Cellular, molecular and quantitative imaging analysis of screening-detected lung adenocarcinoma

筛查发现的肺腺癌的细胞、分子和定量成像分析

• 批准号: 10253260
• 负责人: Fabien Maldonado
• 金额: $ 68.16万
• 依托单位: VANDERBILT UNIVERSITY MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-24 至 2022-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10253260
• 关键词: Academic Medical Centers; Adenocarcinoma; Adjuvant; Adjuvant Therapy; Aggressive behavior; Area; Behavior; Benign; Bioinformatics; Biological; Biological Markers; Biometry; Biopsy; Blood; Cancer Biology; Cancer Center; Cells; Cellular biology; Clinical; Clinical Data; Clinical Management; Collaborations; Country; DNA analysis; Data Element; Databases; Discrimination; Disease; Early Detection Research Network; Environment; Epidemiology; Fertilization; Freezing; Funding; Future; Genes; Genomics; Goals; Health Care Costs; Heterogeneity; Image; Image Analysis; Indolent; Institution; Intervention; Investigation; Laboratories; Light; Lung Adenocarcinoma; Lung nodule; Malignant - descriptor; Malignant Neoplasms; Malignant neoplasm of lung; Measurement; Medical center; Modeling; Molecular; Molecular Analysis; Morbidity - disease rate; Natural History; Nodule; Operative Surgical Procedures; Pathology; Patients; Phenotype; Positioning Attribute; Procedures; Prospective Studies; Proteomics; Provider; Pulmonology; Research; Research Personnel; Resected; Risk; Science; Solid; Standardization; Structure; Technology; Testing; Tissues; Work; base; cancer cell; cancer risk; cellular imaging; chemotherapy; circulating DNA; circulating biomarkers; cohort; design; follow-up; functional genomics; high throughput screening; improved; interdisciplinary approach; low dose computed tomography; lung cancer screening; molecular imaging; molecular marker; mortality risk; multidimensional data; multidisciplinary; new technology; novel; overtreatment; phenotypic biomarker; predictive modeling; programs; prospective; public health relevance; quantitative imaging; repository; screening; screening program; single cell analysis; standard of care; synergism; tumor; tumor DNA; tumor behavior; tumor heterogeneity; tumor microenvironment

 DESCRIPTION (provided by applicant): Suspicious screening-detected lung nodules present a formidable challenge to patients and their providers. The standard of care lacks accuracy in predicting a) malignancy from benign disease and b) indolent vs aggressive behavior. The answer to these questions justifies diametrically opposed strategies (biopsy vs follow up) each of which carries huge consequences including cure of the cancer, risk of death during a procedure or risk of dying from not intervening early in the disease. This application will focus o the behavior of early stage adenocarcinoma of the lung and not on the distinction between benign from malignant nodules. We assembled a unique multidisciplinary group of experts to tackle this problem in an original way. We will develop a retrospective and a prospective repository for both tissue (ADC fresh frozen tissues, blood) and images from which we will derive detailed quantitative structural imaging analysis, targeted genomic analysis and single cell analysis to interrogate the functional genomics of these tumors. The integration of this multidimensional data imaging/molecular/cellular/epidemiology will allow us to identify and validate cellular and molecular determinants of tumor behavior in the context of their inter- and intra-tumor heterogeneity. With these results, we will be able to build integrated models of ADC behavior, validate a new genomic molecular test on circulating DNA and propose prospective studies that would eventually offer a different intervention based on these predictions i.e. surgery, vs no surgery, adjuvant immuno- or chemotherapy vs no adjuvant therapy and therefore reduce overtreatment and ultimately increase the rate of cure and reduce healthcare cost.



项目成果

Lung Cancer Detection using Co-learning from Chest CT Images and Clinical Demographics.

利用胸部 CT 图像和临床人口统计数据的共同学习进行肺癌检测。

• DOI: 10.1117/12.2512965
• 发表时间: 2019
• 期刊: Proceedings of SPIE--the International Society for Optical Engineering
• 作者: Wang,Jiachen;Gao,Riqiang;Huo,Yuankai;Bao,Shunxing;Xiong,Yunxi;Antic,SanjaL;Osterman,TravisJ;Massion,PierreP;Landman,BennettA
• 通讯作者: Landman,BennettA

Radial gradient and radial deviation radiomic features from pre-surgical CT scans are associated with survival among lung adenocarcinoma patients.

• DOI: 10.18632/oncotarget.21629
• 发表时间: 2017-11-10
• 期刊: Oncotarget
• 作者: Tunali I;Stringfield O;Guvenis A;Wang H;Liu Y;Balagurunathan Y;Lambin P;Gillies RJ;Schabath MB
• 通讯作者: Schabath MB

Quantitative computed tomographic descriptors associate tumor shape complexity and intratumor heterogeneity with prognosis in lung adenocarcinoma.

• DOI: 10.1371/journal.pone.0118261
• 发表时间: 2015
• 期刊: PloS one
• 影响因子: 3.7
• 作者: Grove O;Berglund AE;Schabath MB;Aerts HJ;Dekker A;Wang H;Velazquez ER;Lambin P;Gu Y;Balagurunathan Y;Eikman E;Gatenby RA;Eschrich S;Gillies RJ
• 通讯作者: Gillies RJ

Early Detection of Molecular Residual Disease in Localized Lung Cancer by Circulating Tumor DNA Profiling.

• DOI: 10.1158/2159-8290.cd-17-0716
• 发表时间: 2017-12
• 期刊: Cancer discovery
• 影响因子: 28.2
• 作者: Chaudhuri AA;Chabon JJ;Lovejoy AF;Newman AM;Stehr H;Azad TD;Khodadoust MS;Esfahani MS;Liu CL;Zhou L;Scherer F;Kurtz DM;Say C;Carter JN;Merriott DJ;Dudley JC;Binkley MS;Modlin L;Padda SK;Gensheimer MF;West RB;Shrager JB;Neal JW;Wakelee HA;Loo BW Jr;Alizadeh AA;Diehn M
• 通讯作者: Diehn M

Prediction of pathological nodal involvement by CT-based Radiomic features of the primary tumor in patients with clinically node-negative peripheral lung adenocarcinomas.

基于 CT 的临床淋巴结阴性周围型肺腺癌患者原发肿瘤的放射组学特征预测病理淋巴结受累

• DOI: 10.1002/mp.12901
• 发表时间: 2018-06
• 期刊: Medical physics
• 影响因子: 3.8
• 作者: Liu Y;Kim J;Balagurunathan Y;Hawkins S;Stringfield O;Schabath MB;Li Q;Qu F;Liu S;Garcia AL;Ye Z;Gillies RJ
• 通讯作者: Gillies RJ