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Nomenoğlu H, Fındık G, Çetin M, Aydoğdu K, Gülhan SŞE, Bıçakçıoğlu P. Efficiency of pulmonary nodule risk scoring systems in Turkish population. Updates Surg 2024:10.1007/s13304-024-01901-8. [PMID: 38944649 DOI: 10.1007/s13304-024-01901-8] [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: 02/17/2024] [Accepted: 05/21/2024] [Indexed: 07/01/2024]
Abstract
Malignancy risk calculation models were developed using the clinical and radiological features. It was aimed to compare pulmonary nodule risk calculation models and evaluate their effectiveness and applicability for the Turkish population. Between 2014 and 2019, 351 patients who were operated on for pulmonary nodules were evaluated with the following data: age, gender, smoking history, family history of lung cancer, extrapulmonary malignancy and granulomatous disease, nodule diameter, attenuation character, side, localization, spiculation, nodule count, presence of pulmonary emphysema, FDG uptake in PET/CT of the nodule, and definitive pathology data. Malignancy risk scores were calculated using the equations of the Brock, Mayo, and Herder models. The results were evaluated statistically. The mean age of the 351 patients (236 men, 115 women) was 57.84 ± 10.87 (range 14-79) years, and 226 malignant and 125 benign nodules were observed. Significant correlations were found between malignancy and age (p < 0.001), nodule diameter (p < 0.001), gender (p < 0.009), speculation (p < 0.001), emphysema (p < 0.05), FDG uptake (p < 0.001). All three models were found effective in the differentiation (p < 0.001). The ideal threshold value was determined for the Brock (19.5%), Mayo (23.1%), and Herder (56%) models. All models were effective for nodules of > 10 mm, but none of them were for 0-10 mm. Brock was effective in ground-glass nodules (p = 0.02) and all models were effective for semi-solid and solid nodules. None of the groups could provide AUC values as high as those achieved in the original studies. This suggests the need to optimize models and malignancy risk thresholds for Turkish population.
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Affiliation(s)
- Hakan Nomenoğlu
- Department of Thoracic Surgery, University of Health Sciences, Ankara Atatürk Sanatoryum Training and Research Hospital, Ankara, Turkey
| | - Göktürk Fındık
- Department of Thoracic Surgery, University of Health Sciences, Ankara Atatürk Sanatoryum Training and Research Hospital, Ankara, Turkey
| | - Mehmet Çetin
- Department of Thoracic Surgery, Ministry of Health, Nigde Omer Halisdemir Training and Research Hospital, Nigde, Turkey.
| | - Koray Aydoğdu
- Department of Thoracic Surgery, University of Health Sciences, Ankara Etlik City Hospital, Ankara, Turkey
| | - Selim Şakir Erkmen Gülhan
- Department of Thoracic Surgery, University of Health Sciences, Ankara Atatürk Sanatoryum Training and Research Hospital, Ankara, Turkey
| | - Pınar Bıçakçıoğlu
- Department of Thoracic Surgery, University of Health Sciences, Ankara Atatürk Sanatoryum Training and Research Hospital, Ankara, Turkey
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Susai CJ, Velotta JB, Sakoda LC. Clinical Adjuncts to Lung Cancer Screening: A Narrative Review. Thorac Surg Clin 2023; 33:421-432. [PMID: 37806744 PMCID: PMC10926946 DOI: 10.1016/j.thorsurg.2023.03.002] [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] [Indexed: 10/10/2023]
Abstract
The updated US Preventive Services Task Force guidelines on lung cancer screening have significantly expanded the population of screening eligible adults, among whom the balance of benefits and harms associated with lung cancer screening vary considerably. Clinical adjuncts are additional information and tools that can guide decision-making to optimally screen individuals who are most likely to benefit. Proposed adjuncts include integration of clinical history, risk prediction models, shared-decision-making tools, and biomarker tests at key steps in the screening process. Although evidence regarding their clinical utility and implementation is still evolving, they carry significant promise in optimizing screening effectiveness and efficiency for lung cancer.
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Affiliation(s)
- Cynthia J Susai
- UCSF East Bay General Surgery, 1411 East 31st Street QIC 22134, Oakland, CA 94612, USA
| | - Jeffrey B Velotta
- Department of Thoracic Surgery, Kaiser Permanente Northern California, 3600 Broadway, Oakland, CA 94611, USA
| | - Lori C Sakoda
- Division of Research, Kaiser Permanente Northern California, 2000 Broadway, Oakland, CA 94612, USA.
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Wu Z, Wang F, Cao W, Qin C, Dong X, Yang Z, Zheng Y, Luo Z, Zhao L, Yu Y, Xu Y, Li J, Tang W, Shen S, Wu N, Tan F, Li N, He J. Lung cancer risk prediction models based on pulmonary nodules: A systematic review. Thorac Cancer 2022; 13:664-677. [PMID: 35137543 PMCID: PMC8888150 DOI: 10.1111/1759-7714.14333] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2021] [Revised: 01/10/2022] [Accepted: 01/11/2022] [Indexed: 10/25/2022] Open
Abstract
BACKGROUND Screening with low-dose computed tomography (LDCT) is an efficient way to detect lung cancer at an earlier stage, but has a high false-positive rate. Several pulmonary nodules risk prediction models were developed to solve the problem. This systematic review aimed to compare the quality and accuracy of these models. METHODS The keywords "lung cancer," "lung neoplasms," "lung tumor," "risk," "lung carcinoma" "risk," "predict," "assessment," and "nodule" were used to identify relevant articles published before February 2021. All studies with multivariate risk models developed and validated on human LDCT data were included. Informal publications or studies with incomplete procedures were excluded. Information was extracted from each publication and assessed. RESULTS A total of 41 articles and 43 models were included. External validation was performed for 23.2% (10/43) models. Deep learning algorithms were applied in 62.8% (27/43) models; 60.0% (15/25) deep learning based researches compared their algorithms with traditional methods, and received better discrimination. Models based on Asian and Chinese populations were usually built on single-center or small sample retrospective studies, and the majority of the Asian models (12/15, 80.0%) were not validated using external datasets. CONCLUSION The existing models showed good discrimination for identifying high-risk pulmonary nodules, but lacked external validation. Deep learning algorithms are increasingly being used with good performance. More researches are required to improve the quality of deep learning models, particularly for the Asian population.
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Affiliation(s)
- Zheng Wu
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Fei Wang
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Wei Cao
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Chao Qin
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xuesi Dong
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Zhuoyu Yang
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yadi Zheng
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Zilin Luo
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Liang Zhao
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yiwen Yu
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yongjie Xu
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jiang Li
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.,Chinese Academy of Medical Sciences Key Laboratory for National Cancer Big Data Analysis and Implement, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Wei Tang
- PET-CT Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Sipeng Shen
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China.,Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China
| | - Ning Wu
- PET-CT Center, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.,Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Fengwei Tan
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Ni Li
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.,Chinese Academy of Medical Sciences Key Laboratory for National Cancer Big Data Analysis and Implement, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jie He
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.,Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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Zhang K, Wei Z, Nie Y, Shen H, Wang X, Wang J, Yang F, Chen K. Comprehensive analysis of clinical logistic and machine learning based models for the evaluation of pulmonary nodules. JTO Clin Res Rep 2022; 3:100299. [PMID: 35392654 PMCID: PMC8980995 DOI: 10.1016/j.jtocrr.2022.100299] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 02/06/2022] [Accepted: 02/15/2022] [Indexed: 11/30/2022] Open
Abstract
Introduction Over the years, multiple models have been developed for the evaluation of pulmonary nodules (PNs). This study aimed to comprehensively investigate clinical models for estimating the malignancy probability in patients with PNs. Methods PubMed, EMBASE, Cochrane Library, and Web of Science were searched for studies reporting mathematical models for PN evaluation until March 2020. Eligible models were summarized, and network meta-analysis was performed on externally validated models (PROSPERO database CRD42020154731). The cut-off value of 40% was used to separate patients into high prevalence (HP) and low prevalence (LP), and a subgroup analysis was performed. Results A total of 23 original models were proposed in 42 included articles. Age and nodule size were most often used in the models, whereas results of positron emission tomography-computed tomography were used when collected. The Mayo model was validated in 28 studies. The area under the curve values of four most often used models (PKU, Brock, Mayo, VA) were 0.830, 0.785, 0.743, and 0.750, respectively. High-prevalence group (HP) models had better results in HP patients with a pooled sensitivity and specificity of 0.83 (95% confidence interval [CI]: 0.78–0.88) and 0.71 (95% CI: 0.71–0.79), whereas LP models only achieved pooled sensitivity and specificity of 0.70 (95% CI: 0.60–0.79) and 0.70 (95% CI: 0.62–0.77). For LP patients, the pooled sensitivity and specificity decreased from 0.68 (95% CI: 0.57–0.78) and 0.93 (95% CI: 0.87–0.97) to 0.57 (95% CI: 0.21–0.88) and 0.82 (95% CI: 0.65–0.92) when the model changed from LP to HP models. Compared with the clinical models, artificial intelligence-based models have promising preliminary results. Conclusions Mathematical models can facilitate the evaluation of lung nodules. Nevertheless, suitable model should be used on appropriate cohorts to achieve an accurate result.
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Affiliation(s)
- Kai Zhang
- Department of Thoracic Surgery, Peking University People’s Hospital, Beijing, People’s Republic of China
| | - Zihan Wei
- Department of Thoracic Surgery, Peking University People’s Hospital, Beijing, People’s Republic of China
- Peking University Health Science Center, Beijing, People’s Republic of China
| | - Yuntao Nie
- Department of Thoracic Surgery, Peking University People’s Hospital, Beijing, People’s Republic of China
| | - Haifeng Shen
- Department of Thoracic Surgery, Peking University People’s Hospital, Beijing, People’s Republic of China
| | - Xin Wang
- Department of Thoracic Surgery, Peking University People’s Hospital, Beijing, People’s Republic of China
- Peking University Health Science Center, Beijing, People’s Republic of China
| | - Jun Wang
- Department of Thoracic Surgery, Peking University People’s Hospital, Beijing, People’s Republic of China
| | - Fan Yang
- Department of Thoracic Surgery, Peking University People’s Hospital, Beijing, People’s Republic of China
| | - Kezhong Chen
- Department of Thoracic Surgery, Peking University People’s Hospital, Beijing, People’s Republic of China
- Corresponding author. Address for correspondence: Kezhong Chen, MD, Department of Thoracic Surgery, Peking University People’s Hospital, Xi Zhi Men South Avenue, Number 11, Beijing 100044, People’s Republic of China.
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Zhang W, Duan X, Zhang Z, Yang Z, Zhao C, Liang C, Liu Z, Cheng S, Zhang K. Combination of CT and telomerase+ circulating tumor cells improves diagnosis of small pulmonary nodules. JCI Insight 2021; 6:148182. [PMID: 33905377 PMCID: PMC8262359 DOI: 10.1172/jci.insight.148182] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 04/23/2021] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Early diagnosis and treatment are key to the long-term survival of lung cancer patients. Although CT has significantly contributed to the early diagnosis of lung cancer, there are still consequences of excessive or delayed treatment. By improving the sensitivity and specificity of circulating tumor cell (CTC) detection, a solution was proposed for differentiating benign from malignant pulmonary nodules. METHODS In this study, we used telomerase reverse transcriptase–based (TERT-based) CTC detection (TBCD) to distinguish benign from malignant pulmonary nodules < 2 cm and compared this method with the pathological diagnosis as the gold standard. FlowSight and FISH were used to confirm the CTCs detected by TBCD. RESULTS Our results suggest that CTCs based on TBCD can be used as independent biomarkers to distinguish benign from malignant nodules and are significantly superior to serum tumor markers. When the detection threshold was 1, the detection sensitivity and specificity of CTC diagnosis were 0.854 and 0.839, respectively. For pulmonary nodules ≤ 1 cm and 1–2 cm, the sensitivity and specificity of CTCs were both higher than 77%. Additionally, the diagnostic ability of CTC-assisted CT was compared by CT detection. The results show that CT combined with CTCs could significantly improve the differentiation ability of benign and malignant nodules in lung nodules < 2 cm and that the sensitivity and specificity could reach 0.899 and 0.839, respectively. CONCLUSION TBCD can effectively diagnose pulmonary nodules and be used as an effective auxiliary diagnostic scheme for CT diagnosis. FUNDING National Key Research and Development Project grant nos. 2019YFC1315700 and 2017YFC1308702, CAMS Initiative for Innovative Medicine grant no. 2017-I2M-1-005, and National Natural Science Foundation of China grant no. 81472013.
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Affiliation(s)
- Wen Zhang
- Department of Immunology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xinchun Duan
- Department of Thoracic Surgery, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Zhenrong Zhang
- Department of General Thoracic Surgery, China-Japan Friendship Hospital, Beijing, China
| | - Zhenrong Yang
- State Key Laboratory of Molecular Oncology, Department of Etiology and Carcinogenesis, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Changyun Zhao
- Chongqing Deepexam Biotechnology Co. Ltd., Chongqing, China
| | | | - Zhidong Liu
- Department of Thoracic Surgery, Beijing Chest Hospital, Capital Medical University, Beijing, China
| | - Shujun Cheng
- State Key Laboratory of Molecular Oncology, Department of Etiology and Carcinogenesis, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Kaitai Zhang
- State Key Laboratory of Molecular Oncology, Department of Etiology and Carcinogenesis, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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Maldonado F, Varghese C, Rajagopalan S, Duan F, Balar AB, Lakhani DA, Antic SL, Massion PP, Johnson TF, Karwoski RA, Robb RA, Bartholmai BJ, Peikert T. Validation of the BRODERS classifier (Benign versus aggRessive nODule Evaluation using Radiomic Stratification), a novel HRCT-based radiomic classifier for indeterminate pulmonary nodules. Eur Respir J 2021; 57:13993003.02485-2020. [PMID: 33303552 DOI: 10.1183/13993003.02485-2020] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Accepted: 10/01/2020] [Indexed: 12/22/2022]
Abstract
INTRODUCTION Implementation of low-dose chest computed tomography (CT) lung cancer screening and the ever-increasing use of cross-sectional imaging are resulting in the identification of many screen- and incidentally detected indeterminate pulmonary nodules. While the management of nodules with low or high pre-test probability of malignancy is relatively straightforward, those with intermediate pre-test probability commonly require advanced imaging or biopsy. Noninvasive risk stratification tools are highly desirable. METHODS We previously developed the BRODERS classifier (Benign versus aggRessive nODule Evaluation using Radiomic Stratification), a conventional predictive radiomic model based on eight imaging features capturing nodule location, shape, size, texture and surface characteristics. Herein we report its external validation using a dataset of incidentally identified lung nodules (Vanderbilt University Lung Nodule Registry) in comparison to the Brock model. Area under the curve (AUC), as well as sensitivity, specificity, negative and positive predictive values were calculated. RESULTS For the entire Vanderbilt validation set (n=170, 54% malignant), the AUC was 0.87 (95% CI 0.81-0.92) for the Brock model and 0.90 (95% CI 0.85-0.94) for the BRODERS model. Using the optimal cut-off determined by Youden's index, the sensitivity was 92.3%, the specificity was 62.0%, the positive (PPV) and negative predictive values (NPV) were 73.7% and 87.5%, respectively. For nodules with intermediate pre-test probability of malignancy, Brock score of 5-65% (n=97), the sensitivity and specificity were 94% and 46%, respectively, the PPV was 78.4% and the NPV was 79.2%. CONCLUSIONS The BRODERS radiomic predictive model performs well on an independent dataset and may facilitate the management of indeterminate pulmonary nodules.
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Affiliation(s)
- Fabien Maldonado
- Division of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.,These authors contributed equally to this work
| | - Cyril Varghese
- Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN, USA.,These authors contributed equally to this work
| | - Srinivasan Rajagopalan
- Dept of Physiology and Biomechanical Engineering, Mayo Clinic, Rochester, MN, USA.,These authors contributed equally to this work
| | - Fenghai Duan
- Pulmonary Section, Medical Service, Tennessee Valley Healthcare Systems, Nashville Campus, Nashville, TN, USA
| | - Aneri B Balar
- Division of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Dhairya A Lakhani
- Division of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Sanja L Antic
- Division of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Pierre P Massion
- Division of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.,Dept of Biostatistics and Center for Statistical Sciences, Brown University School of Public Health, Providence, RI, USA
| | | | - Ronald A Karwoski
- Dept of Physiology and Biomechanical Engineering, Mayo Clinic, Rochester, MN, USA
| | - Richard A Robb
- Dept of Physiology and Biomechanical Engineering, Mayo Clinic, Rochester, MN, USA
| | | | - Tobias Peikert
- Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN, USA
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Toumazis I, Bastani M, Han SS, Plevritis SK. Risk-Based lung cancer screening: A systematic review. Lung Cancer 2020; 147:154-186. [DOI: 10.1016/j.lungcan.2020.07.007] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Revised: 07/03/2020] [Accepted: 07/04/2020] [Indexed: 12/17/2022]
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