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Quanyang W, Lina Z, Yao H, Jiawei W, Wei T, Linlin Q, Zewei Z, Donghui H, Hongjia L, Shuluan C, Jiaxing Z, Shijun Z. Application of computer-aided detection for NCCN-based follow-up recommendation in subsolid nodules: Effect on inter-observer agreement. Cancer Med 2024; 13:e6967. [PMID: 38348960 PMCID: PMC10832308 DOI: 10.1002/cam4.6967] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 01/08/2024] [Accepted: 01/12/2024] [Indexed: 02/15/2024] Open
Abstract
RATIONALE AND OBJECTIVES Computer-aided detection (CAD) of pulmonary nodules reduces the impact of observer variability, improving the reliability and reproducibility of nodule assessments in clinical practice. Therefore, this study aimed to assess the impact of CAD on inter-observer agreement in the follow-up management of subsolid nodules. MATERIALS AND METHODS A dataset comprising 60 subsolid nodule cases was constructed based on the National Cancer Center lung cancer screening data. Five observers independently assessed all low-dose computed tomography scans and assigned follow-up management strategies to each case according to the National Comprehensive Cancer Network (NCCN) guidelines, using both manual measurements and CAD assistance. The linearly weighted Cohen's kappa test was used to measure agreement between paired observers. Agreement among multiple observers was evaluated using the Fleiss kappa statistic. RESULTS The agreement of the five observers for NCCN follow-up management categorization was moderate when measured manually, with a Fleiss kappa score of 0.437. Utilizing CAD led to a notable enhancement in agreement, achieving a substantial consensus with a Fleiss kappa value of 0.623. After using CAD, the proportion of major and substantial management discrepancies decreased from 27.5% to 15.8% and 4.8% to 1.5%, respectively (p < 0.01). In 23 lung cancer cases presenting as part-solid nodules, CAD significantly elevates the average sensitivity in detecting lung cancer cases presenting as part-solid nodules (overall sensitivity, 82.6% vs. 92.2%; p < 0.05). CONCLUSION The application of CAD significantly improves inter-observer agreement in the follow-up management strategy for subsolid nodules. It also demonstrates the potential to reduce substantial management discrepancies and increase detection sensitivity in lung cancer cases presenting as part-solid nodules.
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Affiliation(s)
- Wu Quanyang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Zhou Lina
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Huang Yao
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Wang Jiawei
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Tang Wei
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Qi Linlin
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Zhang Zewei
- PET‐CT Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Hou Donghui
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Li Hongjia
- PET‐CT Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Chen Shuluan
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Zhang Jiaxing
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Zhao Shijun
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
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Huang W, Deng H, Li Z, Xiong Z, Zhou T, Ge Y, Zhang J, Jing W, Geng Y, Wang X, Tu W, Dong P, Liu S, Fan L. Baseline whole-lung CT features deriving from deep learning and radiomics: prediction of benign and malignant pulmonary ground-glass nodules. Front Oncol 2023; 13:1255007. [PMID: 37664069 PMCID: PMC10470826 DOI: 10.3389/fonc.2023.1255007] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2023] [Accepted: 07/28/2023] [Indexed: 09/05/2023] Open
Abstract
Objective To develop and validate the model for predicting benign and malignant ground-glass nodules (GGNs) based on the whole-lung baseline CT features deriving from deep learning and radiomics. Methods This retrospective study included 385 GGNs from 3 hospitals, confirmed by pathology. We used 239 GGNs from Hospital 1 as the training and internal validation set; 115 and 31 GGNs from Hospital 2 and Hospital 3 as the external test sets 1 and 2, respectively. An additional 32 stable GGNs from Hospital 3 with more than five years of follow-up were used as the external test set 3. We evaluated clinical and morphological features of GGNs at baseline chest CT and extracted the whole-lung radiomics features simultaneously. Besides, baseline whole-lung CT image features are further assisted and extracted using the convolutional neural network. We used the back-propagation neural network to construct five prediction models based on different collocations of the features used for training. The area under the receiver operator characteristic curve (AUC) was used to compare the prediction performance among the five models. The Delong test was used to compare the differences in AUC between models pairwise. Results The model integrated clinical-morphological features, whole-lung radiomic features, and whole-lung image features (CMRI) performed best among the five models, and achieved the highest AUC in the internal validation set, external test set 1, and external test set 2, which were 0.886 (95% CI: 0.841-0.921), 0.830 (95%CI: 0.749-0.893) and 0.879 (95%CI: 0.712-0.968), respectively. In the above three sets, the differences in AUC between the CMRI model and other models were significant (all P < 0.05). Moreover, the accuracy of the CMRI model in the external test set 3 was 96.88%. Conclusion The baseline whole-lung CT features were feasible to predict the benign and malignant of GGNs, which is helpful for more refined management of GGNs.
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Affiliation(s)
- Wenjun Huang
- Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai, China
- School of Medical Imaging, Weifang Medical University, Weifang, Shandong, China
- Department of Radiology, The Second People’s hospital of Deyang, Deyang, Sichuan, China
| | - Heng Deng
- School of Medicine, Shanghai University, Shanghai, China
| | - Zhaobin Li
- Department of Radiation Oncology, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, China
| | - Zhanda Xiong
- Department of Artificial Intelligence Medical Imaging, Tron Technology, Shanghai, China
| | - Taohu Zhou
- Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai, China
- School of Medical Imaging, Weifang Medical University, Weifang, Shandong, China
| | - Yanming Ge
- School of Medical Imaging, Weifang Medical University, Weifang, Shandong, China
- Medical Imaging Center, Affiliated Hospital of Weifang Medical University, Weifang, Shandong, China
| | - Jing Zhang
- Department of Radiology, The Second People’s hospital of Deyang, Deyang, Sichuan, China
| | - Wenbin Jing
- Department of Radiology, The Second People’s hospital of Deyang, Deyang, Sichuan, China
| | - Yayuan Geng
- Clinical Research Institute, Shukun (Beijing) Technology Co., Ltd., Beijing, China
| | - Xiang Wang
- Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai, China
| | - Wenting Tu
- Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai, China
| | - Peng Dong
- School of Medical Imaging, Weifang Medical University, Weifang, Shandong, China
| | - Shiyuan Liu
- Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai, China
| | - Li Fan
- Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai, China
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Yang Y, Huang R, Xiang L, Zeng J, Zhao W, Huang N. Invasive mucinous adenocarcinoma misdiagnosed as pneumonia: A case report. Exp Ther Med 2023; 25:168. [PMID: 36936707 PMCID: PMC10015316 DOI: 10.3892/etm.2023.11867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 02/10/2023] [Indexed: 03/03/2023] Open
Abstract
Common imaging findings of invasive mucinous adenocarcinoma (IMA) include consolidation of the lung parenchyma, nodules, and ground-glass changes. However, the IMA imaging findings in the present case included diffuse, patchy and blurry density shadows through both lungs. To the best of the authors' knowledge, this image pattern has rarely been reported. The patient provided his consent and authorized the publication of photographs featuring his likeness. The present study reported a patient was diagnosed with IMA via pathologic and genetic analyses. Following antibiotic treatment, the lesions in both sides became larger. Further examinations were completed and IMA was confirmed by biopsy pathohistological examination. Pathological specimens were negative for almost all driver genes mutations, except KRAS. The patients and family refused further treatment, including chemotherapy, radiotherapy and interventional chemotherapy and the patient was discharged from The First Affiliated Hospital of Chengdu Medical College. The present case report emphasized that IMA should be suspected when imaging studies show diffuse lesions throughout both lungs. When a patient does not respond to treatment, clinicians should consider alternative diagnoses.
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Affiliation(s)
- Yuping Yang
- Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Chengdu Medical College, Chengdu, Sichuan 610500, P.R. China
| | - Rongfei Huang
- Department of Pathology, The First Affiliated Hospital of Chengdu Medical College, Chengdu, Sichuan 610500, P.R. China
| | - Liping Xiang
- Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Chengdu Medical College, Chengdu, Sichuan 610500, P.R. China
| | - Jun Zeng
- Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Chengdu Medical College, Chengdu, Sichuan 610500, P.R. China
| | - Wei Zhao
- School of Laboratory Medicine, Chengdu Medical College, Chengdu, Sichuan 610500, P.R. China
- Correspondence to: Dr Wei Zhao, School of Laboratory Medicine, Chengdu Medical College, 783 Xindu Road, Chengdu, Sichuan 610500, P.R. China
| | - Na Huang
- Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Chengdu Medical College, Chengdu, Sichuan 610500, P.R. China
- Correspondence to: Dr Wei Zhao, School of Laboratory Medicine, Chengdu Medical College, 783 Xindu Road, Chengdu, Sichuan 610500, P.R. China
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Ding Y, He C, Zhao X, Xue S, Tang J. Adding predictive and diagnostic values of pulmonary ground-glass nodules on lung cancer via novel non-invasive tests. Front Med (Lausanne) 2022; 9:936595. [PMID: 36059824 PMCID: PMC9433577 DOI: 10.3389/fmed.2022.936595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 07/29/2022] [Indexed: 11/13/2022] Open
Abstract
Pulmonary ground-glass nodules (GGNs) are highly associated with lung cancer. Extensive studies using thin-section high-resolution CT images have been conducted to analyze characteristics of different types of GGNs in order to evaluate and determine the predictive and diagnostic values of GGNs on lung cancer. Accurate prediction of their malignancy and invasiveness is critical for developing individualized therapies and follow-up strategies for a better clinical outcome. Through reviewing the recent 5-year research on the association between pulmonary GGNs and lung cancer, we focused on the radiologic and pathological characteristics of different types of GGNs, pointed out the risk factors associated with malignancy, discussed recent genetic analysis and biomarker studies (including autoantibodies, cell-free miRNAs, cell-free DNA, and DNA methylation) for developing novel diagnostic tools. Based on current progress in this research area, we summarized a process from screening, diagnosis to follow-up of GGNs.
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Affiliation(s)
- Yizong Ding
- Department of Thoracic Surgery, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Chunming He
- Department of Thoracic Surgery, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiaojing Zhao
- Department of Thoracic Surgery, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Song Xue
- Department of Cardiovascular Surgery, Reiji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jian Tang
- Department of Thoracic Surgery, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- *Correspondence: Jian Tang,
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Vaidya P, Bera K, Linden PA, Gupta A, Rajiah PS, Jones DR, Bott M, Pass H, Gilkeson R, Jacono F, Hsieh KLC, Lan GY, Velcheti V, Madabhushi A. Combined Radiomic and Visual Assessment for Improved Detection of Lung Adenocarcinoma Invasiveness on Computed Tomography Scans: A Multi-Institutional Study. Front Oncol 2022; 12:902056. [PMID: 35707362 PMCID: PMC9190758 DOI: 10.3389/fonc.2022.902056] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Accepted: 04/19/2022] [Indexed: 12/20/2022] Open
Abstract
Objective The timing and nature of surgical intervention for semisolid abnormalities are dependent upon distinguishing between adenocarcinoma-in-situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (INV). We sought to develop and evaluate a quantitative imaging method to determine invasiveness of small, ground-glass lesions on computed tomography (CT) chest scans. Methods The study comprised 268 patients from 4 institutions with resected (<=3 cm) semisolid lesions with confirmed histopathological diagnosis of MIA/AIS or INV. A total of 248 radiomic texture features from within the tumor nodule (intratumoral) and adjacent to the nodule (peritumoral) were extracted from manually annotated lung nodules of chest CT scans. The datasets were randomly divided, with 40% of patients used for training and 60% used for testing the machine classifier (Training DTrain, N=106; Testing, DTest, N=162). Results The top five radiomic stable features included four intratumoral (Laws and Haralick feature families) and one peritumoral feature within 3 to 6 mm of the nodule (CoLlAGe feature family), which successfully differentiated INV from MIA/AIS nodules with an AUC of 0.917 [0.867-0.967] on DTrain and 0.863 [0.79-0.931] on DTest. The radiomics model successfully differentiated INV from MIA cases (<1 cm AUC: 0.76 [0.53-0.98], 1-2 cm AUC: 0.92 [0.85-0.98], 2-3 cm AUC: 0.95 [0.88-1]). The final integrated model combining the classifier with the radiologists’ score gave the best AUC on DTest (AUC=0.909, p<0.001). Conclusions Addition of advanced image analysis via radiomics to the routine visual assessment of CT scans help better differentiate adenocarcinoma subtypes and can aid in clinical decision making. Further prospective validation in this direction is warranted.
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Affiliation(s)
- Pranjal Vaidya
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Kaustav Bera
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
- Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH, United States
| | - Philip A. Linden
- Department of Surgery, Division of Thoracic and Esophageal Surgery, University Hospitals Cleveland Medical Center, Cleveland, OH, United States
| | - Amit Gupta
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
- Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH, United States
| | | | - David R. Jones
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Matthew Bott
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Harvey Pass
- Department of Cardiothoracic Surgery, New York University (NYU) Langone Health, New York, NY, United States
| | - Robert Gilkeson
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
- Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH, United States
| | - Frank Jacono
- Division of Pulmonary Medicine, Louis Stokes VA Medical Center, Cleveland, OH, United States
| | - Kevin Li-Chun Hsieh
- Department of Radiology, School of Medicine, College of Medicine, Taipei Medical University and Taipei Medical University Hospital, Taipei, Taiwan
| | - Gong-Yau Lan
- Department of Radiology, School of Medicine, College of Medicine, Taipei Medical University and Taipei Medical University Hospital, Taipei, Taiwan
| | - Vamsidhar Velcheti
- New York University (NYU) Langone Perlmutter Cancer Center, New York, NY, United States
| | - Anant Madabhushi
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
- Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, OH, United States
- *Correspondence: Anant Madabhushi,
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LW1497, an Inhibitor of Malate Dehydrogenase, Suppresses TGF-β1-Induced Epithelial-Mesenchymal Transition in Lung Cancer Cells by Downregulating Slug. Antioxidants (Basel) 2021; 10:antiox10111674. [PMID: 34829545 PMCID: PMC8615288 DOI: 10.3390/antiox10111674] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2021] [Revised: 10/15/2021] [Accepted: 10/16/2021] [Indexed: 01/01/2023] Open
Abstract
LW1497 suppresses the expression of the hypoxia-inducing factor (HIF)-1α inhibiting malate dehydrogenase. Although hypoxia and HIF-1α are known to be important in cancer, LW1497 has not been therapeutically applied to cancer yet. Thus, we investigated the effect of LW1497 on the epithelial-mesenchymal transition (EMT) of lung cancer cells. A549 and H1299 lung cancer cells were induced to undergo via TGF-β1 treatment, resulting in the downregulation of E-cadherin and upregulation of N-cadherin and Vimentin concurrently with increases in the migration and invasion capacities of the cells. These effects of TGF-β1 were suppressed upon co-treatment of the cells with LW1497. An RNA-seq analysis revealed that LW1497 induced differential expression of genes related to hypoxia, RNA splicing, angiogenesis, cell migration, and metastasis in the A549 lung cancer cell lines. We confirmed the differential expression of Slug, an EMT-related transcription factor. Results from Western blotting and RT-PCR confirmed that LW1497 inhibited the expression of EMT markers and Slug. After orthotopically transplanting A549 cancer cells into mice, LW1497 was administered to examine whether the lung cancer progression was inhibited. We observed that LW1497 reduced the area of cancer. In addition, the results from immunohistochemical analyses showed that LW1497 downregulated EMT markers and Slug. In conclusion, LW1497 suppresses cancer progression through the inhibition of EMT by downregulating Slug.
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Yoon SH, Kim YJ, Doh K, Kim J, Lee KH, Lee KW, Kim J. Interobserver variability in Lung CT Screening Reporting and Data System categorisation in subsolid nodule-enriched lung cancer screening CTs. Eur Radiol 2021; 31:7184-7191. [PMID: 33733688 DOI: 10.1007/s00330-021-07800-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Revised: 01/25/2021] [Accepted: 02/16/2021] [Indexed: 11/29/2022]
Abstract
OBJECTIVES To assess interobserver agreement in Lung CT Screening Reporting and Data System (Lung-RADS) categorisation in subsolid nodule-enriched low-dose screening CTs. METHODS A retrospective review of low-dose screening CT reports from 2013 to 2017 using keyword searches for subsolid nodules identified 54 baseline CT scans. With an additional 108 negative screening CT scans, a total of 162 CT scans were categorised according to the Lung-RADS by two fellowship-trained thoracic radiologists in consensus. We randomly selected 20, 20, 10, and 10 scans from categories 1/2, 3, 4A, and 4B CT scans, respectively, to ensure balanced category representation. Five radiologists classified the 60 CT scans into Lung-RADS categories. The frequencies of concordance and minor and major discordance were calculated, with major discordance defined as at least 6 months of management discrepancy. We used Cohen's κ statistics to analyse reader agreement. RESULTS An average of 60.3% (181 of 300) of all cases and 45.0% (90 of 200) of positive screens were correctly categorised. The minor and major discordance rates were 12.3% and 27.3% overall and 18.5% and 36.5% in positive screens, respectively. The concordance rate was significantly higher among experienced thoracic radiologists. Overall, the interobserver agreement was moderate (mean κ, 0.45; 95% confidence interval: 0.40-0.51). The proportion of part-solid risk-dominant nodules was significantly higher in cases with low rates of accurate categorisation. CONCLUSION This retrospective study observed variable accuracy and moderate interobserver agreement in radiologist categorisation of subsolid nodules in screening CTs. This inconsistency may affect management recommendations for lung cancer screening. KEY POINTS • Diagnostic performance for Lung-RADS categorisation is variable among radiologists with fair to moderate interobserver agreement in subsolid nodule-enriched CT scans. • Experienced thoracic radiologists showed more accurate and consistent Lung-RADS categorisation than radiology residents. • The relative abundance of part-solid nodules was a potential factor related to increased disagreement in Lung-RADS categorisation.
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Affiliation(s)
- Sung Hyun Yoon
- Department of Radiology, Seoul National University Bundang Hospital, 82, Gumi-ro 173beon-gil, Bundang-gu, Seongnam-si, Gyeongi-do, 13620, Korea
| | - Yong Ju Kim
- Department of Radiology, Seoul National University Bundang Hospital, 82, Gumi-ro 173beon-gil, Bundang-gu, Seongnam-si, Gyeongi-do, 13620, Korea
| | | | - Junghoon Kim
- Department of Radiology, Seoul National University Bundang Hospital, 82, Gumi-ro 173beon-gil, Bundang-gu, Seongnam-si, Gyeongi-do, 13620, Korea
| | - Kyung Hee Lee
- Department of Radiology, Seoul National University Bundang Hospital, 82, Gumi-ro 173beon-gil, Bundang-gu, Seongnam-si, Gyeongi-do, 13620, Korea.,Department of Radiology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam-si, Korea
| | - Kyung Won Lee
- Department of Radiology, Seoul National University Bundang Hospital, 82, Gumi-ro 173beon-gil, Bundang-gu, Seongnam-si, Gyeongi-do, 13620, Korea
| | - Jihang Kim
- Department of Radiology, Seoul National University Bundang Hospital, 82, Gumi-ro 173beon-gil, Bundang-gu, Seongnam-si, Gyeongi-do, 13620, Korea.
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Chaunzwa TL, Hosny A, Xu Y, Shafer A, Diao N, Lanuti M, Christiani DC, Mak RH, Aerts HJWL. Deep learning classification of lung cancer histology using CT images. Sci Rep 2021; 11:5471. [PMID: 33727623 PMCID: PMC7943565 DOI: 10.1038/s41598-021-84630-x] [Citation(s) in RCA: 45] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Accepted: 02/15/2021] [Indexed: 02/07/2023] Open
Abstract
Tumor histology is an important predictor of therapeutic response and outcomes in lung cancer. Tissue sampling for pathologist review is the most reliable method for histology classification, however, recent advances in deep learning for medical image analysis allude to the utility of radiologic data in further describing disease characteristics and for risk stratification. In this study, we propose a radiomics approach to predicting non-small cell lung cancer (NSCLC) tumor histology from non-invasive standard-of-care computed tomography (CT) data. We trained and validated convolutional neural networks (CNNs) on a dataset comprising 311 early-stage NSCLC patients receiving surgical treatment at Massachusetts General Hospital (MGH), with a focus on the two most common histological types: adenocarcinoma (ADC) and Squamous Cell Carcinoma (SCC). The CNNs were able to predict tumor histology with an AUC of 0.71(p = 0.018). We also found that using machine learning classifiers such as k-nearest neighbors (kNN) and support vector machine (SVM) on CNN-derived quantitative radiomics features yielded comparable discriminative performance, with AUC of up to 0.71 (p = 0.017). Our best performing CNN functioned as a robust probabilistic classifier in heterogeneous test sets, with qualitatively interpretable visual explanations to its predictions. Deep learning based radiomics can identify histological phenotypes in lung cancer. It has the potential to augment existing approaches and serve as a corrective aid for diagnosticians.
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Affiliation(s)
- Tafadzwa L. Chaunzwa
- grid.38142.3c000000041936754XArtificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA USA ,grid.62560.370000 0004 0378 8294Department of Radiation Oncology, Dana Farber Cancer Institute and Brigham and Women’s Hospital, Boston, MA USA ,grid.413575.10000 0001 2167 1581Howard Hughes Medical Institute, Chevy Chase, MD USA
| | - Ahmed Hosny
- grid.38142.3c000000041936754XArtificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA USA ,grid.62560.370000 0004 0378 8294Department of Radiation Oncology, Dana Farber Cancer Institute and Brigham and Women’s Hospital, Boston, MA USA
| | - Yiwen Xu
- grid.38142.3c000000041936754XArtificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA USA ,grid.62560.370000 0004 0378 8294Department of Radiation Oncology, Dana Farber Cancer Institute and Brigham and Women’s Hospital, Boston, MA USA
| | - Andrea Shafer
- grid.38142.3c000000041936754XHarvard T.H. Chan School of Public Health, Boston, MA USA
| | - Nancy Diao
- grid.38142.3c000000041936754XHarvard T.H. Chan School of Public Health, Boston, MA USA
| | - Michael Lanuti
- grid.32224.350000 0004 0386 9924Division of Thoracic Surgery, Massachusetts General Hospital, Boston, MA USA
| | - David C. Christiani
- grid.38142.3c000000041936754XHarvard T.H. Chan School of Public Health, Boston, MA USA ,grid.32224.350000 0004 0386 9924Department of Medicine, Massachusetts General Hospital, Boston, MA USA
| | - Raymond H. Mak
- grid.38142.3c000000041936754XArtificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA USA ,grid.62560.370000 0004 0378 8294Department of Radiation Oncology, Dana Farber Cancer Institute and Brigham and Women’s Hospital, Boston, MA USA
| | - Hugo J. W. L. Aerts
- grid.38142.3c000000041936754XArtificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA USA ,grid.62560.370000 0004 0378 8294Department of Radiation Oncology, Dana Farber Cancer Institute and Brigham and Women’s Hospital, Boston, MA USA ,grid.65499.370000 0001 2106 9910Department of Radiology, Dana Farber Cancer Institute and Brigham and Women’s Hospital, Boston, MA USA ,grid.5012.60000 0001 0481 6099Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University, Maastricht, The Netherlands
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Wang L, Li T, Hong J, Zhang M, Ouyang M, Zheng X, Tang K. 18F-FDG PET-based radiomics model for predicting occult lymph node metastasis in clinical N0 solid lung adenocarcinoma. Quant Imaging Med Surg 2021; 11:215-225. [PMID: 33392023 DOI: 10.21037/qims-20-337] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Background This study aimed to develop a preoperative positron emission tomography (PET)-based radiomics model for predicting occult lymph node metastasis (OLM) in clinical N0 (cN0) solid lung adenocarcinoma. Methods The preoperative fluorine-18-fludeoxyglucose (18F-FDG) PET images of 370 patients with cN0 lung adenocarcinoma confirmed by histopathological examination were retrospectively reviewed. Patients were divided into training and validation sets. Radiomics features and relevant data were extracted from PET images. A nomogram was developed in a training set via univariate and multivariate logistic analyses, and its performance was assessed by concordance-index (C-index), calibration curves, and decision curve analysis (DCA) in the training and validation sets. Results The multivariate logistic regression analysis showed that only carcinoembryonic antigen (CEA), metabolic tumor volume (MTV), and the radiomics signature had statistically significant differences between patients with and without OLM (P<0.05). A nomogram was developed based on the logistic analyses, and its C-index was 0.769 in the training set and 0.768 in the validation set. The calibration curve demonstrated good consistency between the nomogram-predicted probability of OLM and the actual rate. The DCA also confirmed the clinical utility of the nomogram. Conclusions A PET/computed tomography (CT)-based radiomics model including CEA, MTV, and the radiomics signature was developed and demonstrated adequate predictive accuracy and clinical net benefit in the present study, and was conveniently used to facilitate the individualized preoperative prediction of OLM.
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Affiliation(s)
- Lili Wang
- Department of PET/CT, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Tiancheng Li
- PET Center, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Junjie Hong
- Department of PET/CT, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Mingyue Zhang
- Department of Radiology, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Mingli Ouyang
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiangwu Zheng
- Department of PET/CT, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Kun Tang
- Department of PET/CT, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
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10
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Huang B, Zhou Z, Liu J, Wu X, Li X, He Q, Zhang P, Tang X. The role of monoamine oxidase A in HPV-16 E7-induced epithelial-mesenchymal transition and HIF-1α protein accumulation in non-small cell lung cancer cells. Int J Biol Sci 2020; 16:2692-2703. [PMID: 32792865 PMCID: PMC7415426 DOI: 10.7150/ijbs.46966] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2020] [Accepted: 07/23/2020] [Indexed: 12/19/2022] Open
Abstract
Our previous studies have found that human papillomavirus (HPV)-16 E7 oncoprotein promotes epithelial-mesenchymal transition (EMT) and hypoxia-inducible factor-1α (HIF-1α) protein accumulation in non-small cell lung cancer (NSCLC) cells and monoamine oxidase A (MAOA) is highly expressed in NSCLC tissues. Here, we further explored the role of MAOA in HPV-16 E7-induced EMT and HIF-1α protein accumulation in A549 and NCI-H460 NSCLC cells. Our results showed that HPV-16 E7 enhanced MAOA expression in NSCLC cells. Additionally, MAOA knockout inhibited HPV-16 E7-induced migration, invasion, and EMT, and significantly reduced HPV-16 E7-induced ROS generation and HIF-1α protein accumulation via promoting its degradation. Furthermore, MAOA knockout suppressed HPV-16 E7-induced ERK1/2 activation. In vivo, MAOA knockout inhibited tumor growth, metastasis, and the expression of EMT-related markers and HIF-1α proteins induced by HPV-16 E7 in NCI-H460 NSCLC subcutaneous xenograft and in situ intrapulmonary models of nude mice. Taken together, our findings provide evidence that MAOA plays a key role in EMT and HIF-1α protein accumulation induced by HPV-16 E7 in NSCLC cells, suggesting that MAOA may be a potential therapeutic target for HPV-related NSCLC.
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Affiliation(s)
- Bingyu Huang
- Institute of Biochemistry and Molecular Biology, Collaborative innovation center for antitumor active substance research and development, Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, Guangdong Medical University, Zhanjiang 524023, P.R. China
| | - Zhiyuan Zhou
- Institute of Biochemistry and Molecular Biology, Collaborative innovation center for antitumor active substance research and development, Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, Guangdong Medical University, Zhanjiang 524023, P.R. China
| | - Jiao Liu
- Institute of Biochemistry and Molecular Biology, Collaborative innovation center for antitumor active substance research and development, Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, Guangdong Medical University, Zhanjiang 524023, P.R. China
| | - Xin Wu
- Marine Medical Research Institute of Guangdong Zhanjiang, Guangdong Key Laboratory for Research and Development of Natural Drugs, Guangdong Medical University, Zhanjiang 524023, P.R. China
| | - Xiangyong Li
- Institute of Biochemistry and Molecular Biology, Collaborative innovation center for antitumor active substance research and development, Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, Guangdong Medical University, Zhanjiang 524023, P.R. China
| | - Qiang He
- Institute of Plastic Surgery, Affiliated Hospital of Guangdong Medical University, Zhanjiang 524001, P.R. China
| | - Peihua Zhang
- Institute of Plastic Surgery, Affiliated Hospital of Guangdong Medical University, Zhanjiang 524001, P.R. China
| | - Xudong Tang
- Institute of Biochemistry and Molecular Biology, Collaborative innovation center for antitumor active substance research and development, Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, Guangdong Medical University, Zhanjiang 524023, P.R. China.,Marine Medical Research Institute of Guangdong Zhanjiang, Guangdong Key Laboratory for Research and Development of Natural Drugs, Guangdong Medical University, Zhanjiang 524023, P.R. China
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11
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Hamid AB, Petreaca RC. Secondary Resistant Mutations to Small Molecule Inhibitors in Cancer Cells. Cancers (Basel) 2020; 12:cancers12040927. [PMID: 32283832 PMCID: PMC7226513 DOI: 10.3390/cancers12040927] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2020] [Revised: 04/05/2020] [Accepted: 04/07/2020] [Indexed: 12/14/2022] Open
Abstract
Secondary resistant mutations in cancer cells arise in response to certain small molecule inhibitors. These mutations inevitably cause recurrence and often progression to a more aggressive form. Resistant mutations may manifest in various forms. For example, some mutations decrease or abrogate the affinity of the drug for the protein. Others restore the function of the enzyme even in the presence of the inhibitor. In some cases, resistance is acquired through activation of a parallel pathway which bypasses the function of the drug targeted pathway. The Catalogue of Somatic Mutations in Cancer (COSMIC) produced a compendium of resistant mutations to small molecule inhibitors reported in the literature. Here, we build on these data and provide a comprehensive review of resistant mutations in cancers. We also discuss mechanistic parallels of resistance.
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12
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Wood DE, Kazerooni EA, Baum SL, Eapen GA, Ettinger DS, Hou L, Jackman DM, Klippenstein D, Kumar R, Lackner RP, Leard LE, Lennes IT, Leung ANC, Makani SS, Massion PP, Mazzone P, Merritt RE, Meyers BF, Midthun DE, Pipavath S, Pratt C, Reddy C, Reid ME, Rotter AJ, Sachs PB, Schabath MB, Schiebler ML, Tong BC, Travis WD, Wei B, Yang SC, Gregory KM, Hughes M. Lung Cancer Screening, Version 3.2018, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw 2019; 16:412-441. [PMID: 29632061 DOI: 10.6004/jnccn.2018.0020] [Citation(s) in RCA: 383] [Impact Index Per Article: 76.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Lung cancer is the leading cause of cancer-related mortality in the United States and worldwide. Early detection of lung cancer is an important opportunity for decreasing mortality. Data support using low-dose computed tomography (LDCT) of the chest to screen select patients who are at high risk for lung cancer. Lung screening is covered under the Affordable Care Act for individuals with high-risk factors. The Centers for Medicare & Medicaid Services (CMS) covers annual screening LDCT for appropriate Medicare beneficiaries at high risk for lung cancer if they also receive counseling and participate in shared decision-making before screening. The complete version of the NCCN Guidelines for Lung Cancer Screening provides recommendations for initial and subsequent LDCT screening and provides more detail about LDCT screening. This manuscript focuses on identifying patients at high risk for lung cancer who are candidates for LDCT of the chest and on evaluating initial screening findings.
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13
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Miao H, Shaolei L, Nan L, Yumei L, Shanyuan Z, Fangliang L, Yue Y. Occult mediastinal lymph node metastasis in FDG-PET/CT node-negative lung adenocarcinoma patients: Risk factors and histopathological study. Thorac Cancer 2019; 10:1453-1460. [PMID: 31127706 PMCID: PMC6558456 DOI: 10.1111/1759-7714.13093] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2019] [Revised: 04/27/2019] [Accepted: 05/02/2019] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND The aim of this study was to investigate predictive factors of occult mediastinal lymph node metastasis (MLNM) in preoperative 18 F-fluorodeoxy-glucose PET/CT node-negative lung adenocarcinoma patients. METHODS We reviewed the clinical data and PET/CT parameters of 360 consecutive pulmonary adenocarcinoma patients who were scheduled to undergo anatomical pulmonary resection and systemic mediastinal node dissection. The nodal metastasis was pathologically defined and all resected tumors were classified according to the 2011 IASLC/ATS/ERS classification. Univariate and multivariate analysis were conducted to evaluate the associations between clinicopathological variables and MLNM. RESULTS Of all 360 patients, 54 (15.0%) had pathological N2 diseases. The serum CEA level, nodule type, hilar nodal SUVmax, tumor SUVmax, size, location and histologic subtype were associated with MLNM significantly on univariate analysis. On multivariate analysis, CEA ≥ 5.0 ng/mL (P < 0.001), solid nodule (P = 0.012), tumor SUVmax ≥ 3.7 (P < 0.027), hilar nodal SUVmax ≥ 2.0 (P < 0.001) and centrally located tumor (P = 0.035) were independent risk factors for MLNM. The area under the ROC curve (AUC) for tumor SUVmax and hilar nodal SUVmax in predicting MLNM was 0.764 and 0.730, respectively, and the combined use of five factors yielded a higher AUC of 0.885. CONCLUSION Increased primary tumor and hilar lymph node SUVmax, solid nodule, centrally located tumor and increased CEA level predicted the increased risk of mediastinal lymph node metastasis. Combined use of these factors improved the diagnostic capacity for predicting N2 disease preoperatively. Invasive mediastinal staging should be considered for patients with these risk factors, even those with a negative mediastinum on PET/CT.
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Affiliation(s)
- Huang Miao
- Department of Thoracic Surgery II, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education)Peking University Cancer Hospital and InstituteBeijingChina
| | - Li Shaolei
- Department of Thoracic Surgery II, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education)Peking University Cancer Hospital and InstituteBeijingChina
| | - Li Nan
- Department of Nuclear Medicine, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education)Peking University Cancer Hospital and InstituteBeijingChina
| | - Lai Yumei
- Department of Pathology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education)Peking University Cancer Hospital and InstituteBeijingChina
| | - Zhang Shanyuan
- Department of Thoracic Surgery II, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education)Peking University Cancer Hospital and InstituteBeijingChina
| | - Lu Fangliang
- Department of Thoracic Surgery II, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education)Peking University Cancer Hospital and InstituteBeijingChina
| | - Yang Yue
- Department of Thoracic Surgery II, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education)Peking University Cancer Hospital and InstituteBeijingChina
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14
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Gao C, Zhuang J, Li H, Liu C, Zhou C, Liu L, Sun C. Exploration of methylation-driven genes for monitoring and prognosis of patients with lung adenocarcinoma. Cancer Cell Int 2018; 18:194. [PMID: 30498398 PMCID: PMC6258452 DOI: 10.1186/s12935-018-0691-z] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2018] [Accepted: 11/19/2018] [Indexed: 12/22/2022] Open
Abstract
Background As one of the most common malignant tumors in humans, lung cancer has experienced a gradual increase in morbidity and mortality. This study examined prognosis-related methylation-driven genes specific to lung adenocarcinoma (LUAD) to provide a basis for prognosis prediction and personalized targeted therapy for LUAD patients. Methods The methylation and survival time data from LUAD patients in the TCGA database were downloaded. The MethylMix algorithm was used to identify the differential methylation status of LUAD and adjacent tissues based on the β-mixture model to obtain disease-related methylation-driven genes. A COX regression model was then used to screen for LUAD prognosis-related methylation-driven genes, and a linear risk model based on five methylation-driven gene expression profiles was constructed. A methylation and gene expression combined survival analysis was performed to further explore the prognostic value of 5 genes independently. Results There were 118 differentially expressed methylation-driven genes in the LUAD tissues and adjacent tissues. Five of the genes, CCDC181, PLAU, S1PR1, ELF3, and KLHDC9, were used to construct a prognostic risk model. Overall, the survival time was significantly lower in the high-risk group compared with that in the low-risk group (P < 0.05). In addition, the methylation and gene expression combined survival analysis found that the combined expression levels of the genes CCDC181, PLAU, and S1PR1 as well as KLHDC9 alone can be used as independent prognostic markers or drug targets. Conclusion Our findings provide an important bioinformatic basis and relevant theoretical basis for guiding subsequent LUAD early diagnosis and prognosis assessments. Electronic supplementary material The online version of this article (10.1186/s12935-018-0691-z) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Chundi Gao
- 1College of First Clinical Medicine, Shandong University of Traditional Chinese Medicine, Jinan, 250014 Shandong People's Republic of China
| | - Jing Zhuang
- 2Department of Oncology, Affiliated Hospital of Weifang Medical University, Weifang, 261031 Shandong People's Republic of China.,Departmen of Oncology, Weifang Traditional Chinese Hospital, Weifang, 261041 Shandong People's Republic of China
| | - Huayao Li
- 1College of First Clinical Medicine, Shandong University of Traditional Chinese Medicine, Jinan, 250014 Shandong People's Republic of China
| | - Cun Liu
- 4College of Traditional Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan, 250014 Shandong People's Republic of China
| | - Chao Zhou
- 2Department of Oncology, Affiliated Hospital of Weifang Medical University, Weifang, 261031 Shandong People's Republic of China.,Departmen of Oncology, Weifang Traditional Chinese Hospital, Weifang, 261041 Shandong People's Republic of China
| | - Lijuan Liu
- 2Department of Oncology, Affiliated Hospital of Weifang Medical University, Weifang, 261031 Shandong People's Republic of China.,Departmen of Oncology, Weifang Traditional Chinese Hospital, Weifang, 261041 Shandong People's Republic of China
| | - Changgang Sun
- 2Department of Oncology, Affiliated Hospital of Weifang Medical University, Weifang, 261031 Shandong People's Republic of China.,Departmen of Oncology, Weifang Traditional Chinese Hospital, Weifang, 261041 Shandong People's Republic of China
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15
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Hammer MM, Palazzo LL, Eckel AL, Barbosa EM, Kong CY. A Decision Analysis of Follow-up and Treatment Algorithms for Nonsolid Pulmonary Nodules. Radiology 2018; 290:506-513. [PMID: 30457486 DOI: 10.1148/radiol.2018180867] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Purpose To evaluate management strategies and treatment options for patients with ground-glass nodules (GGNs) by using decision-analysis models. Materials and Methods A simulation was developed for 1 000 000 hypothetical patients with GGNs undergoing follow-up per the Lung Imaging Reporting and Data System (Lung-RADS) recommendations. The initial age range was 55-75 years (mean, 64 years). Nodules could grow and develop solid components over time. Clinically significant malignancy rates were calibrated to data from the National Lung Screening Trial. Annual versus 3-year-interval follow-up of Lung-RADS category 2 nodules was compared, and different treatment strategies were tested (stereotactic body radiation therapy, surgery, and no therapy). Results Overall, 2.3% (22 584 of 1 000 000) of nodules were clinically significant malignancies; 6.3% (62 559 of 1 000 000) of nodules were treated. Only 30% (18 668 of 62 559) of Lung-RADS category 4B or 4X nodules were clinically significant malignancies. The risk of clinically significant malignancy for persistent nonsolid nodules after baseline was higher than Lung-RADS estimates for categories 2 and 3 (3% vs <1% and 1%-2%, respectively). Overall survival (OS) at 10 years was 72% (527 827 of 737 306; 95% confidence interval [CI]: 71%, 72%) with annual follow-up and 71% (526 507 of 737 306; 95% CI: 71%, 72%) with 3-year-interval follow-up (P < .01). At 10 years, OS among patients whose nodules progressed to Lung-RADS category 4B or 4X was 80% after radiation therapy (49 945 of 62 559; 95% CI: 80%, 80%), 79% after surgery (49 139 of 62 559; 95% CI: 78%, 79%), and 74% after no therapy (46 512 of 62 559; 95% CI: 74%, 75%) (P < .01). Conclusion Simulation modeling suggests that the follow-up interval for evaluating ground-glass nodules can be increased from 1 year to 3 years with minimal change in outcomes. Stereotactic body radiation therapy demonstrated the best outcomes compared with lobectomy and with no therapy for nonsolid nodules. © RSNA, 2018 Online supplemental material is available for this article.
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Affiliation(s)
- Mark M Hammer
- From the Department of Radiology, Brigham and Women's Hospital, Boston, Mass (M.M.H.); Institute for Technology Assessment, Massachusetts General Hospital, 101 Merrimac St, 10th Floor, Boston, MA 02114 (L.L.P., A.L.E., C.Y.K.); Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pa, (E.M.B.); and Harvard Medical School, Boston, Mass (C.Y.K.)
| | - Lauren L Palazzo
- From the Department of Radiology, Brigham and Women's Hospital, Boston, Mass (M.M.H.); Institute for Technology Assessment, Massachusetts General Hospital, 101 Merrimac St, 10th Floor, Boston, MA 02114 (L.L.P., A.L.E., C.Y.K.); Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pa, (E.M.B.); and Harvard Medical School, Boston, Mass (C.Y.K.)
| | - Andrew L Eckel
- From the Department of Radiology, Brigham and Women's Hospital, Boston, Mass (M.M.H.); Institute for Technology Assessment, Massachusetts General Hospital, 101 Merrimac St, 10th Floor, Boston, MA 02114 (L.L.P., A.L.E., C.Y.K.); Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pa, (E.M.B.); and Harvard Medical School, Boston, Mass (C.Y.K.)
| | - Eduardo M Barbosa
- From the Department of Radiology, Brigham and Women's Hospital, Boston, Mass (M.M.H.); Institute for Technology Assessment, Massachusetts General Hospital, 101 Merrimac St, 10th Floor, Boston, MA 02114 (L.L.P., A.L.E., C.Y.K.); Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pa, (E.M.B.); and Harvard Medical School, Boston, Mass (C.Y.K.)
| | - Chung Yin Kong
- From the Department of Radiology, Brigham and Women's Hospital, Boston, Mass (M.M.H.); Institute for Technology Assessment, Massachusetts General Hospital, 101 Merrimac St, 10th Floor, Boston, MA 02114 (L.L.P., A.L.E., C.Y.K.); Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pa, (E.M.B.); and Harvard Medical School, Boston, Mass (C.Y.K.)
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16
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Nakajima EC, Frankland MP, Johnson TF, Antic SL, Chen H, Chen SC, Karwoski RA, Walker R, Landman BA, Clay RD, Bartholmai BJ, Rajagopalan S, Peikert T, Massion PP, Maldonado F. Assessing the inter-observer variability of Computer-Aided Nodule Assessment and Risk Yield (CANARY) to characterize lung adenocarcinomas. PLoS One 2018; 13:e0198118. [PMID: 29856852 PMCID: PMC5983856 DOI: 10.1371/journal.pone.0198118] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2017] [Accepted: 05/14/2018] [Indexed: 12/17/2022] Open
Abstract
Lung adenocarcinoma (ADC), the most common lung cancer type, is recognized increasingly as a disease spectrum. To guide individualized patient care, a non-invasive means of distinguishing indolent from aggressive ADC subtypes is needed urgently. Computer-Aided Nodule Assessment and Risk Yield (CANARY) is a novel computed tomography (CT) tool that characterizes early ADCs by detecting nine distinct CT voxel classes, representing a spectrum of lepidic to invasive growth, within an ADC. CANARY characterization has been shown to correlate with ADC histology and patient outcomes. This study evaluated the inter-observer variability of CANARY analysis. Three novice observers segmented and analyzed independently 95 biopsy-confirmed lung ADCs from Vanderbilt University Medical Center/Nashville Veterans Administration Tennessee Valley Healthcare system (VUMC/TVHS) and the Mayo Clinic (Mayo). Inter-observer variability was measured using intra-class correlation coefficient (ICC). The average ICC for all CANARY classes was 0.828 (95% CI 0.76, 0.895) for the VUMC/TVHS cohort, and 0.852 (95% CI 0.804, 0.901) for the Mayo cohort. The most invasive voxel classes had the highest ICC values. To determine whether nodule size influenced inter-observer variability, an additional cohort of 49 sub-centimeter nodules from Mayo were also segmented by three observers, with similar ICC results. Our study demonstrates that CANARY ADC classification between novice CANARY users has an acceptably low degree of variability, and supports the further development of CANARY for clinical application.
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Affiliation(s)
- Erica C. Nakajima
- Department of Internal Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | | | - Tucker F. Johnson
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Sanja L. Antic
- Department of Internal Medicine, Division of Allergy, Pulmonary and Critical Care, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
- Veterans Affairs Tennessee Valley Health Care System, Nashville, Tennessee, United States of America
| | - Heidi Chen
- Center for Quantitative Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Sheau-Chiann Chen
- Center for Quantitative Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Ronald A. Karwoski
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Ronald Walker
- Veterans Affairs Tennessee Valley Health Care System, Nashville, Tennessee, United States of America
- Department of Radiology, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Bennett A. Landman
- Department of Electrical Engineering, Vanderbilt University, Nashville, Tennessee, United States of America
- Institute of Image Science, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Ryan D. Clay
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Brian J. Bartholmai
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Srinivasan Rajagopalan
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Tobias Peikert
- Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Pierre P. Massion
- Department of Internal Medicine, Division of Allergy, Pulmonary and Critical Care, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
- Veterans Affairs Tennessee Valley Health Care System, Nashville, Tennessee, United States of America
| | - Fabien Maldonado
- Department of Internal Medicine, Division of Allergy, Pulmonary and Critical Care, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
- Veterans Affairs Tennessee Valley Health Care System, Nashville, Tennessee, United States of America
- * E-mail:
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17
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Kim EJ, Park MK, Byun HJ, Kang GJ, Yu L, Kim HJ, Shim JG, Lee H, Lee CH. YdjC chitooligosaccharide deacetylase homolog induces keratin reorganization in lung cancer cells: involvement of interaction between YDJC and CDC16. Oncotarget 2018; 9:22915-22928. [PMID: 29796162 PMCID: PMC5955423 DOI: 10.18632/oncotarget.25145] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2017] [Accepted: 04/03/2018] [Indexed: 01/27/2023] Open
Abstract
Lung cancer is a fatal disease with a high mortality rate. The perinuclear reorganization of keratin 8 (K8) is an important biochemical phenomenon reflecting changes in the physical properties of metastatic cancer. However, there is not much of information about the regulatory molecules involved in phosphorylation and perinuclear reorganization of K8. In this study, we investigated the role and molecular mechanisms of YdjC chitooligosaccha- ride deacetylase homolog (YDJC) in sphingosylphosphorylcholine (SPC)-induced phosphorylation and reorganization of K8, and migration and invasion (SPC-induced events). SPC induced expression of YDJC in a dose- and time-dependent manner. Gene silencing of YDJC suppressed SPC-induced events. YDJC overexpression induced the SPC-induced events. YDJC deacetylase dominant negative mutant (YDJCD13A) did not induce SPC-induced events. YDJC siRNA reduced ERK activation and overexpression of YDJC induced ERK activation. The siRNA of ERK1 or ERK2 suppressed YDJC-induced phosphorylation and reorganization of K8, and migration and invasion. Co-immunoprecipitation revealed that YDJC binds to CDC16. Interestingly, CDC16 siRNA induced SPC-induced events. Overexpression of CDC16 blocked SPC-induced events. KMPLOT analysis based on public microarray data revealed the poor prognosis of lung cancer patients with high expression of YDJC compared with patients with low expression of YDJC. The collective results indicate that YDJC is involved in SPC-induced events in A549 lung cancer cells by interacting with CDC16. YDJC overexpression might be involved in the progression of lung cancer. These results also suggest that suppression of YDJC or boosting of CDC16 interaction with YDJC might be a novel way to prevent progression of lung cancer.
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Affiliation(s)
- Eun Ji Kim
- College of Pharmacy, Dongguk University-Seoul, 04620, Seoul, South Korea
| | | | - Hyun Jung Byun
- College of Pharmacy, Dongguk University-Seoul, 04620, Seoul, South Korea
| | - Gyeoung Jin Kang
- College of Pharmacy, Dongguk University-Seoul, 04620, Seoul, South Korea
| | - Lu Yu
- College of Pharmacy, Dongguk University-Seoul, 04620, Seoul, South Korea
| | - Hyun Ji Kim
- College of Pharmacy, Dongguk University-Seoul, 04620, Seoul, South Korea
| | - Jae Gal Shim
- National Cancer Center, Goyang, 10408, South Korea
| | - Ho Lee
- National Cancer Center, Goyang, 10408, South Korea
| | - Chang Hoon Lee
- College of Pharmacy, Dongguk University-Seoul, 04620, Seoul, South Korea
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18
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Inamura K. Clinicopathological Characteristics and Mutations Driving Development of Early Lung Adenocarcinoma: Tumor Initiation and Progression. Int J Mol Sci 2018; 19:ijms19041259. [PMID: 29690599 PMCID: PMC5979290 DOI: 10.3390/ijms19041259] [Citation(s) in RCA: 83] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2018] [Revised: 04/19/2018] [Accepted: 04/20/2018] [Indexed: 01/01/2023] Open
Abstract
Lung cancer is the leading cause of cancer-related deaths worldwide, with lung adenocarcinoma representing the most common lung cancer subtype. Among all lung adenocarcinomas, the most prevalent subset develops via tumorigenesis and progression from atypical adenomatous hyperplasia (AAH) to adenocarcinoma in situ (AIS), to minimally invasive adenocarcinoma (MIA), to overt invasive adenocarcinoma with a lepidic pattern. This stepwise development is supported by the clinicopathological and molecular characteristics of these tumors. In the 2015 World Health Organization classification, AAH and AIS are both defined as preinvasive lesions, whereas MIA is identified as an early invasive adenocarcinoma that is not expected to recur if removed completely. Recent studies have examined the molecular features of lung adenocarcinoma tumorigenesis and progression. EGFR-mutated adenocarcinoma frequently develops via the multistep progression. Oncogene-induced senescence appears to decrease the frequency of the multistep progression in KRAS- or BRAF-mutated adenocarcinoma, whose tumor evolution may be associated with epigenetic alterations and kinase-inactive mutations. This review summarizes the current knowledge of tumorigenesis and tumor progression in early lung adenocarcinoma, with special focus on its clinicopathological characteristics and their associations with driver mutations (EGFR, KRAS, and BRAF) as well as on its molecular pathogenesis and progression.
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Affiliation(s)
- Kentaro Inamura
- Division of Pathology, The Cancer Institute, Japanese Foundation for Cancer Research, 3-8-31 Ariake, Koto-ku, Tokyo 135-8550, Japan.
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19
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Yu KH, Berry GJ, Rubin DL, Ré C, Altman RB, Snyder M. Association of Omics Features with Histopathology Patterns in Lung Adenocarcinoma. Cell Syst 2017; 5:620-627.e3. [PMID: 29153840 PMCID: PMC5746468 DOI: 10.1016/j.cels.2017.10.014] [Citation(s) in RCA: 75] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2016] [Revised: 07/30/2017] [Accepted: 10/19/2017] [Indexed: 12/16/2022]
Abstract
Adenocarcinoma accounts for more than 40% of lung malignancy, and microscopic pathology evaluation is indispensable for its diagnosis. However, how histopathology findings relate to molecular abnormalities remains largely unknown. Here, we obtained H&E-stained whole-slide histopathology images, pathology reports, RNA sequencing, and proteomics data of 538 lung adenocarcinoma patients from The Cancer Genome Atlas and used these to identify molecular pathways associated with histopathology patterns. We report cell-cycle regulation and nucleotide binding pathways underpinning tumor cell dedifferentiation, and we predicted histology grade using transcriptomics and proteomics signatures (area under curve >0.80). We built an integrative histopathology-transcriptomics model to generate better prognostic predictions for stage I patients (p = 0.0182 ± 0.0021) compared with gene expression or histopathology studies alone, and the results were replicated in an independent cohort (p = 0.0220 ± 0.0070). These results motivate the integration of histopathology and omics data to investigate molecular mechanisms of pathology findings and enhance clinical prognostic prediction.
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Affiliation(s)
- Kun-Hsing Yu
- Biomedical Informatics Program, Stanford University, Stanford, CA 94305-5479, USA; Department of Genetics, Stanford University, Stanford, CA 94305-5120, USA; Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA
| | - Gerald J Berry
- Department of Pathology, Stanford University, Stanford, CA 94305, USA
| | - Daniel L Rubin
- Biomedical Informatics Program, Stanford University, Stanford, CA 94305-5479, USA; Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA; Department of Radiology, Stanford University, Stanford, CA 94305-5105, USA; Department of Medicine (Biomedical Informatics Research), Stanford University, Stanford, CA 94305-5479, USA
| | - Christopher Ré
- Department of Computer Science, Stanford University, Stanford, CA 94305-9025, USA
| | - Russ B Altman
- Biomedical Informatics Program, Stanford University, Stanford, CA 94305-5479, USA; Department of Genetics, Stanford University, Stanford, CA 94305-5120, USA; Department of Computer Science, Stanford University, Stanford, CA 94305-9025, USA; Department of Bioengineering, Stanford University, Stanford, CA 94305-4125, USA
| | - Michael Snyder
- Department of Genetics, Stanford University, Stanford, CA 94305-5120, USA.
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Kim HJ, Byun HJ, Park MK, Kim EJ, Kang GJ, Lee CH. Novel involvement of RhebL1 in sphingosylphosphorylcholine-induced keratin phosphorylation and reorganization: Binding to and activation of AKT1. Oncotarget 2017; 8:20851-20864. [PMID: 28209923 PMCID: PMC5400551 DOI: 10.18632/oncotarget.15364] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2016] [Accepted: 02/06/2017] [Indexed: 11/25/2022] Open
Abstract
Sphingosylphosphorylcholine induces keratin phosphorylation and reorganization, and increases viscoelasticity of metastatic cancer cells such as PANC-1 cells. However, the mechanism involved in sphingosylphosphorylcholine-induced keratin phosphorylation and reorganization is largely unknown. Sphingosylphosphorylcholine dose- and time-dependently induces the expression of RhebL1. The involvement of RhebL1 in sphingosylphosphorylcholine-induced events including keratin 8 (K8) phosphorylation, reorganization, migration and invasion was examined. Gene silencing of RhebL1 suppressed the sphingosylphosphorylcholine-induced events and overexpression of RhebL1 enhanced those events even without sphingosylphosphorylcholine treatment. We examined whether the G protein function of RhebL1 induces K8 phosphorylation using constitutively active RhebL1Q64L and dominant negative RhebL1D60K. G protein activity of RhebL1 is involved in sphingosylphosphorylcholine-induced K8 phosphorylation. We found that RhebL1 binds and activates AKT1. G protein activity of RhebL1 is involved in the binding and activation of AKT1. MK2206 (AKT inhibitor) and gene silencing of AKT1 inhibited the sphingosylphosphorylcholine-induced events, whereas overexpression of activated-AKT1 induced K8 phosphorylation, reorganization, migration and invasion even without sphingosylphosphorylcholine treatment. The collective results indicate that RhebL1 is involved in sphingosylphosphorylcholine-induced events in A549 lung cancer cells via binding to AKT1 leading to activation of it. These results suggest that suppression of RhebL1 or inhibition of RhebL1′s binding to AKT1 might be a novel way that prevents changes in the physical properties of metastatic cancer cells.
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Affiliation(s)
- Hyun Ji Kim
- College of Pharmacy, Dongguk University, Seoul 100-715, Republic of Korea
| | - Hyun Jung Byun
- College of Pharmacy, Dongguk University, Seoul 100-715, Republic of Korea
| | - Mi Kyung Park
- College of Pharmacy, Dongguk University, Seoul 100-715, Republic of Korea
| | - Eun Ji Kim
- College of Pharmacy, Dongguk University, Seoul 100-715, Republic of Korea
| | - Gyeoung Jin Kang
- College of Pharmacy, Dongguk University, Seoul 100-715, Republic of Korea
| | - Chang Hoon Lee
- College of Pharmacy, Dongguk University, Seoul 100-715, Republic of Korea
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Yip SSF, Liu Y, Parmar C, Li Q, Liu S, Qu F, Ye Z, Gillies RJ, Aerts HJWL. Associations between radiologist-defined semantic and automatically computed radiomic features in non-small cell lung cancer. Sci Rep 2017; 7:3519. [PMID: 28615677 PMCID: PMC5471260 DOI: 10.1038/s41598-017-02425-5] [Citation(s) in RCA: 70] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2017] [Accepted: 04/11/2017] [Indexed: 12/26/2022] Open
Abstract
Tumor phenotypes captured in computed tomography (CT) images can be described qualitatively and quantitatively using radiologist-defined "semantic" and computer-derived "radiomic" features, respectively. While both types of features have shown to be promising predictors of prognosis, the association between these groups of features remains unclear. We investigated the associations between semantic and radiomic features in CT images of 258 non-small cell lung adenocarcinomas. The tumor imaging phenotypes were described using 9 qualitative semantic features that were scored by radiologists, and 57 quantitative radiomic features that were automatically calculated using mathematical algorithms. Of the 9 semantic features, 3 were rated on a binary scale (cavitation, air bronchogram, and calcification) and 6 were rated on a categorical scale (texture, border definition, contour, lobulation, spiculation, and concavity). 32-41 radiomic features were associated with the binary semantic features (AUC = 0.56-0.76). The relationship between all radiomic features and the categorical semantic features ranged from weak to moderate (|Spearmen's correlation| = 0.002-0.65). There are associations between semantic and radiomic features, however the associations were not strong despite being significant. Our results indicate that radiomic features may capture distinct tumor phenotypes that fail to be perceived by naked eye that semantic features do not describe and vice versa.
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Affiliation(s)
- Stephen S F Yip
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, and Harvard Medical School, Boston, MA, 02115, USA.
| | - Ying Liu
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin; Tianjin's Clinical Research Center for Cancer, Tianjin, PR China
| | - Chintan Parmar
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, and Harvard Medical School, Boston, MA, 02115, USA
| | - Qian Li
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin; Tianjin's Clinical Research Center for Cancer, Tianjin, PR China
| | - Shichang Liu
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin; Tianjin's Clinical Research Center for Cancer, Tianjin, PR China
| | - Fangyuan Qu
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin; Tianjin's Clinical Research Center for Cancer, Tianjin, PR China
| | - Zhaoxiang Ye
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin; Tianjin's Clinical Research Center for Cancer, Tianjin, PR China
| | - Robert J Gillies
- Department of Cancer Imaging and Metabolism, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
- Department of Radiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Hugo J W L Aerts
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, and Harvard Medical School, Boston, MA, 02115, USA
- Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA
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Deng L, Xiao SM, Qiang JW, Li YA, Zhang Y. Early Lung Adenocarcinoma in Mice: Micro-Computed Tomography Manifestations and Correlation with Pathology. Transl Oncol 2017; 10:311-317. [PMID: 28325667 PMCID: PMC5358927 DOI: 10.1016/j.tranon.2017.02.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2017] [Revised: 02/16/2017] [Accepted: 02/16/2017] [Indexed: 12/17/2022] Open
Abstract
Lung cancer is the most common fatal malignancy for both men and women and adenocarcinoma is the most common histologic type. Early diagnosis of lung cancer can significantly improve the survival rate of patients. This study aimed to investigate the micro-computed tomography (micro-CT) manifestations of early lung adenocarcinoma (LAC) in mice and to provide a new perspective for early clinical diagnosis. Early LAC models in 10 mice were established by subcutaneously injecting 1-methyl-3-nitro-1-nitrosoguanidine (MNNG) solution. Micro-CT scan and multiple planar reconstruction (MPR) were used for mouse lungs. Micro-CT features of early LAC, especially the relationships between tumor and bronchus, were analyzed and correlated with pathology. Micro-CT findings of early LAC were divided into three types: non-solid (n = 8, 6%), partly solid (n = 85, 64%) and totally solid (n = 39, 30%). Tumor-bronchus relationships, which could be observed in 110 of 132(83%) LAC, were classified into four patterns: type I (n = 16, 15%), bronchus was truncated at the margin of the tumor; type II (n = 33, 30%), bronchus penetrated into the tumor with tapered narrowing and interruption; type III (n = 38, 35%), bronchus penetrated into the tumor with a patent and intact lumen; type IV (n = 99, 90%), bronchus ran at the border of the tumor with an intact or compressed lumen. Micro-CT manifestations of early LAC correlated well with pathological findings. Micro-CT can clearly demonstrate the features of mouse early LAC and bronchus-tumor relationships, and can also provide a new tool and perspective for the study of early LAC.
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Affiliation(s)
- Lin Deng
- Department of Radiology, Jinshan Hospital & Shanghai Medical College, Fudan University, Shanghai, 201508, China
| | - Shi Man Xiao
- Department of Radiology, Jinshan Hospital & Shanghai Medical College, Fudan University, Shanghai, 201508, China
| | - Jin Wei Qiang
- Department of Radiology, Jinshan Hospital & Shanghai Medical College, Fudan University, Shanghai, 201508, China.
| | - Yong Ai Li
- Department of Radiology, Jinshan Hospital & Shanghai Medical College, Fudan University, Shanghai, 201508, China
| | - Yu Zhang
- Department of Radiology, Jinshan Hospital & Shanghai Medical College, Fudan University, Shanghai, 201508, China
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Interstitial lung disease pattern turned out to be a predominantly lepidic lung adenocarcinoma. Respir Med Case Rep 2017; 21:56-58. [PMID: 28393007 PMCID: PMC5377009 DOI: 10.1016/j.rmcr.2017.02.016] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2017] [Revised: 02/22/2017] [Accepted: 02/27/2017] [Indexed: 11/21/2022] Open
Abstract
We report a case of a 46-year-old woman without any medical history who presented to our Respiratory Department with exertional dyspnoea for the last 6 weeks associated with non-productive cough. Chest radiography showed bilateral diffuse interstitial opacity. Bronchoalveolar lavage and transbronchial biopsies performed during flexible bronchoscopy as a step in the diagnostic workup of idiopathic interstitial pneumonia showed cells of pulmonary adenocarcinoma.
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24
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Silva APS, Coelho PV, Anazetti M, Simioni PU. Targeted therapies for the treatment of non-small-cell lung cancer: Monoclonal antibodies and biological inhibitors. Hum Vaccin Immunother 2016; 13:843-853. [PMID: 27831000 PMCID: PMC5404364 DOI: 10.1080/21645515.2016.1249551] [Citation(s) in RCA: 44] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023] Open
Abstract
The usual treatments for patients with non-small-cell lung cancer (NSCLC), such as advanced lung adenocarcinoma, are unspecific and aggressive, and include lung resection, radiotherapy and chemotherapy. Recently, treatment with monoclonal antibodies and biological inhibitors has emerged as an effective alternative, generating effective results with few side effects. In recent years, several clinical trials using monoclonal antibodies presented potential benefits to NSCLC, and 4 of them are already approved for the treatment of NSCLC, such as cetuximab, bevacizumab, nivolumab and pembrolizumab. Also, biological inhibitors are attractive tolls for biological applications. Among the approved inhibitors are crizotinib, erlotinib, afatinib and gefitinib, and side effects are usually mild to intense. Nevertheless, biological molecule treatments are under development, and several new monoclonal antibodies and biological inhibitors are in trial to treat NSCLC. Also under trial study are as follows: anti-epidermal growth factor receptor (EGFR) antibodies (nimotuzumab and ficlatuzumab), anti-IGF 1 receptor (IGF-1R) monoclonal antibody (figitumumab), anti-NR-LU-10 monoclonal antibody (nofetumomab) as well as antibodies directly affecting the cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) molecule (ipilimumab and tremelimumab), to receptor activator of nuclear factor-kappa B ligand (RANKL) (denosumab) or to polymerase enzyme (veliparib and olaparib). Among new inhibitors under investigation are poly-ADP ribose polymerase (PARP) inhibitors (veliparib and olaparib) and phosphatidylinositol 3-kinase (PI3K) inhibitor (buparlisib). However, the success of immunotherapies still requires extensive research and additional controlled trials to evaluate the long-term benefits and side effects.
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Affiliation(s)
- Ana P S Silva
- a Department of Biomedical Science , Faculty of Americana , Americana , SP , Brazil
| | - Priscila V Coelho
- a Department of Biomedical Science , Faculty of Americana , Americana , SP , Brazil
| | - Maristella Anazetti
- a Department of Biomedical Science , Faculty of Americana , Americana , SP , Brazil.,b Department of Health Science , Faculty DeVry Metrocamp , Campinas , SP , Brazil
| | - Patricia U Simioni
- a Department of Biomedical Science , Faculty of Americana , Americana , SP , Brazil.,c Department of Genetics , Evolution and Bioagents, Institute of Biology, University of Campinas (UNICAMP) , Campinas , SP , Brazil.,d Department of Biochemistry and Microbiology , Institute of Biosciences, Universidade Estadual Paulista, UNESP , Rio Claro , SP , Brazil
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25
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Ziv E, Erinjeri JP, Yarmohammadi H, Boas FE, Petre EN, Gao S, Shady W, Sofocleous CT, Jones DR, Rudin CM, Solomon SB. Lung Adenocarcinoma: Predictive Value of KRAS Mutation Status in Assessing Local Recurrence in Patients Undergoing Image-guided Ablation. Radiology 2016; 282:251-258. [PMID: 27440441 DOI: 10.1148/radiol.2016160003] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
Purpose To establish the relationship between KRAS mutation status and local recurrence after image-guided ablation of lung adenocarcinoma. Materials and Methods This study consisted of a HIPAA-compliant institutional review board-approved retrospective review of 56 primary lung adenocarcinomas in 54 patients (24 men, 30 women; median age, 72 years; range, 54-87 years) treated with percutaneous image-guided ablation and with available genetic mutational analysis. KRAS mutation status and additional clinical and technical variables-Eastern Cooperative Oncology Group (ECOG) status, smoking history, stage at diagnosis, status (new primary or not), history of radiation, history of surgery, prior systemic treatment, modality of ablation, size of nodule, ablation margin, and presence of ground-glass appearance-were recorded and evaluated in relation to time to local recurrence, which was calculated from the time of ablation to the first radiographic evidence of recurrence. Predictors of outcome were identified by using a proportional hazards model for both univariate and multivariate analysis, with death as a competing risk. Results Technical success was 100%. Of the 56 ablated tumors, 37 (66%) were wild type for KRAS and 19 (34%) were KRAS mutants. The 1-year and 3-year cumulative incidences of recurrence were 20% and 35% for wild-type KRAS compared with 40% and 63% for KRAS mutant tumors. KRAS mutation status was a significant predictor of local recurrence at both univariate (P = .05; subdistribution hazard ratio [sHR], 2.32) and multivariate (P = .006; sHR, 3.75) analysis. At multivariate analysis, size (P = .026; sHR, 2.54) and ECOG status (P = .012; sHR, 2.23) were also independent significant predictors, whereas minimum margin (P = .066) was not. Conclusion The results of this study show that there is a relationship between KRAS mutation status and local recurrence after image-guided ablation of lung adenocarcinoma. Specifically, KRAS mutation status of the ablated lesion is a significant predictor of time to local recurrence, independent of size and margin. © RSNA, 2016.
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Affiliation(s)
- Etay Ziv
- From the Interventional Radiology Service, Department of Radiology (E.Z., J.P.E., H.Y., F.E.B., E.N.P., S.G., W.S., C.T.S., S.B.S.), Thoracic Service, Department of Surgery (D.R.J.), and Thoracic Oncology Service, Division of Solid Tumor Oncology, Department of Medicine (C.M.R.), Memorial Sloan-Kettering Cancer Center, 1275 York Ave, Howard-118, New York, NY 10065; and the Interventional Therapy Department, Key Laboratory of Carcinogenesis and Translational Research, Ministry of Education, Peking University Cancer Hospital and Institute, Beijing, China (S.G.)
| | - Joseph P Erinjeri
- From the Interventional Radiology Service, Department of Radiology (E.Z., J.P.E., H.Y., F.E.B., E.N.P., S.G., W.S., C.T.S., S.B.S.), Thoracic Service, Department of Surgery (D.R.J.), and Thoracic Oncology Service, Division of Solid Tumor Oncology, Department of Medicine (C.M.R.), Memorial Sloan-Kettering Cancer Center, 1275 York Ave, Howard-118, New York, NY 10065; and the Interventional Therapy Department, Key Laboratory of Carcinogenesis and Translational Research, Ministry of Education, Peking University Cancer Hospital and Institute, Beijing, China (S.G.)
| | - Hooman Yarmohammadi
- From the Interventional Radiology Service, Department of Radiology (E.Z., J.P.E., H.Y., F.E.B., E.N.P., S.G., W.S., C.T.S., S.B.S.), Thoracic Service, Department of Surgery (D.R.J.), and Thoracic Oncology Service, Division of Solid Tumor Oncology, Department of Medicine (C.M.R.), Memorial Sloan-Kettering Cancer Center, 1275 York Ave, Howard-118, New York, NY 10065; and the Interventional Therapy Department, Key Laboratory of Carcinogenesis and Translational Research, Ministry of Education, Peking University Cancer Hospital and Institute, Beijing, China (S.G.)
| | - F Edward Boas
- From the Interventional Radiology Service, Department of Radiology (E.Z., J.P.E., H.Y., F.E.B., E.N.P., S.G., W.S., C.T.S., S.B.S.), Thoracic Service, Department of Surgery (D.R.J.), and Thoracic Oncology Service, Division of Solid Tumor Oncology, Department of Medicine (C.M.R.), Memorial Sloan-Kettering Cancer Center, 1275 York Ave, Howard-118, New York, NY 10065; and the Interventional Therapy Department, Key Laboratory of Carcinogenesis and Translational Research, Ministry of Education, Peking University Cancer Hospital and Institute, Beijing, China (S.G.)
| | - Elena N Petre
- From the Interventional Radiology Service, Department of Radiology (E.Z., J.P.E., H.Y., F.E.B., E.N.P., S.G., W.S., C.T.S., S.B.S.), Thoracic Service, Department of Surgery (D.R.J.), and Thoracic Oncology Service, Division of Solid Tumor Oncology, Department of Medicine (C.M.R.), Memorial Sloan-Kettering Cancer Center, 1275 York Ave, Howard-118, New York, NY 10065; and the Interventional Therapy Department, Key Laboratory of Carcinogenesis and Translational Research, Ministry of Education, Peking University Cancer Hospital and Institute, Beijing, China (S.G.)
| | - Song Gao
- From the Interventional Radiology Service, Department of Radiology (E.Z., J.P.E., H.Y., F.E.B., E.N.P., S.G., W.S., C.T.S., S.B.S.), Thoracic Service, Department of Surgery (D.R.J.), and Thoracic Oncology Service, Division of Solid Tumor Oncology, Department of Medicine (C.M.R.), Memorial Sloan-Kettering Cancer Center, 1275 York Ave, Howard-118, New York, NY 10065; and the Interventional Therapy Department, Key Laboratory of Carcinogenesis and Translational Research, Ministry of Education, Peking University Cancer Hospital and Institute, Beijing, China (S.G.)
| | - Waleed Shady
- From the Interventional Radiology Service, Department of Radiology (E.Z., J.P.E., H.Y., F.E.B., E.N.P., S.G., W.S., C.T.S., S.B.S.), Thoracic Service, Department of Surgery (D.R.J.), and Thoracic Oncology Service, Division of Solid Tumor Oncology, Department of Medicine (C.M.R.), Memorial Sloan-Kettering Cancer Center, 1275 York Ave, Howard-118, New York, NY 10065; and the Interventional Therapy Department, Key Laboratory of Carcinogenesis and Translational Research, Ministry of Education, Peking University Cancer Hospital and Institute, Beijing, China (S.G.)
| | - Constantinos T Sofocleous
- From the Interventional Radiology Service, Department of Radiology (E.Z., J.P.E., H.Y., F.E.B., E.N.P., S.G., W.S., C.T.S., S.B.S.), Thoracic Service, Department of Surgery (D.R.J.), and Thoracic Oncology Service, Division of Solid Tumor Oncology, Department of Medicine (C.M.R.), Memorial Sloan-Kettering Cancer Center, 1275 York Ave, Howard-118, New York, NY 10065; and the Interventional Therapy Department, Key Laboratory of Carcinogenesis and Translational Research, Ministry of Education, Peking University Cancer Hospital and Institute, Beijing, China (S.G.)
| | - David R Jones
- From the Interventional Radiology Service, Department of Radiology (E.Z., J.P.E., H.Y., F.E.B., E.N.P., S.G., W.S., C.T.S., S.B.S.), Thoracic Service, Department of Surgery (D.R.J.), and Thoracic Oncology Service, Division of Solid Tumor Oncology, Department of Medicine (C.M.R.), Memorial Sloan-Kettering Cancer Center, 1275 York Ave, Howard-118, New York, NY 10065; and the Interventional Therapy Department, Key Laboratory of Carcinogenesis and Translational Research, Ministry of Education, Peking University Cancer Hospital and Institute, Beijing, China (S.G.)
| | - Charles M Rudin
- From the Interventional Radiology Service, Department of Radiology (E.Z., J.P.E., H.Y., F.E.B., E.N.P., S.G., W.S., C.T.S., S.B.S.), Thoracic Service, Department of Surgery (D.R.J.), and Thoracic Oncology Service, Division of Solid Tumor Oncology, Department of Medicine (C.M.R.), Memorial Sloan-Kettering Cancer Center, 1275 York Ave, Howard-118, New York, NY 10065; and the Interventional Therapy Department, Key Laboratory of Carcinogenesis and Translational Research, Ministry of Education, Peking University Cancer Hospital and Institute, Beijing, China (S.G.)
| | - Stephen B Solomon
- From the Interventional Radiology Service, Department of Radiology (E.Z., J.P.E., H.Y., F.E.B., E.N.P., S.G., W.S., C.T.S., S.B.S.), Thoracic Service, Department of Surgery (D.R.J.), and Thoracic Oncology Service, Division of Solid Tumor Oncology, Department of Medicine (C.M.R.), Memorial Sloan-Kettering Cancer Center, 1275 York Ave, Howard-118, New York, NY 10065; and the Interventional Therapy Department, Key Laboratory of Carcinogenesis and Translational Research, Ministry of Education, Peking University Cancer Hospital and Institute, Beijing, China (S.G.)
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