1
|
Foldyna B, Hadzic I, Zeleznik R, Langenbach MC, Raghu VK, Mayrhofer T, Lu MT, Aerts HJWL. Deep learning analysis of epicardial adipose tissue to predict cardiovascular risk in heavy smokers. Commun Med (Lond) 2024; 4:44. [PMID: 38480863 PMCID: PMC10937640 DOI: 10.1038/s43856-024-00475-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Accepted: 03/04/2024] [Indexed: 03/17/2024] Open
Abstract
BACKGROUND Heavy smokers are at increased risk for cardiovascular disease and may benefit from individualized risk quantification using routine lung cancer screening chest computed tomography. We investigated the prognostic value of deep learning-based automated epicardial adipose tissue quantification and compared it to established cardiovascular risk factors and coronary artery calcium. METHODS We investigated the prognostic value of automated epicardial adipose tissue quantification in heavy smokers enrolled in the National Lung Screening Trial and followed for 12.3 (11.9-12.8) years. The epicardial adipose tissue was segmented and quantified on non-ECG-synchronized, non-contrast low-dose chest computed tomography scans using a validated deep-learning algorithm. Multivariable survival regression analyses were then utilized to determine the associations of epicardial adipose tissue volume and density with all-cause and cardiovascular mortality (myocardial infarction and stroke). RESULTS Here we show in 24,090 adult heavy smokers (59% men; 61 ± 5 years) that epicardial adipose tissue volume and density are independently associated with all-cause (adjusted hazard ratios: 1.10 and 1.38; P < 0.001) and cardiovascular mortality (adjusted hazard ratios: 1.14 and 1.78; P < 0.001) beyond demographics, clinical risk factors, body habitus, level of education, and coronary artery calcium score. CONCLUSIONS Our findings suggest that automated assessment of epicardial adipose tissue from low-dose lung cancer screening images offers prognostic value in heavy smokers, with potential implications for cardiovascular risk stratification in this high-risk population.
Collapse
Affiliation(s)
- Borek Foldyna
- Cardiovascular Imaging Research Center (CIRC), Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
| | - Ibrahim Hadzic
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
- Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University, Maastricht, The Netherlands
| | - Roman Zeleznik
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Marcel C Langenbach
- Cardiovascular Imaging Research Center (CIRC), Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Institute for Diagnostic and Interventional Radiology, University Hospital Cologne, Cologne, Germany
| | - Vineet K Raghu
- Cardiovascular Imaging Research Center (CIRC), Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Thomas Mayrhofer
- Cardiovascular Imaging Research Center (CIRC), Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- School of Business Studies, Stralsund University of Applied Sciences, Stralsund, Germany
| | - Michael T Lu
- Cardiovascular Imaging Research Center (CIRC), Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Hugo J W L Aerts
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
- Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University, Maastricht, The Netherlands
| |
Collapse
|
2
|
Wang XW, Wang T, Schaub DP, Chen C, Sun Z, Ke S, Hecker J, Maaser-Hecker A, Zeleznik OA, Zeleznik R, Litonjua AA, DeMeo DL, Lasky-Su J, Silverman EK, Liu YY, Weiss ST. Benchmarking omics-based prediction of asthma development in children. Respir Res 2023; 24:63. [PMID: 36842969 PMCID: PMC9969629 DOI: 10.1186/s12931-023-02368-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Accepted: 02/16/2023] [Indexed: 02/27/2023] Open
Abstract
BACKGROUND Asthma is a heterogeneous disease with high morbidity. Advancement in high-throughput multi-omics approaches has enabled the collection of molecular assessments at different layers, providing a complementary perspective of complex diseases. Numerous computational methods have been developed for the omics-based patient classification or disease outcome prediction. Yet, a systematic benchmarking of those methods using various combinations of omics data for the prediction of asthma development is still lacking. OBJECTIVE We aimed to investigate the computational methods in disease status prediction using multi-omics data. METHOD We systematically benchmarked 18 computational methods using all the 63 combinations of six omics data (GWAS, miRNA, mRNA, microbiome, metabolome, DNA methylation) collected in The Vitamin D Antenatal Asthma Reduction Trial (VDAART) cohort. We evaluated each method using standard performance metrics for each of the 63 omics combinations. RESULTS Our results indicate that overall Logistic Regression, Multi-Layer Perceptron, and MOGONET display superior performance, and the combination of transcriptional, genomic and microbiome data achieves the best prediction. Moreover, we find that including the clinical data can further improve the prediction performance for some but not all the omics combinations. CONCLUSIONS Specific omics combinations can reach the optimal prediction of asthma development in children. And certain computational methods showed superior performance than other methods.
Collapse
Affiliation(s)
- Xu-Wen Wang
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Tong Wang
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Darius P Schaub
- Department of Mathematics, University of Hamburg, 21109, Hamburg, Germany
| | - Can Chen
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Zheng Sun
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Shanlin Ke
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Julian Hecker
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Anna Maaser-Hecker
- Genetics and Aging Research Unit, Department of Neurology, McCance Center for Brain Health, Mass General Institute for Neurodegenerative Disease, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - Oana A Zeleznik
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Roman Zeleznik
- Department of Radiation Oncology, Brigham and Women's Hospital, Boston, MA, USA
| | - Augusto A Litonjua
- Division of Pediatric Pulmonology, Golisano Children's Hospital, Rochester, NY, USA
| | - Dawn L DeMeo
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Jessica Lasky-Su
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Edwin K Silverman
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Yang-Yu Liu
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA.
- Center for Artificial Intelligence and Modeling, The Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA.
| | - Scott T Weiss
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA.
| |
Collapse
|
3
|
Guthier C, McKenzie E, Zeleznik R, Bitterman D, Bredfeldt J, Aerts H, Atkins K, Mak R. Deep Learning-Based Automated Cardiac Sub-Structure Contouring with Dosimetric and Clinical Outcomes Validation. Int J Radiat Oncol Biol Phys 2022. [DOI: 10.1016/j.ijrobp.2022.07.417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2022]
|
4
|
Guthier C, Zeleznik R, Bitterman D, Aerts H, Bredfeldt J, Mak R. PO-1636 Clinical implementation of AI-based contouring workflows from commissioning to automated routine QA. Radiother Oncol 2022. [DOI: 10.1016/s0167-8140(22)03600-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
|
5
|
Ye Z, Qian JM, Hosny A, Zeleznik R, Plana D, Likitlersuang J, Zhang Z, Mak RH, Aerts HJWL, Kann BH. Deep Learning-based Detection of Intravenous Contrast Enhancement on CT Scans. Radiol Artif Intell 2022; 4:e210285. [PMID: 35652117 DOI: 10.1148/ryai.210285] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Revised: 03/24/2022] [Accepted: 04/14/2022] [Indexed: 11/11/2022]
Abstract
Identifying the presence of intravenous contrast material on CT scans is an important component of data curation for medical imaging-based artificial intelligence model development and deployment. Use of intravenous contrast material is often poorly documented in imaging metadata, necessitating impractical manual annotation by clinician experts. Authors developed a convolutional neural network (CNN)-based deep learning platform to identify intravenous contrast enhancement on CT scans. For model development and validation, authors used six independent datasets of head and neck (HN) and chest CT scans, totaling 133 480 axial two-dimensional sections from 1979 scans, which were manually annotated by clinical experts. Five CNN models were trained first on HN scans for contrast enhancement detection. Model performances were evaluated at the patient level on a holdout set and external test set. Models were then fine-tuned on chest CT data and externally validated. This study found that Digital Imaging and Communications in Medicine metadata tags for intravenous contrast material were missing or erroneous for 1496 scans (75.6%). An EfficientNetB4-based model showed the best performance, with areas under the curve (AUCs) of 0.996 and 1.0 in HN holdout (n = 216) and external (n = 595) sets, respectively, and AUCs of 1.0 and 0.980 in the chest holdout (n = 53) and external (n = 402) sets, respectively. This automated, scan-to-prediction platform is highly accurate at CT contrast enhancement detection and may be helpful for artificial intelligence model development and clinical application. Keywords: CT, Head and Neck, Supervised Learning, Transfer Learning, Convolutional Neural Network (CNN), Machine Learning Algorithms, Contrast Material Supplemental material is available for this article. © RSNA, 2022.
Collapse
Affiliation(s)
- Zezhong Ye
- Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Harvard Institutes of Medicine, Boston, Mass (Z.Y., J.M.Q., A.H., R.Z., D.P., J.L., Z.Z., R.H.M., H.J.W.L.A., B.H.K.); Departments of Radiation Oncology (Z.Y., J.M.Q., A.H., R.Z., J.L., Z.Z., R.H.M., H.J.W.L.A., B.H.K.) and Radiology (H.J.W.L.A.), Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115; Harvard-MIT Division of Health Sciences & Technology, Cambridge, Mass (D.P.); and Department of Radiology and Nuclear Medicine, School for Cardiovascular Diseases (CARIM) & School for Oncology and Reproduction (GROW), Maastricht University, Maastricht, the Netherlands (H.J.W.L.A.)
| | - Jack M Qian
- Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Harvard Institutes of Medicine, Boston, Mass (Z.Y., J.M.Q., A.H., R.Z., D.P., J.L., Z.Z., R.H.M., H.J.W.L.A., B.H.K.); Departments of Radiation Oncology (Z.Y., J.M.Q., A.H., R.Z., J.L., Z.Z., R.H.M., H.J.W.L.A., B.H.K.) and Radiology (H.J.W.L.A.), Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115; Harvard-MIT Division of Health Sciences & Technology, Cambridge, Mass (D.P.); and Department of Radiology and Nuclear Medicine, School for Cardiovascular Diseases (CARIM) & School for Oncology and Reproduction (GROW), Maastricht University, Maastricht, the Netherlands (H.J.W.L.A.)
| | - Ahmed Hosny
- Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Harvard Institutes of Medicine, Boston, Mass (Z.Y., J.M.Q., A.H., R.Z., D.P., J.L., Z.Z., R.H.M., H.J.W.L.A., B.H.K.); Departments of Radiation Oncology (Z.Y., J.M.Q., A.H., R.Z., J.L., Z.Z., R.H.M., H.J.W.L.A., B.H.K.) and Radiology (H.J.W.L.A.), Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115; Harvard-MIT Division of Health Sciences & Technology, Cambridge, Mass (D.P.); and Department of Radiology and Nuclear Medicine, School for Cardiovascular Diseases (CARIM) & School for Oncology and Reproduction (GROW), Maastricht University, Maastricht, the Netherlands (H.J.W.L.A.)
| | - Roman Zeleznik
- Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Harvard Institutes of Medicine, Boston, Mass (Z.Y., J.M.Q., A.H., R.Z., D.P., J.L., Z.Z., R.H.M., H.J.W.L.A., B.H.K.); Departments of Radiation Oncology (Z.Y., J.M.Q., A.H., R.Z., J.L., Z.Z., R.H.M., H.J.W.L.A., B.H.K.) and Radiology (H.J.W.L.A.), Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115; Harvard-MIT Division of Health Sciences & Technology, Cambridge, Mass (D.P.); and Department of Radiology and Nuclear Medicine, School for Cardiovascular Diseases (CARIM) & School for Oncology and Reproduction (GROW), Maastricht University, Maastricht, the Netherlands (H.J.W.L.A.)
| | - Deborah Plana
- Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Harvard Institutes of Medicine, Boston, Mass (Z.Y., J.M.Q., A.H., R.Z., D.P., J.L., Z.Z., R.H.M., H.J.W.L.A., B.H.K.); Departments of Radiation Oncology (Z.Y., J.M.Q., A.H., R.Z., J.L., Z.Z., R.H.M., H.J.W.L.A., B.H.K.) and Radiology (H.J.W.L.A.), Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115; Harvard-MIT Division of Health Sciences & Technology, Cambridge, Mass (D.P.); and Department of Radiology and Nuclear Medicine, School for Cardiovascular Diseases (CARIM) & School for Oncology and Reproduction (GROW), Maastricht University, Maastricht, the Netherlands (H.J.W.L.A.)
| | - Jirapat Likitlersuang
- Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Harvard Institutes of Medicine, Boston, Mass (Z.Y., J.M.Q., A.H., R.Z., D.P., J.L., Z.Z., R.H.M., H.J.W.L.A., B.H.K.); Departments of Radiation Oncology (Z.Y., J.M.Q., A.H., R.Z., J.L., Z.Z., R.H.M., H.J.W.L.A., B.H.K.) and Radiology (H.J.W.L.A.), Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115; Harvard-MIT Division of Health Sciences & Technology, Cambridge, Mass (D.P.); and Department of Radiology and Nuclear Medicine, School for Cardiovascular Diseases (CARIM) & School for Oncology and Reproduction (GROW), Maastricht University, Maastricht, the Netherlands (H.J.W.L.A.)
| | - Zhongyi Zhang
- Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Harvard Institutes of Medicine, Boston, Mass (Z.Y., J.M.Q., A.H., R.Z., D.P., J.L., Z.Z., R.H.M., H.J.W.L.A., B.H.K.); Departments of Radiation Oncology (Z.Y., J.M.Q., A.H., R.Z., J.L., Z.Z., R.H.M., H.J.W.L.A., B.H.K.) and Radiology (H.J.W.L.A.), Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115; Harvard-MIT Division of Health Sciences & Technology, Cambridge, Mass (D.P.); and Department of Radiology and Nuclear Medicine, School for Cardiovascular Diseases (CARIM) & School for Oncology and Reproduction (GROW), Maastricht University, Maastricht, the Netherlands (H.J.W.L.A.)
| | - Raymond H Mak
- Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Harvard Institutes of Medicine, Boston, Mass (Z.Y., J.M.Q., A.H., R.Z., D.P., J.L., Z.Z., R.H.M., H.J.W.L.A., B.H.K.); Departments of Radiation Oncology (Z.Y., J.M.Q., A.H., R.Z., J.L., Z.Z., R.H.M., H.J.W.L.A., B.H.K.) and Radiology (H.J.W.L.A.), Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115; Harvard-MIT Division of Health Sciences & Technology, Cambridge, Mass (D.P.); and Department of Radiology and Nuclear Medicine, School for Cardiovascular Diseases (CARIM) & School for Oncology and Reproduction (GROW), Maastricht University, Maastricht, the Netherlands (H.J.W.L.A.)
| | - Hugo J W L Aerts
- Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Harvard Institutes of Medicine, Boston, Mass (Z.Y., J.M.Q., A.H., R.Z., D.P., J.L., Z.Z., R.H.M., H.J.W.L.A., B.H.K.); Departments of Radiation Oncology (Z.Y., J.M.Q., A.H., R.Z., J.L., Z.Z., R.H.M., H.J.W.L.A., B.H.K.) and Radiology (H.J.W.L.A.), Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115; Harvard-MIT Division of Health Sciences & Technology, Cambridge, Mass (D.P.); and Department of Radiology and Nuclear Medicine, School for Cardiovascular Diseases (CARIM) & School for Oncology and Reproduction (GROW), Maastricht University, Maastricht, the Netherlands (H.J.W.L.A.)
| | - Benjamin H Kann
- Artificial Intelligence in Medicine Program, Mass General Brigham, Harvard Medical School, Harvard Institutes of Medicine, Boston, Mass (Z.Y., J.M.Q., A.H., R.Z., D.P., J.L., Z.Z., R.H.M., H.J.W.L.A., B.H.K.); Departments of Radiation Oncology (Z.Y., J.M.Q., A.H., R.Z., J.L., Z.Z., R.H.M., H.J.W.L.A., B.H.K.) and Radiology (H.J.W.L.A.), Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St, Boston, MA 02115; Harvard-MIT Division of Health Sciences & Technology, Cambridge, Mass (D.P.); and Department of Radiology and Nuclear Medicine, School for Cardiovascular Diseases (CARIM) & School for Oncology and Reproduction (GROW), Maastricht University, Maastricht, the Netherlands (H.J.W.L.A.)
| |
Collapse
|
6
|
Atkins KM, Weiss J, Zeleznik R, Bitterman DS, Chaunzwa TL, Huynh E, Guthier C, Kozono DE, Lewis JH, Tamarappoo BK, Nohria A, Hoffmann U, Aerts HJWL, Mak RH. Elevated Coronary Artery Calcium Quantified by a Validated Deep Learning Model From Lung Cancer Radiotherapy Planning Scans Predicts Mortality. JCO Clin Cancer Inform 2022; 6:e2100095. [PMID: 35084935 DOI: 10.1200/cci.21.00095] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
PURPOSE Coronary artery calcium (CAC) quantified on computed tomography (CT) scans is a robust predictor of atherosclerotic coronary disease; however, the feasibility and relevance of quantitating CAC from lung cancer radiotherapy planning CT scans is unknown. We used a previously validated deep learning (DL) model to assess whether CAC is a predictor of all-cause mortality and major adverse cardiac events (MACEs). METHODS Retrospective analysis of non-contrast-enhanced radiotherapy planning CT scans from 428 patients with locally advanced lung cancer is performed. The DL-CAC algorithm was previously trained on 1,636 cardiac-gated CT scans and tested on four clinical trial cohorts. Plaques ≥ 1 cubic millimeter were measured to generate an Agatston-like DL-CAC score and grouped as DL-CAC = 0 (very low risk) and DL-CAC ≥ 1 (elevated risk). Cox and Fine and Gray regressions were adjusted for lung cancer and cardiovascular factors. RESULTS The median follow-up was 18.1 months. The majority (61.4%) had a DL-CAC ≥ 1. There was an increased risk of all-cause mortality with DL-CAC ≥ 1 versus DL-CAC = 0 (adjusted hazard ratio, 1.51; 95% CI, 1.01 to 2.26; P = .04), with 2-year estimates of 56.2% versus 45.4%, respectively. There was a trend toward increased risk of major adverse cardiac events with DL-CAC ≥ 1 versus DL-CAC = 0 (hazard ratio, 1.80; 95% CI, 0.87 to 3.74; P = .11), with 2-year estimates of 7.3% versus 1.2%, respectively. CONCLUSION In this proof-of-concept study, CAC was effectively measured from routinely acquired radiotherapy planning CT scans using an automated model. Elevated CAC, as predicted by the DL model, was associated with an increased risk of mortality, suggesting a potential benefit for automated cardiac risk screening before cancer therapy begins.
Collapse
Affiliation(s)
- Katelyn M Atkins
- Department of Radiation Oncology, Cedars-Sinai Medical Center, Los Angeles, CA.,Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women's Hospital, Boston, MA
| | - Jakob Weiss
- Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women's Hospital, Boston, MA.,Artificial Intelligence in Medicine (AIM) Program, Brigham and Women's Hospital, Harvard Medical School, Boston, MA.,Department of Diagnostic and Interventional Radiology, University Hospital, Freiburg, Germany
| | - Roman Zeleznik
- Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women's Hospital, Boston, MA.,Artificial Intelligence in Medicine (AIM) Program, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Danielle S Bitterman
- Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women's Hospital, Boston, MA.,Artificial Intelligence in Medicine (AIM) Program, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Tafadzwa L Chaunzwa
- Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women's Hospital, Boston, MA.,Artificial Intelligence in Medicine (AIM) Program, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Elizabeth Huynh
- Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women's Hospital, Boston, MA
| | - Christian Guthier
- Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women's Hospital, Boston, MA
| | - David E Kozono
- Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women's Hospital, Boston, MA
| | - John H Lewis
- Department of Radiation Oncology, Cedars-Sinai Medical Center, Los Angeles, CA
| | | | - Anju Nohria
- Department of Cardiovascular Medicine, Dana-Farber Cancer Institute and Brigham and Women's Hospital, Boston, MA
| | - Udo Hoffmann
- Artificial Intelligence in Medicine (AIM) Program, Brigham and Women's Hospital, Harvard Medical School, Boston, MA.,Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA
| | - Hugo J W L Aerts
- Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women's Hospital, Boston, MA.,Artificial Intelligence in Medicine (AIM) Program, Brigham and Women's Hospital, Harvard Medical School, Boston, MA.,Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University, Maastricht, the Netherlands
| | - Raymond H Mak
- Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women's Hospital, Boston, MA.,Artificial Intelligence in Medicine (AIM) Program, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| |
Collapse
|
7
|
Zeleznik R, Weiss J, Taron J, Guthier C, Bitterman DS, Hancox C, Kann BH, Kim DW, Punglia RS, Bredfeldt J, Foldyna B, Eslami P, Lu MT, Hoffmann U, Mak R, Aerts HJWL. Deep-learning system to improve the quality and efficiency of volumetric heart segmentation for breast cancer. NPJ Digit Med 2021; 4:43. [PMID: 33674717 PMCID: PMC7935874 DOI: 10.1038/s41746-021-00416-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Accepted: 02/08/2021] [Indexed: 02/06/2023] Open
Abstract
Although artificial intelligence algorithms are often developed and applied for narrow tasks, their implementation in other medical settings could help to improve patient care. Here we assess whether a deep-learning system for volumetric heart segmentation on computed tomography (CT) scans developed in cardiovascular radiology can optimize treatment planning in radiation oncology. The system was trained using multi-center data (n = 858) with manual heart segmentations provided by cardiovascular radiologists. Validation of the system was performed in an independent real-world dataset of 5677 breast cancer patients treated with radiation therapy at the Dana-Farber/Brigham and Women’s Cancer Center between 2008–2018. In a subset of 20 patients, the performance of the system was compared to eight radiation oncology experts by assessing segmentation time, agreement between experts, and accuracy with and without deep-learning assistance. To compare the performance to segmentations used in the clinic, concordance and failures (defined as Dice < 0.85) of the system were evaluated in the entire dataset. The system was successfully applied without retraining. With deep-learning assistance, segmentation time significantly decreased (4.0 min [IQR 3.1–5.0] vs. 2.0 min [IQR 1.3–3.5]; p < 0.001), and agreement increased (Dice 0.95 [IQR = 0.02]; vs. 0.97 [IQR = 0.02], p < 0.001). Expert accuracy was similar with and without deep-learning assistance (Dice 0.92 [IQR = 0.02] vs. 0.92 [IQR = 0.02]; p = 0.48), and not significantly different from deep-learning-only segmentations (Dice 0.92 [IQR = 0.02]; p ≥ 0.1). In comparison to real-world data, the system showed high concordance (Dice 0.89 [IQR = 0.06]) across 5677 patients and a significantly lower failure rate (p < 0.001). These results suggest that deep-learning algorithms can successfully be applied across medical specialties and improve clinical care beyond the original field of interest.
Collapse
Affiliation(s)
- Roman Zeleznik
- Artificial Intelligence in Medicine (AIM) Program, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.,Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.,Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Jakob Weiss
- Artificial Intelligence in Medicine (AIM) Program, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.,Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.,Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.,Department of Diagnostic and Interventional Radiology, University Hospital, Freiburg, Germany
| | - Jana Taron
- Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.,Department of Diagnostic and Interventional Radiology, University Hospital, Freiburg, Germany
| | - Christian Guthier
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Danielle S Bitterman
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Cindy Hancox
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Benjamin H Kann
- Artificial Intelligence in Medicine (AIM) Program, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.,Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Daniel W Kim
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Rinaa S Punglia
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Jeremy Bredfeldt
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Borek Foldyna
- Artificial Intelligence in Medicine (AIM) Program, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.,Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Parastou Eslami
- Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Michael T Lu
- Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Udo Hoffmann
- Artificial Intelligence in Medicine (AIM) Program, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.,Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Raymond Mak
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Hugo J W L Aerts
- Artificial Intelligence in Medicine (AIM) Program, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. .,Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. .,Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA. .,Department of Radiology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA. .,Department of Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University, Maastricht, The Netherlands.
| |
Collapse
|
8
|
Zeleznik R, Foldyna B, Eslami P, Weiss J, Alexander I, Taron J, Parmar C, Alvi RM, Banerji D, Uno M, Kikuchi Y, Karady J, Zhang L, Scholtz JE, Mayrhofer T, Lyass A, Mahoney TF, Massaro JM, Vasan RS, Douglas PS, Hoffmann U, Lu MT, Aerts HJWL. Deep convolutional neural networks to predict cardiovascular risk from computed tomography. Nat Commun 2021; 12:715. [PMID: 33514711 PMCID: PMC7846726 DOI: 10.1038/s41467-021-20966-2] [Citation(s) in RCA: 75] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2020] [Accepted: 01/05/2021] [Indexed: 11/30/2022] Open
Abstract
Coronary artery calcium is an accurate predictor of cardiovascular events. While it is visible on all computed tomography (CT) scans of the chest, this information is not routinely quantified as it requires expertise, time, and specialized equipment. Here, we show a robust and time-efficient deep learning system to automatically quantify coronary calcium on routine cardiac-gated and non-gated CT. As we evaluate in 20,084 individuals from distinct asymptomatic (Framingham Heart Study, NLST) and stable and acute chest pain (PROMISE, ROMICAT-II) cohorts, the automated score is a strong predictor of cardiovascular events, independent of risk factors (multivariable-adjusted hazard ratios up to 4.3), shows high correlation with manual quantification, and robust test-retest reliability. Our results demonstrate the clinical value of a deep learning system for the automated prediction of cardiovascular events. Implementation into clinical practice would address the unmet need of automating proven imaging biomarkers to guide management and improve population health.
Collapse
Affiliation(s)
- Roman Zeleznik
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Borek Foldyna
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Parastou Eslami
- Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Jakob Weiss
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University of Tübingen, Tübingen, Germany
| | - Ivanov Alexander
- Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Jana Taron
- Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University of Tübingen, Tübingen, Germany
| | - Chintan Parmar
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Raza M Alvi
- Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Dahlia Banerji
- Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Mio Uno
- Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Yasuka Kikuchi
- Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Center for Cause of Death Investigation, Faculty of Medicine, Hokkaido University, Sapporo, Hokkaido, Japan
| | - Julia Karady
- Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Cardiovascular Imaging Research Group, Heart and Vascular Center, Semmelweis University, Budapest, Hungary
| | - Lili Zhang
- Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Jan-Erik Scholtz
- Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Thomas Mayrhofer
- Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- School of Business Studies, Stralsund University of Applied Sciences, Stralsund, Germany
| | - Asya Lyass
- Department of Mathematics and Statistics, Boston University, Boston, MA, USA
| | - Taylor F Mahoney
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Joseph M Massaro
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Ramachandran S Vasan
- National Heart, Lung, and Blood Institute and Boston University, Framingham Heart Study, Framingham, MA, USA
- Departments of Cardiology and Preventive Medicine, Department of Medicine, Boston University School of Medicine, Boston, MA, USA
| | - Pamela S Douglas
- Department of Medicine, Division of Cardiology, Duke University School of Medicine, Duke Clinical Research Institute, Durham, NC, USA
| | - Udo Hoffmann
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Michael T Lu
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Hugo J W L Aerts
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA.
- Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
- Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.
- Department of Radiology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.
- Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University, Maastricht, The Netherlands.
| |
Collapse
|
9
|
Zeleznik R, Weiss J, Taron J, Guthier C, Hancox C, Bitterman D, Kim D, Kann B, Punglia R, Bredfeldt J, Foldyna B, Lu M, Hoffmann U, Mak R, Aerts H. Deep Learning Based Heart Segmentation Algorithm to Improve Radiation Treatment Planning. Int J Radiat Oncol Biol Phys 2020. [DOI: 10.1016/j.ijrobp.2020.07.833] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
|
10
|
Eslami P, Parmar C, Foldyna B, Scholtz JE, Ivanov A, Zeleznik R, Lu MT, Ferencik M, Vasan RS, Baltrusaitis K, Massaro JM, D’Agostino RB, Mayrhofer T, O’Donnell CJ, Aerts HJWL, Hoffmann U. Radiomics of Coronary Artery Calcium in the Framingham Heart Study. Radiol Cardiothorac Imaging 2020; 2:e190119. [PMID: 32715301 PMCID: PMC7051158 DOI: 10.1148/ryct.2020190119] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2019] [Revised: 10/14/2019] [Accepted: 10/21/2019] [Indexed: 12/19/2022]
Abstract
PURPOSE To extract radiomic features from coronary artery calcium (CAC) on CT images and to determine whether this approach could improve the ability to identify individuals at risk for a composite endpoint of clinical events. MATERIALS AND METHODS Participants from the Offspring and Third Generation cohorts of the community-based Framingham Heart Study underwent noncontrast cardiac CT (2002-2005) and were followed for more than a median of 9.1 years for composite major events. A total of 624 participants with CAC Agatston score (AS) of greater than 0 and good or excellent CT image quality were included for manual CAC segmentation and extraction of a predefined set of radiomic features reflecting intensity, shape, and texture. In a discovery cohort (n = 318), machine learning was used to select the 20 most informative and nonredundant CAC radiomic features, classify features predicting events, and define a radiomic-based score (RS). Performance of the RS was tested independently for the prediction of events in a validation cohort (n = 306). RESULTS The RS had a median value of 0.08 (interquartile range, 0.007-0.71) and a weak and modest correlation with Framingham risk score (FRS) (r = 0.2) and AS (r = 0.39), respectively. The continuous RS unadjusted, adjusted for age and sex, FRS, AS, and FRS plus AS were significantly associated with events (hazard ratio [HR] = 2.2, P < .001; HR = 1.8, P = .002; HR = 2.0, P < .001; HR = 1.7, P = .02; and HR = 1.8, P = .01, respectively). In participants with AS of less than 300, RS association with events remained significant when unadjusted and adjusted for age and sex, FRS, AS, and FRS plus AS (HR = 2.4, 2.8, 2.8, 2.3, and 2.6; P < .001, respectively). In the same subgroup of participants, adding the RS to AS resulted in a significant improvement in the discriminatory ability for events as compared with the AS (area under the receiver operating curve: 0.80 vs 0.68, respectively; P = .03). CONCLUSION A radiomic-based score, including the complex properties of CAC, may constitute an imaging biomarker to be further developed to identify individuals at risk for major adverse cardiovascular events in a community-based cohort. Supplemental material is available for this article. © RSNA, 2020.
Collapse
Affiliation(s)
- Parastou Eslami
- From the Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, 165 Cambridge St, Suite 400, Boston, MA 02114 (P.E., B.F., J.E.S., A.I., M.T.L., M.F., T.M., U.H.); Department of Radiation Oncology and Radiology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, Mass (C.P., R.Z., H.J.W.L.A.); Knight Cardiovascular Institute, Oregon Health and Science University, Portland, Ore (M.F.); National Heart, Lung, and Blood Institute’s and Boston University’s Framingham Heart Study, Framingham, Mass (R.S.V., C.J.O.); Department of Mathematics, Boston University, Boston, Mass (K.B., J.M.M., R.B.D.); Institute for Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt, Germany (J.E.S.); and School of Business Studies, Stralsund University of Applied Sciences, Stralsund, Germany (T.M.)
| | - Chintan Parmar
- From the Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, 165 Cambridge St, Suite 400, Boston, MA 02114 (P.E., B.F., J.E.S., A.I., M.T.L., M.F., T.M., U.H.); Department of Radiation Oncology and Radiology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, Mass (C.P., R.Z., H.J.W.L.A.); Knight Cardiovascular Institute, Oregon Health and Science University, Portland, Ore (M.F.); National Heart, Lung, and Blood Institute’s and Boston University’s Framingham Heart Study, Framingham, Mass (R.S.V., C.J.O.); Department of Mathematics, Boston University, Boston, Mass (K.B., J.M.M., R.B.D.); Institute for Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt, Germany (J.E.S.); and School of Business Studies, Stralsund University of Applied Sciences, Stralsund, Germany (T.M.)
| | - Borek Foldyna
- From the Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, 165 Cambridge St, Suite 400, Boston, MA 02114 (P.E., B.F., J.E.S., A.I., M.T.L., M.F., T.M., U.H.); Department of Radiation Oncology and Radiology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, Mass (C.P., R.Z., H.J.W.L.A.); Knight Cardiovascular Institute, Oregon Health and Science University, Portland, Ore (M.F.); National Heart, Lung, and Blood Institute’s and Boston University’s Framingham Heart Study, Framingham, Mass (R.S.V., C.J.O.); Department of Mathematics, Boston University, Boston, Mass (K.B., J.M.M., R.B.D.); Institute for Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt, Germany (J.E.S.); and School of Business Studies, Stralsund University of Applied Sciences, Stralsund, Germany (T.M.)
| | - Jan-Erik Scholtz
- From the Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, 165 Cambridge St, Suite 400, Boston, MA 02114 (P.E., B.F., J.E.S., A.I., M.T.L., M.F., T.M., U.H.); Department of Radiation Oncology and Radiology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, Mass (C.P., R.Z., H.J.W.L.A.); Knight Cardiovascular Institute, Oregon Health and Science University, Portland, Ore (M.F.); National Heart, Lung, and Blood Institute’s and Boston University’s Framingham Heart Study, Framingham, Mass (R.S.V., C.J.O.); Department of Mathematics, Boston University, Boston, Mass (K.B., J.M.M., R.B.D.); Institute for Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt, Germany (J.E.S.); and School of Business Studies, Stralsund University of Applied Sciences, Stralsund, Germany (T.M.)
| | - Alexander Ivanov
- From the Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, 165 Cambridge St, Suite 400, Boston, MA 02114 (P.E., B.F., J.E.S., A.I., M.T.L., M.F., T.M., U.H.); Department of Radiation Oncology and Radiology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, Mass (C.P., R.Z., H.J.W.L.A.); Knight Cardiovascular Institute, Oregon Health and Science University, Portland, Ore (M.F.); National Heart, Lung, and Blood Institute’s and Boston University’s Framingham Heart Study, Framingham, Mass (R.S.V., C.J.O.); Department of Mathematics, Boston University, Boston, Mass (K.B., J.M.M., R.B.D.); Institute for Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt, Germany (J.E.S.); and School of Business Studies, Stralsund University of Applied Sciences, Stralsund, Germany (T.M.)
| | - Roman Zeleznik
- From the Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, 165 Cambridge St, Suite 400, Boston, MA 02114 (P.E., B.F., J.E.S., A.I., M.T.L., M.F., T.M., U.H.); Department of Radiation Oncology and Radiology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, Mass (C.P., R.Z., H.J.W.L.A.); Knight Cardiovascular Institute, Oregon Health and Science University, Portland, Ore (M.F.); National Heart, Lung, and Blood Institute’s and Boston University’s Framingham Heart Study, Framingham, Mass (R.S.V., C.J.O.); Department of Mathematics, Boston University, Boston, Mass (K.B., J.M.M., R.B.D.); Institute for Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt, Germany (J.E.S.); and School of Business Studies, Stralsund University of Applied Sciences, Stralsund, Germany (T.M.)
| | - Michael T. Lu
- From the Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, 165 Cambridge St, Suite 400, Boston, MA 02114 (P.E., B.F., J.E.S., A.I., M.T.L., M.F., T.M., U.H.); Department of Radiation Oncology and Radiology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, Mass (C.P., R.Z., H.J.W.L.A.); Knight Cardiovascular Institute, Oregon Health and Science University, Portland, Ore (M.F.); National Heart, Lung, and Blood Institute’s and Boston University’s Framingham Heart Study, Framingham, Mass (R.S.V., C.J.O.); Department of Mathematics, Boston University, Boston, Mass (K.B., J.M.M., R.B.D.); Institute for Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt, Germany (J.E.S.); and School of Business Studies, Stralsund University of Applied Sciences, Stralsund, Germany (T.M.)
| | - Maros Ferencik
- From the Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, 165 Cambridge St, Suite 400, Boston, MA 02114 (P.E., B.F., J.E.S., A.I., M.T.L., M.F., T.M., U.H.); Department of Radiation Oncology and Radiology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, Mass (C.P., R.Z., H.J.W.L.A.); Knight Cardiovascular Institute, Oregon Health and Science University, Portland, Ore (M.F.); National Heart, Lung, and Blood Institute’s and Boston University’s Framingham Heart Study, Framingham, Mass (R.S.V., C.J.O.); Department of Mathematics, Boston University, Boston, Mass (K.B., J.M.M., R.B.D.); Institute for Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt, Germany (J.E.S.); and School of Business Studies, Stralsund University of Applied Sciences, Stralsund, Germany (T.M.)
| | - Ramachandran S. Vasan
- From the Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, 165 Cambridge St, Suite 400, Boston, MA 02114 (P.E., B.F., J.E.S., A.I., M.T.L., M.F., T.M., U.H.); Department of Radiation Oncology and Radiology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, Mass (C.P., R.Z., H.J.W.L.A.); Knight Cardiovascular Institute, Oregon Health and Science University, Portland, Ore (M.F.); National Heart, Lung, and Blood Institute’s and Boston University’s Framingham Heart Study, Framingham, Mass (R.S.V., C.J.O.); Department of Mathematics, Boston University, Boston, Mass (K.B., J.M.M., R.B.D.); Institute for Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt, Germany (J.E.S.); and School of Business Studies, Stralsund University of Applied Sciences, Stralsund, Germany (T.M.)
| | - Kristin Baltrusaitis
- From the Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, 165 Cambridge St, Suite 400, Boston, MA 02114 (P.E., B.F., J.E.S., A.I., M.T.L., M.F., T.M., U.H.); Department of Radiation Oncology and Radiology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, Mass (C.P., R.Z., H.J.W.L.A.); Knight Cardiovascular Institute, Oregon Health and Science University, Portland, Ore (M.F.); National Heart, Lung, and Blood Institute’s and Boston University’s Framingham Heart Study, Framingham, Mass (R.S.V., C.J.O.); Department of Mathematics, Boston University, Boston, Mass (K.B., J.M.M., R.B.D.); Institute for Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt, Germany (J.E.S.); and School of Business Studies, Stralsund University of Applied Sciences, Stralsund, Germany (T.M.)
| | - Joseph M. Massaro
- From the Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, 165 Cambridge St, Suite 400, Boston, MA 02114 (P.E., B.F., J.E.S., A.I., M.T.L., M.F., T.M., U.H.); Department of Radiation Oncology and Radiology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, Mass (C.P., R.Z., H.J.W.L.A.); Knight Cardiovascular Institute, Oregon Health and Science University, Portland, Ore (M.F.); National Heart, Lung, and Blood Institute’s and Boston University’s Framingham Heart Study, Framingham, Mass (R.S.V., C.J.O.); Department of Mathematics, Boston University, Boston, Mass (K.B., J.M.M., R.B.D.); Institute for Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt, Germany (J.E.S.); and School of Business Studies, Stralsund University of Applied Sciences, Stralsund, Germany (T.M.)
| | - Ralph B. D’Agostino
- From the Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, 165 Cambridge St, Suite 400, Boston, MA 02114 (P.E., B.F., J.E.S., A.I., M.T.L., M.F., T.M., U.H.); Department of Radiation Oncology and Radiology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, Mass (C.P., R.Z., H.J.W.L.A.); Knight Cardiovascular Institute, Oregon Health and Science University, Portland, Ore (M.F.); National Heart, Lung, and Blood Institute’s and Boston University’s Framingham Heart Study, Framingham, Mass (R.S.V., C.J.O.); Department of Mathematics, Boston University, Boston, Mass (K.B., J.M.M., R.B.D.); Institute for Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt, Germany (J.E.S.); and School of Business Studies, Stralsund University of Applied Sciences, Stralsund, Germany (T.M.)
| | - Thomas Mayrhofer
- From the Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, 165 Cambridge St, Suite 400, Boston, MA 02114 (P.E., B.F., J.E.S., A.I., M.T.L., M.F., T.M., U.H.); Department of Radiation Oncology and Radiology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, Mass (C.P., R.Z., H.J.W.L.A.); Knight Cardiovascular Institute, Oregon Health and Science University, Portland, Ore (M.F.); National Heart, Lung, and Blood Institute’s and Boston University’s Framingham Heart Study, Framingham, Mass (R.S.V., C.J.O.); Department of Mathematics, Boston University, Boston, Mass (K.B., J.M.M., R.B.D.); Institute for Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt, Germany (J.E.S.); and School of Business Studies, Stralsund University of Applied Sciences, Stralsund, Germany (T.M.)
| | - Christopher J. O’Donnell
- From the Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, 165 Cambridge St, Suite 400, Boston, MA 02114 (P.E., B.F., J.E.S., A.I., M.T.L., M.F., T.M., U.H.); Department of Radiation Oncology and Radiology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, Mass (C.P., R.Z., H.J.W.L.A.); Knight Cardiovascular Institute, Oregon Health and Science University, Portland, Ore (M.F.); National Heart, Lung, and Blood Institute’s and Boston University’s Framingham Heart Study, Framingham, Mass (R.S.V., C.J.O.); Department of Mathematics, Boston University, Boston, Mass (K.B., J.M.M., R.B.D.); Institute for Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt, Germany (J.E.S.); and School of Business Studies, Stralsund University of Applied Sciences, Stralsund, Germany (T.M.)
| | - Hugo J. W. L. Aerts
- From the Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, 165 Cambridge St, Suite 400, Boston, MA 02114 (P.E., B.F., J.E.S., A.I., M.T.L., M.F., T.M., U.H.); Department of Radiation Oncology and Radiology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, Mass (C.P., R.Z., H.J.W.L.A.); Knight Cardiovascular Institute, Oregon Health and Science University, Portland, Ore (M.F.); National Heart, Lung, and Blood Institute’s and Boston University’s Framingham Heart Study, Framingham, Mass (R.S.V., C.J.O.); Department of Mathematics, Boston University, Boston, Mass (K.B., J.M.M., R.B.D.); Institute for Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt, Germany (J.E.S.); and School of Business Studies, Stralsund University of Applied Sciences, Stralsund, Germany (T.M.)
| | - Udo Hoffmann
- From the Cardiovascular Imaging Research Center, Massachusetts General Hospital, Harvard Medical School, 165 Cambridge St, Suite 400, Boston, MA 02114 (P.E., B.F., J.E.S., A.I., M.T.L., M.F., T.M., U.H.); Department of Radiation Oncology and Radiology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, Mass (C.P., R.Z., H.J.W.L.A.); Knight Cardiovascular Institute, Oregon Health and Science University, Portland, Ore (M.F.); National Heart, Lung, and Blood Institute’s and Boston University’s Framingham Heart Study, Framingham, Mass (R.S.V., C.J.O.); Department of Mathematics, Boston University, Boston, Mass (K.B., J.M.M., R.B.D.); Institute for Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt, Germany (J.E.S.); and School of Business Studies, Stralsund University of Applied Sciences, Stralsund, Germany (T.M.)
| |
Collapse
|
11
|
Kamran SC, Coroller T, Milani N, Agrawal V, Baldini EH, Chen AB, Johnson BE, Kozono D, Franco I, Chopra N, Zeleznik R, Aerts HJWL, Mak R. The impact of quantitative CT-based tumor volumetric features on the outcomes of patients with limited stage small cell lung cancer. Radiat Oncol 2020; 15:14. [PMID: 31937336 PMCID: PMC6961251 DOI: 10.1186/s13014-020-1460-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2019] [Accepted: 01/06/2020] [Indexed: 11/10/2022] Open
Abstract
INTRODUCTION Limited stage small cell lung cancer (LS-SCLC) has a poor prognosis. Additional prognostic markers are needed for risk-stratification and treatment intensification. This study compares quantitative CT-based volumetric tumor measurements versus International Association for the Study of Lung Cancer (IASLC) TNM staging to predict outcomes. MATERIALS & METHODS A cohort of 105 patients diagnosed with LS-SCLC and treated with chemoradiation (CRT) from 2000 to 2013 were analyzed retrospectively. Patients were staged by the Union for International Cancer Control (UICC) TNM Classification, 8th edition. Tumor volumes and diameters were extracted from radiation planning CT imaging. Univariable and multivariable models were used to analyze relationships between CT features and overall survival (OS), locoregional recurrence (LRR), in-field LRR, any progression, and distant metastasis (DM). RESULTS Median follow-up was 21.3 months. Two-year outcomes were as follows: 38% LRR, 31% in-field LRR, 52% DM, 62% any progression, and 47% OS (median survival 16.5 months). On univariable analysis, UICC T-stage and N-stage were not associated with any clinical outcome. UICC overall stage was only statistically associated with in-field LRR. One imaging feature (3D maximum tumor diameter) was found to be significantly associated with LRR (HR 1.10, p = 0.003), in-field LRR (HR 1.10, p = 0.007), DM (HR 1.10, p = 0.02), any progression (HR 1.10, p = 0.008), and OS (HR 1.10, p = 0.03). On multivariable analysis, this feature remained significantly associated with all outcomes. CONCLUSION For LS-SCLC, quantitative CT-based volumetric tumor measurements were significantly associated with outcomes after CRT and may be better predictors of outcome than TNM stage.
Collapse
Affiliation(s)
- Sophia C Kamran
- Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Cox 3, Boston, MA, 02114, USA. .,Harvard Medical School, Boston, MA, USA.
| | - Thibaud Coroller
- Harvard Medical School, Boston, MA, USA.,Brigham and Women's Hospital/Dana-Farber Cancer Institute, Harvard Medical School, 450 Brookline Avenue, Boston, MA, 02215, USA
| | - Nastaran Milani
- Brigham and Women's Hospital/Dana-Farber Cancer Institute, Harvard Medical School, 450 Brookline Avenue, Boston, MA, 02215, USA
| | - Vishesh Agrawal
- Brigham and Women's Hospital/Dana-Farber Cancer Institute, Harvard Medical School, 450 Brookline Avenue, Boston, MA, 02215, USA
| | - Elizabeth H Baldini
- Harvard Medical School, Boston, MA, USA.,Brigham and Women's Hospital/Dana-Farber Cancer Institute, Harvard Medical School, 450 Brookline Avenue, Boston, MA, 02215, USA
| | | | - Bruce E Johnson
- Harvard Medical School, Boston, MA, USA.,Brigham and Women's Hospital/Dana-Farber Cancer Institute, Harvard Medical School, 450 Brookline Avenue, Boston, MA, 02215, USA
| | - David Kozono
- Harvard Medical School, Boston, MA, USA.,Brigham and Women's Hospital/Dana-Farber Cancer Institute, Harvard Medical School, 450 Brookline Avenue, Boston, MA, 02215, USA
| | - Idalid Franco
- Brigham and Women's Hospital/Dana-Farber Cancer Institute, Harvard Medical School, 450 Brookline Avenue, Boston, MA, 02215, USA
| | - Nitish Chopra
- Brigham and Women's Hospital/Dana-Farber Cancer Institute, Harvard Medical School, 450 Brookline Avenue, Boston, MA, 02215, USA
| | - Roman Zeleznik
- Brigham and Women's Hospital/Dana-Farber Cancer Institute, Harvard Medical School, 450 Brookline Avenue, Boston, MA, 02215, USA
| | - Hugo J W L Aerts
- Harvard Medical School, Boston, MA, USA.,Brigham and Women's Hospital/Dana-Farber Cancer Institute, Harvard Medical School, 450 Brookline Avenue, Boston, MA, 02215, USA
| | - Raymond Mak
- Harvard Medical School, Boston, MA, USA. .,Brigham and Women's Hospital/Dana-Farber Cancer Institute, Harvard Medical School, 450 Brookline Avenue, Boston, MA, 02215, USA.
| |
Collapse
|
12
|
Xu Y, Hosny A, Zeleznik R, Parmar C, Coroller T, Franco I, Mak RH, Aerts HJWL. Deep Learning Predicts Lung Cancer Treatment Response from Serial Medical Imaging. Clin Cancer Res 2019; 25:3266-3275. [PMID: 31010833 DOI: 10.1158/1078-0432.ccr-18-2495] [Citation(s) in RCA: 238] [Impact Index Per Article: 47.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2018] [Revised: 12/19/2018] [Accepted: 01/28/2019] [Indexed: 11/16/2022]
Abstract
PURPOSE Tumors are continuously evolving biological systems, and medical imaging is uniquely positioned to monitor changes throughout treatment. Although qualitatively tracking lesions over space and time may be trivial, the development of clinically relevant, automated radiomics methods that incorporate serial imaging data is far more challenging. In this study, we evaluated deep learning networks for predicting clinical outcomes through analyzing time series CT images of patients with locally advanced non-small cell lung cancer (NSCLC).Experimental Design: Dataset A consists of 179 patients with stage III NSCLC treated with definitive chemoradiation, with pretreatment and posttreatment CT images at 1, 3, and 6 months follow-up (581 scans). Models were developed using transfer learning of convolutional neural networks (CNN) with recurrent neural networks (RNN), using single seed-point tumor localization. Pathologic response validation was performed on dataset B, comprising 89 patients with NSCLC treated with chemoradiation and surgery (178 scans). RESULTS Deep learning models using time series scans were significantly predictive of survival and cancer-specific outcomes (progression, distant metastases, and local-regional recurrence). Model performance was enhanced with each additional follow-up scan into the CNN model (e.g., 2-year overall survival: AUC = 0.74, P < 0.05). The models stratified patients into low and high mortality risk groups, which were significantly associated with overall survival [HR = 6.16; 95% confidence interval (CI), 2.17-17.44; P < 0.001]. The model also significantly predicted pathologic response in dataset B (P = 0.016). CONCLUSIONS We demonstrate that deep learning can integrate imaging scans at multiple timepoints to improve clinical outcome predictions. AI-based noninvasive radiomics biomarkers can have a significant impact in the clinic given their low cost and minimal requirements for human input.
Collapse
Affiliation(s)
- Yiwen Xu
- Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts
| | - Ahmed Hosny
- Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts.,Radiology and Nuclear Medicine, GROW, Maastricht University Medical Centre, Maastricht, the Netherlands
| | - Roman Zeleznik
- Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts.,Radiology and Nuclear Medicine, GROW, Maastricht University Medical Centre, Maastricht, the Netherlands
| | - Chintan Parmar
- Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts
| | - Thibaud Coroller
- Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts
| | - Idalid Franco
- Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts
| | - Raymond H Mak
- Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts
| | - Hugo J W L Aerts
- Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts. .,Radiology and Nuclear Medicine, GROW, Maastricht University Medical Centre, Maastricht, the Netherlands.,Department of Radiology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts
| |
Collapse
|
13
|
Xu Y, Hosny A, Coroller T, Zeleznik R, Mak R, Aerts H. Deep Learning Based Tracking of Imaging Phenotypes to Improve Therapy Survival Prediction. Int J Radiat Oncol Biol Phys 2018. [DOI: 10.1016/j.ijrobp.2018.07.1429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
|
14
|
Hosny A, Parmar C, Coroller TP, Grossmann P, Zeleznik R, Kumar A, Bussink J, Gillies RJ, Mak RH, Aerts HJWL. Deep learning for lung cancer prognostication: A retrospective multi-cohort radiomics study. PLoS Med 2018; 15:e1002711. [PMID: 30500819 PMCID: PMC6269088 DOI: 10.1371/journal.pmed.1002711] [Citation(s) in RCA: 299] [Impact Index Per Article: 49.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/29/2018] [Accepted: 11/05/2018] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND Non-small-cell lung cancer (NSCLC) patients often demonstrate varying clinical courses and outcomes, even within the same tumor stage. This study explores deep learning applications in medical imaging allowing for the automated quantification of radiographic characteristics and potentially improving patient stratification. METHODS AND FINDINGS We performed an integrative analysis on 7 independent datasets across 5 institutions totaling 1,194 NSCLC patients (age median = 68.3 years [range 32.5-93.3], survival median = 1.7 years [range 0.0-11.7]). Using external validation in computed tomography (CT) data, we identified prognostic signatures using a 3D convolutional neural network (CNN) for patients treated with radiotherapy (n = 771, age median = 68.0 years [range 32.5-93.3], survival median = 1.3 years [range 0.0-11.7]). We then employed a transfer learning approach to achieve the same for surgery patients (n = 391, age median = 69.1 years [range 37.2-88.0], survival median = 3.1 years [range 0.0-8.8]). We found that the CNN predictions were significantly associated with 2-year overall survival from the start of respective treatment for radiotherapy (area under the receiver operating characteristic curve [AUC] = 0.70 [95% CI 0.63-0.78], p < 0.001) and surgery (AUC = 0.71 [95% CI 0.60-0.82], p < 0.001) patients. The CNN was also able to significantly stratify patients into low and high mortality risk groups in both the radiotherapy (p < 0.001) and surgery (p = 0.03) datasets. Additionally, the CNN was found to significantly outperform random forest models built on clinical parameters-including age, sex, and tumor node metastasis stage-as well as demonstrate high robustness against test-retest (intraclass correlation coefficient = 0.91) and inter-reader (Spearman's rank-order correlation = 0.88) variations. To gain a better understanding of the characteristics captured by the CNN, we identified regions with the most contribution towards predictions and highlighted the importance of tumor-surrounding tissue in patient stratification. We also present preliminary findings on the biological basis of the captured phenotypes as being linked to cell cycle and transcriptional processes. Limitations include the retrospective nature of this study as well as the opaque black box nature of deep learning networks. CONCLUSIONS Our results provide evidence that deep learning networks may be used for mortality risk stratification based on standard-of-care CT images from NSCLC patients. This evidence motivates future research into better deciphering the clinical and biological basis of deep learning networks as well as validation in prospective data.
Collapse
Affiliation(s)
- Ahmed Hosny
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Chintan Parmar
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Thibaud P. Coroller
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Patrick Grossmann
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Roman Zeleznik
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Avnish Kumar
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Johan Bussink
- Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Robert J. Gillies
- Department of Cancer Physiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida, United States of America
| | - Raymond H. Mak
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Hugo J. W. L. Aerts
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| |
Collapse
|
15
|
Abstract
Patients with inflammatory arthritic disease of the feet invariably require conservative office management. The simplest method of obtaining relief has been to use commercially available extra-depth shoes combined with custommade, removable, closed-celled polyethylene foam arch supports. These supports are soft, light, and can be accurately adjusted to each patient's pathology. The technique and modifications used for relieving the commonest problems, such as anterior metatarsalgia and plantar heel pain, are discussed.
Collapse
|
16
|
|
17
|
Johnson RM, Hart DL, Owen JR, Lerner E, Chapin W, Zeleznik R. The yale cervical orthosis: an evaluation of its effectiveness in restricting cervical motion in normal subjects and a comparison with other cervical orthoses. Phys Ther 1978; 58:865-71. [PMID: 662928 DOI: 10.1093/ptj/58.7.865] [Citation(s) in RCA: 17] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
The Yale cervical orthosis is a lightweight polyethylene foam Philadelphia collar with molded fiberglass extensions over the thorax. This orthosis was studied on 17 normal subjects in the extremes of the ranges of flexion, extension, rotation, and lateral bending using roentgenograms and axial photographs to assess how effectively it limited motion of the neck. Overall, it satisfactorily controlled cervical motion and was similar to the most effective rigid cervical orthoses. Flexion and extension ranges were compared at different segmental levels of the spine. The Yale orthosis was most successful in restricting flexion in the area of the middle and lower cervical spine and was acceptable in controlling extension range. The orthosis was least effective in controlling motion in the upper spine, particularly at the atlantoaxial articulation. The Yale orthosis is recommended for postsurgical protection of the middle and lower cervical spine and in select situations of spinal instability, but it is not recommended for control of odontoid fractures or atlantoaxial subluxation.
Collapse
|
18
|
Zeleznik R. Calcium hypochlorite for disinfection of hydrotherapy equipment. Phys Ther 1973; 53:80-1. [PMID: 4345450 DOI: 10.1093/ptj/53.1.80] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
|