Introduction
Author | Aims | Imaging modality | Number (training and validation sets, where available) | Conclusion |
---|---|---|---|---|
Lewis et al. [9] | To distinguish hepatocellular carcinoma (HCC) from other primary liver cancers (intrahepatic cholangiocarcinoma [ICC] and combined HCC-ICC) through volumetric quantitative apparent diffusion coefficient (ADC) histogram parameters and LI-RADS categorization | MRI | 63 | Combination of quantitative ADC histogram parameters and LI-RADS categorization yielded the best prediction accuracy for distinction of HCC compared to ICC and combined HCC-ICC |
Wu et al. [10] | To evaluate the feasibility of using radiomics with precontrast MRI for classifying HCC and hepatic haemangioma (HH) | MRI | 369 | Radiomics-based assessments could be used to distinguish between HCC and HH on precontrast images, thereby allowing noninvasively efficient identification and minimizing errors from visual inspection |
Oyama et al. [11] | To evaluate the accuracy for classification of hepatic tumours | MRI | 37 HCCs, 23 metastatic tumours, and 33 HHs | Using texture analysis or topological data analysis allows for classification of the three hepatic tumours with considerable accuracy |
Wu et al. [12] | To predict histopathological grading for HCC cases | MRI | 170 | A computed radiomics signature itself or combined with clinical factors could help to classify the patients into high-grade or low-grade HCC |
Indications for radiotherapy
Radiomics for treatment planning
Target volume definition: automatic segmentation of target volumes and organs at risk
Author | Aims | Imaging modality | Number, (training (T) and validation (V) set, where available) | Conclusion |
---|---|---|---|---|
Chen et al. [71] | To develop a radiomics model based on gadolinium-ethoxybenzyl-diethylenetriamine (Gd-EOB-DTPA)-enhanced MRI for pretreatment prediction of immunoscore in HCC | MRI | 207 T: 150 V: 57 | MRI-based combined radiomics nomogram shows effectiveness in predicting immunoscore in HCC |
Shan et al. [66] | To predict recurrence of HCC (hepatocellular carcinoma) after curative treatment | CECT | 156 T: 109 V: 47 | A radiomics model effectively predicts early recurrence (ER) of HCC and is more efficient than conventional imaging features and models |
Xu et al. [68] | To predict microvascular invasion (MVI) and clinical outcomes in patients with HCC | CECT | 495 T: 350 V: 145 | The computational approach demonstrates good performance for predicting MVI and clinical outcomes |
Vivanti et al. [88] | To automatically delineate liver tumours in longitudinal CT studies | CECT | 31 | The system showed the ability to predict failures and the ability to correct them |
Vorontsov et al. [89] | To bring up a semi-automatic tumour segmentation method | CECT | 40 | The proposed method can deal with highly variable data |
Bakr et al. [69] | To predict MVI | CECT | 28 | RF (Radiomic features) computed with single-phased or combined-phased images were correlated with MVI |
Peng et al. [70] | To develop and validate a radiomics nomogram for the preoperative prediction of prognosis in patients with HCC undergoing partial hepatectomy | CECT | 304 T: 184 V: 120 | Radiomics nomogram showed excellent performance for the individualized and non-invasive estimation of disease-free survival, which may help clinicians better identify patients with HBV-related HCC who can benefit from the surgery |
Zhou et al. [67] | To predict ER of HCC | CECT | 215 | Radiomics signature was a significant predictor for ER in HCC |
Liu et al. [86] | To develop and validate a learning-based method to derive electron density from routine anatomical MRI for potential MRI-based SBRT treatment planning; CT and MRI for CT synthesis | (co-registered) CT and MRI | 21 | Image similarity and dosimetric agreement between synthetic CT and original CT |
Fu et al. [90] | To expedite the contouring process for MRI-guided adaptive radiotherapy (MR-IGART), a convolutional neural network deep-learning model is proposed to accurately segment the liver, kidneys, stomach, bowel and duodenum in 3D MR images | CEMRI | 120 T: 100 V: 10 Test: 10 | The proposed method can automatically segment the liver, kidneys, stomach, bowel, and duodenum in 3D MR images with good accuracy |
Zhang et al. [91] | To build a knowledge-based model of liver cancer for auto-planning | CECT | 70 T: 20 | Auto-planning shows availability and effectiveness |
Li et al. [65] | CT textural feature analysis for the stratification of single large HCCs >5 cm, and the subsequent determination of patient suitability for liver resection (LR) or transcatheter arterial chemoembolization (TACE) | CECT | 130 | Texture analysis demonstrated the feasibility of using HCC patient stratification for determining the suitability of LR vs. TACE |
Adaptive radiotherapy: dose painting
Author | Aims | Imaging modality | Number, (training (T) and validation (V) set, where available) | Conclusion |
---|---|---|---|---|
Cai et al. [99] | To develop and validate a radiomics-based nomogram for the preoperative prediction of posthepatectomy liver failure (PHLF) in patients with HCC | CECT | 112 T: 80 V: 32 | A nomogram based on the Radiomics-score, model for end-stage liver disease (MELD), and performance status (PS) can predict PHLF |
Ibragimov et al. [100] | To predict toxicity beyond the existing dose/volume histograms | CECT | 125 | A framework offers clinically accurate tools for hepatobiliary toxicity prediction and automatic identification of anatomical regions that are critical to spare during stereotactic body radiation therapy |
Park et al. [101] | To develop and validate a radiomics-based model for staging liver fibrosis | Gadoxetic acid-enhanced hepatobiliary phase MRI | 436 | Radiomics analysis of gadoxetic acid-enhanced hepatobiliary phase images allows for accurate diagnosis of liver fibrosis |
Dogan et al. [94] | To determine the changes in image texture features (delta-radiomics) measured on daily low-field MRI and whether delta-radiomics features could be used to assess treatment response and predict patient outcomes | MRI | 10 | Dogan et al. demonstrated that three delta-radiomics texture features extracted from low-field MRI during SBRT in liver were able to differentiate between local disease control and local control failure |
Monitoring/follow-up
Author | Aims | Imaging modality | Number, (training (T) and validation (V) set, where available) | Conclusion |
---|---|---|---|---|
Reimer et al. [105] | To determine whether post-treatment MRI-based texture analysis of liver metastases may be suitable for predicting therapy response to transarterial radioembolization (TARE) during follow-up | CEMRI | 37 | The model indicates the potential of MRI-based texture analysis at arterial- and venous-phase MRI for the early prediction of progressive disease after TARE |
Cozzi et al. [106] | To predict overall survival and local control | Non-contrast CT | 138 | Survival could be predicted using a radiomics signature made by a single shape-based feature |
Kim et al. [107] | To predict survival (overall and progression-free survival) | CECT | 88 | A combination of clinical and radiomic features better predicted survival |
Mokrane et al. [108] | To enhance clinicians’ decision-making by diagnosing HCC in cirrhotic patients with indeterminate liver nodules using quantitative imaging features | CECT | 178 T: 142 V: 36 | Radiomics can be used to non-invasively diagnose HCC in cirrhotic patients with indeterminate liver nodules, which could be used to optimize patient management |
Donghui et al. [13] | To identify aggressive behaviour and predict recurrence of HCC after liver transplantation (LT) | CECT | 133 T: 93 V: 40 | Radiomics signature extracted from CT images may be a potential imaging biomarker for liver cancer invasion and enable accurate prediction of HCC recurrence after LT |
Zhao et al. [109] | To investigate the combined predictive performance of qualitative and quantitative MRI features and prognostic immunohistochemical markers for the ER of intrahepatic mass-forming cholangiocarcinoma (IMCC) | CEMRI | 47 | The combined model was the superior predictive model of ER |