CT-based multimodal deep learning for non-invasive overall survival prediction in advanced hepatocellular carcinoma patients treated with immunotherapy
Xia Y, Zhou J, Xun X, Zhang J, Wei T, Gao R, Reddy B, Liu C, Kim G, Yu Z. CT-based multimodal deep learning for non-invasive overall survival prediction in advanced hepatocellular carcinoma patients treated with immunotherapy. Insights Into Imaging 2024, 15: 214. PMID: 39186192, PMCID: PMC11347550, DOI: 10.1186/s13244-024-01784-8.Peer-Reviewed Original ResearchConvolutional-recurrent neural networkAdvanced hepatocellular carcinomaSpatial-temporal informationHepatocellular carcinomaCT scanOverall survival predictionRECIST criteriaClinical variablesPatients treated with immunotherapyExtract spatial-temporal informationFollow-up CT imagesPrognostic modelAdvanced HCC patientsRisk group stratificationDeep learning-based modelTest setDisease statusMethodsThis retrospective studyLog-rank testMultimodal deep learningMulti-modal inputsSurvival predictionDeep learning modelsAnalysis of CT scansPatient's disease statusPredicting long-term progression of Alzheimer’s disease using a multimodal deep learning model incorporating interaction effects
Wang Y, Gao R, Wei T, Johnston L, Yuan X, Zhang Y, Yu Z. Predicting long-term progression of Alzheimer’s disease using a multimodal deep learning model incorporating interaction effects. Journal Of Translational Medicine 2024, 22: 265. PMID: 38468358, PMCID: PMC10926590, DOI: 10.1186/s12967-024-05025-w.Peer-Reviewed Original ResearchConceptsAlzheimer's diseaseGenetic polymorphism dataProgression of Alzheimer's diseaseMild cognitive impairmentPolymorphism dataAlzheimer's Disease Neuroimaging InitiativeAD progressionArea under the receiver operating characteristic curvePrediction of AD progressionDeep learning modelsADNI-1AlzheimerPatient careLearning modelsMCI to ADInteraction effectsADNI-3Increase prediction accuracyMild cognitive impairment to ADEarly interventionCognitive impairmentClinical assessment