RSI-AI: Predicting clinically significant prostate cancer to guide biopsy decisions by combining advanced tissue microstructure imaging with deep learning
RSI-AI: Predicting clinically significant prostate cancer to guide biopsy decisions by combining advanced tissue microstructure imaging with deep learning
批准号:
10254808
负责人:
Nathan Scott White
金额:
$25.17万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2021
资助国家:
美国
项目状态:
已结题
起止时间:
2021-08-15 至 2023-07-31
关键词:
3-DimensionalAffectAgeAlgorithmsBiopsyBlindedClassificationClinicalClinical DataClinical TreatmentCommunitiesComputer softwareDataData SetDetectionDiagnosisDiagnosticDiffusion Magnetic Resonance ImagingEarly DiagnosisFamilyFutureGeneticGlandGleason Grade for Prostate CancerGoalsHealth Care CostsImageImaging TechniquesIndolentLabelLegal patentLesionLocationMagnetic Resonance ImagingMalignant - descriptorMalignant NeoplasmsMalignant neoplasm of prostateMedical DeviceMethodsModelingOutputPSA screeningPathologicPathologyPerformancePhaseProductionProstateQuality of lifeRadical ProstatectomyRadiology SpecialtyReaderRecording of previous eventsRestriction Spectrum ImagingRisk AssessmentRisk FactorsScanningScreening for Prostate CancerSensitivity and SpecificitySystemTestingTissuesTrainingUnited StatesUpdateValidationaccurate diagnosisbasecancer siteclinical predictorsclinical riskclinically significantcloud platformcommercializationconvolutional neural networkdeep learningdemographicsimaging modalityimprovedinnovationmenneural networkneural network architecturenoninvasive diagnosisnovelovertreatmentprospectiveprostate biopsyprostate cancer riskprostate lesionsradiologistrisk prediction modelsafety netsoftware developmentspectrographtooltumorusabilityvirtual biopsywastingwater diffusion
中文摘要
前列腺活组织检查对前列腺癌的诊断至关重要,但通常不清楚是谁
英文摘要
Prostate biopsies are critical for the diagnosis of prostate cancer, but it is often unclear who
should be biopsied and where in the gland the biopsy should be targeted. This results in missed
diagnoses, unnecessary biopsies, and overdiagnosis and overtreatment of cancer that is not life
threatening. The goal of this proposal is to develop a set of quantitative and non-invasive tools,
RSI-AI and RSI-AI+, to help clinicians determine who should be biopsied for prostate cancer
and the locations of clinically significant lesions. RSI-AI uses deep learning to predict the
location and pathological grade of prostate cancer lesions from restriction spectrum imaging
(RSI) data. RSI is an advanced diffusion magnetic resonance imaging (MRI) technique that
models the restricted diffusion of water molecules to improve microtissue classification and
tumor detection. By utilizing RSI data in the deep learning model, RSI-AI will produce
pathological grade predictions that are more accurate than models trained with conventional MRI
data. RSI-AI+ integrates the pathological grade predictions from RSI-AI with clinical data
including age, family history, genetics, and prostate volume to accurately and comprehensively
quantify current and future risk for prostate cancer. Phase I of this proposal will develop and
validate the RSI-AI and RSI-AI+ models and compare their performance to models trained with
conventional MRI data. Phase II of this proposal will deploy RSI-AI and RSI-AI+ to the
Cortechs cloud platform, demonstrate their clinical usability and utility, and generate the
materials required for a 510K FDA submission. The clinical software generated through this
proposal will ultimately improve diagnostic yields, reduce unnecessary biopsies and
overtreatment of indolent prostate cancer, while facilitating early detection and appropriate
treatment of clinically significant prostate cancer
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RSI-AI: Predicting clinically significant prostate cancer to guide biopsy decisions by combining advanced tissue microstructure imaging with deep learning
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批准号:10896571
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项目类别:
-
资助金额:$73.58万
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财政年份:2021
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负责人:Nathan Scott White
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依托单位:
Towards a Virtual Biopsy: An improved multimodal imaging biomarker to guide treatment decisions in neuro-oncology by combining advanced tissue microstructure imaging with deep learning
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批准号:10325327
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项目类别:
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资助金额:$25.47万
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财政年份:2021
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负责人:Nathan Scott White
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依托单位:
海外基金