Ruth Lim, MD
Assistant Professor of Radiology and Biomedical ImagingCards
About
Research
Publications
2024
The role of 18F-FDG PET in minimizing variability in gross tumor volume delineation of soft tissue sarcomas
Najem E, Marin T, Zhuo Y, Lahoud R, Tian F, Beddok A, Rozenblum L, Xing F, Moteabbed M, Lim R, Liu X, Woo J, Lostetter S, Lamane A, Chen Y, Ma C, El Fakhri G. The role of 18F-FDG PET in minimizing variability in gross tumor volume delineation of soft tissue sarcomas. Radiotherapy And Oncology 2024, 194: 110186. PMID: 38412906, PMCID: PMC11042980, DOI: 10.1016/j.radonc.2024.110186.Peer-Reviewed Original ResearchGross tumor volume delineationGross tumor volumeDice similarity coefficientF-FDG PET imagingSoft tissue sarcomasInter-reader variabilityGTV delineationRadiation therapy treatment planningF-FDGF-FDG PETTherapy treatment planningPerformance level estimationTumor volume delineationTissue sarcomasPET imagingVolume delineationSimultaneous truthHausdorff distanceDice similarity coefficient scoreAccurate gross tumor volumeImaging modality groupsWilcoxon signed-rank testStatistically significant decreaseSigned-rank testTumor volume
2023
A Comprehensive Primer on Radiation Oncology for Non-Radiation Oncologists
Beddok A, Lim R, Thariat J, Shih H, Fakhri G. A Comprehensive Primer on Radiation Oncology for Non-Radiation Oncologists. Cancers 2023, 15: 4906. PMID: 37894273, PMCID: PMC10605284, DOI: 10.3390/cancers15204906.Peer-Reviewed Original ResearchProton therapyRadiation therapyRadiation oncologistsIntensity-modulated proton therapyNon-oncologistsNon-radiation oncologistsImage-guided RTFollow-upExternal beam therapyArc therapyThree-dimensional conformal RTCT simulationPatient alignmentTarget volumeIntensity-modulated RTRT planningCompletion of RTPotential late effects of treatmentMedical physicistsLifelong follow-upLate effects of treatmentPatient follow-upBeam therapyNon-radiativeOptimize treatment outcomes
2022
Automation of generative adversarial network-based synthetic data-augmentation for maximizing the diagnostic performance with paranasal imaging
Kong H, Kim J, Moon H, Park H, Kim J, Lim R, Woo J, Fakhri G, Kim D, Kim S. Automation of generative adversarial network-based synthetic data-augmentation for maximizing the diagnostic performance with paranasal imaging. Scientific Reports 2022, 12: 18118. PMID: 36302815, PMCID: PMC9613909, DOI: 10.1038/s41598-022-22222-z.Peer-Reviewed Original ResearchConceptsSynthetic data augmentationData augmentationLack of training dataConventional data augmentationDeep learning methodsTraining dataLearning methodsPipeline approachAlgorithm trainingGraphical dataAutomationWaters' view radiographsModel performanceAutomated pipelinePerformancePerformance parametersAlgorithmDatasetAugmentationDataMethodPipelineRulesIndustrial workersHuman biodistribution and radiation dosimetry of the demyelination tracer [18F]3F4AP
Brugarolas P, Wilks M, Noel J, Kaiser J, Vesper D, Ramos-Torres K, Guehl N, Macdonald-Soccorso M, Sun Y, Rice P, Yokell D, Lim R, Normandin M, El Fakhri G. Human biodistribution and radiation dosimetry of the demyelination tracer [18F]3F4AP. European Journal Of Nuclear Medicine And Molecular Imaging 2022, 50: 344-351. PMID: 36197499, PMCID: PMC9816249, DOI: 10.1007/s00259-022-05980-w.Peer-Reviewed Original ResearchConceptsRadiation dosimetryTime-activity curvesAdverse eventsEffective doseMultiple bed positionsComprehensive metabolic panelNonhuman primatesHealthy human volunteersNo adverse eventsDynamic PET scansVoltage-gated potassiumAnimal models of neurological diseasesNonhuman primate studiesModels of neurological diseasesHuman biodistributionAverage effective doseMetabolic panelDosimetryOLINDA softwareHealthy volunteersUrinary bladderPET scansDemyelinating lesionsBed positionAnimal modelsACT: Semi-supervised Domain-Adaptive Medical Image Segmentation with Asymmetric Co-training
Liu X, Xing F, Shusharina N, Lim R, Jay Kuo C, El Fakhri G, Woo J. ACT: Semi-supervised Domain-Adaptive Medical Image Segmentation with Asymmetric Co-training. Lecture Notes In Computer Science 2022, 13435: 66-76. PMID: 36780245, PMCID: PMC9911133, DOI: 10.1007/978-3-031-16443-9_7.Peer-Reviewed Original ResearchSemi-supervised domain adaptationUnsupervised domain adaptationSemi-supervised learningMedical image segmentationDomain adaptationDomain shiftLabel supervisionTarget domainImage segmentationDomain dataLeverage different knowledgePseudo-label noiseSignificant domain shiftSupervised joint trainingLabeled source domainUnlabeled target dataUnlabeled target domainLabeled target samplesTarget domain dataSource domain dataState-of-the-artMRI segmentation taskSubstantial performance gainsPseudo-labelsLabel noiseJoint EANM/SIOPE/RAPNO practice guidelines/SNMMI procedure standards for imaging of paediatric gliomas using PET with radiolabelled amino acids and [18F]FDG: version 1.0
Piccardo A, Albert N, Borgwardt L, Fahey F, Hargrave D, Galldiks N, Jehanno N, Kurch L, Law I, Lim R, Lopci E, Marner L, Morana G, Young Poussaint T, Seghers V, Shulkin B, Warren K, Traub-Weidinger T, Zucchetta P. Joint EANM/SIOPE/RAPNO practice guidelines/SNMMI procedure standards for imaging of paediatric gliomas using PET with radiolabelled amino acids and [18F]FDG: version 1.0. European Journal Of Nuclear Medicine And Molecular Imaging 2022, 49: 3852-3869. PMID: 35536420, PMCID: PMC9399211, DOI: 10.1007/s00259-022-05817-6.Peer-Reviewed Original ResearchConceptsAmino acid positron emission tomographyPositron emission tomographyPaediatric neuro-oncologyEuropean Society for Paediatric Oncology (SIOPENeuro-oncologyLevel of evidenceBrain tumor groupPositron emission tomography imagingEvidence-based recommendationsPaediatric patientsTumor groupResponse assessmentClinician guidelinesPaediatric gliomasBrain gliomasImaging specialistsEmission tomographyRadiolabeled amino acidsConsensus opinionEANMPaediatric oncologyPET radiopharmaceuticalsGliomaNuclear medicineOncologySNMMI procedure standard/EANM practice guideline on pediatric [99mTc]Tc-DMSA renal cortical scintigraphy: an update
Vali R, Armstrong I, Bar-Sever Z, Biassoni L, Borgwardt L, Brown J, Grant F, Mandell G, Majd M, Nadel H, Ng T, Roca-Bielsa I, Rohringer T, Santos A, Seghers V, Shaikh N, Ted Treves S, Zaffino-Nevrotski T, Zucchetta P, Lim R. SNMMI procedure standard/EANM practice guideline on pediatric [99mTc]Tc-DMSA renal cortical scintigraphy: an update. Clinical And Translational Imaging 2022, 10: 173-184. DOI: 10.1007/s40336-022-00484-x.Peer-Reviewed Original ResearchEuropean Association of Nuclear MedicineRenal cortical scintigraphyCortical scintigraphyStandard of careImprove patient careEvidence-based recommendationsProfessional medical associationsLevel of trainingLevel of evidenceMedicine careTechnology subsequent to publicationPatient careLegal standard of careMedicine physiciansNuclear medicineMedicine practicePractice guidelinesDiagnostic nuclear medicine imagingCareComplexity of human conditionsExpert consensusMedical professionalsNuclear medicine physiciansPractice medicineMedical AssociationThe role of renal contour change in the diagnosis of cortical scarring after urinary tract infection.
Muniz G, Charron M, Lim R, Shammas A, Liu H, Shaikh N. The role of renal contour change in the diagnosis of cortical scarring after urinary tract infection. Am J Nucl Med Mol Imaging 2022, 12: 41-43. PMID: 35295886, PMCID: PMC8918400.Peer-Reviewed Original ResearchUrinary tract infectionRenal scarringTract infectionsRenal contourDiagnosis of renal scarringPermanent renal scarringProspective clinical trialFollow-up scansNuclear medicine physiciansTechnetium-99mPresence of abnormalitiesRIVUR studyContour abnormalitiesCortical scarringClinical trialsTechnetium-99Medicine physiciansGold standardEligibility criteriaScarsContour changesAbnormalitiesDiagnosisInfectionChildren
2021
Deep learning-based GTV contouring modeling inter- and intra- observer variability in sarcomas
Marin T, Zhuo Y, Lahoud R, Tian F, Ma X, Xing F, Moteabbed M, Liu X, Grogg K, Shusharina N, Woo J, Lim R, Ma C, Chen Y, El Fakhri G. Deep learning-based GTV contouring modeling inter- and intra- observer variability in sarcomas. Radiotherapy And Oncology 2021, 167: 269-276. PMID: 34808228, PMCID: PMC8934266, DOI: 10.1016/j.radonc.2021.09.034.Peer-Reviewed Original ResearchConceptsGross tumor volumeRadiation therapy treatment planningGross tumor volume contoursGross tumor volume delineationTherapy treatment planningIntra-observer variabilityConsensus contoursGTV contoursPre-operative CT imagesSoft tissue sarcomasRadiation oncologistsTumor volumeBone sarcomasTreatment planningAccurate contoursCT imagesDelineation procedureSarcomaSoft tissueConfidence levelRadiationPatientsHausdorff distanceMultiple contoursX-rayDetecting lumbar lesions in 99mTc‐MDP SPECT by deep learning: Comparison with physicians
Petibon Y, Fahey F, Cao X, Levin Z, Sexton‐Stallone B, Falone A, Zukotynski K, Kwatra N, Lim R, Bar‐Sever Z, Chemli Y, Treves S, Fakhri G, Ouyang J. Detecting lumbar lesions in 99mTc‐MDP SPECT by deep learning: Comparison with physicians. Medical Physics 2021, 48: 4249-4261. PMID: 34101855, DOI: 10.1002/mp.15033.Peer-Reviewed Original ResearchConceptsSingle-photon emission computed tomographyLow back painLumbar lesionsPediatric patientsTc-MDPEvaluate low back painCause of low back painTc-MDP scanLesion-presentEmission computed tomographyConvolutional neural networkClinical likelihoodBack painInterreader variabilityDeep convolutional neural networkLumbar locationLesionsStress lesionsFocal lesionsDeep learningPatientsLumbar stressPhysiciansDL systemsLROC studies