Altered metabolism and machine learning for pancreatic cancer early detection
改变新陈代谢和机器学习以实现胰腺癌早期检测
基本信息
- 批准号:10705708
- 负责人:
- 金额:$ 81.16万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2022
- 资助国家:美国
- 起止时间:2022-09-16 至 2027-08-31
- 项目状态:未结题
- 来源:
- 关键词:AddressArchitectureAreaAtrophicBiological MarkersBloodBlood specimenCancer CenterCancer DetectionCancer EtiologyCessation of lifeClinicalClinical DataCollaborationsCommunitiesComputerized Medical RecordDataData CollectionData SetDedicationsDetectionDevelopmentDiagnosisDiameterDiseaseDuct (organ) structureEarly DiagnosisEarly identificationEventExcisionExocrine pancreatic insufficiencyFecesFingerprintFutureGeneral PopulationGeneticGenetic RiskGoalsHumanImageImmune responseIncidenceIndividualLiverLocalized DiseaseLocationMachine LearningMalignant NeoplasmsMalignant neoplasm of pancreasMeasuresMetabolicMetabolic dysfunctionMetabolismMetastatic Neoplasm to the LiverMethylationModelingMuscular AtrophyNeoplasm MetastasisOperative Surgical ProceduresOrganPancreasPancreatectomyPancreatic CystPancreatic Ductal AdenocarcinomaPancreatic cystic neoplasiaPancreatic ductPatient-Focused OutcomesPatientsPeptide HydrolasesPerformancePeripheralPopulationProbabilityRecurrenceRecurrent Malignant NeoplasmRecurrent diseaseResearchRiskRisk AssessmentRoleSamplingSampling StudiesScreening for cancerTechniquesTechnologyTerminologyTestingTimeTissuesTranslatingTumor TissueUnited StatesWorkX-Ray Computed Tomographybiobankbiomarker identificationbiomarker validationcancer biomarkerscancer recurrencecarcinogenesiscell free DNAchronic pancreatitisclinical implementationdata infrastructuredata modelingdata resourcedetection testearly onsetexperiencefederated learninghigh riskhigh risk populationimaging approachimaging studyimprovedimproved outcomeinnovationmachine learning modelmachine learning predictionmethylation patternmortalitymouse modelnanosensorsnovel strategiespancreas developmentpancreatic ductal adenocarcinoma modelpancreatic neoplasmpatient populationpatient subsetsrisk predictionrisk stratificationsample collectionscreeningstandard of carestool samplestructured datasurveillance imagingtumorunstructured data
项目摘要
PROJECT SUMMARY
Pancreatic cancer is the 3rd leading cause of cancer death in the United States. The high mortality of
pancreatic ductal adenocarcinoma (PDAC) is largely a consequence of diagnosis at an advanced stage when
the tumor is no longer treatable for cure. Currently, asymptomatic screening for PDAC is not recommended for
the general population, with screening pursued only for a small subset of patients with pancreatic cystic lesions
or strong genetic risk for PDAC. Even when cancer is identified early, patients can have rapid recurrence after
surgical resection, most often with liver metastases. To improve outcomes for patients with PDAC, a number of
important advancements are urgently needed, including improved risk assessment to identify those at elevated
risk for PDAC, new non-invasive biomarkers to select individuals for intensive imaging surveillance, and better
strategies to identify those with localized tumors who are at risk for rapid recurrence after surgical resection. In
the current proposal, we directly address these critical areas of need, focusing on: (a) machine learning models
for risk assessment from electronic medical record data (Aim 1), (b) development of non-invasive biomarkers
from stool and computed tomography (CT) imaging that measure metabolic alterations caused by early PDAC
(Aim 2), and (c) characterization of CT imaging and circulating cell-free DNA methylation patterns to predict
presence of occult metastases at the time of surgical resection (Aim 3). Furthermore, we will collect and make
available clinical data, blood samples, stool samples, imaging studies, and tumor tissue from multiple patient
populations critical to PDAC early detection research, including those with early-stage PDAC, chronic
pancreatitis, genetic PDAC risk, pancreatic cystic lesions, and non-cancer controls (Aim 4). To accomplish the
proposed work, we have assembled a highly experienced and collaborative team that is fully committed to
working together and with other Pancreatic Cancer Detection Consortium units. Thus, we will leverage cutting-
edge machine learning approaches, develop multiple innovative, non-invasive biomarker technologies, and
collect a large array of data and clinical samples for collaborative activities within and outside the Pancreatic
Cancer Detection Consortium. With a highly dedicated expert team and clear scientific plan, we expect to
achieve our near-term goal of reducing pancreatic cancer mortality by finding PDAC earlier and treating it more
effectively for cure.
项目总结
项目成果
期刊论文数量(12)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Comprehensive human cell-type methylation atlas reveals origins of circulating cell-free DNA in health and disease.
- DOI:10.1038/s41467-018-07466-6
- 发表时间:2018-11-29
- 期刊:
- 影响因子:16.6
- 作者:Moss J;Magenheim J;Neiman D;Zemmour H;Loyfer N;Korach A;Samet Y;Maoz M;Druid H;Arner P;Fu KY;Kiss E;Spalding KL;Landesberg G;Zick A;Grinshpun A;Shapiro AMJ;Grompe M;Wittenberg AD;Glaser B;Shemer R;Kaplan T;Dor Y
- 通讯作者:Dor Y
Association Between Polycystic Ovary Syndrome and Risk of Pancreatic Cancer.
多囊卵巢综合症与胰腺癌风险之间的关联。
- DOI:10.1001/jamaoncol.2022.4540
- 发表时间:2022
- 期刊:
- 影响因子:28.4
- 作者:Peeri,NoahC;Landicino,MarcoV;Saldia,CAmethyst;Kurtz,RobertC;Rolston,VineetS;Du,Mengmeng
- 通讯作者:Du,Mengmeng
Liquid biopsy reveals collateral tissue damage in cancer.
- DOI:10.1172/jci.insight.153559
- 发表时间:2022-01-25
- 期刊:
- 影响因子:8
- 作者:Lubotzky A;Zemmour H;Neiman D;Gotkine M;Loyfer N;Piyanzin S;Ochana BL;Lehmann-Werman R;Cohen D;Moss J;Magenheim J;Loftus MF;Brais L;Ng K;Mostoslavsky R;Wolpin BM;Zick A;Maoz M;Grinshpun A;Kustanovich A;Makranz C;Cohen JE;Peretz T;Hubert A;Temper M;Salah A;Avniel-Polak S;Grozinsky-Glasberg S;Spalding KL;Rokach A;Kaplan T;Glaser B;Shemer R;Dor Y
- 通讯作者:Dor Y
Reply to the letter to the editor 'Borderline resectable pancreatic cancer: an evolving concept' by Petrucciani et al.
回复 Petrucciani 等人给编辑的信“边缘性可切除胰腺癌:一个不断发展的概念”。
- DOI:10.1093/annonc/mdx273
- 发表时间:2017
- 期刊:
- 影响因子:0
- 作者:Gilbert,JW;Wolpin,B;Clancy,T;Wang,J;Mamon,H;Shinagare,AB;Jagannathan,J;Rosenthal,M
- 通讯作者:Rosenthal,M
Islet cells share promoter hypomethylation independently of expression, but exhibit cell-type-specific methylation in enhancers.
- DOI:10.1073/pnas.1713736114
- 发表时间:2017-12-19
- 期刊:
- 影响因子:11.1
- 作者:Neiman D;Moss J;Hecht M;Magenheim J;Piyanzin S;Shapiro AMJ;de Koning EJP;Razin A;Cedar H;Shemer R;Dor Y
- 通讯作者:Dor Y
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Michael H. Rosenthal其他文献
Diagnosis and management of duodenal adenocarcinomas: a comprehensive review for the radiologist
- DOI:
10.1007/s00261-014-0309-4 - 发表时间:
2014-11-27 - 期刊:
- 影响因子:2.200
- 作者:
Chong Hyun Suh;Sree Harsha Tirumani;Atul B. Shinagare;Kyung Won Kim;Michael H. Rosenthal;Nikhil H. Ramaiya;Akshay D. Baheti - 通讯作者:
Akshay D. Baheti
An Aggressive Presentation of Colorectal Cancer With an Atypical Lymphoproliferative Pattern of Metastatic Disease: A Case Report and Review of the Literature
- DOI:
10.1016/j.clcc.2014.05.002 - 发表时间:
2014-09-01 - 期刊:
- 影响因子:
- 作者:
Sonali M. Shah;Michael H. Rosenthal;Gabriel K. Griffin;Eric D. Jacobsen;Nadine J. McCleary - 通讯作者:
Nadine J. McCleary
568 ASSOCIATION OF VISCERAL ADIPOSITY WITH INCIDENT AND RECURRENT DIVERTICULITIS IN AN ELECTRONIC HEALTH RECORDBASED COHORT STUDY
- DOI:
10.1016/s0016-5085(24)00800-x - 发表时间:
2024-05-18 - 期刊:
- 影响因子:
- 作者:
Jane Ha;Christopher P. Bridge;Katherine P. Andriole;Avinash Kambadakone;Florian J. Fintelmann;Michael H. Rosenthal;Randy L. Gollub;Edward Giovannucci;Lisa L. Strate;Wenjie Ma;Andrew T. Chan - 通讯作者:
Andrew T. Chan
The management of retroperitoneal lymphadenopathy in spermatocytic seminoma of the testicle
- DOI:
10.1016/j.clinimag.2013.11.006 - 发表时间:
2014-03-01 - 期刊:
- 影响因子:
- 作者:
Farhana Sharmeen;Michael H. Rosenthal;Stephanie A.H. Howard - 通讯作者:
Stephanie A.H. Howard
Michael H. Rosenthal的其他文献
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{{ truncateString('Michael H. Rosenthal', 18)}}的其他基金
Altered metabolism and machine learning for pancreatic cancer early detection
改变新陈代谢和机器学习以实现胰腺癌早期检测
- 批准号:
10526719 - 财政年份:2022
- 资助金额:
$ 81.16万 - 项目类别:
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