Jason Johnson, MD, MBA, DABR
Cards
About
Research
Publications
2026
Tucatinib-Trastuzumab-Capecitabine for Treatment of Leptomeningeal Metastasis in HER2+ Breast Cancer: TBCRC049 Phase 2 Study Results.
O'Brien BJ, Murthy RK, Berry DA, Singareeka-Raghavendra A, Monroe MG, Johnson J, Schwartz-Gomez J, Topletz-Erickson A, Lobbous M, Melisko M, Morikawa A, Ferguson SD, de Groot JF, Krop I, Valero V, Rimawi M, Wolff A, Tripathy D, Lin NU, Stringer-Reasor E. Tucatinib-Trastuzumab-Capecitabine for Treatment of Leptomeningeal Metastasis in HER2+ Breast Cancer: TBCRC049 Phase 2 Study Results. Nat Cancer. In press.Peer-Reviewed Original Research In PressAssociations of local breast density with patient characteristics and risk of breast cancer among women undergoing diagnostic breast biopsy.
Burke A, Fan S, Pfeiffer RM, Bodelon C, Chan AHL, Hill ML, Khan N, Tikhonov A, Harris AR, Highnam RP, Vacek PM, Weaver DL, Johnson JM, Herschorn SD, Sprague BL, Sherman ME, Mullooly M, Gierach GL. Associations of local breast density with patient characteristics and risk of breast cancer among women undergoing diagnostic breast biopsy. Breast Cancer Research. 2025 Jan 5. [Preprint - Accepted December 19, 2024]Peer-Reviewed Original Research In Press
2025
Radiologist-Validated Automatic Lumbar T1-Weighted Spinal MRI Segmentation Tool via an Attention U-Net Algorithm
Kalluvila A, Wang E, Hurley M, Freeman C, Johnson J. Radiologist-Validated Automatic Lumbar T1-Weighted Spinal MRI Segmentation Tool via an Attention U-Net Algorithm. Diagnostics 2025, 15: 3046. PMID: 41374427, PMCID: PMC12691307, DOI: 10.3390/diagnostics15233046.Peer-Reviewed Original ResearchAttention U-NetU-NetLoss functionMSE lossMRI segmentationSegmentation loss functionSegmentation toolU-Net algorithmDeep learningSegmentation applicationsSegmentation performanceSegmentation accuracyProcess of segmentationArtificial intelligenceSSIMDice coefficientHigher accuracyAlgorithmPrevalence of disc herniationMethods:MSESuperior resultsResults:ArchitectureFunction of choiceLateral mandibular ridge: A unique feature of the auriculocondylar syndrome
Yadav M, Patel D, Wong K, de Oliveira Santo I, Morrison K, Green G, Cunningham M, Johnson J. Lateral mandibular ridge: A unique feature of the auriculocondylar syndrome. European Journal Of Radiology 2025, 194: 112476. PMID: 41124729, DOI: 10.1016/j.ejrad.2025.112476.Peer-Reviewed Original ResearchConceptsAuriculocondylar syndromeTemporomandibular jointCT markersHigh-resolution multidetector CTGenetic confirmationPediatric craniofacial surgeonPierre Robin sequencePositive predictive valueTreacher Collins syndromeFisher's exact testConfidence intervalsCraniofacial microsomiaArch syndromeCT findingsMultidetector CTRobin sequenceCraniofacial surgeonsCraniofacial malformationsExact testCraniofacial CTPathogenic variantsConsecutive individualsPredictive valueMandibular anomaliesSyndromeUltra-High Resolution 9.4T Brain MRI Segmentation via a Newly Engineered Multi-Scale Residual Nested U-Net with Gated Attention
Kalluvila A, Patel J, Johnson J. Ultra-High Resolution 9.4T Brain MRI Segmentation via a Newly Engineered Multi-Scale Residual Nested U-Net with Gated Attention. Bioengineering 2025, 12: 1014. PMID: 41155013, PMCID: PMC12561009, DOI: 10.3390/bioengineering12101014.Peer-Reviewed Original ResearchGround-truth masksSegmentation modelState-of-the-art performanceSpatial-channel attention mechanismMulti-scale residual blockT dataDice scoreMRI segmentationChannel attention mechanismDeep learning-based modelNested U-NetGlobal feature extractionState-of-the-artLearning-based modelsT imagesResidual blockAttention mechanismFeature extractionT datasetGray-white matter interfaceU-NetWilcoxon signed-rank testGated attentionMRI scannerGroundParsonage-Turner Syndrome in the Pediatric Population: A Case Report
Safinia, C., Jakovljevic, M., Farzaneh, H., Lesenskyj DeArias, A., Bamford, N. S., & Johnson, J. M. (2025). Parsonage-Turner Syndrome in the Pediatric Population: A Case Report. Journal of Radiology Case Reports, in press.Peer-Reviewed Case Reports and Technical NotesBrain CT for Diagnosis of Intracranial Disease in Ambulatory Cancer Patients: Assessment of the Diagnostic Value of Scanning Without Contrast Prior to With Contrast.
Wang E, Darbandi A, Tu L, Ballester L, Morales C, Chen M, Gule-Monroe M, Johnson J. Brain CT for Diagnosis of Intracranial Disease in Ambulatory Cancer Patients: Assessment of the Diagnostic Value of Scanning Without Contrast Prior to With Contrast. American Journal Of Neuroradiology 2025, ajnr.a8995. PMID: 40925682, DOI: 10.3174/ajnr.a8995.Peer-Reviewed Original ResearchNon-contrast CTIncremental cost-effectiveness ratioContrast-enhanced studiesBrain CTCancer patientsIntracranial diseaseDiagnostic valueAmbulatory cancer patientsCT reportsCancer stageContrast-enhanced brain CTMD Anderson Cancer CenterContrast-enhanced CTAsymptomatic cancer patientsPatient radiation exposureBoard-certified neuroradiologistsBrain CT examinationsCost-effectiveness ratioCT findingsCT examinationsDiagnosis of intracranial diseaseNon-contrast CT findingsCancer CenterPatientsRadiation exposureComparative Accuracy of 18-Gauge versus 20-Gauge Ultrasound-Guided Fine-Needle Aspiration in the Detection of Persistent Lymph Node Metastasis in Head and Neck Squamous Cell Carcinoma after Chemoradiation.
Nguyen J, Patel D, Lano K, Ansari S, Wang E, Yadav M, Gillison M, Lai S, Fuller C, Moreno A, Gule-Monroe M, Johnson J. Comparative Accuracy of 18-Gauge versus 20-Gauge Ultrasound-Guided Fine-Needle Aspiration in the Detection of Persistent Lymph Node Metastasis in Head and Neck Squamous Cell Carcinoma after Chemoradiation. American Journal Of Neuroradiology 2025, ajnr.a8990. PMID: 40876946, DOI: 10.3174/ajnr.a8990.Peer-Reviewed Original ResearchHead & neck squamous cell carcinomaUltrasound-guided fine-needle aspirationNeck squamous cell carcinomaNegative predictive valuePositive predictive valueFine-needle aspirationSquamous cell carcinomaNeck dissectionCell carcinomaNeedle aspirationPredictive valueDiagnostic accuracy of ultrasound-guided fine-needle aspirationPreoperative ultrasound-guided fine-needle aspirationHead and neck squamous cell carcinomaPersistent lymph node metastasisAccuracy of ultrasound-guided fine-needle aspirationCervical nodal metastasisExcellent positive predictive valueSalvage neck dissectionTreated with chemoradiotherapyPost-radiation fibrosisFollow-up imagingLymph node metastasisPersistent adenopathyMetastatic diseaseASNR Consensus Statement: Integrating Neuro-PET Interpretation into Neuroradiology Training and Practice
Ivanidze J, Franceschi AM, Wintermark M, Jordan JE, Aboian M, Anderson J, Assadsangabi R, Benayoun MD, Benzinger TL, Buchpiguel CA, Chiang GC-Y, Ebani EJ, Famuyide A, Galldiks N, Hu LS, Johnson DS, Johnson JM, Khalaf A, Knight-Greenfield A, Kuo P, Lohmann, P, Moradi, F, Nabavizadeh A, Nickerson JP, Perez-Carrillo GJG, Pyatigorskaya N, Raghavan P, Roytman M, Sheperd T, Singh G, Starkey J, Veronesi MC, Whitlow CT, Yildiz S, Zeineh M, Zaharchuk G, Raghavan P, Barajas RF. State of Practice: Integrating Neuro-PET Interpretation into Neuroradiology Training and Practice: A Collaborative, Patient-Centered Approach. AJNR Am J Neuroradiol. Accepted 18 July 2025 (in press).Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsStudy of 18 F-fluciclovine PET for serial assessment of glioblastoma tumor volumes during surgery and radiotherapy
Dagher S, Johnson J, Mohamed R, Ansari S, Mawlawi O, Liu H, Wintermark M, Schellingerhout D, Flynt L, Yeboa D, Weinberg J, Ferguson S, Gule-Monroe M. Study of 18 F-fluciclovine PET for serial assessment of glioblastoma tumor volumes during surgery and radiotherapy. Journal Of Neuro-Oncology 2025, 175: 549-560. PMID: 40591209, PMCID: PMC12420720, DOI: 10.1007/s11060-025-05146-2.Peer-Reviewed Original ResearchConceptsBiological tumor volumeTumor volumePost-radiationConventional MRIPre-radiationTreatment planningF-fluciclovine PETAdjunct to conventional MRIPost-treatment settingGlioblastoma tumor volumeF-fluciclovineTumor burdenTumor infiltrationFLAIR volumeTreatment courseFLAIR abnormalitiesMedian decreaseT1 volumesPurposeRecent evidenceSurgeryMRIGlioblastomaTumorVolumetric changesMultiple timepoints
Academic Achievements & Community Involvement
Clinical Care
Overview
Jason Johnson, MD, is a neuroradiologist who specializes in imaging techniques to diagnose and evaluate conditions affecting the brain and nervous system, including brain tumors, stroke, and other neurological disorders.
He utilizes advanced imaging methods to provide detailed insights into these conditions, aiding in accurate diagnosis and effective treatment planning.
An associate professor of radiology and biomedical imaging at Yale School of Medicine, Dr. Johnson completed his medical training at the University of Arizona, followed by a residency in radiology at the University of Vermont and fellowship training in neuroradiology at both the University of California, San Francisco, and Massachusetts General Hospital/Harvard Medical School.
Dr. Johnson has been recognized with the Magna Cum Laude Merit Award by the International Society of Magnetic Resonance in Medicine.
Clinical Specialties
Neuroradiology
Fact Sheets
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