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Braunschweig R, Kildal D, Janka R. Artificial intelligence (AI) in diagnostic imaging. ROFO-FORTSCHR RONTG 2024; 196:664-670. [PMID: 38346684 DOI: 10.1055/a-2208-6487] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/26/2024]
Affiliation(s)
- Rainer Braunschweig
- Institute of Radiology, University Hospitals Erlangen Department of Radiology, Erlangen, Germany
| | - Daniela Kildal
- Radiology, Valais Hospital, Visp, Switzerland
- Klinik für diagnostische und interventionelle Radiologie, University Hospital Ulm, Germany
| | - Rolf Janka
- Institute of Radiology, University Hospitals Erlangen Department of Radiology, Erlangen, Germany
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Li M, Deng X, Zhou D, Liu X, Dai J, Liu Q. A Novel Methylation-based Model for Prognostic Prediction in Lung Adenocarcinoma. Curr Genomics 2024; 25:26-40. [PMID: 38544827 PMCID: PMC10964088 DOI: 10.2174/0113892029277397231228062412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 11/30/2023] [Accepted: 12/05/2023] [Indexed: 08/25/2024] Open
Abstract
Objectives Specific methylation sites have shown promise in the early diagnosis of lung adenocarcinoma (LUAD). However, their utility in predicting LUAD prognosis remains unclear. This study aimed to construct a reliable methylation-based predictor for accurately predicting the prognosis of LUAD patients. Methods DNA methylation data and survival data from LUAD patients were obtained from the TCGA and a GEO series. A DNA methylation-based signature was developed using univariate least absolute shrinkage and selection operators and multivariate Cox regression models. Results Eight CpG sites were identified and validated as optimal prognostic signatures for the overall survival of LUAD patients. Receiver operating characteristic analysis demonstrated the high predictive ability of the eight-site methylation signature combined with clinical factors for overall survival. Conclusion This research successfully identified a novel eight-site methylation signature for predicting the overall survival of LUAD patients through bioinformatic integrated analysis of gene methylation markers used in the early diagnosis of lung cancer.
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Affiliation(s)
- Manyuan Li
- Department of Thoracic Surgery, Xinqiao Hospital, Third Military Medical University (Army Medical University), Chongqing, 400037, China
| | - Xufeng Deng
- Department of Thoracic Surgery, Xinqiao Hospital, Third Military Medical University (Army Medical University), Chongqing, 400037, China
| | - Dong Zhou
- Department of Thoracic Surgery, Xinqiao Hospital, Third Military Medical University (Army Medical University), Chongqing, 400037, China
| | - Xiaoqing Liu
- Department of Thoracic Surgery, Xinqiao Hospital, Third Military Medical University (Army Medical University), Chongqing, 400037, China
| | - Jigang Dai
- Department of Thoracic Surgery, Xinqiao Hospital, Third Military Medical University (Army Medical University), Chongqing, 400037, China
| | - Quanxing Liu
- Department of Thoracic Surgery, Xinqiao Hospital, Third Military Medical University (Army Medical University), Chongqing, 400037, China
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Majanga V, Viriri S. A Survey of Dental Caries Segmentation and Detection Techniques. ScientificWorldJournal 2022; 2022:8415705. [PMID: 35450417 PMCID: PMC9017544 DOI: 10.1155/2022/8415705] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2022] [Revised: 02/21/2022] [Accepted: 03/10/2022] [Indexed: 01/15/2023] Open
Abstract
Dental caries detection, in the past, has been a challenging task given the amount of information got from various radiographic images. Several methods have been introduced to improve the quality of images for faster caries detection. Deep learning has become the methodology of choice when it comes to analysis of medical images. This survey gives an in-depth look into the use of deep learning for object detection, segmentation, and classification. It further looks into literature on segmentation and detection methods of dental images through deep learning. From the literature studied, we found out that methods were grouped according to the type of dental caries (proximal, enamel), type of X-ray images used (extraoral, intraoral), and segmentation method (threshold-based, cluster-based, boundary-based, and region-based). From the works reviewed, the main focus has been found to be on threshold-based segmentation methods. Most of the reviewed papers have preferred the use of intraoral X-ray images over extraoral X-ray images to perform segmentation on dental images of already isolated parts of the teeth. This paper presents an in-depth analysis of recent research in deep learning for dental caries segmentation and detection. It involves discussing the methods and algorithms used in segmenting and detecting dental caries. It also discusses various existing models used and how they compare with each other in terms of system performance and evaluation. We also discuss the limitations of these methods, as well as future perspectives on how to improve their performance.
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Affiliation(s)
- Vincent Majanga
- Statistics and Computer Science, University of KwaZulu-Natal, Durban 4000, South Africa
| | - Serestina Viriri
- Statistics and Computer Science, University of KwaZulu-Natal, Durban 4000, South Africa
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A Novel Nodule Edge Sharpness Radiomic Biomarker Improves Performance of Lung-RADS for Distinguishing Adenocarcinomas from Granulomas on Non-Contrast CT Scans. Cancers (Basel) 2021; 13:cancers13112781. [PMID: 34205005 PMCID: PMC8199879 DOI: 10.3390/cancers13112781] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Accepted: 05/31/2021] [Indexed: 11/18/2022] Open
Abstract
Simple Summary The great majority of pulmonary nodules on screening CT scans are benign (95%). Due to inaccurate diagnoses of granulomas from adenocarcinomas on CT scans, many patients with benign nodules are subjected to unnecessary surgical procedures. The aim of this retrospective study is to evaluate the discriminability of a new radiomic feature, nodule edge/interface sharpness (NIS), for distinguishing lung adenocarcinomas from benign granulomas on non-contrast CT scans. Moreover, we aim to evaluate whether NIS can improve the performance of Lung-RADS, by reclassifying benign nodules that were initially assessed as suspicious. In a cohort of 352 patients with diagnostic non-contrast CT scans, NIS radiomics was able to classify nodules with an area under the receiver operating characteristic curve (ROC AUC) of 0.77, and when combined with intra-tumoral textural and shape features, classification performance increased to AUC of 0.84. Additionally, the NIS classifier correctly reclassified 46% of those lesions that were actually benign but deemed suspicious by Lung-RADS. Combining NIS with Lung-RADS has the potential to alter patient management by significantly decreasing unnecessary biopsies/follow up imaging. Abstract The aim of this study is to evaluate whether NIS radiomics can distinguish lung adenocarcinomas from granulomas on non-contrast CT scans, and also to improve the performance of Lung-RADS by reclassifying benign nodules that were initially assessed as suspicious. The screening or standard diagnostic non-contrast CT scans of 362 patients was divided into training (St, N = 145), validation (Sv, N = 145), and independent validation (Siv, N = 62) sets from different institutions. Nodules were identified and manually segmented on CT images by a radiologist. A series of 264 features relating to the edge sharpness transition from the inside to the outside of the nodule were extracted. The top 10 features were used to train a linear discriminant analysis (LDA) machine learning classifier on St. In conjunction with the LDA classifier, NIS radiomics classified nodules with an AUC of 0.82 ± 0.04, 0.77, and 0.71 respectively on St, Sv, and Siv. We evaluated the ability of the NIS classifier to determine the proportion of the patients in Sv that were identified initially as suspicious by Lung-RADS but were reclassified as benign by applying the NIS scores. The NIS classifier was able to correctly reclassify 46% of those lesions that were actually benign but deemed suspicious by Lung-RADS alone on Sv.
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Khodrog OA, Cui F, Xu N, Han Q, Liu J, Gong T, Yuan Q. Prediction of squamous cell carcinoma cases from squamous cell hyperplasia in throat lesions using CT radiomics model. Saudi Med J 2021; 42:284-292. [PMID: 33632907 PMCID: PMC7989270 DOI: 10.15537/smj.2021.42.3.20200617] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Accepted: 02/01/2021] [Indexed: 12/23/2022] Open
Abstract
Objectives: To differentiate squamous cell hyperplasia (SCH) (benign) from squamous cell carcinoma (SCC) malignant) using textural features extracted from CT images and thereby, facilitate the preoperative medical diagnosis and treatment of throat cancers without the need for sample biopsies. Methods: In total, 100 throat cancer patients were selected for this retrospective study. The cases were collected from the Second Hospital of Jilin University, Changchun, China, from June 2017 to January 2019. The patients were separated into a training and validation cohort consisting of 70 and 30 cases, respectively. The Artificial Intelligence Kit software (A.K. software) was used to extract the radiomics features from the CT images. These features were further processed using the minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) methods to obtain a subset of optimal features. The radiomics model was validated based on area-under-the-curve (AUC) values, accuracy, specificity, and sensitivity using the R-studio software. Results: The diagnostic accuracy, specificity, PPV, NPV, and AUC values obtained for the training cohort was 0.91, 0.9, 0.93, 0.9, and 0.96 CT angiography (CTA), 0.93, 0.93, 0.95, 0.90, and 0.96 computed tomography normal (CTN), and 0.92, 0.87, 0.91, 0.96, and 0.96 CT venogram (CTV). These values were subsequently confirmed in the validation cohort. Conclusion: The radiomics-based prediction model proposed in this study successfully differentiated between SCH and SCC throat cancers using CT imaging, thereby facilitating the development of accurate preoperative diagnosis based on specific biomarkers and cancer phenotypes.
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Affiliation(s)
- Osama A. Khodrog
- From the Department of Radiology (Khodrog, Cui, Xu, Han, Liu, Gong, Yuan), the Second Hospital of Jilin University, Changchun, China and from the Department of Medical Imaging (Khodrog), Faculty of Applied Medical Health, Palestine Ahliya University, Bethlehem, Palestine.
| | - Fengzhi Cui
- From the Department of Radiology (Khodrog, Cui, Xu, Han, Liu, Gong, Yuan), the Second Hospital of Jilin University, Changchun, China and from the Department of Medical Imaging (Khodrog), Faculty of Applied Medical Health, Palestine Ahliya University, Bethlehem, Palestine.
| | - Nannan Xu
- From the Department of Radiology (Khodrog, Cui, Xu, Han, Liu, Gong, Yuan), the Second Hospital of Jilin University, Changchun, China and from the Department of Medical Imaging (Khodrog), Faculty of Applied Medical Health, Palestine Ahliya University, Bethlehem, Palestine.
| | - Qinghe Han
- From the Department of Radiology (Khodrog, Cui, Xu, Han, Liu, Gong, Yuan), the Second Hospital of Jilin University, Changchun, China and from the Department of Medical Imaging (Khodrog), Faculty of Applied Medical Health, Palestine Ahliya University, Bethlehem, Palestine.
| | - Jianhua Liu
- From the Department of Radiology (Khodrog, Cui, Xu, Han, Liu, Gong, Yuan), the Second Hospital of Jilin University, Changchun, China and from the Department of Medical Imaging (Khodrog), Faculty of Applied Medical Health, Palestine Ahliya University, Bethlehem, Palestine.
| | - Tingting Gong
- From the Department of Radiology (Khodrog, Cui, Xu, Han, Liu, Gong, Yuan), the Second Hospital of Jilin University, Changchun, China and from the Department of Medical Imaging (Khodrog), Faculty of Applied Medical Health, Palestine Ahliya University, Bethlehem, Palestine.
| | - Qinghai Yuan
- From the Department of Radiology (Khodrog, Cui, Xu, Han, Liu, Gong, Yuan), the Second Hospital of Jilin University, Changchun, China and from the Department of Medical Imaging (Khodrog), Faculty of Applied Medical Health, Palestine Ahliya University, Bethlehem, Palestine.
- Address correspondence and reprint request to: Dr. Qinghai Yuan, Department of Radiology, Norman Bethune College of Medicine, The Second Hospital of Jilin University, Changchun, China. E-mail: ORCID ID: http://orcid.org/0000-0002-5337-5354
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Khorrami M, Bera K, Thawani R, Rajiah P, Gupta A, Fu P, Linden P, Pennell N, Jacono F, Gilkeson RC, Velcheti V, Madabhushi A. Distinguishing granulomas from adenocarcinomas by integrating stable and discriminating radiomic features on non-contrast computed tomography scans. Eur J Cancer 2021; 148:146-158. [PMID: 33743483 DOI: 10.1016/j.ejca.2021.02.008] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Revised: 01/28/2021] [Accepted: 02/02/2021] [Indexed: 12/13/2022]
Abstract
OBJECTIVE To identify stable and discriminating radiomic features on non-contrast CT scans to develop more generalisable radiomic classifiers for distinguishing granulomas from adenocarcinomas. METHODS In total, 412 patients with adenocarcinomas and granulomas from three institutions were retrospectively included. Segmentations of the lung nodules were performed manually by an expert radiologist in a 2D axial view. Radiomic features were extracted from intra- and perinodular regions. A total of 145 patients were used as part of the training set (Str), whereas 205 patients were used as part of test set I (Ste1) and 62 patients were used as part of independent test set II (Ste2). To mitigate the variation of CT acquisition parameters, we defined 'stable' radiomic features as those for which the feature expression remains relatively unchanged between different sites, as assessed using a Wilcoxon rank-sum test. These stable features were used to develop more generalisable radiomic classifiers that were more resilient to variations in lung CT scans. Features were ranked based on two criteria, firstly based on discriminability (i.e. maximising AUC) alone and subsequently based on maximising both feature stability and discriminability. Different machine-learning classifiers (Linear discriminant analysis, Quadratic discriminant analysis, Support vector machines and random forest) were trained with features selected using the two different criteria and then compared on the two independent test sets for distinguishing granulomas from adenocarcinomas, in terms of area under the receiver operating characteristic curve. RESULTS In the test sets, classifiers constructed using the criteria involving maximising feature stability and discriminability simultaneously achieved higher AUC compared with the discriminating alone criteria (Ste1 [n = 205]: maximum AUCs of 0.85versus . 0.80; p-value = 0.047 and Ste2 [n = 62]: maximum AUCs of 0.87 versus. 0.79; p-value = 0.021). These differences held for features extracted from scans with <3 mm slice thickness (AUC = 0.88 versus. 0.80; p-value = 0.039, n = 100) and for the ≥3 mm cases (AUC = 0.81 versus. 0.76; p-value = 0.034, n = 105). In both experiments, shape and peritumoural texture features had a higher stability compared with intratumoural texture features. CONCLUSIONS Our study suggests that explicitly accounting for both stability and discriminability results in more generalisable radiomic classifiers to distinguish adenocarcinomas from granulomas on non-contrast CT scans. Our results also showed that peritumoural texture and shape features were less affected by the scanner parameters compared with intratumoural texture features; however, they were also less discriminating compared with intratumoural features.
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Affiliation(s)
- Mohammadhadi Khorrami
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Kaustav Bera
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Rajat Thawani
- OHSU Knight Cancer Institute, Oregon Health & Science University, Oregon, USA
| | - Prabhakar Rajiah
- Department of Radiology, UT Southwestern Medical Center, Dallas, TX, USA
| | - Amit Gupta
- Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - Pingfu Fu
- Department of Population and Quantitative Health Sciences, CWRU, Cleveland, OH, USA
| | - Philip Linden
- Thoracic and Esophageal Surgery Department, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - Nathan Pennell
- Department of Hematology and Medical Oncology, Cleveland Clinic, Cleveland, OH, USA
| | - Frank Jacono
- Pulmonary Section, Cleveland Veterans Affairs Medical Center, Cleveland, OH, USA
| | - Robert C Gilkeson
- Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | | | - Anant Madabhushi
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA; Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, OH, USA.
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Liu J, Xu H, Qing H, Li Y, Yang X, He C, Ren J, Zhou P. Comparison of Radiomic Models Based on Low-Dose and Standard-Dose CT for Prediction of Adenocarcinomas and Benign Lesions in Solid Pulmonary Nodules. Front Oncol 2021; 10:634298. [PMID: 33604303 PMCID: PMC7884759 DOI: 10.3389/fonc.2020.634298] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Accepted: 12/14/2020] [Indexed: 12/26/2022] Open
Abstract
Objectives This study aimed to develop radiomic models based on low-dose CT (LDCT) and standard-dose CT to distinguish adenocarcinomas from benign lesions in patients with solid solitary pulmonary nodules and compare the performance among these radiomic models and Lung CT Screening Reporting and Data System (Lung-RADS). The reproducibility of radiomic features between LDCT and standard-dose CT were also evaluated. Methods A total of 141 consecutive pathologically confirmed solid solitary pulmonary nodules were enrolled including 50 adenocarcinomas and 48 benign nodules in primary cohort and 22 adenocarcinomas and 21 benign nodules in validation cohort. LDCT and standard-dose CT scans were conducted using same acquisition parameters and reconstruction method except for radiation dose. All nodules were automatically segmented and 104 original radiomic features were extracted. The concordance correlation coefficient was used to quantify reproducibility of radiomic features between LDCT and standard-dose CT. Radiomic features were selected to build radiomic signature, and clinical characteristics and radiomic signature were combined to develop radiomic nomogram for LDCT and standard-dose CT, respectively. The performance of radiomic models and Lung-RADS was assessed by area under curve (AUC) of receiver operating characteristic curve, sensitivity, and specificity. Results Shape and first order features, and neighboring gray tone difference matrix features were highly reproducible between LDCT and standard-dose CT. No significant differences of AUCs were found among radiomic signature and nomogram of LDCT and standard-dose CT in both primary and validation cohort (0.915 vs. 0.919 vs. 0.898 vs. 0.909 and 0.976 vs. 0.976 vs. 0.985 vs. 0.987, respectively). These radiomic models had higher specificity than Lung-RADS (all correct P < 0.05), while there were no significant differences of sensitivity between Lung-RADS and radiomic models. Conclusions The diagnostic performance of LDCT-based radiomic models to differentiate adenocarcinomas from benign lesions in solid pulmonary nodules were equivalent to that of standard-dose CT. The LDCT-based radiomic model with higher specificity and lower false-positive rate than Lung-RADS might help reduce overdiagnosis and overtreatment of solid pulmonary nodules in lung cancer screening.
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Affiliation(s)
- Jieke Liu
- Department of Radiology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Hao Xu
- Department of Radiology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Haomiao Qing
- Department of Radiology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Yong Li
- Department of Radiology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Xi Yang
- Department of Radiology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Changjiu He
- Department of Radiology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Jing Ren
- Department of Radiology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Peng Zhou
- Department of Radiology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
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Beig N, Bera K, Tiwari P. Introduction to radiomics and radiogenomics in neuro-oncology: implications and challenges. Neurooncol Adv 2020; 2:iv3-iv14. [PMID: 33521636 PMCID: PMC7829475 DOI: 10.1093/noajnl/vdaa148] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
Neuro-oncology largely consists of malignancies of the brain and central nervous system including both primary as well as metastatic tumors. Currently, a significant clinical challenge in neuro-oncology is to tailor therapies for patients based on a priori knowledge of their survival outcome or treatment response to conventional or experimental therapies. Radiomics or the quantitative extraction of subvisual data from conventional radiographic imaging has recently emerged as a powerful data-driven approach to offer insights into clinically relevant questions related to diagnosis, prediction, prognosis, as well as assessing treatment response. Furthermore, radiogenomic approaches provide a mechanism to establish statistical correlations of radiomic features with point mutations and next-generation sequencing data to further leverage the potential of routine MRI scans to serve as "virtual biopsy" maps. In this review, we provide an introduction to radiomic and radiogenomic approaches in neuro-oncology, including a brief description of the workflow involving preprocessing, tumor segmentation, and extraction of "hand-crafted" features from the segmented region of interest, as well as identifying radiogenomic associations that could ultimately lead to the development of reliable prognostic and predictive models in neuro-oncology applications. Lastly, we discuss the promise of radiomics and radiogenomic approaches in personalizing treatment decisions in neuro-oncology, as well as the challenges with clinical adoption, which will rely heavily on their demonstrated resilience to nonstandardization in imaging protocols across sites and scanners, as well as in their ability to demonstrate reproducibility across large multi-institutional cohorts.
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Affiliation(s)
- Niha Beig
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
| | - Kaustav Bera
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
| | - Pallavi Tiwari
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
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Khorrami M, Bera K, Leo P, Vaidya P, Patil P, Thawani R, Velu P, Rajiah P, Alilou M, Choi H, Feldman MD, Gilkeson RC, Linden P, Fu P, Pass H, Velcheti V, Madabhushi A. Stable and discriminating radiomic predictor of recurrence in early stage non-small cell lung cancer: Multi-site study. Lung Cancer 2020; 142:90-97. [PMID: 32120229 DOI: 10.1016/j.lungcan.2020.02.018] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2019] [Revised: 02/03/2020] [Accepted: 02/25/2020] [Indexed: 01/14/2023]
Abstract
OBJECTIVES To evaluate whether combining stability and discriminability criteria in building radiomic classifiers will improve the prognosis of cancer recurrence in early stage non-small cell lung cancer on non-contrast computer tomography (CT). MATERIALS AND METHODS CT scans of 610 patients with early stage (IA, IB, IIA) NSCLC from four independent cohorts were evaluated. A total of 350 patients from Cleveland Clinic Foundation and University of Pennsylvania were divided into two equal sets for training (D1) and validation set (D2). 80 patients from The Cancer Genome Atlas Lung Adenocarcinoma and Squamous Cell Carcinoma and 195 patients from The Cancer Imaging Archive, were used as independent second (D3) and third (D4) validation sets. A linear discriminant analysis (LDA) classifier was built based on the most stable and discriminate features. In addition, a radiomic risk score (RRS) was generated by using least absolute shrinkage and selection operator, Cox regression model to predict time to progression (TTP) following surgery. RESULTS A feature selection strategy focusing on both feature discriminability and stability resulted in the classifier having a higher discriminability on validation datasets compared to the discriminability alone criteria in discriminating cancer recurrence (D2, AUC of 0.75 vs. 0.65; D3, 0.74 vs. 0.62; D4, 0.76 vs. 0.63). The RRS generated by most stable-discriminating features was significantly associated with TTP compared to discriminating alone criteria (HR = 1.66, C-index of 0.72 vs. HR = 1.04, C-index of 0.62). CONCLUSION Accounting for both stability and discriminability yielded a more generalizable classifier for predicting cancer recurrence and TTP in early stage NSCLC.
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Affiliation(s)
- Mohammadhadi Khorrami
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Kaustav Bera
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Patrick Leo
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Pranjal Vaidya
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Pradnya Patil
- Department of Solid Tumor Oncology, Cleveland Clinic, Cleveland, OH, USA
| | - Rajat Thawani
- Department of Internal Medicine, Maimonides Medical Center, Brooklyn, NY, USA
| | - Priya Velu
- Pathology and Laboratory Medicine, Hospital of the University of Pennsylvania, USA
| | - Prabhakar Rajiah
- Department of Radiology, UT Southwestern Medical Center, Dallas, TX, USA
| | - Mehdi Alilou
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Humberto Choi
- Department of Pulmonary Medicine, Cleveland Clinic, Cleveland, OH, USA
| | - Michael D Feldman
- Pathology and Laboratory Medicine, Hospital of the University of Pennsylvania, USA
| | | | - Philip Linden
- Thoracic and Esophageal Surgery Department, University Hospitals of Cleveland, OH, USA
| | - Pingfu Fu
- Department of Population and Quantitative Health Sciences, CWRU, Cleveland, OH, USA
| | - Harvey Pass
- Department of Hematology and Oncology, NYU Langone Health, New York, NY, USA
| | - Vamsidhar Velcheti
- Department of Hematology and Oncology, NYU Langone Health, New York, NY, USA
| | - Anant Madabhushi
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA.
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Khorrami M, Prasanna P, Gupta A, Patil P, Velu PD, Thawani R, Corredor G, Alilou M, Bera K, Fu P, Feldman M, Velcheti V, Madabhushi A. Changes in CT Radiomic Features Associated with Lymphocyte Distribution Predict Overall Survival and Response to Immunotherapy in Non-Small Cell Lung Cancer. Cancer Immunol Res 2020; 8:108-119. [PMID: 31719058 PMCID: PMC7718609 DOI: 10.1158/2326-6066.cir-19-0476] [Citation(s) in RCA: 171] [Impact Index Per Article: 42.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2019] [Revised: 09/04/2019] [Accepted: 11/05/2019] [Indexed: 12/26/2022]
Abstract
No predictive biomarkers can robustly identify patients with non-small cell lung cancer (NSCLC) who will benefit from immune checkpoint inhibitor (ICI) therapies. Here, in a machine learning setting, we compared changes ("delta") in the radiomic texture (DelRADx) of CT patterns both within and outside tumor nodules before and after two to three cycles of ICI therapy. We found that DelRADx patterns could predict response to ICI therapy and overall survival (OS) for patients with NSCLC. We retrospectively analyzed data acquired from 139 patients with NSCLC at two institutions, who were divided into a discovery set (D1 = 50) and two independent validation sets (D2 = 62, D3 = 27). Intranodular and perinodular texture descriptors were extracted, and the relative differences were computed. A linear discriminant analysis (LDA) classifier was trained with 8 DelRADx features to predict RECIST-derived response. Association of delta-radiomic risk score (DRS) with OS was determined. The association of DelRADx features with tumor-infiltrating lymphocyte (TIL) density on the diagnostic biopsies (n = 36) was also evaluated. The LDA classifier yielded an AUC of 0.88 ± 0.08 in distinguishing responders from nonresponders in D1, and 0.85 and 0.81 in D2 and D3 DRS was associated with OS [HR: 1.64; 95% confidence interval (CI), 1.22-2.21; P = 0.0011; C-index = 0.72). Peritumoral Gabor features were associated with the density of TILs on diagnostic biopsy samples. Our results show that DelRADx could be used to identify early functional responses in patients with NSCLC.
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Affiliation(s)
- Mohammadhadi Khorrami
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
| | - Prateek Prasanna
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
| | - Amit Gupta
- Department of Radiology-Cardiothoracic Imaging, University Hospitals, Cleveland, Ohio
| | - Pradnya Patil
- Department of Solid Tumor Oncology, Cleveland Clinic, Cleveland, Ohio
| | - Priya D Velu
- Pathology and Laboratory Medicine, Weill Cornell Medicine Physicians, New York, New York
| | - Rajat Thawani
- Department of Internal Medicine, Maimonides Medical Center, Brooklyn, New York
| | - German Corredor
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
| | - Mehdi Alilou
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
| | - Kaustav Bera
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
| | - Pingfu Fu
- Department of Population and Quantitative Health Sciences, CWRU, Cleveland, Ohio
| | - Michael Feldman
- Pathology and Laboratory Medicine, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania
| | - Vamsidhar Velcheti
- Department of Hematology and Oncology, NYU Langone Health, New York, New York
| | - Anant Madabhushi
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio.
- Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, Ohio
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11
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Sollini M, Antunovic L, Chiti A, Kirienko M. Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics. Eur J Nucl Med Mol Imaging 2019; 46:2656-2672. [PMID: 31214791 PMCID: PMC6879445 DOI: 10.1007/s00259-019-04372-x] [Citation(s) in RCA: 156] [Impact Index Per Article: 31.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2019] [Accepted: 05/23/2019] [Indexed: 02/06/2023]
Abstract
PURPOSE The aim of this systematic review was to analyse literature on artificial intelligence (AI) and radiomics, including all medical imaging modalities, for oncological and non-oncological applications, in order to assess how far the image mining research stands from routine medical application. To do this, we applied a trial phases classification inspired from the drug development process. METHODS Among the articles we considered for inclusion from PubMed were multimodality AI and radiomics investigations, with a validation analysis aimed at relevant clinical objectives. Quality assessment of selected papers was performed according to the QUADAS-2 criteria. We developed the phases classification criteria for image mining studies. RESULTS Overall 34,626 articles were retrieved, 300 were selected applying the inclusion/exclusion criteria, and 171 high-quality papers (QUADAS-2 ≥ 7) were identified and analysed. In 27/171 (16%), 141/171 (82%), and 3/171 (2%) studies the development of an AI-based algorithm, radiomics model, and a combined radiomics/AI approach, respectively, was described. A total of 26/27(96%) and 1/27 (4%) AI studies were classified as phase II and III, respectively. Consequently, 13/141 (9%), 10/141 (7%), 111/141 (79%), and 7/141 (5%) radiomics studies were classified as phase 0, I, II, and III, respectively. All three radiomics/AI studies were categorised as phase II trials. CONCLUSIONS The results of the studies are promising but still not mature enough for image mining tools to be implemented in the clinical setting and be widely used. The transfer learning from the well-known drug development process, with some specific adaptations to the image mining discipline could represent the most effective way for radiomics and AI algorithms to become the standard of care tools.
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Affiliation(s)
- Martina Sollini
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20090, Pieve Emanuele, Milan, Italy
| | - Lidija Antunovic
- Nuclear Medicine, Humanitas Clinical and Research Center IRCCS, Rozzano, Milan, Italy
| | - Arturo Chiti
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20090, Pieve Emanuele, Milan, Italy
- Nuclear Medicine, Humanitas Clinical and Research Center IRCCS, Rozzano, Milan, Italy
| | - Margarita Kirienko
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20090, Pieve Emanuele, Milan, Italy.
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12
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Khorrami M, Jain P, Bera K, Alilou M, Thawani R, Patil P, Ahmad U, Murthy S, Stephans K, Fu P, Velcheti V, Madabhushi A. Predicting pathologic response to neoadjuvant chemoradiation in resectable stage III non-small cell lung cancer patients using computed tomography radiomic features. Lung Cancer 2019; 135:1-9. [PMID: 31446979 PMCID: PMC6711393 DOI: 10.1016/j.lungcan.2019.06.020] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2018] [Revised: 05/08/2019] [Accepted: 06/23/2019] [Indexed: 12/25/2022]
Abstract
OBJECTIVE The use of a neoadjuvant chemoradiation followed by surgery in patients with stage IIIA NSCLC is controversial and the benefit of surgery is limited. There are currently no clinically validated biomarkers to select patients for such an approach. In this study we evaluate computed tomography (CT) derived intratumoral and peritumoral texture and nodule shape features in their ability to predict major pathological response (MPR). MPR being defined as ≤10% of residual viable tumor, assessed at the time of surgery. MATERIAL AND METHODS Ninety patients with stage III NSCLC treated with chemoradiation prior to surgical resection were selected. The patients were divided randomly into two equal sets, one for training and one for independent testing. The radiomic texture and shape features were extracted from within the nodule (intra) and from the parenchymal regions immediately surrounding the nodule (peritumoral). A univariate regression analysis was performed on the image and clinicopathologic variables and then included into a multivariable logistic regression (MLR) for binary outcome prediction of MPR. The radiomic signature risk-score was generated by using a multivariate Cox regression model and association of the signature with OS and DFS was also evaluated. RESULTS Thirteen stable and predictive intratumoral and peritumoral radiomic texture features were found to be predictive of MPR. The MLR classifier yielded an AUC of 0.90 ± 0.025 within the training set and a corresponding AUC = 0.86 in prediction of MPR within the test set. The radiomic signature was also significantly associated with OS (HR = 11.18, 95% CI = 3.17, 44.1; p-value = 0.008) and DFS (HR = 2.78, 95% CI = 1.11, 4.12; p-value = 0.0042) in the testing set. CONCLUSION Texture features extracted within and around the lung tumor on CT images appears to be associated with the likelihood of MPR, OS and DFS to chemoradiation.
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Affiliation(s)
- Mohammadhadi Khorrami
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Prantesh Jain
- Department of Hematology/Oncology, University Hospitals Seidman Cancer Center, Case Comprehensive Cancer Center, Cleveland, OH, USA
| | - Kaustav Bera
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Mehdi Alilou
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Rajat Thawani
- Maimonides Medical Center, 4802 10th Ave, Brooklyn, NY 11219, USA
| | - Pradnya Patil
- Department of Solid Tumor Oncology, Cleveland Clinic, Cleveland, OH, USA
| | - Usman Ahmad
- Department of Thoracic and Cardiovascular Surgery, Cleveland Clinic, Cleveland, OH, USA
| | - Sudish Murthy
- Department of Thoracic and Cardiovascular Surgery, Cleveland Clinic, Cleveland, OH, USA
| | - Kevin Stephans
- Department of Radiation Oncology, Cleveland Clinic, Cleveland, OH, USA
| | - Pinfu Fu
- Department of Population and Quantitative Health Sciences, CWRU, Cleveland, OH, USA
| | - Vamsidhar Velcheti
- Department of Hematology and Oncology, NYU Langone Health, New York, NY, USA
| | - Anant Madabhushi
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA; Louis Stokes Cleveland Veterans Administration Medical Center, OH, USA.
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13
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Chirra P, Leo P, Yim M, Bloch BN, Rastinehad AR, Purysko A, Rosen M, Madabhushi A, Viswanath SE. Multisite evaluation of radiomic feature reproducibility and discriminability for identifying peripheral zone prostate tumors on MRI. J Med Imaging (Bellingham) 2019; 6:024502. [PMID: 31259199 PMCID: PMC6566001 DOI: 10.1117/1.jmi.6.2.024502] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2018] [Accepted: 05/15/2019] [Indexed: 12/18/2022] Open
Abstract
Recent advances in the field of radiomics have enabled the development of a number of prognostic and predictive imaging-based tools for a variety of diseases. However, wider clinical adoption of these tools is contingent on their generalizability across multiple sites and scanners. This may be particularly relevant in the context of radiomic features derived from T1- or T2-weighted magnetic resonance images (MRIs), where signal intensity values are known to lack tissue-specific meaning and vary based on differing acquisition protocols between institutions. We present the first empirical study of benchmarking five different radiomic feature families in terms of both reproducibility and discriminability in a multisite setting, specifically, for identifying prostate tumors in the peripheral zone on MRI. Our cohort comprised 147 patient T2-weighted MRI datasets from four different sites, all of which are first preprocessed to correct for acquisition-related artifacts such as bias field, differing voxel resolutions, and intensity drift (nonstandardness). About 406 three-dimensional voxel-wise radiomic features from five different families (gray, Haralick, gradient, Laws, and Gabor) were evaluated in a cross-site setting to determine (a) how reproducible they are within a relatively homogeneous nontumor tissue region and (b) how well they could discriminate tumor regions from nontumor regions. Our results demonstrate that a majority of the popular Haralick features are reproducible in over 99% of all cross-site comparisons, as well as achieve excellent cross-site discriminability (classification accuracy of ≈ 0.8 ). By contrast, a majority of Laws features are highly variable across sites (reproducible in < 75 % of all cross-site comparisons) as well as resulting in low cross-site classifier accuracies ( < 0.6 ), likely due to a large number of noisy filter responses that can be extracted. These trends suggest that only a subset of radiomic features and associated parameters may be both reproducible and discriminable enough for use within machine learning classifier schemes.
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Affiliation(s)
- Prathyush Chirra
- Case Western Reserve University, Department of Biomedical Engineering, Cleveland, Ohio, United States
| | - Patrick Leo
- Case Western Reserve University, Department of Biomedical Engineering, Cleveland, Ohio, United States
| | - Michael Yim
- Northeast Ohio Medical University, College of Medicine, Rootstown, Ohio, United States
| | - B. Nicolas Bloch
- Boston University School of Medicine, Department of Radiology, Boston, Massachusetts, United States
| | - Ardeshir R. Rastinehad
- Icahn School of Medicine at Mount Sinai, Department of Urology, New York, New York, United States
| | - Andrei Purysko
- Cleveland Clinic, Department of Radiology, Cleveland, Ohio, United States
| | - Mark Rosen
- Hospital of the University of Pennsylvania, Department of Radiology, Philadelphia, Pennsylvania, United States
| | - Anant Madabhushi
- Case Western Reserve University, Department of Biomedical Engineering, Cleveland, Ohio, United States
- Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, Ohio, United States
| | - Satish E. Viswanath
- Case Western Reserve University, Department of Biomedical Engineering, Cleveland, Ohio, United States
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14
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Khorrami M, Khunger M, Zagouras A, Patil P, Thawani R, Bera K, Rajiah P, Fu P, Velcheti V, Madabhushi A. Combination of Peri- and Intratumoral Radiomic Features on Baseline CT Scans Predicts Response to Chemotherapy in Lung Adenocarcinoma. Radiol Artif Intell 2019; 1:e180012. [PMID: 32076657 DOI: 10.1148/ryai.2019180012] [Citation(s) in RCA: 70] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2018] [Revised: 01/16/2019] [Accepted: 02/04/2019] [Indexed: 12/11/2022]
Abstract
Purpose To identify the role of radiomics texture features both within and outside the nodule in predicting (a) time to progression (TTP) and overall survival (OS) as well as (b) response to chemotherapy in patients with non-small cell lung cancer (NSCLC). Materials and Methods Data in a total of 125 patients who had been treated with pemetrexed-based platinum doublet chemotherapy at Cleveland Clinic were retrospectively analyzed. The patients were divided randomly into two sets with the constraint that there were an equal number of responders and nonresponders in the training set. The training set comprised 53 patients with NSCLC, and the validation set comprised 72 patients. A machine learning classifier trained with radiomic texture features extracted from intra- and peritumoral regions of non-contrast-enhanced CT images was used to predict response to chemotherapy. The radiomic risk-score signature was generated by using least absolute shrinkage and selection operator with the Cox regression model; association of the radiomic signature with TTP and OS was also evaluated. Results A combination of radiomic features in conjunction with a quadratic discriminant analysis classifier yielded a mean maximum area under the receiver operating characteristic curve (AUC) of 0.82 ± 0.09 (standard deviation) in the training set and a corresponding AUC of 0.77 in the independent testing set. The radiomics signature was also significantly associated with TTP (hazard ratio [HR], 2.8; 95% confidence interval [CI]: 1.95, 4.00; P < .0001) and OS (HR, 2.35; 95% CI: 1.41, 3.94; P = .0011). Additionally, decision curve analysis demonstrated that in terms of clinical usefulness, the radiomics signature had a higher overall net benefit in prediction of high-risk patients to receive treatment than the clinicopathologic measurements. Conclusion This study suggests that radiomic texture features extracted from within and around the nodule on baseline CT scans are (a) predictive of response to chemotherapy and (b) associated with TTP and OS for patients with NSCLC.© RSNA, 2019Supplemental material is available for this article.
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Affiliation(s)
- Mohammadhadi Khorrami
- Department of Biomedical Engineering, Case Western Reserve University School of Engineering, 2071 Martin Luther King Dr, Cleveland, OH 44106-7207 (M. Khorrami, K.B., A.M.); Departments of Internal Medicine (M. Khunger) and Solid Tumor Oncology (A.Z., P.P.), Cleveland Clinic, Cleveland, Ohio; Department of Internal Medicine, Maimonides Medical Center, Brooklyn, NY (R.T.); Department of Radiology, UT Southwestern Medical Center, Dallas, Tex (P.R.); Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio (P.F.); Department of Hematology and Oncology, New York University, New York, NY (V.V.); Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, Ohio (A.M.)
| | - Monica Khunger
- Department of Biomedical Engineering, Case Western Reserve University School of Engineering, 2071 Martin Luther King Dr, Cleveland, OH 44106-7207 (M. Khorrami, K.B., A.M.); Departments of Internal Medicine (M. Khunger) and Solid Tumor Oncology (A.Z., P.P.), Cleveland Clinic, Cleveland, Ohio; Department of Internal Medicine, Maimonides Medical Center, Brooklyn, NY (R.T.); Department of Radiology, UT Southwestern Medical Center, Dallas, Tex (P.R.); Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio (P.F.); Department of Hematology and Oncology, New York University, New York, NY (V.V.); Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, Ohio (A.M.)
| | - Alexia Zagouras
- Department of Biomedical Engineering, Case Western Reserve University School of Engineering, 2071 Martin Luther King Dr, Cleveland, OH 44106-7207 (M. Khorrami, K.B., A.M.); Departments of Internal Medicine (M. Khunger) and Solid Tumor Oncology (A.Z., P.P.), Cleveland Clinic, Cleveland, Ohio; Department of Internal Medicine, Maimonides Medical Center, Brooklyn, NY (R.T.); Department of Radiology, UT Southwestern Medical Center, Dallas, Tex (P.R.); Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio (P.F.); Department of Hematology and Oncology, New York University, New York, NY (V.V.); Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, Ohio (A.M.)
| | - Pradnya Patil
- Department of Biomedical Engineering, Case Western Reserve University School of Engineering, 2071 Martin Luther King Dr, Cleveland, OH 44106-7207 (M. Khorrami, K.B., A.M.); Departments of Internal Medicine (M. Khunger) and Solid Tumor Oncology (A.Z., P.P.), Cleveland Clinic, Cleveland, Ohio; Department of Internal Medicine, Maimonides Medical Center, Brooklyn, NY (R.T.); Department of Radiology, UT Southwestern Medical Center, Dallas, Tex (P.R.); Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio (P.F.); Department of Hematology and Oncology, New York University, New York, NY (V.V.); Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, Ohio (A.M.)
| | - Rajat Thawani
- Department of Biomedical Engineering, Case Western Reserve University School of Engineering, 2071 Martin Luther King Dr, Cleveland, OH 44106-7207 (M. Khorrami, K.B., A.M.); Departments of Internal Medicine (M. Khunger) and Solid Tumor Oncology (A.Z., P.P.), Cleveland Clinic, Cleveland, Ohio; Department of Internal Medicine, Maimonides Medical Center, Brooklyn, NY (R.T.); Department of Radiology, UT Southwestern Medical Center, Dallas, Tex (P.R.); Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio (P.F.); Department of Hematology and Oncology, New York University, New York, NY (V.V.); Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, Ohio (A.M.)
| | - Kaustav Bera
- Department of Biomedical Engineering, Case Western Reserve University School of Engineering, 2071 Martin Luther King Dr, Cleveland, OH 44106-7207 (M. Khorrami, K.B., A.M.); Departments of Internal Medicine (M. Khunger) and Solid Tumor Oncology (A.Z., P.P.), Cleveland Clinic, Cleveland, Ohio; Department of Internal Medicine, Maimonides Medical Center, Brooklyn, NY (R.T.); Department of Radiology, UT Southwestern Medical Center, Dallas, Tex (P.R.); Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio (P.F.); Department of Hematology and Oncology, New York University, New York, NY (V.V.); Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, Ohio (A.M.)
| | - Prabhakar Rajiah
- Department of Biomedical Engineering, Case Western Reserve University School of Engineering, 2071 Martin Luther King Dr, Cleveland, OH 44106-7207 (M. Khorrami, K.B., A.M.); Departments of Internal Medicine (M. Khunger) and Solid Tumor Oncology (A.Z., P.P.), Cleveland Clinic, Cleveland, Ohio; Department of Internal Medicine, Maimonides Medical Center, Brooklyn, NY (R.T.); Department of Radiology, UT Southwestern Medical Center, Dallas, Tex (P.R.); Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio (P.F.); Department of Hematology and Oncology, New York University, New York, NY (V.V.); Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, Ohio (A.M.)
| | - Pingfu Fu
- Department of Biomedical Engineering, Case Western Reserve University School of Engineering, 2071 Martin Luther King Dr, Cleveland, OH 44106-7207 (M. Khorrami, K.B., A.M.); Departments of Internal Medicine (M. Khunger) and Solid Tumor Oncology (A.Z., P.P.), Cleveland Clinic, Cleveland, Ohio; Department of Internal Medicine, Maimonides Medical Center, Brooklyn, NY (R.T.); Department of Radiology, UT Southwestern Medical Center, Dallas, Tex (P.R.); Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio (P.F.); Department of Hematology and Oncology, New York University, New York, NY (V.V.); Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, Ohio (A.M.)
| | - Vamsidhar Velcheti
- Department of Biomedical Engineering, Case Western Reserve University School of Engineering, 2071 Martin Luther King Dr, Cleveland, OH 44106-7207 (M. Khorrami, K.B., A.M.); Departments of Internal Medicine (M. Khunger) and Solid Tumor Oncology (A.Z., P.P.), Cleveland Clinic, Cleveland, Ohio; Department of Internal Medicine, Maimonides Medical Center, Brooklyn, NY (R.T.); Department of Radiology, UT Southwestern Medical Center, Dallas, Tex (P.R.); Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio (P.F.); Department of Hematology and Oncology, New York University, New York, NY (V.V.); Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, Ohio (A.M.)
| | - Anant Madabhushi
- Department of Biomedical Engineering, Case Western Reserve University School of Engineering, 2071 Martin Luther King Dr, Cleveland, OH 44106-7207 (M. Khorrami, K.B., A.M.); Departments of Internal Medicine (M. Khunger) and Solid Tumor Oncology (A.Z., P.P.), Cleveland Clinic, Cleveland, Ohio; Department of Internal Medicine, Maimonides Medical Center, Brooklyn, NY (R.T.); Department of Radiology, UT Southwestern Medical Center, Dallas, Tex (P.R.); Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio (P.F.); Department of Hematology and Oncology, New York University, New York, NY (V.V.); Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, Ohio (A.M.)
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15
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Beig N, Khorrami M, Alilou M, Prasanna P, Braman N, Orooji M, Rakshit S, Bera K, Rajiah P, Ginsberg J, Donatelli C, Thawani R, Yang M, Jacono F, Tiwari P, Velcheti V, Gilkeson R, Linden P, Madabhushi A. Perinodular and Intranodular Radiomic Features on Lung CT Images Distinguish Adenocarcinomas from Granulomas. Radiology 2019; 290:783-792. [PMID: 30561278 PMCID: PMC6394783 DOI: 10.1148/radiol.2018180910] [Citation(s) in RCA: 206] [Impact Index Per Article: 41.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2018] [Revised: 10/15/2018] [Accepted: 10/25/2018] [Indexed: 12/18/2022]
Abstract
Purpose To evaluate ability of radiomic (computer-extracted imaging) features to distinguish non-small cell lung cancer adenocarcinomas from granulomas at noncontrast CT. Materials and Methods For this retrospective study, screening or standard diagnostic noncontrast CT images were collected for 290 patients (mean age, 68 years; range, 18-92 years; 125 men [mean age, 67 years; range, 18-90 years] and 165 women [mean age, 68 years; range, 33-92 years]) from two institutions between 2007 and 2013. Histopathologic analysis was available for one nodule per patient. Corresponding nodule of interest was identified on axial CT images by a radiologist with manual annotation. Nodule shape, wavelet (Gabor), and texture-based (Haralick and Laws energy) features were extracted from intra- and perinodular regions. Features were pruned to train machine learning classifiers with 145 patients. In a test set of 145 patients, classifier results were compared against a convolutional neural network (CNN) and diagnostic readings of two radiologists. Results Support vector machine classifier with intranodular radiomic features achieved an area under the receiver operating characteristic curve (AUC) of 0.75 on the test set. Combining radiomics of intranodular with perinodular regions improved the AUC to 0.80. On the same test set, CNN resulted in an AUC of 0.76. Radiologist readers achieved AUCs of 0.61 and 0.60, respectively. Conclusion Radiomic features from intranodular and perinodular regions of nodules can distinguish non-small cell lung cancer adenocarcinomas from benign granulomas at noncontrast CT. © RSNA, 2018 Online supplemental material is available for this article. See also the editorial by Nishino in this issue.
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Affiliation(s)
- Niha Beig
- From the Department of Biomedical Engineering, Case Western Reserve
University, 2071 Martin Luther King Dr, Wickenden 523, Cleveland, OH 44106-7207
(N. Beig, M.K., M.A., P.P., N. Braman, M.O., K.B., P.T., A.M.); Taussig Cancer
Institute–Cleveland Clinic, Cleveland, Ohio (S.R.); Division of Thoracic
and Esophageal Surgery (J.G., P.L.), Division of Pulmonary Critical Care and
Sleep Medicine (C.D., F.J.), Department of Pathology (M.Y.), and Department of
Radiology (R.G.), University Hospitals of Cleveland, Cleveland, Ohio; Pulmonary
Section, Cleveland Veterans Affairs Medical Center, Cleveland, Ohio (F.J.);
Department of Radiology, UT Southwestern Medical Center, Dallas, Tex (P.R.);
Department of Internal Medicine, Maimonides Medical Center, Brooklyn, NY (R.T.);
and Hematology and Oncology, New York University, Perlmutter Cancer Center, New
York, NY (V.V.)
| | - Mohammadhadi Khorrami
- From the Department of Biomedical Engineering, Case Western Reserve
University, 2071 Martin Luther King Dr, Wickenden 523, Cleveland, OH 44106-7207
(N. Beig, M.K., M.A., P.P., N. Braman, M.O., K.B., P.T., A.M.); Taussig Cancer
Institute–Cleveland Clinic, Cleveland, Ohio (S.R.); Division of Thoracic
and Esophageal Surgery (J.G., P.L.), Division of Pulmonary Critical Care and
Sleep Medicine (C.D., F.J.), Department of Pathology (M.Y.), and Department of
Radiology (R.G.), University Hospitals of Cleveland, Cleveland, Ohio; Pulmonary
Section, Cleveland Veterans Affairs Medical Center, Cleveland, Ohio (F.J.);
Department of Radiology, UT Southwestern Medical Center, Dallas, Tex (P.R.);
Department of Internal Medicine, Maimonides Medical Center, Brooklyn, NY (R.T.);
and Hematology and Oncology, New York University, Perlmutter Cancer Center, New
York, NY (V.V.)
| | - Mehdi Alilou
- From the Department of Biomedical Engineering, Case Western Reserve
University, 2071 Martin Luther King Dr, Wickenden 523, Cleveland, OH 44106-7207
(N. Beig, M.K., M.A., P.P., N. Braman, M.O., K.B., P.T., A.M.); Taussig Cancer
Institute–Cleveland Clinic, Cleveland, Ohio (S.R.); Division of Thoracic
and Esophageal Surgery (J.G., P.L.), Division of Pulmonary Critical Care and
Sleep Medicine (C.D., F.J.), Department of Pathology (M.Y.), and Department of
Radiology (R.G.), University Hospitals of Cleveland, Cleveland, Ohio; Pulmonary
Section, Cleveland Veterans Affairs Medical Center, Cleveland, Ohio (F.J.);
Department of Radiology, UT Southwestern Medical Center, Dallas, Tex (P.R.);
Department of Internal Medicine, Maimonides Medical Center, Brooklyn, NY (R.T.);
and Hematology and Oncology, New York University, Perlmutter Cancer Center, New
York, NY (V.V.)
| | - Prateek Prasanna
- From the Department of Biomedical Engineering, Case Western Reserve
University, 2071 Martin Luther King Dr, Wickenden 523, Cleveland, OH 44106-7207
(N. Beig, M.K., M.A., P.P., N. Braman, M.O., K.B., P.T., A.M.); Taussig Cancer
Institute–Cleveland Clinic, Cleveland, Ohio (S.R.); Division of Thoracic
and Esophageal Surgery (J.G., P.L.), Division of Pulmonary Critical Care and
Sleep Medicine (C.D., F.J.), Department of Pathology (M.Y.), and Department of
Radiology (R.G.), University Hospitals of Cleveland, Cleveland, Ohio; Pulmonary
Section, Cleveland Veterans Affairs Medical Center, Cleveland, Ohio (F.J.);
Department of Radiology, UT Southwestern Medical Center, Dallas, Tex (P.R.);
Department of Internal Medicine, Maimonides Medical Center, Brooklyn, NY (R.T.);
and Hematology and Oncology, New York University, Perlmutter Cancer Center, New
York, NY (V.V.)
| | - Nathaniel Braman
- From the Department of Biomedical Engineering, Case Western Reserve
University, 2071 Martin Luther King Dr, Wickenden 523, Cleveland, OH 44106-7207
(N. Beig, M.K., M.A., P.P., N. Braman, M.O., K.B., P.T., A.M.); Taussig Cancer
Institute–Cleveland Clinic, Cleveland, Ohio (S.R.); Division of Thoracic
and Esophageal Surgery (J.G., P.L.), Division of Pulmonary Critical Care and
Sleep Medicine (C.D., F.J.), Department of Pathology (M.Y.), and Department of
Radiology (R.G.), University Hospitals of Cleveland, Cleveland, Ohio; Pulmonary
Section, Cleveland Veterans Affairs Medical Center, Cleveland, Ohio (F.J.);
Department of Radiology, UT Southwestern Medical Center, Dallas, Tex (P.R.);
Department of Internal Medicine, Maimonides Medical Center, Brooklyn, NY (R.T.);
and Hematology and Oncology, New York University, Perlmutter Cancer Center, New
York, NY (V.V.)
| | - Mahdi Orooji
- From the Department of Biomedical Engineering, Case Western Reserve
University, 2071 Martin Luther King Dr, Wickenden 523, Cleveland, OH 44106-7207
(N. Beig, M.K., M.A., P.P., N. Braman, M.O., K.B., P.T., A.M.); Taussig Cancer
Institute–Cleveland Clinic, Cleveland, Ohio (S.R.); Division of Thoracic
and Esophageal Surgery (J.G., P.L.), Division of Pulmonary Critical Care and
Sleep Medicine (C.D., F.J.), Department of Pathology (M.Y.), and Department of
Radiology (R.G.), University Hospitals of Cleveland, Cleveland, Ohio; Pulmonary
Section, Cleveland Veterans Affairs Medical Center, Cleveland, Ohio (F.J.);
Department of Radiology, UT Southwestern Medical Center, Dallas, Tex (P.R.);
Department of Internal Medicine, Maimonides Medical Center, Brooklyn, NY (R.T.);
and Hematology and Oncology, New York University, Perlmutter Cancer Center, New
York, NY (V.V.)
| | - Sagar Rakshit
- From the Department of Biomedical Engineering, Case Western Reserve
University, 2071 Martin Luther King Dr, Wickenden 523, Cleveland, OH 44106-7207
(N. Beig, M.K., M.A., P.P., N. Braman, M.O., K.B., P.T., A.M.); Taussig Cancer
Institute–Cleveland Clinic, Cleveland, Ohio (S.R.); Division of Thoracic
and Esophageal Surgery (J.G., P.L.), Division of Pulmonary Critical Care and
Sleep Medicine (C.D., F.J.), Department of Pathology (M.Y.), and Department of
Radiology (R.G.), University Hospitals of Cleveland, Cleveland, Ohio; Pulmonary
Section, Cleveland Veterans Affairs Medical Center, Cleveland, Ohio (F.J.);
Department of Radiology, UT Southwestern Medical Center, Dallas, Tex (P.R.);
Department of Internal Medicine, Maimonides Medical Center, Brooklyn, NY (R.T.);
and Hematology and Oncology, New York University, Perlmutter Cancer Center, New
York, NY (V.V.)
| | - Kaustav Bera
- From the Department of Biomedical Engineering, Case Western Reserve
University, 2071 Martin Luther King Dr, Wickenden 523, Cleveland, OH 44106-7207
(N. Beig, M.K., M.A., P.P., N. Braman, M.O., K.B., P.T., A.M.); Taussig Cancer
Institute–Cleveland Clinic, Cleveland, Ohio (S.R.); Division of Thoracic
and Esophageal Surgery (J.G., P.L.), Division of Pulmonary Critical Care and
Sleep Medicine (C.D., F.J.), Department of Pathology (M.Y.), and Department of
Radiology (R.G.), University Hospitals of Cleveland, Cleveland, Ohio; Pulmonary
Section, Cleveland Veterans Affairs Medical Center, Cleveland, Ohio (F.J.);
Department of Radiology, UT Southwestern Medical Center, Dallas, Tex (P.R.);
Department of Internal Medicine, Maimonides Medical Center, Brooklyn, NY (R.T.);
and Hematology and Oncology, New York University, Perlmutter Cancer Center, New
York, NY (V.V.)
| | - Prabhakar Rajiah
- From the Department of Biomedical Engineering, Case Western Reserve
University, 2071 Martin Luther King Dr, Wickenden 523, Cleveland, OH 44106-7207
(N. Beig, M.K., M.A., P.P., N. Braman, M.O., K.B., P.T., A.M.); Taussig Cancer
Institute–Cleveland Clinic, Cleveland, Ohio (S.R.); Division of Thoracic
and Esophageal Surgery (J.G., P.L.), Division of Pulmonary Critical Care and
Sleep Medicine (C.D., F.J.), Department of Pathology (M.Y.), and Department of
Radiology (R.G.), University Hospitals of Cleveland, Cleveland, Ohio; Pulmonary
Section, Cleveland Veterans Affairs Medical Center, Cleveland, Ohio (F.J.);
Department of Radiology, UT Southwestern Medical Center, Dallas, Tex (P.R.);
Department of Internal Medicine, Maimonides Medical Center, Brooklyn, NY (R.T.);
and Hematology and Oncology, New York University, Perlmutter Cancer Center, New
York, NY (V.V.)
| | - Jennifer Ginsberg
- From the Department of Biomedical Engineering, Case Western Reserve
University, 2071 Martin Luther King Dr, Wickenden 523, Cleveland, OH 44106-7207
(N. Beig, M.K., M.A., P.P., N. Braman, M.O., K.B., P.T., A.M.); Taussig Cancer
Institute–Cleveland Clinic, Cleveland, Ohio (S.R.); Division of Thoracic
and Esophageal Surgery (J.G., P.L.), Division of Pulmonary Critical Care and
Sleep Medicine (C.D., F.J.), Department of Pathology (M.Y.), and Department of
Radiology (R.G.), University Hospitals of Cleveland, Cleveland, Ohio; Pulmonary
Section, Cleveland Veterans Affairs Medical Center, Cleveland, Ohio (F.J.);
Department of Radiology, UT Southwestern Medical Center, Dallas, Tex (P.R.);
Department of Internal Medicine, Maimonides Medical Center, Brooklyn, NY (R.T.);
and Hematology and Oncology, New York University, Perlmutter Cancer Center, New
York, NY (V.V.)
| | - Christopher Donatelli
- From the Department of Biomedical Engineering, Case Western Reserve
University, 2071 Martin Luther King Dr, Wickenden 523, Cleveland, OH 44106-7207
(N. Beig, M.K., M.A., P.P., N. Braman, M.O., K.B., P.T., A.M.); Taussig Cancer
Institute–Cleveland Clinic, Cleveland, Ohio (S.R.); Division of Thoracic
and Esophageal Surgery (J.G., P.L.), Division of Pulmonary Critical Care and
Sleep Medicine (C.D., F.J.), Department of Pathology (M.Y.), and Department of
Radiology (R.G.), University Hospitals of Cleveland, Cleveland, Ohio; Pulmonary
Section, Cleveland Veterans Affairs Medical Center, Cleveland, Ohio (F.J.);
Department of Radiology, UT Southwestern Medical Center, Dallas, Tex (P.R.);
Department of Internal Medicine, Maimonides Medical Center, Brooklyn, NY (R.T.);
and Hematology and Oncology, New York University, Perlmutter Cancer Center, New
York, NY (V.V.)
| | - Rajat Thawani
- From the Department of Biomedical Engineering, Case Western Reserve
University, 2071 Martin Luther King Dr, Wickenden 523, Cleveland, OH 44106-7207
(N. Beig, M.K., M.A., P.P., N. Braman, M.O., K.B., P.T., A.M.); Taussig Cancer
Institute–Cleveland Clinic, Cleveland, Ohio (S.R.); Division of Thoracic
and Esophageal Surgery (J.G., P.L.), Division of Pulmonary Critical Care and
Sleep Medicine (C.D., F.J.), Department of Pathology (M.Y.), and Department of
Radiology (R.G.), University Hospitals of Cleveland, Cleveland, Ohio; Pulmonary
Section, Cleveland Veterans Affairs Medical Center, Cleveland, Ohio (F.J.);
Department of Radiology, UT Southwestern Medical Center, Dallas, Tex (P.R.);
Department of Internal Medicine, Maimonides Medical Center, Brooklyn, NY (R.T.);
and Hematology and Oncology, New York University, Perlmutter Cancer Center, New
York, NY (V.V.)
| | - Michael Yang
- From the Department of Biomedical Engineering, Case Western Reserve
University, 2071 Martin Luther King Dr, Wickenden 523, Cleveland, OH 44106-7207
(N. Beig, M.K., M.A., P.P., N. Braman, M.O., K.B., P.T., A.M.); Taussig Cancer
Institute–Cleveland Clinic, Cleveland, Ohio (S.R.); Division of Thoracic
and Esophageal Surgery (J.G., P.L.), Division of Pulmonary Critical Care and
Sleep Medicine (C.D., F.J.), Department of Pathology (M.Y.), and Department of
Radiology (R.G.), University Hospitals of Cleveland, Cleveland, Ohio; Pulmonary
Section, Cleveland Veterans Affairs Medical Center, Cleveland, Ohio (F.J.);
Department of Radiology, UT Southwestern Medical Center, Dallas, Tex (P.R.);
Department of Internal Medicine, Maimonides Medical Center, Brooklyn, NY (R.T.);
and Hematology and Oncology, New York University, Perlmutter Cancer Center, New
York, NY (V.V.)
| | - Frank Jacono
- From the Department of Biomedical Engineering, Case Western Reserve
University, 2071 Martin Luther King Dr, Wickenden 523, Cleveland, OH 44106-7207
(N. Beig, M.K., M.A., P.P., N. Braman, M.O., K.B., P.T., A.M.); Taussig Cancer
Institute–Cleveland Clinic, Cleveland, Ohio (S.R.); Division of Thoracic
and Esophageal Surgery (J.G., P.L.), Division of Pulmonary Critical Care and
Sleep Medicine (C.D., F.J.), Department of Pathology (M.Y.), and Department of
Radiology (R.G.), University Hospitals of Cleveland, Cleveland, Ohio; Pulmonary
Section, Cleveland Veterans Affairs Medical Center, Cleveland, Ohio (F.J.);
Department of Radiology, UT Southwestern Medical Center, Dallas, Tex (P.R.);
Department of Internal Medicine, Maimonides Medical Center, Brooklyn, NY (R.T.);
and Hematology and Oncology, New York University, Perlmutter Cancer Center, New
York, NY (V.V.)
| | - Pallavi Tiwari
- From the Department of Biomedical Engineering, Case Western Reserve
University, 2071 Martin Luther King Dr, Wickenden 523, Cleveland, OH 44106-7207
(N. Beig, M.K., M.A., P.P., N. Braman, M.O., K.B., P.T., A.M.); Taussig Cancer
Institute–Cleveland Clinic, Cleveland, Ohio (S.R.); Division of Thoracic
and Esophageal Surgery (J.G., P.L.), Division of Pulmonary Critical Care and
Sleep Medicine (C.D., F.J.), Department of Pathology (M.Y.), and Department of
Radiology (R.G.), University Hospitals of Cleveland, Cleveland, Ohio; Pulmonary
Section, Cleveland Veterans Affairs Medical Center, Cleveland, Ohio (F.J.);
Department of Radiology, UT Southwestern Medical Center, Dallas, Tex (P.R.);
Department of Internal Medicine, Maimonides Medical Center, Brooklyn, NY (R.T.);
and Hematology and Oncology, New York University, Perlmutter Cancer Center, New
York, NY (V.V.)
| | - Vamsidhar Velcheti
- From the Department of Biomedical Engineering, Case Western Reserve
University, 2071 Martin Luther King Dr, Wickenden 523, Cleveland, OH 44106-7207
(N. Beig, M.K., M.A., P.P., N. Braman, M.O., K.B., P.T., A.M.); Taussig Cancer
Institute–Cleveland Clinic, Cleveland, Ohio (S.R.); Division of Thoracic
and Esophageal Surgery (J.G., P.L.), Division of Pulmonary Critical Care and
Sleep Medicine (C.D., F.J.), Department of Pathology (M.Y.), and Department of
Radiology (R.G.), University Hospitals of Cleveland, Cleveland, Ohio; Pulmonary
Section, Cleveland Veterans Affairs Medical Center, Cleveland, Ohio (F.J.);
Department of Radiology, UT Southwestern Medical Center, Dallas, Tex (P.R.);
Department of Internal Medicine, Maimonides Medical Center, Brooklyn, NY (R.T.);
and Hematology and Oncology, New York University, Perlmutter Cancer Center, New
York, NY (V.V.)
| | - Robert Gilkeson
- From the Department of Biomedical Engineering, Case Western Reserve
University, 2071 Martin Luther King Dr, Wickenden 523, Cleveland, OH 44106-7207
(N. Beig, M.K., M.A., P.P., N. Braman, M.O., K.B., P.T., A.M.); Taussig Cancer
Institute–Cleveland Clinic, Cleveland, Ohio (S.R.); Division of Thoracic
and Esophageal Surgery (J.G., P.L.), Division of Pulmonary Critical Care and
Sleep Medicine (C.D., F.J.), Department of Pathology (M.Y.), and Department of
Radiology (R.G.), University Hospitals of Cleveland, Cleveland, Ohio; Pulmonary
Section, Cleveland Veterans Affairs Medical Center, Cleveland, Ohio (F.J.);
Department of Radiology, UT Southwestern Medical Center, Dallas, Tex (P.R.);
Department of Internal Medicine, Maimonides Medical Center, Brooklyn, NY (R.T.);
and Hematology and Oncology, New York University, Perlmutter Cancer Center, New
York, NY (V.V.)
| | - Philip Linden
- From the Department of Biomedical Engineering, Case Western Reserve
University, 2071 Martin Luther King Dr, Wickenden 523, Cleveland, OH 44106-7207
(N. Beig, M.K., M.A., P.P., N. Braman, M.O., K.B., P.T., A.M.); Taussig Cancer
Institute–Cleveland Clinic, Cleveland, Ohio (S.R.); Division of Thoracic
and Esophageal Surgery (J.G., P.L.), Division of Pulmonary Critical Care and
Sleep Medicine (C.D., F.J.), Department of Pathology (M.Y.), and Department of
Radiology (R.G.), University Hospitals of Cleveland, Cleveland, Ohio; Pulmonary
Section, Cleveland Veterans Affairs Medical Center, Cleveland, Ohio (F.J.);
Department of Radiology, UT Southwestern Medical Center, Dallas, Tex (P.R.);
Department of Internal Medicine, Maimonides Medical Center, Brooklyn, NY (R.T.);
and Hematology and Oncology, New York University, Perlmutter Cancer Center, New
York, NY (V.V.)
| | - Anant Madabhushi
- From the Department of Biomedical Engineering, Case Western Reserve
University, 2071 Martin Luther King Dr, Wickenden 523, Cleveland, OH 44106-7207
(N. Beig, M.K., M.A., P.P., N. Braman, M.O., K.B., P.T., A.M.); Taussig Cancer
Institute–Cleveland Clinic, Cleveland, Ohio (S.R.); Division of Thoracic
and Esophageal Surgery (J.G., P.L.), Division of Pulmonary Critical Care and
Sleep Medicine (C.D., F.J.), Department of Pathology (M.Y.), and Department of
Radiology (R.G.), University Hospitals of Cleveland, Cleveland, Ohio; Pulmonary
Section, Cleveland Veterans Affairs Medical Center, Cleveland, Ohio (F.J.);
Department of Radiology, UT Southwestern Medical Center, Dallas, Tex (P.R.);
Department of Internal Medicine, Maimonides Medical Center, Brooklyn, NY (R.T.);
and Hematology and Oncology, New York University, Perlmutter Cancer Center, New
York, NY (V.V.)
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