Artificial Intelligence-based decision support for chemotherapy-response assessment in Brain Tumors
基于人工智能的脑肿瘤化疗反应评估决策支持
基本信息
- 批准号:10589512
- 负责人:
- 金额:--
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2023
- 资助国家:美国
- 起止时间:2023-07-01 至 2027-06-30
- 项目状态:未结题
- 来源:
- 关键词:AdjuvantAdjuvant ChemotherapyAdoptionAftercareAnatomyAnxietyAppearanceArtificial IntelligenceAwardBenignBiologicalBiophysicsBiopsyBrainBrain NeoplasmsCellsCharacteristicsClinicalClinical TrialsClinical assessmentsCombination Drug TherapyCombined Modality TherapyCoupledDataDiagnosisDiagnosticDiscriminationDiseaseDisease ProgressionEntropyExcisionExposure toGadoliniumGenomicsGlioblastomaGliomaGulf War veteranHealthcare SystemsHeterogeneityHistologicHistologyHumanImageInstitutionInterventionIonizing radiationIraqLesionLinkMGMT geneMRI ScansMagnetic Resonance ImagingMalignant NeoplasmsMalignant neoplasm of brainMapsMeasurementMedical centerMethylationMolecularMorbidity - disease rateMorphologyNeurologicOhioOncologistOperative Surgical ProceduresPathway interactionsPatient SelectionPatientsPatternPerfusionPersonsPharmaceutical PreparationsPharmacotherapyPhenotypePlayPopulationQuality of lifeRadiationRadiation therapyReaderReadingRecurrent tumorRefractoryResistanceResortRiskRoleScanningShapesSiteSurfaceSurvival RateTennesseeTextureTime ManagementTissuesTreatment-related toxicityTumor BiologyUnnecessary SurgeryValidationVasodilationVeteransVisualWarWorkbrain magnetic resonance imagingbrain parenchymachemoradiationchemotherapyclinically actionablecohortcomorbiditycompanion diagnosticsconventional therapycostdeep learningdiagnostic toolfinancial toxicityfollow-uphigh riskimaging biomarkerimprovedinnovationmortalitynerve agentnervous system disorderneuroimagingpredictive markerprognosticpromoterradiation effectradiologistradiomicsresponders and non-respondersresponse biomarkersegregationside effectstandard of caresuccesstreatment effecttreatment responsetumortumor microenvironment
项目摘要
ABSTRACT: In 2020, over 23,000 patients in the US will be diagnosed with Glioblastoma (GBM), a highly
aggressive brain tumor, with a dismal median survival of 15-18 months. Studies focusing on Gulf War Veterans
especially those exposed to nerve agents in Iraq in 1991 have shown a higher risk of brain tumors among
neurological diseases and a distinct neurological brain pattern as compared with the other Veterans. The
standard-of-care for GBM consists of surgical resection followed by radiotherapy combined with concomitant
and adjuvant chemotherapy. However, ~50% of GBM patients do not respond favorably to chemoradiation
following surgery. A priori identification of non-responders could allow for selection of these patients as potential
candidates for genomically-driven drug therapies (over 64 ongoing clinical trials in the US) over conventional
treatment. Further, chemotherapy costs >$100K/year. There is hence an unmet need to develop and validate
predictive biomarkers to identify up front which Veteran patients will not benefit from chemotherapy. Another
significant challenge in GBM management is the differentiation of suspicious lesions on post-treatment MRI, as
tumor recurrence or treatment-induced radiation effects. In the absence of reliable diagnosis, patients with a
benign treatment effect have to undergo an unnecessary surgical confirmation biopsy. The co-morbidities due
to unnecessary biopsies disproportionately impact Veteran GBM patients who tend to be older and have
increased comorbidity burden. Consequently, developing a companion diagnostic solution using clinical MRI
could represent a compelling solution in substantially improve quality-of-life years for Veteran GBM patients by
sparing them of the side-effects of surgery, while providing timely management in patients with tumor recurrence.
Recently, we have developed a new “Neuro-Image Risk Classifier” (NeuRisC), that uses artificial-intelligence
(AI)-driven computational features corresponding to the micro-architectural measurements of disorder in the local
intensity gradients (i.e. gradient entropy) on Gadolinium (Gd)-T1w MRI; the initial version of NeuRisC has been
shown to (a) be prognostic of GBM survival on n=203 studies (p<0.001), and (2) have an accuracy of 85% (a
37% improvement over expert readers) on n=58 studies in distinguishing radiation effects from tumor recurrence.
In this VA project, we propose to further improve, and validate the accuracy of NeuRisC by expanding our initial
feature set (using Gd-T1w MRI alone) by including (1) additional features from anatomical (T2w, FLAIR) and
functional MR sequences (perfusion), (2) a new class of biophysical deformation attributes from “normal” brain
parenchyma, and (3) peritumoral features from outside the lesion. In Aim 1, we will develop (NeuRisC)predict as a
predictive image-based marker of benefit to chemotherapy by combining intra- and peri-tumor gradient entropy
and biophysical deformation attributes from “normal” brain parenchyma. Similarly, (NeuRisC)diagnose will be
developed in Aim 2 by including lesion and peri-lesional features from pre- and multiple post-treatment MRIs, to
improve discrimination of radiation effects and tumor recurrence. Overcoming limitations in previous work
pertaining to small cohorts & lack of spatially mapped ex-vivo histology, NeuRisC modules will be validated on
a large multi-institutional cohort of >1000 studies with co-localized tissue and MRI scans obtained across multiple
biopsies/lesion. This cohort will also allow for establishing associations of NeuRisC features with underlying
histological/molecular tumor characteristics - a prerequisite for clinical adoption. Lastly, NeuRisC modules will
be deployed at Northeast Ohio & Tennessee VA Healthcare Systems to validate their utility as decision support.
On an independent cohort of N=200 MRIs from Veteran patients, interpretation results from oncologists and
radiologists at these two VA sites will be compared, with and without NeuRisC, to evaluate added benefit of
NeuRisC as decision support. Criteria for success will be to demonstrate that NeuRisC is able to (a) predict
GBM patients that respond favorably to chemoradiation with >90% accuracy, and (b) is non-inferior to accuracy
for invasive biopsies (85-90% accuracy), thereby avoiding biopsies in patients with a benign radiation effect.
摘要:2020年,美国将有超过23,000名患者被诊断为胶质母细胞瘤(GBM),这是一种高度恶性肿瘤
项目成果
期刊论文数量(0)
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Pallavi Tiwari其他文献
Pallavi Tiwari的其他文献
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{{ truncateString('Pallavi Tiwari', 18)}}的其他基金
RadxTools for assessing tumor treatment response on imaging
用于评估影像学肿瘤治疗反应的 RadxTools
- 批准号:
10477947 - 财政年份:2020
- 资助金额:
-- - 项目类别:
RadxTools for assessing tumor treatment response on imaging
用于评估影像学肿瘤治疗反应的 RadxTools
- 批准号:
10206077 - 财政年份:2020
- 资助金额:
-- - 项目类别:
RadxTools for assessing tumor treatment response on imaging
用于评估影像学肿瘤治疗反应的 RadxTools
- 批准号:
10593646 - 财政年份:2020
- 资助金额:
-- - 项目类别:
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