curve; OS: overall survival; PFS: progression-free survival; DFS: disease-free survival