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Yoo H, Kim KH, Singh R, Digumarthy SR, Kalra MK. Validation of a Deep Learning Algorithm for the Detection of Malignant Pulmonary Nodules in Chest Radiographs. JAMA Netw Open 2020; 3:e2017135. [PMID: 32970157 PMCID: PMC7516603 DOI: 10.1001/jamanetworkopen.2020.17135] [Citation(s) in RCA: 62] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
IMPORTANCE The improvement of pulmonary nodule detection, which is a challenging task when using chest radiographs, may help to elevate the role of chest radiographs for the diagnosis of lung cancer. OBJECTIVE To assess the performance of a deep learning-based nodule detection algorithm for the detection of lung cancer on chest radiographs from participants in the National Lung Screening Trial (NLST). DESIGN, SETTING, AND PARTICIPANTS This diagnostic study used data from participants in the NLST ro assess the performance of a deep learning-based artificial intelligence (AI) algorithm for the detection of pulmonary nodules and lung cancer on chest radiographs using separate training (in-house) and validation (NLST) data sets. Baseline (T0) posteroanterior chest radiographs from 5485 participants (full T0 data set) were used to assess lung cancer detection performance, and a subset of 577 of these images (nodule data set) were used to assess nodule detection performance. Participants aged 55 to 74 years who currently or formerly (ie, quit within the past 15 years) smoked cigarettes for 30 pack-years or more were enrolled in the NLST at 23 US centers between August 2002 and April 2004. Information on lung cancer diagnoses was collected through December 31, 2009. Analyses were performed between August 20, 2019, and February 14, 2020. EXPOSURES Abnormality scores produced by the AI algorithm. MAIN OUTCOMES AND MEASURES The performance of an AI algorithm for the detection of lung nodules and lung cancer on radiographs, with lung cancer incidence and mortality as primary end points. RESULTS A total of 5485 participants (mean [SD] age, 61.7 [5.0] years; 3030 men [55.2%]) were included, with a median follow-up duration of 6.5 years (interquartile range, 6.1-6.9 years). For the nodule data set, the sensitivity and specificity of the AI algorithm for the detection of pulmonary nodules were 86.2% (95% CI, 77.8%-94.6%) and 85.0% (95% CI, 81.9%-88.1%), respectively. For the detection of all cancers, the sensitivity was 75.0% (95% CI, 62.8%-87.2%), the specificity was 83.3% (95% CI, 82.3%-84.3%), the positive predictive value was 3.8% (95% CI, 2.6%-5.0%), and the negative predictive value was 99.8% (95% CI, 99.6%-99.9%). For the detection of malignant pulmonary nodules in all images of the full T0 data set, the sensitivity was 94.1% (95% CI, 86.2%-100.0%), the specificity was 83.3% (95% CI, 82.3%-84.3%), the positive predictive value was 3.4% (95% CI, 2.2%-4.5%), and the negative predictive value was 100.0% (95% CI, 99.9%-100.0%). In digital radiographs of the nodule data set, the AI algorithm had higher sensitivity (96.0% [95% CI, 88.3%-100.0%] vs 88.0% [95% CI, 75.3%-100.0%]; P = .32) and higher specificity (93.2% [95% CI, 89.9%-96.5%] vs 82.8% [95% CI, 77.8%-87.8%]; P = .001) for nodule detection compared with the NLST radiologists. For malignant pulmonary nodule detection on digital radiographs of the full T0 data set, the sensitivity of the AI algorithm was higher (100.0% [95% CI, 100.0%-100.0%] vs 94.1% [95% CI, 82.9%-100.0%]; P = .32) compared with the NLST radiologists, and the specificity (90.9% [95% CI, 89.6%-92.1%] vs 91.0% [95% CI, 89.7%-92.2%]; P = .91), positive predictive value (8.2% [95% CI, 4.4%-11.9%] vs 7.8% [95% CI, 4.1%-11.5%]; P = .65), and negative predictive value (100.0% [95% CI, 100.0%-100.0%] vs 99.9% [95% CI, 99.8%-100.0%]; P = .32) were similar to those of NLST radiologists. CONCLUSIONS AND RELEVANCE In this study, the AI algorithm performed better than NLST radiologists for the detection of pulmonary nodules on digital radiographs. When used as a second reader, the AI algorithm may help to detect lung cancer.
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Affiliation(s)
| | | | - Ramandeep Singh
- Division of Thoracic Imaging, Department of Radiology, Massachusetts General Hospital, Boston
- Harvard Medical School, Boston, Massachusetts
| | - Subba R. Digumarthy
- Division of Thoracic Imaging, Department of Radiology, Massachusetts General Hospital, Boston
- Harvard Medical School, Boston, Massachusetts
| | - Mannudeep K. Kalra
- Division of Thoracic Imaging, Department of Radiology, Massachusetts General Hospital, Boston
- Harvard Medical School, Boston, Massachusetts
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Yi E, Han SM, Chang JE, Kim HT, Kim JK, Seo SJ, Chung JH, Jheon S. Synchrotron tomographic images from human lung adenocarcinoma: Three-dimensional reconstruction and histologic correlations. Microsc Res Tech 2017; 80:1141-1148. [PMID: 28730614 DOI: 10.1002/jemt.22910] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2017] [Revised: 05/11/2017] [Accepted: 07/03/2017] [Indexed: 01/09/2023]
Abstract
High-resolution tomographic images using synchrotron X-rays are expected to provide detailed reflection of microstructures, thereby allowing for the examination of histologic structures without destruction of the specimen. This study aims to evaluate the synchrotron tomographic images of mixed ground-glass opacity excised on 5-mm sections in comparison to pathologic examination. The Institutional Review Board of our institute approved this retrospective study, and written informed consent was obtained from each patient whose lung tissue would be used. Obtained lung cancer specimens were brought to the multiple Wiggler 6C beam line at the Pohang Light Source (PLS-II) in Korea, and phase contrast X-ray images were obtained in November 2016. The X-ray emanated from a bending magnet of the electron storage ring with electron energy of 3 GeV, and a typical beam current was 320 mA. Reconstructed tomographic images were compared with images from histologic slides obtained from the same samples. Pulmonary microstructures including terminal bronchioles, alveolar sacs, and vasculature were identified with phase contrast X-ray images. Images from normal lung tissue and mixed ground-glass opacity were clearly distinguishable. Hyperplasia of the interalveolar septum and dysplasia of microstructure were clearly identified. The imaging findings correlated well with hematoxylin-eosin stained specimens. Tomographic images using synchrotron radiation have the potential for clinical applications. With refinement, this technique may become a diagnostic tool for detection of lung cancer.
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Affiliation(s)
- Eunjue Yi
- Department of Thoracic and Cardiovascular Surgery, Korea University Anam Hospital, Seoul, 02841, Republic of Korea
| | - Sung-Mi Han
- Anatomy, School of Medicine, Catholic University of Daegu, Gyeongbuk, 38430, Republic of Korea
| | - Ji-Eun Chang
- Department of Thoracic and Cardiovascular Surgery, Seoul National University Bundang Hospital, Gyeonggi-do, 13602, Republic of Korea
| | - Hong-Tae Kim
- Anatomy, School of Medicine, Catholic University of Daegu, Gyeongbuk, 38430, Republic of Korea
| | - Jong-Ki Kim
- Biomedical Engineering and Radiology, School of Medicine, Catholic University of Daegu, Gyeongbuk, 38430, Republic of Korea
| | - Seung-Jun Seo
- Biomedical Engineering and Radiology, School of Medicine, Catholic University of Daegu, Gyeongbuk, 38430, Republic of Korea
| | - Jin-Haeng Chung
- Department of Pathology, Seoul National University Bundang Hospital, Gyeonggi-do, 13602, Republic of Korea.,Department of Pathology, Seoul National University College of Medicine, Seoul, 03080, Republic of Korea
| | - Sanghoon Jheon
- Department of Thoracic and Cardiovascular Surgery, Seoul National University Bundang Hospital, Gyeonggi-do, 13602, Republic of Korea.,Department of Thoracic and Cardiovascular Surgery, College of Medicine, Seoul National University, Seoul, 03080, Republic of Korea
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Detterbeck F, Molins L. Video-assisted thoracic surgery and open chest surgery in lung cancer treatment: present and future. J Vis Surg 2016; 2:173. [PMID: 29078558 DOI: 10.21037/jovs.2016.11.03] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2016] [Accepted: 10/01/2016] [Indexed: 11/06/2022]
Abstract
Surgical resection remains the most effective treatment of early stage lung cancer. The surgical approach has evolved, and now consists primarily of video-assisted thoracic surgery (VATS) and more limited incisions even with open techniques. Both approaches have their place. Many factors contribute to deciding whether one or the other is better for a particular tumor, patient and in a particular setting and region. Video assisted surgery, where appropriate, is associated with fewer complications and a shorter hospital stay, and similar long term survival. But modern open surgery is also associated with good results. This article reviews the data and discusses considerations to weigh in finding the right balance between the video-assisted and the open approaches.
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Affiliation(s)
- Frank Detterbeck
- Section of Thoracic Surgery, Department of Surgery, Yale University School of Medicine, New Haven, CT, USA
| | - Laureano Molins
- Thoracic Surgery, Hospital Clínic & Sagrat Cor, University of Barcelona, Barcelona, Spain
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Reid AE, Tanoue L, Detterbeck F, Michaud GC, McCorkle R. The Role of the Advanced Practitioner in a Comprehensive Lung Cancer Screening and Pulmonary Nodule Program. J Adv Pract Oncol 2015; 5:440-6. [PMID: 26328217 PMCID: PMC4530114 DOI: 10.6004/jadpro.2014.5.6.4] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Affiliation(s)
- Amanda E Reid
- 1University of Texas MD Anderson Cancer Center, Houston, Texas; 2Yale University School of Medicine, New Haven, Connecticut; 3Yale University School of Nursing, New Haven, Connecticut
| | - Lynn Tanoue
- 1University of Texas MD Anderson Cancer Center, Houston, Texas; 2Yale University School of Medicine, New Haven, Connecticut; 3Yale University School of Nursing, New Haven, Connecticut
| | - Frank Detterbeck
- 1University of Texas MD Anderson Cancer Center, Houston, Texas; 2Yale University School of Medicine, New Haven, Connecticut; 3Yale University School of Nursing, New Haven, Connecticut
| | - Gaetane Celine Michaud
- 1University of Texas MD Anderson Cancer Center, Houston, Texas; 2Yale University School of Medicine, New Haven, Connecticut; 3Yale University School of Nursing, New Haven, Connecticut
| | - Ruth McCorkle
- 1University of Texas MD Anderson Cancer Center, Houston, Texas; 2Yale University School of Medicine, New Haven, Connecticut; 3Yale University School of Nursing, New Haven, Connecticut
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Sathe A, Zhang YA, Ma X, Ray P, Cadinu D, Wang YW, Yao X, Liu X, Tang H, Wang Y, Huang Y, Liu C, Gu J, Akerman M, Mo Y, Cheng C, Xuan Z, Chen L, Xiao G, Xie Y, Girard L, Wang H, Lam S, Wistuba II, Zhang L, Gazdar AF, Zhang MQ. SCT Promoter Methylation is a Highly Discriminative Biomarker for Lung and Many Other Cancers. ACTA ACUST UNITED AC 2015; 1:30-33. [PMID: 33758771 DOI: 10.1109/lls.2015.2488438] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Aberrant DNA methylation has long been implicated in cancers. In this work we present a highly discriminative DNA methylation biomarker for non-small cell lung cancers and fourteen other cancers. Based on 69 NSCLC cell lines and 257 cancer-free lung tissues we identified a CpG island in SCT gene promoter which was verified by qMSP experiment in 15 NSCLC cell lines and 3 immortalized human respiratory epithelium cells. In addition, we found that SCT promoter was methylated in 23 cancer cell lines involving >10 cancer types profiled by ENCODE. We found that SCT promoter is hyper-methylated in primary tumors from TCGA lung cancer cohort. Additionally, we found that SCT promoter is methylated at high frequencies in fifteen malignancies and is not methylated in~1000 non-cancerous tissues across >30 organ types. Our study indicates that SCT promoter methylation is a highly discriminative biomarker for lung and many other cancers.
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Affiliation(s)
- Adwait Sathe
- Center for Systems Biology, Department of Molecular and Cell Biology, The University of Texas at Dallas, 800 W Campbell Rd., Richardson, TX, 75080, USA
| | - Yu-An Zhang
- The Hamon Center for Therapeutic Oncology Research and Department of Pathology, University of Texas Southwestern Medical Center, Dallas, Texas 75390, USA
| | - Xiaotu Ma
- Center for Systems Biology, Department of Molecular and Cell Biology, The University of Texas at Dallas, 800 W Campbell Rd., Richardson, TX, 75080, USA
| | - Pradipta Ray
- Center for Systems Biology, Department of Molecular and Cell Biology, The University of Texas at Dallas, 800 W Campbell Rd., Richardson, TX, 75080, USA
| | - Daniela Cadinu
- Center for Systems Biology, Department of Molecular and Cell Biology, The University of Texas at Dallas, 800 W Campbell Rd., Richardson, TX, 75080, USA
| | - Yi-Wei Wang
- The Hamon Center for Therapeutic Oncology Research and Department of Pathology, University of Texas Southwestern Medical Center, Dallas, Texas 75390, USA
| | - Xiao Yao
- Center for Systems Biology, Department of Molecular and Cell Biology, The University of Texas at Dallas, 800 W Campbell Rd., Richardson, TX, 75080, USA
| | - Xiaoyun Liu
- The Hamon Center for Therapeutic Oncology Research and Department of Pathology, University of Texas Southwestern Medical Center, Dallas, Texas 75390, USA
| | - Hao Tang
- Department of Clinical Science, University of Texas Southwestern Medical Center, Dallas, Texas 75390, USA
| | - Yunfei Wang
- Center for Systems Biology, Department of Molecular and Cell Biology, The University of Texas at Dallas, 800 W Campbell Rd., Richardson, TX, 75080, USA
| | - Ying Huang
- Center for Systems Biology, Department of Molecular and Cell Biology, The University of Texas at Dallas, 800 W Campbell Rd., Richardson, TX, 75080, USA
| | - Changning Liu
- Center for Systems Biology, Department of Molecular and Cell Biology, The University of Texas at Dallas, 800 W Campbell Rd., Richardson, TX, 75080, USA
| | - Jin Gu
- Division of Bioinformatics, Center for Synthetic and Systems Biology, TNLIST, Tsinghua University, Beijing 100084, China
| | - Martin Akerman
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Yifan Mo
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Chao Cheng
- Department of Genetics, Geisel School of Medicine at Dartmouth, Hanover, NH, USA
| | - Zhenyu Xuan
- Center for Systems Biology, Department of Molecular and Cell Biology, The University of Texas at Dallas, 800 W Campbell Rd., Richardson, TX, 75080, USA
| | - Lei Chen
- Laboratory of Signal Transduction, Eastern Hepatobiliary Surgery Hospital, SMMU, Shanghai 200438, China
| | - Guanghua Xiao
- Department of Clinical Science, University of Texas Southwestern Medical Center, Dallas, Texas 75390, USA
| | - Yang Xie
- Department of Clinical Science, University of Texas Southwestern Medical Center, Dallas, Texas 75390, USA
| | - Luc Girard
- The Hamon Center for Therapeutic Oncology Research and Department of Pathology, University of Texas Southwestern Medical Center, Dallas, Texas 75390, USA
| | - Hongyang Wang
- Laboratory of Signal Transduction, Eastern Hepatobiliary Surgery Hospital, SMMU, Shanghai 200438, China
| | - Stephen Lam
- BC Cancer Research Center, BC Cancer Agency, Vancouver, BC V521L3, Canada
| | - Ignacio I Wistuba
- Department of Translational Molecular Pathology, Thoracic/Head and Neck Medical Oncology, The University of Texas, MD Anderson Cancer Center, Houston TX 77030, USA
| | - Li Zhang
- Center for Systems Biology, Department of Molecular and Cell Biology, The University of Texas at Dallas, 800 W Campbell Rd., Richardson, TX, 75080, USA
| | - Adi F Gazdar
- The Hamon Center for Therapeutic Oncology Research and Department of Pathology, University of Texas Southwestern Medical Center, Dallas, Texas 75390, USA
| | - Michael Q Zhang
- Center for Systems Biology, Department of Molecular and Cell Biology, The University of Texas at Dallas, 800 W Campbell Rd., Richardson, TX, 75080, USA.,Division of Bioinformatics, Center for Synthetic and Systems Biology, TNLIST, Tsinghua University, Beijing 100084, China
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Xu X, Cheng J, Thrall MJ, Liu Z, Wang X, Wong ST. Multimodal non-linear optical imaging for label-free differentiation of lung cancerous lesions from normal and desmoplastic tissues. BIOMEDICAL OPTICS EXPRESS 2013; 4:2855-68. [PMID: 24409386 PMCID: PMC3862152 DOI: 10.1364/boe.4.002855] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/16/2013] [Revised: 10/14/2013] [Accepted: 10/25/2013] [Indexed: 05/07/2023]
Abstract
Lung carcinoma is the leading cause of cancer-related death in the United States, and non-small cell carcinoma accounts for 85% of all lung cancer cases. One major characteristic of non-small cell carcinoma is the appearance of desmoplasia and deposition of dense extracellular collagen around the tumor. The desmoplastic response provides a radiologic target but may impair sampling during traditional image-guided needle biopsy and is difficult to differentiate from normal tissues using single label free imaging modality; for translational purposes, label-free techniques provide a more promising route to clinics. We thus investigated the potential of using multimodal, label free optical microscopy that incorporates Coherent Anti-Stokes Raman Scattering (CARS), Two-Photon Excited AutoFluorescence (TPEAF), and Second Harmonic Generation (SHG) techniques for differentiating lung cancer from normal and desmoplastic tissues. Lung tissue samples from patients were imaged using CARS, TPEAF, and SHG for comparison and showed that the combination of the three non-linear optics techniques is essential for attaining reliable differentiation. These images also illustrated good pathological correlation with hematoxylin and eosin (H&E) stained sections from the same tissue samples. Automated image analysis algorithms were developed for quantitative segmentation and feature extraction to enable lung tissue differentiation. Our results indicate that coupled with automated morphology analysis, the proposed tri-modal nonlinear optical imaging technique potentially offers a powerful translational strategy to differentiate cancer lesions reliably from surrounding non-tumor and desmoplastic tissues.
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Affiliation(s)
- Xiaoyun Xu
- Systems Medicine and Bioengineering Department, Houston Methodist Research Institute, Weill Cornell Medical College, Houston, Texas 77030 USA
- Authors contributed equally to this work
| | - Jie Cheng
- Systems Medicine and Bioengineering Department, Houston Methodist Research Institute, Weill Cornell Medical College, Houston, Texas 77030 USA
- Authors contributed equally to this work
| | - Michael J. Thrall
- Department of Pathology and Genomic Medicine, Houston Methodist Hospital, Weill Cornell Medical College, Houston, Texas 77030 USA
| | - Zhengfan Liu
- Systems Medicine and Bioengineering Department, Houston Methodist Research Institute, Weill Cornell Medical College, Houston, Texas 77030 USA
- School of Optoelectronics, Beijing Institute of Technology, Beijing, China
| | - Xi Wang
- Systems Medicine and Bioengineering Department, Houston Methodist Research Institute, Weill Cornell Medical College, Houston, Texas 77030 USA
| | - Stephen T.C. Wong
- Systems Medicine and Bioengineering Department, Houston Methodist Research Institute, Weill Cornell Medical College, Houston, Texas 77030 USA
- Department of Pathology and Genomic Medicine, Houston Methodist Hospital, Weill Cornell Medical College, Houston, Texas 77030 USA
- Department of Radiology, Houston Methodist Hospital, Weill Cornell Medical College, Houston, Texas 77030 USA
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