MingDe Lin, PhD
Associate Professor Adjunct, Radiology & Biomedical Imaging, Yale School of MedicineAbout
Biography
Dr. Lin received the B.S. degree from Rensselaer Polytechnic Institute and the Ph.D. degree from Duke University, all in biomedical engineering. He is currently the Director for Clinical Research in North America for Visage Imaging and is stationed at Yale-New Haven Hospital where he oversees, coordinates, drives and directs research collaborations with high profile academic hospitals in North America to develop new solutions for diagnostic image analysis and guidance that improve clinical and operational outcomes while reducing cost of care. This includes Artificial Intelligence (AI) and Machine Learning (ML) applications in the Radiology enterprise diagnostic imaging solutions space. Dr. Lin identifies opportunities for academic-industry research partnerships, and acts as the liaison between Visage Imaging researchers and clinical collaborators to translate ideas to prototype for clinical validation, with the goal of technology transfer to product. A highlight is Ming coordinated the clinical data curation and ground-truth annotation for building a fully automatic breast density AI classifier that provides an ACR BI-RADS Atlas 5th Edition breast density category to aid radiologists in the assessment of breast tissue composition from full field digital mammography and digital breast tomosynthesis systems and drove the clinical validation with Yale radiologists and Visage developers that led to transfer to product and regulatory approvals in 21 months following IRB approval and has been in full clinical production use at Yale since April 2021. Moreover, Dr. Lin directed the study with Yale radiologists to assess the AI algorithm’s post-clinical deployment performance, and we found there was 99.35% agreement in classifying the breast density between the AI and the radiologist. This was the first FDA-cleared AI algorithm that reported having >1000 patients for validating the AI from two different clinical sites: Yale and New York University (NYU), and it was the first for a major PACS vendor to offer a self-developed, FDA-cleared AI algorithm natively into their PACS: Visage Breast Density, K201411, 510(k) clearance, January 2021, Health Canada Licensed, October 2020, Australian TGA approval, July 2020, CE Mark Cleared, May 2020.
Another effort Dr. Lin is working on is multi-institution AI research to develop robust deep learning methods for generating patient-specific virtual-high-count PET images from standard PET images, thereby saving imaging time, reducing radiation dose, and increasing scanner longevity. This work is being conducted in an NIH R01 academic-industry partnership grant where Dr. Lin is the Visage Imaging, Inc. Principal Investigator (PI), and the other partners are Yale New Haven, Massachusetts General Brigham, and University of California Davis hospitals.
Dr. Lin also is directly involved in research to develop better ways to treat patients with liver cancer using transcatheter arterial chemoembolization (TACE), and in this context, he is also the chief engineer and operations manager of the Yale Interventional Oncology Research Lab. Dr. Lin is Principal Investigator (PI) on two NIH R01 grants and the Industry PI on its renewal NIH R01 grant (three grants in total) to improve the diagnosis, treatment, and response assessment after transcatheter arterial chemoembolization for patients with liver cancer. Dr. Lin is the inventor of 3D quantification TACE therapy response tool (qEASL) and in collaboration with clinical partners, validated, and showed clinical relevance (ability to predict patient survival) that led to transfer to commercial product (FDA 510(k) cleared December 2016 - Multi-Modality Tumor Tracking (MMTT) application). Prior to Visage Imaging, Dr. Lin was the Philips research site manager and senior researcher stationed onsite at Yale where he managed the research portfolio and partnership Philips has with Yale.
Appointments
Radiology & Biomedical Imaging
Associate Professor AdjunctPrimary
Other Departments & Organizations
Education & Training
- PhD
- Duke University (2008)
- BS
- Rensselaer Polytechnic Institute (2001)
Research
Overview
Dr. Lin is the inventor of 3D quantification TACE therapy response tool (qEASL) and in collaboration with clinical partners, co-created, validated, and showed clinical relevance (ability to predict patient survival) that led to full transfer to commercial product.
ORCID
0000-0001-7641-2595- View Lab Website
Yale Interventional Oncology Research Lab
Research at a Glance
Yale Co-Authors
Publications Timeline
Julius Chapiro, MD/PhD
Todd Schlachter, MD
Tal Zeevi, MSc
Sanjay Aneja, MD
Fabian Max Laage Gaupp, MD
Antonio Omuro, MD
Publications
2024
Tumor response assessment in hepatocellular carcinoma treated with immunotherapy: imaging biomarkers for clinical decision-making
Sobirey R, Matuschewski N, Gross M, Lin M, Kao T, Kasolowsky V, Strazzabosco M, Stein S, Savic L, Gebauer B, Jaffe A, Duncan J, Madoff D, Chapiro J. Tumor response assessment in hepatocellular carcinoma treated with immunotherapy: imaging biomarkers for clinical decision-making. European Radiology 2024, 1-11. PMID: 39033181, DOI: 10.1007/s00330-024-10955-6.Peer-Reviewed Original ResearchConceptsMedian overall survivalTumor response criteriaTumor response assessmentHepatocellular carcinoma patientsHepatocellular carcinomaTumor responseOverall survivalResponse criteriaResponse assessmentNon-respondersPoorer median overall survivalPrediction of tumor responsePredictive valueHepatocellular carcinoma immunotherapyDisease controlPrognostic of survivalClinical baseline parametersLog-rank testKaplan-Meier curvesMultivariate Cox regressionPredicting overall survivalCox regression modelsSurvival benefitStratify patientsMRI pre-Outcomes of Repeat Conventional Transarterial Chemoembolization in Patients with Liver Metastases
Ghabili K, Windham-Herman A, Konstantinidis M, Murali N, Borde T, Adam L, Laage-Gaupp F, Lin M, Chapiro J, Georgiades C, Nezami N. Outcomes of Repeat Conventional Transarterial Chemoembolization in Patients with Liver Metastases. Annals Of Hepatology 2024, 101529. PMID: 39033928, DOI: 10.1016/j.aohep.2024.101529.Peer-Reviewed Original ResearchConceptsConventional transarterial chemoembolizationLiver metastasesNeuroendocrine tumorsColorectal carcinomaTransarterial chemoembolizationOverall survivalLung cancerAssociated with improved patient survivalManagement of liver metastasesMetastatic liver lesionsSingle-institution analysisNonresponding patientsSurvival outcomesPatient survivalResponse assessmentTarget lesionsMetastasisLiver lesionsPatientsResponse rateChemoembolizationSurvivalLiverLesionsCancerArtificial Intelligence-based Morpho-volumetric Analysis of Pre- and Post-EVAR Infrarenal Abdominal Aortic Aneurysms Characterized on Computed Tomography Angiography
Weiss D, Hager T, Aboian M, Lin M, Renninghoff D, Holler W, Fischer U, Deuschl C, Aneja S, Aboian E. Artificial Intelligence-based Morpho-volumetric Analysis of Pre- and Post-EVAR Infrarenal Abdominal Aortic Aneurysms Characterized on Computed Tomography Angiography. Journal Of Vascular Surgery 2024, 79: e133-e134. DOI: 10.1016/j.jvs.2024.03.165.Peer-Reviewed Original ResearchComparison of Volumetric and 2D Measurements and Longitudinal Trajectories in the Response Assessment of BRAF V600E-Mutant Pediatric Gliomas in the Pacific Pediatric Neuro-Oncology Consortium Clinical Trial
Ramakrishnan D, Brüningk S, von Reppert M, Memon F, Maleki N, Aneja S, Kazerooni A, Nabavizadeh A, Lin M, Bousabarah K, Molinaro A, Nicolaides T, Prados M, Mueller S, Aboian M. Comparison of Volumetric and 2D Measurements and Longitudinal Trajectories in the Response Assessment of BRAF V600E-Mutant Pediatric Gliomas in the Pacific Pediatric Neuro-Oncology Consortium Clinical Trial. American Journal Of Neuroradiology 2024, 45: 475-482. PMID: 38453411, PMCID: PMC11288571, DOI: 10.3174/ajnr.a8189.Peer-Reviewed Original ResearchAltmetricConceptsArea under the curvePediatric gliomasBT-RADSResponse assessmentPartial responseClinical trialsVolumetric analysisReceiver operating characteristic analysisBrain Tumor ReportingReceiver operating characteristic curveModel estimation timeOperating characteristic analysisEvaluate treatment efficacyStable diseasePartial respondersManual volumetric segmentationNo significant differenceSolid tumorsProspective studyTumor ReportingClinical decision-makingTreatment efficacyGliomaSignificant differenceCharacteristic curveVolumetric Abdominal Aortic Aneurysm Analysis in Post Evar Surveillance Settings
Weiss D, Aboian M, Lin M, Holler W, Renninghoff D, Harris S, Fischer U, Chaar C, Deuschl C, Aboian E. Volumetric Abdominal Aortic Aneurysm Analysis in Post Evar Surveillance Settings. Annals Of Vascular Surgery 2024, 100: 265. DOI: 10.1016/j.avsg.2023.12.040.Peer-Reviewed Original ResearchA large open access dataset of brain metastasis 3D segmentations on MRI with clinical and imaging information
Ramakrishnan D, Jekel L, Chadha S, Janas A, Moy H, Maleki N, Sala M, Kaur M, Petersen G, Merkaj S, von Reppert M, Baid U, Bakas S, Kirsch C, Davis M, Bousabarah K, Holler W, Lin M, Westerhoff M, Aneja S, Memon F, Aboian M. A large open access dataset of brain metastasis 3D segmentations on MRI with clinical and imaging information. Scientific Data 2024, 11: 254. PMID: 38424079, PMCID: PMC10904366, DOI: 10.1038/s41597-024-03021-9.Peer-Reviewed Original ResearchAltmetricMeSH Keywords and ConceptsConceptsWhole-brain radiotherapyStereotactic radiosurgeryT1 post-contrastBrain metastasesPost-contrastSide effectsImage informationArtificial intelligenceAssociated with cognitive side effectsContrast-enhancing lesionsQuality of datasetsCognitive side effectsFLAIR MR imagesValidation of AI modelsBrain radiotherapyLimitations of algorithmsStandard treatmentAI modelsMR imagingAI networksContrast enhancementClinical settingSegmentation workflowDatasetClinical adoption
2023
Comparison of volumetric and 2D-based response methods in the PNOC-001 pediatric low-grade glioma clinical trial
von Reppert M, Ramakrishnan D, Brüningk S, Memon F, Fadel S, Maleki N, Bahar R, Avesta A, Jekel L, Sala M, Lost J, Tillmanns N, Kaur M, Aneja S, Kazerooni A, Nabavizadeh A, Lin M, Hoffmann K, Bousabarah K, Swanson K, Haas-Kogan D, Mueller S, Aboian M. Comparison of volumetric and 2D-based response methods in the PNOC-001 pediatric low-grade glioma clinical trial. Neuro-Oncology Advances 2023, 6: vdad172. PMID: 38221978, PMCID: PMC10785766, DOI: 10.1093/noajnl/vdad172.Peer-Reviewed Original ResearchAltmetricApplication of novel PACS-based informatics platform to identify imaging based predictors of CDKN2A allelic status in glioblastomas
Tillmanns N, Lost J, Tabor J, Vasandani S, Vetsa S, Marianayagam N, Yalcin K, Erson-Omay E, von Reppert M, Jekel L, Merkaj S, Ramakrishnan D, Avesta A, de Oliveira Santo I, Jin L, Huttner A, Bousabarah K, Ikuta I, Lin M, Aneja S, Turowski B, Aboian M, Moliterno J. Application of novel PACS-based informatics platform to identify imaging based predictors of CDKN2A allelic status in glioblastomas. Scientific Reports 2023, 13: 22942. PMID: 38135704, PMCID: PMC10746716, DOI: 10.1038/s41598-023-48918-4.Peer-Reviewed Original ResearchCitationsAltmetricMeSH Keywords and ConceptsConceptsInformatics platformDeep learning algorithmsImaging featuresCDKN2A alterationsLearning algorithmHeterozygous lossHomozygous deletionLarge datasetsDeep white matter invasionGBM molecular subtypesNew informaticsQualitative imaging biomarkersWhole-exome sequencingQualitative imaging featuresGBM resectionRadiographic evidenceWorse prognosisPACSMolecular subtypesPial invasionImaging biomarkersCDKN2A mutationsAllele statusNoninvasive identificationMagnetic resonance imagesIntraprocedural C‐arm dual‐phase cone‐beam enhancement patterns correlate with tumor absorbed dose after radioembolization
Bastiaannet R, Lin M, Frey E, de Jong H. Intraprocedural C‐arm dual‐phase cone‐beam enhancement patterns correlate with tumor absorbed dose after radioembolization. Medical Physics 2023, 51: 3045-3052. PMID: 38064591, PMCID: PMC10994751, DOI: 10.1002/mp.16882.Peer-Reviewed Original ResearchConceptsContrast-enhanced cone-beam CTHepatocellular carcinomaPET/CT scansNon-tumor liver tissuesPersonalized treatment planningIntra-procedural imagingBlood flow patternsPretreatment tumorRetrospective studyLimits of agreementTumor responseBland-Altman analysisClinical trialsTumor doseCT scanEnhancement patternHepatic vasculatureHCC tumorsCatheter positionTumor tissueLiver tissueTumorsHepatic radioembolizationPatientsDoseSystematic Literature Review of Machine Learning Algorithms Using Pretherapy Radiologic Imaging for Glioma Molecular Subtype Prediction
Lost J, Verma T, Jekel L, von Reppert M, Tillmanns N, Merkaj S, Petersen G, Bahar R, Gordem A, Haider M, Subramanian H, Brim W, Ikuta I, Omuro A, Conte G, Marquez-Nostra B, Avesta A, Bousabarah K, Nabavizadeh A, Kazerooni A, Aneja S, Bakas S, Lin M, Sabel M, Aboian M. Systematic Literature Review of Machine Learning Algorithms Using Pretherapy Radiologic Imaging for Glioma Molecular Subtype Prediction. American Journal Of Neuroradiology 2023, 44: 1126-1134. PMID: 37770204, PMCID: PMC10549943, DOI: 10.3174/ajnr.a8000.Peer-Reviewed Original ResearchCitationsAltmetric
Links & Media
Media
News
- February 01, 2021Source: PME Receives FDA Clearance for Breast Density Algorithm
FDA Clearance for Breast Density Classification AI Algorithm
- January 18, 2021Source: Tau Beta Pi Wins Award, and Advisor MingDe Lin Elected to Board
Advisor MingDe Lin elected to national board of directors of Tau Beta Pi Engineering Honor Society
- October 26, 2020
Image Processing and Analysis Group Wins International Best Paper Award for 2nd Straight Year
- August 16, 2019
Department Provides Launching Platform for YSM Grad
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