Qing Lyu
Assistant ProfessorCards
About
Research
Publications
2026
APOE ε4 Modulates Beta‐Amyloid Clearance via Cerebrospinal Fluid Dynamics: Insights from Nonhuman Primate and Clinical Studies
Kim, Jeongchul, et al. "APOE ε4 Modulates Beta‐Amyloid Clearance via Cerebrospinal Fluid Dynamics: Insights from Nonhuman Primate and Clinical Studies." Alzheimer's & Dementia 21 (2025): e104986.Peer-Reviewed Original Research
2025
APOE ε4 Modulates Beta‐Amyloid Clearance via Cerebrospinal Fluid Dynamics: Insights from Nonhuman Primate and Clinical Studies
Kim J, Lipford M, Barcus R, Yuan H, Lyu Q, Hudson J, Lockhart S, Hughes T, Sutphen C, Frye B, Sai K, Rudolph M, Shively C, Register T, Mielke M, Craft S, Whitlow C. APOE ε4 Modulates Beta‐Amyloid Clearance via Cerebrospinal Fluid Dynamics: Insights from Nonhuman Primate and Clinical Studies. Alzheimer's & Dementia 2025, 21: e104986. PMCID: PMC12780961, DOI: 10.1002/alz70856_104986.Peer-Reviewed Original ResearchA large scale multi-dataset investigation of brain metastases distribution based on primary cancer type
Lyu Q, Yuan H, Lin Z, Barcus R, Hudson J, Jiang Y, Kim J, Whitlow C. A large scale multi-dataset investigation of brain metastases distribution based on primary cancer type. BMC Cancer 2025, 25: 1821. PMID: 41291518, PMCID: PMC12648834, DOI: 10.1186/s12885-025-15222-5.Peer-Reviewed Original ResearchCancer brain metastasesBreast cancer brain metastasesBrain metastasesPattern of brain metastasesLung cancer brain metastasisMelanoma brain metastasesPrimary tumor typePrimary cancer typeTemporal lobeMetastasis distributionTumor volumeTumor typesPoor prognosisKidney cancerClinical challengeHeterogeneous distribution patternCancer typesMelanomaStatistical differenceBreastLungKidneyFrontal lobePredicting Immunotherapy-Induced Pneumonitis Based on Chest CT and Non-Imaging Data
Lyu Q, Yuan H, Lin Z, Ponnatapura J, Whitlow C. Predicting Immunotherapy-Induced Pneumonitis Based on Chest CT and Non-Imaging Data. Cancers 2025, 17: 2980. PMID: 41008824, PMCID: PMC12468472, DOI: 10.3390/cancers17182980.Peer-Reviewed Original ResearchImmune checkpoint inhibitorsICI-pneumonitisCT scanTreatment of non-small cell lung cancer patientsComputed tomographyNon-small cell lung cancer patientsRisk of developing pneumonitisCell lung cancer patientsHigh riskICI-based immunotherapyImmunotherapy-induced pneumonitisEarly identification of patientsSignificant survival benefitScreening computed tomographyEarly identificationIdentification of patientsReceiver operating characteristic curveArea under the receiver operating characteristic curveLung cancer patientsOperating characteristics curveCheckpoint inhibitorsChest CTSurvival benefitClinical featuresClinical dataMultimodal radiopathomics signature for prediction of response to immunotherapy-based combination therapy in gastric cancer using interpretable machine learning
. Multimodal radiopathomics signature for prediction of response to immunotherapy-based combination therapy in gastric cancer using interpretable machine learning. Cancer Letters 2025, 631: 217930. PMID: 40675469, DOI: 10.1016/j.canlet.2025.217930.Peer-Reviewed Original ResearchMRI-Based Radiomics Ensemble Model for Predicting Radiation Necrosis in Brain Metastasis Patients Treated with Stereotactic Radiosurgery and Immunotherapy
Chen Y, Helis C, Cramer C, Munley M, Choi A, Tan J, Xing F, Lyu Q, Whitlow C, Willey J, Chan M, Jiang Y. MRI-Based Radiomics Ensemble Model for Predicting Radiation Necrosis in Brain Metastasis Patients Treated with Stereotactic Radiosurgery and Immunotherapy. Cancers 2025, 17: 1974. PMID: 40563624, PMCID: PMC12191015, DOI: 10.3390/cancers17121974.Peer-Reviewed Original ResearchArea under the curveRadiation necrosisNegative predictive valuePositive predictive valueBrain metastasesConclusions:Occurrence of radiation necrosisRadiomic featuresPredictive valueResults:SRS sessionBrain metastasis patientsHighest area under the curveMethod:Radiosurgery sessionStereotactic radiosurgeryRadiation therapyMetastasis patientsTreatment modalitiesValidation cohortClinical factorsNeurological deficitsMetastasisClinical informationNecrosisInvestigating the role of structural wall stress on aortic growth prognosis in acute uncomplicated type B aortic dissection.
Liu M, Du Y, Cebull HL, Wu Y, Mazlout A, Kalyanasundaram A, Agarwal R, Dong H, Piccinelli M, Oshinski JN, Elefteriades JA, Gleason RL, Leshnower BG. Investigating the role of structural wall stress on aortic growth prognosis in acute uncomplicated type B aortic dissection. Res Sq 2025 PMID: 40470228, DOI: 10.21203/rs.3.rs-6569327/v1.Peer-Reviewed Original ResearchMedical multimodal multitask foundation model for lung cancer screening
Niu C, Lyu Q, Carothers C, Kaviani P, Tan J, Yan P, Kalra M, Whitlow C, Wang G. Medical multimodal multitask foundation model for lung cancer screening. Nature Communications 2025, 16: 1523. PMID: 39934138, PMCID: PMC11814333, DOI: 10.1038/s41467-025-56822-w.Peer-Reviewed Original ResearchConceptsLung cancer screeningMultimodal dataCancer screeningState-of-the-art modelsCombination of multimodal dataState-of-the-artHigh-dimensional imagesLung cancer riskLow-dose computed tomographyMultitask learningMultitask datasetsModel architectureHealthcare qualityBig dataCancer riskClinical data typesData typesMortality risk predictionData elementsComputed tomographyRisk predictionMinimal dataFoundation modelClinical managementCT series
2024
Hunting imaging biomarkers in pulmonary fibrosis: Benchmarks of the AIIB23 challenge.
Nan Y, Xing X, Wang S, Tang Z, Felder FN, Zhang S, Ledda RE, Ding X, Yu R, Liu W, Shi F, Sun T, Cao Z, Zhang M, Gu Y, Zhang H, Gao J, Wang P, Tang W, Yu P, Kang H, Chen J, Lu X, Zhang B, Mamalakis M, Prinzi F, Carlini G, Cuneo L, Banerjee A, Xing Z, Zhu L, Mesbah Z, Jain D, Mayet T, Yuan H, Lyu Q, Qayyum A, Mazher M, Wells A, Walsh SL, Yang G. Hunting imaging biomarkers in pulmonary fibrosis: Benchmarks of the AIIB23 challenge. Med Image Anal 2024, 97: 103253. PMID: 38968907, DOI: 10.1016/j.media.2024.103253.Peer-Reviewed Original Research
2023
Noise Suppression With Similarity-Based Self-Supervised Deep Learning
Niu C, Li M, Fan F, Wu W, Guo X, Lyu Q, Wang G. Noise Suppression With Similarity-Based Self-Supervised Deep Learning. IEEE Transactions On Medical Imaging 2023, 42: 1590-1602. PMID: 37015446, PMCID: PMC10288330, DOI: 10.1109/tmi.2022.3231428.Peer-Reviewed Original ResearchConceptsDenoising approachDenoising methodPhoton-counting CT imagingLearning methodsPhoton-counting computed tomographySelf-supervised deep learningSupervised learning methodsImage denoisingDownstream tasksNoisy samplesDeep learningCT imagesPractical datasetsDenoisingNoise assumptionNoise suppressionRadiation doseCorrelated noiseNoiseLow-dose CTLearningDiverse applications
Get In Touch
Contacts
Email