2025
Emerging Trends in Artificial Intelligence in Neuro-Oncology
Chadha S, Sritharan D, Hager T, D’Souza R, Aneja S. Emerging Trends in Artificial Intelligence in Neuro-Oncology. Current Oncology Reports 2025, 1-12. PMID: 40504358, DOI: 10.1007/s11912-025-01688-w.Peer-Reviewed Original ResearchArtificial intelligenceNeuro-oncologyLeverage natural language processingExtract actionable insightsNatural language processingTreatment response evaluationOptimal treatment planHardware efficiencyLanguage processingComputational pathologyModel generalizabilityActionable insightsAutomated tumor segmentationTumor segmentationRisk stratificationDiagnostic accuracyMolecular classificationTreatment planningClinical reportsComputational techniquesResponse evaluationReviewThis articlePatient outcomesClinical workflowAccelerating drug discoveryPrediction of Lymph Node Metastasis in Non–Small Cell Lung Carcinoma Using Primary Tumor Somatic Mutation Data
Lee V, Moore N, Doyle J, Hicks D, Oh P, Bodofsky S, Hossain S, Patel A, Aneja S, Homer R, Park H. Prediction of Lymph Node Metastasis in Non–Small Cell Lung Carcinoma Using Primary Tumor Somatic Mutation Data. JCO Clinical Cancer Informatics 2025, 9: e2400303. PMID: 40446175, DOI: 10.1200/cci-24-00303.Peer-Reviewed Original ResearchConceptsNon-small cell lung cancerLymph node metastasisArea under the receiver operating characteristic curveNode metastasisTreatment strategiesNon-small cell lung carcinomaPrediction of lymph node metastasisSurvival analysisSNP dataLymph node metastasis statusAssociated with lymph node metastasisCell lung carcinomaCell lung cancerLymph node metastasis predictionReceiver operating characteristic curveDiagnostic methodsPersonalized treatment strategiesSingle-nucleotide polymorphism (SNPChi-square testMedian AUCLung carcinomaClinical outcomesNon-smallRisk stratificationLogistic regression modelsEnhancing prognostic accuracy in lung cancer brain metastases through histopathologic feature integration and AI-based image analysis.
Breuer G, Sritharan D, Chadha S, Hager T, Fu D, Aneja S. Enhancing prognostic accuracy in lung cancer brain metastases through histopathologic feature integration and AI-based image analysis. Journal Of Clinical Oncology 2025, 43: e13608-e13608. DOI: 10.1200/jco.2025.43.16_suppl.e13608.Peer-Reviewed Original ResearchTumor-infiltrating lymphocytesCox proportional hazards modelsClinical featuresHistopathological featuresProportional hazards modelLung adenocarcinoma metastasisWhole slide imagesBrain metastasesAdenocarcinoma metastasisManagement of brain metastasesLung cancer brain metastasisBrain metastasis diagnosisCancer brain metastasesHazards modelEstimate overall survivalSystemic disease controlOverall survival estimatesGround-truth labelsAttention-based modelAttention-based networkMetastasis biopsiesSlide imagesNeural network-based modelOverall survivalAI-based image analysisImage-Based Search in Radiology: Identification of Brain Tumor Subtypes within Databases Using MRI-Based Radiomic Features.
von Reppert M, Chadha S, Willms K, Avesta A, Maleki N, Zeevi T, Lost J, Tillmanns N, Jekel L, Merkaj S, Lin M, Hoffmann K, Aneja S, Aboian M. Image-Based Search in Radiology: Identification of Brain Tumor Subtypes within Databases Using MRI-Based Radiomic Features. American Journal Of Neuroradiology 2025, 46: 1421-1428. PMID: 40250848, DOI: 10.3174/ajnr.a8805.Peer-Reviewed Original ResearchConceptsDimensionality reduction techniquesQuery caseImage search enginesImage-based searchReduction techniquesQuery subsetsRetrieval performanceQuery onesDimensionality reductionVisual similarityQuerySearch enginesSearch approachSearch methodTumor segmentationImage-basedRadiomic featuresNearest neighborsTumor typesDimensionalityBrain tumorsData setsNinety-five patientsLikert scoresMRI-based radiomic featuresImproved Survival and Prognostication in Melanoma Patients With Brain Metastases: An Update of the Melanoma Graded Prognostic Assessment
Sperduto P, Marqueen K, Chang E, Li J, Davies M, Ebner D, Breen W, Lamba N, Shih H, Edwards D, Kim M, Mahal A, Rahman R, Ankrah N, Boggs D, Lewis C, Hyer D, Buatti J, Johri F, Soliman H, Masucci L, Roberge D, Aneja S, Chiang V, Phuong C, Braunstein S, Dajani S, Sachdev S, Wan Z, Niedzwiecki D, Vaios E, Kirkpatrick J, Pasetsky J, Wang T, Kutuk T, Kotecha R, Ross R, Rusthoven C, Nakano T, Tawbi H, Mehta M. Improved Survival and Prognostication in Melanoma Patients With Brain Metastases: An Update of the Melanoma Graded Prognostic Assessment. Journal Of Clinical Oncology 2025, 43: 1910-1919. PMID: 40245362, PMCID: PMC12119226, DOI: 10.1200/jco-24-01351.Peer-Reviewed Original ResearchConceptsGraded Prognostic AssessmentMelanoma brain metastasesPrognostic factorsPrognostic assessmentAbsence of extracranial metastasesGPA 0Multi-institutional retrospective databaseTreatments associated with survivalAnalysis of prognostic factorsMedian follow-up timeModern multimodality therapyKarnofsky performance statusClinical trial eligibilityBrain metastasesExtracranial metastasesMedian survivalMelanoma patientsMultimodal therapyPerformance statusMultidisciplinary treatmentImproved survivalRetrospective databaseTrial eligibilityTherapeutic modalitiesClinical trialsIsolated subsegmental pulmonary embolism identification based on international classification of diseases (ICD)-10 codes and imaging reports
Rashedi S, Bejjani A, Hunsaker A, Aghayev A, Khairani C, McGonagle B, Lo Y, Mahajan S, Caraballo C, Jimenez J, Krishnathasan D, Zarghami M, Monreal M, Barco S, Secemsky E, Klok F, Muriel A, Hussain M, Appah-Sampong A, Rahaghi F, Sadeghipour P, Lin Z, Mojibian H, Aneja S, Konstantinides S, Goldhaber S, Wang L, Zhou L, Jimenez D, Krumholz H, Piazza G, Bikdeli B, Investigators T. Isolated subsegmental pulmonary embolism identification based on international classification of diseases (ICD)-10 codes and imaging reports. Thrombosis Research 2025, 247: 109271. PMID: 39862754, DOI: 10.1016/j.thromres.2025.109271.Peer-Reviewed Original ResearchInternational Classification of Diseases (ICD)-10 codesICD-10International ClassificationPositive predictive valueDischarge diagnosisAccuracy of ICD-10Radiology reportsPrincipal discharge diagnosisSecondary discharge diagnosisIsolated subsegmental pulmonary embolismMedical records of adult patientsHealth systemRecords of adult patientsPredictive valueBlinded re-evaluationExpert radiologistsSubsegmental pulmonary embolismQuality improvementMedical recordsChart reviewPresence of PEProximal PEPulmonary embolismAdult patientsImaging Reporting
2024
Machine Learning Models for 3-Month Outcome Prediction Using Radiomics of Intracerebral Hemorrhage and Perihematomal Edema from Admission Head Computed Tomography (CT)
Dierksen F, Sommer J, Tran A, Lin H, Haider S, Maier I, Aneja S, Sanelli P, Malhotra A, Qureshi A, Claassen J, Park S, Murthy S, Falcone G, Sheth K, Payabvash S. Machine Learning Models for 3-Month Outcome Prediction Using Radiomics of Intracerebral Hemorrhage and Perihematomal Edema from Admission Head Computed Tomography (CT). Diagnostics 2024, 14: 2827. PMID: 39767188, PMCID: PMC11674633, DOI: 10.3390/diagnostics14242827.Peer-Reviewed Original ResearchIntegrated discrimination indexNet reclassification indexPerihematomal edemaHead Computed TomographyIntracerebral hemorrhageComputed tomographyClinical variablesClinical predictors of poor outcomeOutcome predictionAcute supratentorial intracerebral hemorrhageAdmission head computed tomographyRadiomic featuresNon-contrast head computed tomographyPredictors of poor outcomeModified Rankin Scale scoreIntracerebral Hemorrhage ScoreSupratentorial intracerebral hemorrhageIntracerebral hemorrhage patientsClinical risk factorsRankin Scale scoreReceiver operating characteristic areaOperating characteristics areaSecondary brain injuryHematoma evacuationPatient selectionA Hybrid Transformer-Convolutional Neural Network for Segmentation of Intracerebral Hemorrhage and Perihematomal Edema on Non-Contrast Head Computed Tomography (CT) with Uncertainty Quantification to Improve Confidence
Tran A, Desser D, Zeevi T, Abou Karam G, Dierksen F, Dell'Orco A, Kniep H, Hanning U, Fiehler J, Zietz J, Sanelli P, Malhotra A, Duncan J, Aneja S, Falcone G, Qureshi A, Sheth K, Nawabi J, Payabvash S. A Hybrid Transformer-Convolutional Neural Network for Segmentation of Intracerebral Hemorrhage and Perihematomal Edema on Non-Contrast Head Computed Tomography (CT) with Uncertainty Quantification to Improve Confidence. Bioengineering 2024, 11: 1274. PMID: 39768092, PMCID: PMC11672977, DOI: 10.3390/bioengineering11121274.Peer-Reviewed Original ResearchNon-contrast head computed tomographyPerihematomal edemaHead Computed TomographyIntracerebral hemorrhageComputed tomographyVolume similarityUniversity Medical Center Hamburg-EppendorfSecondary brain injuryYale cohortInfratentorial locationMulticentre trialCT scanTreatment planningNon-contrastHamburg-EppendorfImaging markersHemorrhagic strokeHemorrhageEdemaCohortBrain injuryDice coefficientComparative Effectiveness of SBRT
Shen J, Sritharan D, Yu J, Aneja S. Comparative Effectiveness of SBRT. 2024, 455-467. DOI: 10.1007/978-3-031-67743-4_33.Peer-Reviewed Original ResearchStereotactic body radiation therapyEffect of stereotactic body radiation therapyAlternative to other treatment modalitiesStereotactic body radiation therapy treatmentLong-term follow-up of patientsFollow-up of patientsLong-term follow-upTreatment of brainOligometastatic diseaseRadiation therapyTreatment of cancerRetrospective seriesTreatment modalitiesRenal cancerCost-effectiveness studiesDatabase analysisCancerTreatmentProstateTherapyRADT-12. DERIVING IMAGING BIOMARKERS FOR PRIMARY CENTRAL NERVOUS SYSTEM LYMPHOMA USING DEEP LEARNING
Zhu J, Hager T, Chadha S, Sritharan D, Weiss D, Hossain S, Osenberg K, Moore N, Aneja S. RADT-12. DERIVING IMAGING BIOMARKERS FOR PRIMARY CENTRAL NERVOUS SYSTEM LYMPHOMA USING DEEP LEARNING. Neuro-Oncology 2024, 26: viii74-viii74. PMCID: PMC11553274, DOI: 10.1093/neuonc/noae165.0296.Peer-Reviewed Original ResearchPrimary central nervous system lymphomaWhole-brain radiotherapyTreated with chemotherapyOverall survivalHigh-risk groupPatient phenotypesCentral nervous system lymphomaPCNSL treatmentRisk of neurocognitive side effectsImaging biomarkersC-statisticOne-year OSTwo-year OSNervous system lymphomaAssociated with improved outcomesLog-rank testNeurocognitive side effectsTime-dependent AUCBrain radiotherapySystem lymphomaTumor volumeTumor sizeRisk stratificationAnalyses assessed differencesSub-analysisValidating International Classification of Diseases Code 10th Revision algorithms for accurate identification of pulmonary embolism
Bikdeli B, Khairani C, Bejjani A, Lo Y, Mahajan S, Caraballo C, Jimenez J, Krishnathasan D, Zarghami M, Rashedi S, Jimenez D, Barco S, Secemsky E, Klok F, Hunsaker A, Aghayev A, Muriel A, Hussain M, Appah-Sampong A, Lu Y, Lin Z, Mojibian H, Aneja S, Khera R, Konstantinides S, Goldhaber S, Wang L, Zhou L, Monreal M, Piazza G, Krumholz H, Investigators P. Validating International Classification of Diseases Code 10th Revision algorithms for accurate identification of pulmonary embolism. Journal Of Thrombosis And Haemostasis 2024, 23: 556-564. PMID: 39505153, DOI: 10.1016/j.jtha.2024.10.013.Peer-Reviewed Original ResearchDischarge codesInternational ClassificationICD-10Yale New Haven Health SystemPositive predictive valueMass General Brigham hospitalsAccuracy of ICD-10ICD-10 codesPulmonary embolismHealth systemImage codingElectronic databasesF1 scorePre-specified protocolExcellent positive predictive valueIndependent physiciansHighest F1 scoreIdentification of pulmonary embolismAcute pulmonary embolismSecondary codePE codesScoresIdentified PERevised algorithmCUTS: A Deep Learning and Topological Framework for Multigranular Unsupervised Medical Image Segmentation
Liu C, Amodio M, Shen L, Gao F, Avesta A, Aneja S, Wang J, Del Priore L, Krishnaswamy S. CUTS: A Deep Learning and Topological Framework for Multigranular Unsupervised Medical Image Segmentation. Lecture Notes In Computer Science 2024, 15008: 155-165. DOI: 10.1007/978-3-031-72111-3_15.Peer-Reviewed Original ResearchMedical image segmentationImage segmentationLack of labeled dataUnsupervised deep learning frameworkSegmenting medical imagesDeep learning frameworkBrain MRI imagesRetinal fundus imagesContrastive learningLearning frameworkUnsupervised methodDeep learningExpert annotationsData topologyMedical imagesGranularity levelsEmbedding mapHausdorff distanceFundus imagesDice coefficientImage dataEmbeddingAnnotationLearningMRI imagesApplying Language Models to Radiology Text for Identifying Oligometastatic Non-Small Cell Lung Cancer
Moore N, Laird J, Verma N, Hager T, Sritharan D, Lee V, Maresca R, Chadha S, Park H, Aneja S. Applying Language Models to Radiology Text for Identifying Oligometastatic Non-Small Cell Lung Cancer. International Journal Of Radiation Oncology • Biology • Physics 2024, 120: e644. DOI: 10.1016/j.ijrobp.2024.07.1417.Peer-Reviewed Original ResearchNon-small cell lung cancerOligometastatic diseaseCell lung cancerRadiologic impressionTumor RegistryTest cohortOligometastatic non-small cell lung cancerIV non-small cell lung cancerStage IV non-small cell lung cancerLung cancerConvolutional neural networkMetastasis-directed therapyOligometastatic NSCLC patientsMonths of diagnosisLanguage modelClinician reviewNSCLC patientsPatient cohortClinical dataScreening patientsSubgroup analysisBrain MRIClinical textBurden of diseaseClinical relevanceA Comparison of Machine Learning Models to Predict Lymph Node Metastasis with Primary Tumor Transcriptome in Non-Small Cell Lung Cancer
Lee V, Park H, Aneja S, Patel A. A Comparison of Machine Learning Models to Predict Lymph Node Metastasis with Primary Tumor Transcriptome in Non-Small Cell Lung Cancer. International Journal Of Radiation Oncology • Biology • Physics 2024, 120: e638. DOI: 10.1016/j.ijrobp.2024.07.1402.Peer-Reviewed Original ResearchNon-small cell lung cancerLymph nodal metastasisCell lung cancerMedian AUCNodal metastasisPrimary tumorEndobronchial ultrasoundNon-small cell lung cancer stagingLung cancerDiagnostic uncertaintyPrediction of nodal metastasisPET-avid lesionsTreatment strategy formulationPrimary lung adenocarcinomaEBUS-guided biopsiesLymph node metastasisPrimary tumor tissuesReceiver operating characteristic curveTumor transcriptomic dataPET-CTNode metastasisNeedle aspirationCT scanNon-smallClinical decision-makingAcceleration of Volumetric Abdominal Aortic Aneurysm Measurements by Leveraging Artificial Intelligence
Weiss D, Hager T, Aboian M, Lin M, Bousabarah K, Renninghoff D, Holler W, Simmons K, Loh S, Fischer U, Deuschl C, Aneja S, Aboian E. Acceleration of Volumetric Abdominal Aortic Aneurysm Measurements by Leveraging Artificial Intelligence. Journal Of Vascular Surgery 2024, 80: e37-e38. DOI: 10.1016/j.jvs.2024.06.066.Peer-Reviewed Original ResearchCancer Informatics: Novel Methods and Applications of Artificial Intelligence in Cancer Care Delivery
Aneja S, Parikh R. Cancer Informatics: Novel Methods and Applications of Artificial Intelligence in Cancer Care Delivery. Yearbook Of Medical Informatics 2024, 33: 099-101. PMID: 40199295, PMCID: PMC12020643, DOI: 10.1055/s-0044-1800727.Peer-Reviewed Original ResearchConceptsCancer informaticsApplication of artificial intelligenceInformatics methodologiesCancer care deliveryImprove cancer outcomesImpact patient outcomesArtificial intelligenceCare deliveryCancer outcomesNovel methodResearch contributionsExternal reviewersSection editorsPatient outcomesResearch teamInformaticsSelection processSubfield of bioinformaticsClinical validationScientific contributionsEditorial boardScalabilityCancerClinical implementationOutcomesArtificial 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 ResearchLymph node metastasis prediction with non-small cell lung cancer histopathology imaging.
Lee V, King A, Sritharan D, Moore N, Chadha S, Maresca R, Hager T, Aneja S. Lymph node metastasis prediction with non-small cell lung cancer histopathology imaging. Journal Of Clinical Oncology 2024, 42: 8063-8063. DOI: 10.1200/jco.2024.42.16_suppl.8063.Peer-Reviewed Original ResearchNon-small cell lung cancerLymph nodal metastasisNational Lung Screening TrialLymph node metastasisNodal metastasisNode metastasisEndobronchial ultrasound-guided transbronchial needle aspirationUltrasound-guided transbronchial needle aspirationTreatment strategy formulationTransbronchial needle aspirationCell lung cancerDistant metastasis statusLymph node metastasis predictionPrimary tumor dataHematoxylin and eosin (H&EInvasive diagnostic techniquesInfluence clinical decisionsN0 diseaseN3 diseaseEosin (H&EPrimary resectionDistant metastasisPET-CTNeedle aspirationCT scanDifferentiation of IDH-mutant Glioma Subtypes Using Unsupervised Dimensionality Reduction of MRI Biomarkers
Willms K, Zeevi T, Chadha S, von Reppert M, Lost J, Tillmanns N, Merkaj S, Huttner A, Aneja S, Aboian M. Differentiation of IDH-mutant Glioma Subtypes Using Unsupervised Dimensionality Reduction of MRI Biomarkers. Proceedings Of The International Society For Magnetic Resonance In Medicine ... Scientific Meeting And Exhibition. 2024 DOI: 10.58530/2024/3094.Peer-Reviewed Original ResearchAutomated MR Spectroscopy single-voxel placement in suspected diffuse glioma based on tumor biology
Chadha S, Jacobs S, Zeevi T, Tillmanns N, Merkaj S, Lost J, Lin M, Bousabarah K, Holler W, Memon F, Aneja S, Aboian M. Automated MR Spectroscopy single-voxel placement in suspected diffuse glioma based on tumor biology. Proceedings Of The International Society For Magnetic Resonance In Medicine ... Scientific Meeting And Exhibition. 2024 DOI: 10.58530/2024/5120.Peer-Reviewed Original ResearchTumor biologyDiffuse gliomasSingle-voxel magnetic resonance spectroscopyManagement of diffuse gliomasMagnetic resonance spectroscopyNon-invasive diagnosisVoxel placementMetabolite quantificationSingle-voxelMR imagingRadiology techniciansTumorGliomaPlacementResonance spectroscopyPoor-quality spectraClinicDiagnosis
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