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Table 1 Characteristics of studies on the current applications of radiogenomics in cancer

From: A review of radiomics and genomics applications in cancers: the way towards precision medicine

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)

  1. 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