Research graph
References from Deep learning of pretreatment multiphase CT images for predicting response to lenvatinib and immune checkpoint inhibitors in unresectable hepatocellular carcinoma. Local targets link to admitted publications; unresolved targets remain external evidence.
Phase Ib study of lenvatinib plus pembrolizumab in patients with unresectable hepatocellular carcinoma
10.1200/jco.20.00808 · 2020 · External reference
Lenvatinib with or without immune checkpoint inhibitors for patients with unresectable hepatocellular carcinoma in real-world clinical practice
10.1007/s00262-021-03060-w · 2022 · External reference
Evaluation and prediction of treatment response for hepatocellular carcinoma
10.2463/mrms.rev.2022-0118 · 2023 · External reference
Predicting cancer outcomes with radiomics and artificial intelligence in radiology
10.1038/s41571-021-00560-7 · 2022 · External reference
Development of a dual-energy spectral CT based nomogram for the preoperative discrimination of mutated and wild-type KRAS in patients with colorectal cancer
10.1016/j.clinimag.2020.08.023 · 2021 · External reference
Development and validation of a CT-based radiomic nomogram for preoperative prediction of early recurrence in advanced gastric cancer
10.1016/j.radonc.2019.11.023 · 2020 · External reference
Convolutional neural network for discriminating nasopharyngeal carcinoma and benign hyperplasia on MRI
10.1007/s00330-020-07451-y · 2021 · External reference
Radiomics on multi-modalities MR sequences can subtype patients with non-metastatic nasopharyngeal carcinoma (NPC) into distinct survival subgroups
10.1007/s00330-019-06075-1 · 2019 · External reference
Deep learning radiomic nomogram can predict the number of lymph node metastasis in locally advanced gastric cancer: an international multicenter study
10.1016/j.annonc.2020.04.003 · 2020 · External reference
Application of computed tomography-based radiomics signature analysis in the prediction of the response of small cell lung cancer patients to first-line chemotherapy
2019 · External reference
Deep learning predicts lung cancer treatment response from serial medical imaging
10.1158/1078-0432.ccr-18-2495 · 2019 · External reference
Targeted therapy using deep learning tools: state of art approach
10.2174/1389450123666220513110432 · 2022 · External reference
Novel clinical and radiomic predictors of rapid disease progression phenotypes among lung cancer patients treated with immunotherapy: an early report
10.1016/j.lungcan.2019.01.010 · 2019 · External reference
10.1200/jco.18.01010
10.1200/jco.18.01010 · External reference
MVI-mind: a novel deep-learning strategy using computed tomography (CT)-based radiomics for end-to-end high efficiency prediction of microvascular invasion in hepatocellular carcinoma
2022 · External reference
Non-invasive measurement of PD-L1 status and prediction of immunotherapy response using deep learning of PET/CT images
2021 · External reference
Computed tomography-based deep-learning prediction of neoadjuvant chemoradiotherapy treatment response in esophageal squamous cell carcinoma
10.1016/j.radonc.2020.09.014 · 2021 · External reference
10.1016/j.ejca.2021.01.028
10.1016/j.ejca.2021.01.028 · External reference
Early prediction of neoadjuvant chemotherapy response for advanced breast cancer using PET/MRI image deep learning
10.1038/s41598-020-77875-5 · 2020 · External reference
Immunodominance and functional alterations of tumor-associated antigen-specific CD8+ T-cell responses in hepatocellular carcinoma
10.1002/hep.26731 · 2014 · External reference
10.1016/j.ejca.2008.10.026
10.1016/j.ejca.2008.10.026 · External reference
10.1111/apt.17668
10.1111/apt.17668 · External reference
Lower risk of hepatocellular carcinoma with tenofovir than entecavir treatment in subsets of chronic hepatitis B patients: an updated meta-analysis
10.1111/jgh.15783 · 2022 · External reference
Grad-CAM: visual explanations from deep networks via gradient-based localization
2017 · External reference
Unresolved reference
External reference
Deep residual learning for image recognition
2016 · External reference
10.1109/iccv.2019.00140
10.1109/iccv.2019.00140 · External reference
MRI-based nomogram predicts the risk of progression of unresectable hepatocellular carcinoma after combined lenvatinib and anti-PD-1 antibody therapy
10.1016/j.acra.2021.09.004 · 2022 · External reference
Predicting the initial treatment response to transarterial chemoembolization in intermediate-stage hepatocellular carcinoma by the integration of radiomics and deep learning
10.3389/fonc.2021.730282 · 2021 · External reference
Multimodal
2019 · External reference
A CT-based deep learning radiomics nomogram for predicting the response to neoadjuvant chemotherapy in patients with locally advanced gastric cancer: A multicenter cohort study
10.1016/j.eclinm.2022.101348 · 2022 · External reference
Development and validation of a deep learning CT signature to predict survival and chemotherapy benefit in gastric cancer: a multicenter, retrospective study
10.1097/sla.0000000000003778 · 2021 · External reference
Rockette HE, Jr. Detecting hepatocellular carcinoma: value of unenhanced or arterial phase CT imaging or both used in conjunction with conventional portal venous phase contrast-enhanced CT imaging
10.2214/ajr.167.1.8659425 · 1996 · External reference
CT prediction of histological grade of hypervascular hepatocellular carcinoma: utility of the portal phase
10.1007/s11604-012-0149-5 · 2013 · External reference
Impact of portal-phase signal intensity of dynamic gadoxetic acid-enhanced magnetic resonance imaging in hepatocellular carcinoma
10.1002/jhbp.1345 · 2023 · External reference
Dual-phase helical CT of the liver: value of arterial phase scans in the detection of small (< or = 1.5 cm) malignant hepatic neoplasms
10.2214/ajr.164.4.7726040 · 1995 · External reference
10.1016/j.crad.2017.07.003
10.1016/j.crad.2017.07.003 · External reference
Hepatocellular carcinoma: CT texture analysis as a predictor of survival after surgical resection
10.1007/s00330-018-5679-5 · 2019 · External reference
Advanced hepatocellular carcinoma: pretreatment contrast-enhanced CT texture parameters as predictive biomarkers of survival in patients treated with sorafenib
10.1148/radiol.2018171320 · 2018 · External reference
10.1038/538020a
10.1038/538020a · External reference
An explainable deep-learning algorithm for the detection of acute intracranial haemorrhage from small datasets
10.1038/s41551-018-0324-9 · 2019 · External reference
Noninvasive prediction of HCC with progenitor phenotype based on gadoxetic acid-enhanced MRI
10.1007/s00330-019-06414-2 · 2020 · External reference
Heterogeneity of hepatocellular carcinoma contributes to cancer progression
10.1016/j.critrevonc.2015.01.009 · 2015 · External reference
10.1158/1078-0432.ccr-14-0122
10.1158/1078-0432.ccr-14-0122 · External reference
Pretreatment heterogeneous enhancement pattern of hepatocellular carcinoma may be a useful new predictor of early response to lenvatinib and overall prognosis
10.1159/000505190 · 2020 · External reference
10.1002/cam4.5227
10.1002/cam4.5227 · External reference
Development and validation of a contrast-enhanced CT-based radiomics nomogram for prediction of therapeutic efficacy of anti-PD-1 antibodies in advanced HCC patients
10.3389/fimmu.2020.613946 · 2020 · External reference
10.14309/ctg.0000000000000006
10.14309/ctg.0000000000000006 · External reference
10.1016/j.jhep.2019.12.014
10.1016/j.jhep.2019.12.014 · External reference
Trans-arterial chemoembolization as a loco-regional inducer of immunogenic cell death in hepatocellular carcinoma: implications for immunotherapy
10.1136/jitc-2021-003311 · 2021 · External reference
