Program Official

Principal Investigator

John J
Heine
Awardee Organization

H. Lee Moffitt Cancer Ctr & Res Inst
United States

Fiscal Year
2024
Activity Code
U01
Early Stage Investigator Grants (ESI)
Not Applicable
Project End Date

Quantitative Imaging Clinical Validation Center at Moffitt Cancer Center

An overarching goal of cancer screening is to detect cancer at an early stage while it is localized, treatable, and curable. However, cancer screening is associated with false positives, high rates of indeterminate findings, overdiagnosis, and overtreatment, which are serious limitations that need to be addressed to improve early detection efforts. Because medical imaging is a key component of early detection for many cancers, quantitative imaging/radiomics can provide biomarkers to address many of these limitations with early detection. Our group, Quantitative Imaging Clinical Validation Center at Moffitt Cancer Center (QICVC-MCC), helped pioneer image biomarker approaches leveraged in the prior funding cycle to create the first and only EDRN Clinical Validation Center (CVC) dedicated to the validation of image biomarkers. For breast cancer, we validated several breast density-type risk markers and diagnostic models in women classified as BI-RADS 4, noting the three subcategories within this classification were strong diagnostic markers, and constructed a bio-image repository for this subgroup. For lung cancer, we conducted extensive studies applying conventional radiomics for risk prediction, discrimination between malignant and benign nodules, distinguishing between indolent and aggressive lung cancers, predicting tumor mutations, and predicting treatment response. In this renewal, we will expand our CVC from validated conventional feature-based radiomics as a benchmark to compare end-to-end deep learning (DL) methods, expand to other populations, and implement AI platforms for analyzing breast, lung, and other organ site images. In breast imaging (Aim 1), we will expand our efforts from parametric modeling to machine learning/DL for improved risk, early detection, and diagnostic predictions and continue our data repository developments. In lung imaging (Aim 2), we will expand our efforts from lung cancer screening to incidentally detected nodules and surgically resected early-stage lung cancer. Additionally, in Aim 3 we will seek out additional opportunities within the EDRN to conduct studies of image biomarkers in other organ sites beyond breast and lung (e.g., prostate, pancreas, and cutaneous) to address emerging Network objectives. The EDRN has proven that it is greater than the sum of the individual projects. As such, in Aim 4 we propose to build a repository for the housing and sharing of images, algorithms, radiomics, clinical data, and information on biospecimens. In this CVC renewal, we will systematically validate radiomic features and novel image metrics in the early detection of cancer. This research is significant because such information may be able to complement existing clinical guidelines and lead to new strategies to apply noninvasive image biomarkers. The research of the QICVC-MCC is performed at an NCI-Designated Comprehensive Cancer Center, which is an outstanding environment to conduct such studies given the access to large patient populations and outstanding resources, and the clinical setting to deploy such biomarkers for improved personalized cancer care.

Publications

  • Lu H, Kim J, Qi J, Li Q, Liu Y, Schabath MB, Ye Z, Gillies RJ, Balagurunathan Y. Multi-Window CT Based Radiological Traits for Improving Early Detection in Lung Cancer Screening. Cancer management and research. 2020 Nov 27;12:12225-12238. doi: 10.2147/CMAR.S246609. eCollection 2020. PMID: 33273859
  • Heine J, Fowler EEE, Weinfurtner RJ, Hume E, Tworoger SS. Breast Density Analysis Using Digital Breast Tomosynthesis. bioRxiv : the preprint server for biology. 2023 Feb 16. PMID: 36824710
  • Alahmari SS, Cherezov D, Goldgof D, Hall L, Gillies RJ, Schabath MB. Delta Radiomics Improves Pulmonary Nodule Malignancy Prediction in Lung Cancer Screening. IEEE access : practical innovations, open solutions. 2018;6:77796-77806. Epub 2018 Nov 29. PMID: 30607311
  • Avasthi KK, Choi J, Glushko T, Manley BJ, Yu A, Pow-Sang J, Gatenby R, Wang L, Balagurunathan Y. Extracellular microvesicle microRNAs, along with imaging metrics, improve detection of aggressive prostate cancer. medRxiv : the preprint server for health sciences. 2024 Aug 23. PMID: 39228742
  • Azam S, Peng C, Rosner BA, Goncalves MD, Phillips E, Eliassen H, Heine J, Hankinson SE, Tamimi RM. Plasma C-peptide mammographic features and risk of breast cancer. NPJ breast cancer. 2024 Oct 17;10(1):91. PMID: 39420200
  • Reyes ME, Schabath MB. Optimal lung cancer screening intervals following a negative low-dose computed tomography result. Journal of thoracic disease. 2019 Sep;11(Suppl 15):S1916-S1918. PMID: 31632785
  • Lu H, Mu W, Balagurunathan Y, Qi J, Abdalah MA, Garcia AL, Ye Z, Gillies RJ, Schabath MB. Multi-window CT based Radiomic signatures in differentiating indolent versus aggressive lung cancers in the National Lung Screening Trial: a retrospective study. Cancer imaging : the official publication of the International Cancer Imaging Society. 2019 Jun 28;19(1):45. PMID: 31253194
  • Bi WL, Hosny A, Schabath MB, Giger ML, Birkbak NJ, Mehrtash A, Allison T, Arnaout O, Abbosh C, Dunn IF, Mak RH, Tamimi RM, Tempany CM, Swanton C, Hoffmann U, Schwartz LH, Gillies RJ, Huang RY, Aerts HJWL. Artificial intelligence in cancer imaging: Clinical challenges and applications. CA: a cancer journal for clinicians. 2019 Mar;69(2):127-157. Epub 2019 Feb 5. PMID: 30720861
  • Fowler EEE, Hathaway C, Tillman F, Weinfurtner R, Sellers TA, Heine J. Spatial Correlation and Breast Cancer Risk. Biomedical physics & engineering express. 2019 Jul;5. (4). Epub 2019 May 22. PMID: 33304615
  • Schabath MB, Cote ML. Cancer Progress and Priorities: Lung Cancer. Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology. 2019 Oct;28(10):1563-1579. PMID: 31575553
  • Moreno S, Bonfante M, Zurek E, Cherezov D, Goldgof D, Hall L, Schabath M. A Radiogenomics Ensemble to Predict EGFR and KRAS Mutations in NSCLC. Tomography (Ann Arbor, Mich.). 2021 Apr 29;7(2):154-168. PMID: 33946756
  • Fowler EEE, Smallwood A, Miltich C, Drukteinis J, Sellers TA, Heine J. Generalized breast density metrics. Physics in medicine and biology. 2018 Dec 19;64(1):015006. PMID: 30523909
  • Cherezov D, Hawkins S, Goldgof D, Hall L, Balagurunathan Y, Gillies RJ, Schabath MB. Improving malignancy prediction through feature selection informed by nodule size ranges in NLST. Conference proceedings. IEEE International Conference on Systems, Man, and Cybernetics. 2016 Oct;2016:001939-1944. Epub 2017 Feb 9. PMID: 30473607
  • Yaghjyan L, Wang Z, Warner ET, Rosner B, Heine J, Tamimi RM. Reproductive Factors Related to Childbearing and a Novel Automated Mammographic Measure, V. Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology. 2024 Jun 3;33(6):804-811. PMID: 38497795
  • Mu W, Schabath MB, Gillies RJ. Images Are Data: Challenges and Opportunities in the Clinical Translation of Radiomics. Cancer research. 2022 Jun 6;82(11):2066-2068. PMID: 35661199
  • Fowler EE, Smallwood A, Khan N, Miltich C, Drukteinis J, Sellers TA, Heine J. Calibrated Breast Density Measurements. Academic radiology. 2019 Sep;26(9):1181-1190. Epub 2018 Dec 10. PMID: 30545682
  • Fowler EEE, Smallwood AM, Khan NZ, Kilpatrick K, Sellers TA, Heine J. Technical challenges in generalizing calibration techniques for breast density measurements. Medical physics. 2019 Feb;46(2):679-688. Epub 2019 Jan 11. PMID: 30525207
  • Cherezov D, Goldgof D, Hall L, Gillies R, Schabath M, Müller H, Depeursinge A. Revealing Tumor Habitats from Texture Heterogeneity Analysis for Classification of Lung Cancer Malignancy and Aggressiveness. Scientific reports. 2019 Mar 14;9(1):4500. PMID: 30872600
  • Cherezov D, Hawkins SH, Goldgof DB, Hall LO, Liu Y, Li Q, Balagurunathan Y, Gillies RJ, Schabath MB. Delta radiomic features improve prediction for lung cancer incidence: A nested case-control analysis of the National Lung Screening Trial. Cancer medicine. 2018 Dec;7(12):6340-6356. Epub 2018 Dec 1. PMID: 30507033
  • Schabath MB, Aberle DR. MILD trial, strong confirmation of lung cancer screening efficacy. Nature reviews. Clinical oncology. 2019 Sep;16(9):529-530. PMID: 31118491
  • Braithwaite D, Karanth SD, Slatore CG, Zhang D, Bian J, Meza R, Jeon J, Tammemagi M, Schabath M, Wheeler M, Guo Y, Hochhegger B, Kaye FJ, Silvestri GA, Gould MK. Personalised Lung Cancer Screening (PLuS) study to assess the importance of coexisting chronic conditions to clinical practice and policy: protocol for a multicentre observational study. BMJ open. 2022 Jun 22;12(6):e064142. PMID: 35732383
  • Tunali I, Hall LO, Napel S, Cherezov D, Guvenis A, Gillies RJ, Schabath MB. Stability and reproducibility of computed tomography radiomic features extracted from peritumoral regions of lung cancer lesions. Medical physics. 2019 Nov;46(11):5075-5085. Epub 2019 Sep 23. PMID: 31494946
  • Gillies RJ, Schabath MB. Radiomics Improves Cancer Screening and Early Detection. Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology. 2020 Dec;29(12):2556-2567. Epub 2020 Sep 11. PMID: 32917666
  • Tunali I, Tan Y, Gray JE, Katsoulakis E, Eschrich SA, Saller J, Aerts HJWL, Boyle T, Qi J, Guvenis A, Gillies RJ, Schabath MB. Hypoxia-Related Radiomics and Immunotherapy Response: A Multicohort Study of Non-Small Cell Lung Cancer. JNCI cancer spectrum. 2021 May 13;5. (4). doi: 10.1093/jncics/pkab048. eCollection 2021 Aug. PMID: 34409252
  • Liu Y, Wang H, Li Q, McGettigan MJ, Balagurunathan Y, Garcia AL, Thompson ZJ, Heine JJ, Ye Z, Gillies RJ, Schabath MB. Radiologic Features of Small Pulmonary Nodules and Lung Cancer Risk in the National Lung Screening Trial: A Nested Case-Control Study. Radiology. 2018 Jan;286(1):298-306. Epub 2017 Aug 24. PMID: 28837413
  • Paul R, Liu Y, Li Q, Hall L, Goldgof D, Balagurunathan Y, Schabath M, Gillies R. Representation of Deep Features using Radiologist defined Semantic Features. Proceedings of ... International Joint Conference on Neural Networks. International Joint Conference on Neural Networks. 2018 Jul;2018. Epub 2018 Sep 15. PMID: 30443437
  • Paul R, Hall L, Goldgof D, Schabath M, Gillies R. Predicting Nodule Malignancy using a CNN Ensemble Approach. Proceedings of ... International Joint Conference on Neural Networks. International Joint Conference on Neural Networks. 2018 Jul;2018. Epub 2018 Oct 15. PMID: 30443438
  • Mu W, Jiang L, Zhang J, Shi Y, Gray JE, Tunali I, Gao C, Sun Y, Tian J, Zhao X, Sun X, Gillies RJ, Schabath MB. Non-invasive decision support for NSCLC treatment using PET/CT radiomics. Nature communications. 2020 Oct 16;11(1):5228. PMID: 33067442
  • Li Q, Balagurunathan Y, Liu Y, Qi J, Schabath MB, Ye Z, Gillies RJ. Comparison Between Radiological Semantic Features and Lung-RADS in Predicting Malignancy of Screen-Detected Lung Nodules in the National Lung Screening Trial. Clinical lung cancer. 2018 Mar;19(2):148-156.e3. Epub 2017 Oct 13. PMID: 29137847
  • Pérez-Morales J, Lu H, Mu W, Tunali I, Kutuk T, Eschrich SA, Balagurunathan Y, Gillies RJ, Schabath MB. Volume doubling time and radiomic features predict tumor behavior of screen-detected lung cancers. Cancer biomarkers : section A of Disease markers. 2022;33(4):489-501. PMID: 35491768
  • Tunali I, Gray JE, Qi J, Abdalah M, Jeong DK, Guvenis A, Gillies RJ, Schabath MB. Novel clinical and radiomic predictors of rapid disease progression phenotypes among lung cancer patients treated with immunotherapy: An early report. Lung cancer (Amsterdam, Netherlands). 2019 Mar;129:75-79. Epub 2019 Jan 23. PMID: 30797495
  • Balagurunathan Y, Schabath MB, Wang H, Liu Y, Gillies RJ. Quantitative Imaging features Improve Discrimination of Malignancy in Pulmonary nodules. Scientific reports. 2019 Jun 12;9(1):8528. PMID: 31189944
  • Paul R, Schabath M, Balagurunathan Y, Liu Y, Li Q, Gillies R, Hall LO, Goldgof DB. Explaining Deep Features Using Radiologist-Defined Semantic Features and Traditional Quantitative Features. Tomography (Ann Arbor, Mich.). 2019 Mar;5(1):192-200. PMID: 30854457
  • Tomaszewski MR, Latifi K, Boyer E, Palm RF, El Naqa I, Moros EG, Hoffe SE, Rosenberg SA, Frakes JM, Gillies RJ. Delta radiomics analysis of Magnetic Resonance guided radiotherapy imaging data can enable treatment response prediction in pancreatic cancer. Radiation oncology (London, England). 2021 Dec 15;16(1):237. PMID: 34911546
  • Tunali I, Stringfield O, Guvenis A, Wang H, Liu Y, Balagurunathan Y, Lambin P, Gillies RJ, Schabath MB. Radial gradient and radial deviation radiomic features from pre-surgical CT scans are associated with survival among lung adenocarcinoma patients. Oncotarget. 2017 Oct 6;8(56):96013-96026. doi: 10.18632/oncotarget.21629. eCollection 2017 Nov 10. PMID: 29221183
  • McNitt-Gray M, Napel S, Jaggi A, Mattonen SA, Hadjiiski L, Muzi M, Goldgof D, Balagurunathan Y, Pierce LA, Kinahan PE, Jones EF, Nguyen A, Virkud A, Chan HP, Emaminejad N, Wahi-Anwar M, Daly M, Abdalah M, Yang H, Lu L, Lv W, Rahmim A, Gastounioti A, Pati S, Bakas S, Kontos D, Zhao B, Kalpathy-Cramer J, Farahani K. Standardization in Quantitative Imaging: A Multicenter Comparison of Radiomic Features from Different Software Packages on Digital Reference Objects and Patient Data Sets. Tomography (Ann Arbor, Mich.). 2020 Jun;6(2):118-128. PMID: 32548288
  • Paul R, Hawkins SH, Schabath MB, Gillies RJ, Hall LO, Goldgof DB. Predicting malignant nodules by fusing deep features with classical radiomics features. Journal of medical imaging (Bellingham, Wash.). 2018 Jan;5(1):011021. Epub 2018 Mar 21. PMID: 29594181
  • Ho WLJ, Fetisov N, Hall LO, Goldgof D, Schabath MB. Evaluating clinical and radiomic features for predicting lung cancer recurrence pre- and post-tumor resection. Proceedings of SPIE--the International Society for Optical Engineering. 2024 Feb;12926. Epub 2024 Apr 2. PMID: 38993353
  • Cherezov D, Paul R, Fetisov N, Gillies RJ, Schabath MB, Goldgof DB, Hall LO. Lung Nodule Sizes Are Encoded When Scaling CT Image for CNN's. Tomography (Ann Arbor, Mich.). 2020 Jun;6(2):209-215. PMID: 32548298
  • Paul R, Shafiq-Ul Hassan M, Moros EG, Gillies RJ, Hall LO, Goldgof DB. Deep Feature Stability Analysis Using CT Images of a Physical Phantom Across Scanner Manufacturers, Cartridges, Pixel Sizes, and Slice Thickness. Tomography (Ann Arbor, Mich.). 2020 Jun;6(2):250-260. PMID: 32548303
  • Lu H, Parra NA, Qi J, Gage K, Li Q, Fan S, Feuerlein S, Pow-Sang J, Gillies R, Choi JW, Balagurunathan Y. Repeatability of Quantitative Imaging Features in Prostate Magnetic Resonance Imaging. Frontiers in oncology. 2020 May 7;10:551. doi: 10.3389/fonc.2020.00551. eCollection 2020. PMID: 32457827
  • Balagurunathan Y, Mitchell R, El Naqa I. Requirements and reliability of AI in the medical context. Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB). 2021 Mar;83:72-78. Epub 2021 Mar 13. PMID: 33721700
  • Kammer MN, Lakhani DA, Balar AB, Antic SL, Kussrow AK, Webster RL, Mahapatra S, Barad U, Shah C, Atwater T, Diergaarde B, Qian J, Kaizer A, New M, Hirsch E, Feser WJ, Strong J, Rioth M, Miller YE, Balagurunathan Y, Rowe DJ, Helmey S, Chen SC, Bauza J, Deppen SA, Sandler K, Maldonado F, Spira A, Billatos E, Schabath MB, Gillies RJ, Wilson DO, Walker RC, Landman B, Chen H, Grogan EL, Barón AE, Bornhop DJ, Massion PP. Integrated Biomarkers for the Management of Indeterminate Pulmonary Nodules. American journal of respiratory and critical care medicine. 2021 Dec 1;204(11):1306-1316. PMID: 34464235
  • Qin R, Titler MG, Shever LL, Kim T. Estimating effects of nursing intervention via propensity score analysis. Nursing research. 2008 Nov-Dec;57(6):444-52. PMID: 19018219
  • Heine J, Fowler E, Scott CG, Jensen MR, Shepherd J, Hruska CB, Winham SJ, Brandt KR, Wu FF, Norman AD, Pankratz VS, Miglioretti DL, Kerlikowske K, Vachon CM. Mammographic Variation Measures, Breast Density, and Breast Cancer Risk. AJR. American journal of roentgenology. 2021 Aug;217(2):326-335. Epub 2021 Jun 23. PMID: 34161135
  • Heine J, Fowler EEE, Weinfurtner RJ, Hume E, Tworoger SS. Breast density analysis of digital breast tomosynthesis. Scientific reports. 2023 Oct 31;13(1):18760. PMID: 37907569
  • Traverso A, Wee L, Dekker A, Gillies R. Repeatability and Reproducibility of Radiomic Features: A Systematic Review. International journal of radiation oncology, biology, physics. 2018 Nov 15;102(4):1143-1158. Epub 2018 Jun 5. PMID: 30170872
  • Balagurunathan Y, Beers A, Mcnitt-Gray M, Hadjiiski L, Napel S, Goldgof D, Perez G, Arbelaez P, Mehrtash A, Kapur T, Yang E, Moon JW, Perez GB, Delgado-Gonzalo R, Farhangi MM, Amini AA, Ni R, Feng X, Bagari A, Vaidhya K, Veasey B, Safta W, Frigui H, Enguehard J, Gholipour A, Castillo LS, Daza LA, Pinsky P, Kalpathy-Cramer J, Farahani K. Lung Nodule Malignancy Prediction in Sequential CT Scans: Summary of ISBI 2018 Challenge. IEEE transactions on medical imaging. 2021 Dec;40(12):3748-3761. Epub 2021 Nov 30. PMID: 34264825
  • Beichel RR, Smith BJ, Bauer C, Ulrich EJ, Ahmadvand P, Budzevich MM, Gillies RJ, Goldgof D, Grkovski M, Hamarneh G, Huang Q, Kinahan PE, Laymon CM, Mountz JM, Muzi JP, Muzi M, Nehmeh S, Oborski MJ, Tan Y, Zhao B, Sunderland JJ, Buatti JM. Multi-site quality and variability analysis of 3D FDG PET segmentations based on phantom and clinical image data. Medical physics. 2017 Feb;44(2):479-496. PMID: 28205306
  • Liu Y, Kim J, Balagurunathan Y, Hawkins S, Stringfield O, Schabath MB, Li Q, Qu F, Liu S, Garcia AL, Ye Z, Gillies RJ. Prediction of pathological nodal involvement by CT-based Radiomic features of the primary tumor in patients with clinically node-negative peripheral lung adenocarcinomas. Medical physics. 2018 Jun;45(6):2518-2526. Epub 2018 Apr 29. PMID: 29624702
  • Paul R, Schabath MB, Gillies R, Hall LO, Goldgof DB. Hybrid models for lung nodule malignancy prediction utilizing convolutional neural network ensembles and clinical data. Journal of medical imaging (Bellingham, Wash.). 2020 Mar;7(2):024502. Epub 2020 Apr 6. PMID: 32280729
  • Shafiq-Ul-Hassan M, Zhang GG, Latifi K, Ullah G, Hunt DC, Balagurunathan Y, Abdalah MA, Schabath MB, Goldgof DG, Mackin D, Court LE, Gillies RJ, Moros EG. Intrinsic dependencies of CT radiomic features on voxel size and number of gray levels. Medical physics. 2017 Mar;44(3):1050-1062. PMID: 28112418
  • Pérez-Morales J, Tunali I, Stringfield O, Eschrich SA, Balagurunathan Y, Gillies RJ, Schabath MB. Peritumoral and intratumoral radiomic features predict survival outcomes among patients diagnosed in lung cancer screening. Scientific reports. 2020 Jun 29;10(1):10528. PMID: 32601340
  • Paul R, Schabath M, Gillies R, Hall L, Goldgof D. Convolutional Neural Network ensembles for accurate lung nodule malignancy prediction 2 years in the future. Computers in biology and medicine. 2020 Jul;122:103882. Epub 2020 Jun 24. PMID: 32658721
  • Yaghjyan L, Smotherman C, Heine J, Colditz GA, Rosner B, Tamimi RM. Associations of Oral Contraceptives with Mammographic Breast Density in Premenopausal Women. Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology. 2022 Feb;31(2):436-442. Epub 2021 Dec 3. PMID: 34862209
  • Oh H, Rice MS, Warner ET, Bertrand KA, Fowler EE, Eliassen AH, Rosner BA, Heine JJ, Tamimi RM. Early-Life and Adult Anthropometrics in Relation to Mammographic Image Intensity Variation in the Nurses' Health Studies. Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology. 2020 Feb;29(2):343-351. Epub 2019 Dec 11. PMID: 31826913
  • Tunali I, Gillies RJ, Schabath MB. Application of Radiomics and Artificial Intelligence for Lung Cancer Precision Medicine. Cold Spring Harbor perspectives in medicine. 2021 Aug 2;11. (8). PMID: 33431509