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Yao Y, Yang Y, Hu Q, Xie X, Jiang W, Liu C, Li X, Wang Y, Luo L, Li J. A nomogram combining CT-based radiomic features with clinical features for the differentiation of benign and malignant cystic pulmonary nodules. J Cardiothorac Surg 2024; 19:392. [PMID: 38937772 PMCID: PMC11210004 DOI: 10.1186/s13019-024-02936-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 06/15/2024] [Indexed: 06/29/2024] Open
Abstract
BACKGROUND Currently, the differentiation between benign and malignant cystic pulmonary nodules poses a significant challenge for clinicians. The objective of this retrospective study was to construct a predictive model for determining the likelihood of malignancy in patients with cystic pulmonary nodules. METHODS The current study involved 129 patients diagnosed with cystic pulmonary nodules between January 2017 and June 2023 at the Neijiang First People's Hospital. The study gathered the clinical data, preoperative imaging features of chest CT, and postoperative histopathological results for both cohorts. Univariate and multivariate logistic regression analyses were employed to identify independent risk factors, from which a prediction model and nomogram were developed. In addition, The model's performance was assessed through receiver operating characteristic (ROC) curve analysis, calibration curve analysis, and decision curve analysis (DCA). RESULTS A cohort of 129 patients presenting with cystic pulmonary nodules, consisting of 92 malignant and 37 benign lesions, was examined. Logistic data analysis identified a cystic airspace with a mural nodule, spiculation, mural morphology, and the number of cystic cavities as significant independent predictors for discriminating between benign and malignant cystic lung nodules. The nomogram prediction model demonstrated a high level of predictive accuracy, as evidenced by an area under the ROC curve (AUC) of 0.874 (95% CI: 0.804-0.944). Furthermore, the calibration curve of the model displayed satisfactory calibration. DCA proved that the prediction model was useful for clinical application. CONCLUSION In summary, the risk prediction model for benign and malignant cystic pulmonary nodules has the potential to assist clinicians in the diagnosis of such nodules and enhance clinical decision-making processes.
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Affiliation(s)
- Yi Yao
- Department of Cardiothoracic Surgery, The First People's Hospital of Neijiang, No. 1866, West Section of Hanan Avenue, Shizhong District, Neijiang, Sichuan, 641000, China
| | - Yanhui Yang
- Department of Cardiothoracic Surgery, The First People's Hospital of Neijiang, No. 1866, West Section of Hanan Avenue, Shizhong District, Neijiang, Sichuan, 641000, China
| | - Qiuxia Hu
- Department of Obstetrics and Gynecology, The First People's Hospital of Neijiang, No. 1866, West Section of Hanan Avenue, Shizhong District, Neijiang, Sichuan, 641000, China
| | - Xiaoyang Xie
- Department of Cardiothoracic Surgery, The First People's Hospital of Neijiang, No. 1866, West Section of Hanan Avenue, Shizhong District, Neijiang, Sichuan, 641000, China
| | - Wenjian Jiang
- Department of Cardiothoracic Surgery, The First People's Hospital of Neijiang, No. 1866, West Section of Hanan Avenue, Shizhong District, Neijiang, Sichuan, 641000, China
| | - Caiyang Liu
- Department of Cardiothoracic Surgery, The First People's Hospital of Neijiang, No. 1866, West Section of Hanan Avenue, Shizhong District, Neijiang, Sichuan, 641000, China
| | - Xiaoliang Li
- Department of Cardiothoracic Surgery, The First People's Hospital of Neijiang, No. 1866, West Section of Hanan Avenue, Shizhong District, Neijiang, Sichuan, 641000, China
| | - Yi Wang
- Department of Cardiothoracic Surgery, The First People's Hospital of Neijiang, No. 1866, West Section of Hanan Avenue, Shizhong District, Neijiang, Sichuan, 641000, China
| | - Lei Luo
- Department of Cardiothoracic Surgery, The First People's Hospital of Neijiang, No. 1866, West Section of Hanan Avenue, Shizhong District, Neijiang, Sichuan, 641000, China
| | - Ji Li
- Department of Cardiothoracic Surgery, The First People's Hospital of Neijiang, No. 1866, West Section of Hanan Avenue, Shizhong District, Neijiang, Sichuan, 641000, China.
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Zhang Y, Qu L, Zhang H, Wang Y, Gao G, Wang X, Zhang T. Construction of a predictive model of 2-3 cm ground-glass nodules developing into invasive lung adenocarcinoma using high-resolution CT. Front Med (Lausanne) 2024; 11:1403020. [PMID: 38975053 PMCID: PMC11224554 DOI: 10.3389/fmed.2024.1403020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Accepted: 06/06/2024] [Indexed: 07/09/2024] Open
Abstract
Background The purpose of this study was to analyze the imaging risk factors for the development of 2-3 cm ground-glass nodules (GGN) for invasive lung adenocarcinoma and to establish a nomogram prediction model to provide a reference for the pathological prediction of 2-3 cm GGN and the selection of surgical procedures. Methods We reviewed the demographic, imaging, and pathological information of 596 adult patients who underwent 2-3 cm GGN resection, between 2018 and 2022, in the Department of Thoracic Surgery, Second Affiliated Hospital of the Air Force Medical University. Based on single factor analysis, the regression method was used to analyze multiple factors, and a nomogram prediction model for 2-3 cm GGN was established. Results (1) The risk factors for the development of 2-3 cm GGN during the invasion stage of the lung adenocarcinoma were pleural depression sign (OR = 1.687, 95%CI: 1.010-2.820), vacuole (OR = 2.334, 95%CI: 1.222-4.460), burr sign (OR = 2.617, 95%CI: 1.008-6.795), lobulated sign (OR = 3.006, 95%CI: 1.098-8.227), bronchial sign (OR = 3.134, 95%CI: 1.556-6.310), diameter of GGN (OR = 3.118, 95%CI: 1.151-8.445), and CTR (OR = 172.517, 95%CI: 48.023-619.745). (2) The 2-3 cm GGN risk prediction model was developed based on the risk factors with an AUC of 0.839; the calibration curve Y was close to the X-line, and the decision curve was drawn in the range of 0.0-1.0. Conclusion We analyzed the risk factors for the development of 2-3 cm GGN during the invasion stage of the lung adenocarcinoma. The predictive model developed based on the above factors had some clinical significance.
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Affiliation(s)
- Yifan Zhang
- Department of Thoracic Surgery, Tangdu Hospital, Air Force Medical University, Xi’an, China
| | - Lin Qu
- Department of Thoracic Surgery, Tangdu Hospital, Air Force Medical University, Xi’an, China
| | - Haihua Zhang
- Department of Thoracic Surgery, Tangdu Hospital, Air Force Medical University, Xi’an, China
| | - Ying Wang
- Department of Respiratory Medicine, Tangdu Hospital, Air Force Medical University, Xi’an, China
| | - Guizhou Gao
- Department of Thoracic Surgery, Tangdu Hospital, Air Force Medical University, Xi’an, China
| | - Xiaodong Wang
- Department of Thoracic Surgery, Tangdu Hospital, Air Force Medical University, Xi’an, China
| | - Tao Zhang
- Department of Thoracic Surgery, Tangdu Hospital, Air Force Medical University, Xi’an, China
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Liao Y, Li Z, Song L, Xue Y, Chen X, Feng G. Development and validation of a model for predicting upstage in minimally invasive lung adenocarcinoma in Chinese people. World J Surg Oncol 2024; 22:135. [PMID: 38778366 PMCID: PMC11112920 DOI: 10.1186/s12957-024-03414-5] [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: 02/03/2024] [Accepted: 05/20/2024] [Indexed: 05/25/2024] Open
Abstract
BACKGROUND Sublobar resection for ground-glass opacity became a recommend surgery choice supported by the JCOG0804/JCOG0802/JCOG1211 results. Sublobar resection includes segmentectomy and wedge resection, wedge resection is suitable for non-invasive lesions, but in clinical practice, when pathologists are uncertain about the intraoperative frozen diagnosis of invasive lesions, difficulty in choosing the appropriate operation occurs. The purpose of this study was to analyze how to select invasive lesions with clinic-pathological characters. METHODS A retrospective study was conducted on 134 cases of pulmonary nodules diagnosed with minimally invasive adenocarcinoma by intraoperative freezing examination. The patients were divided into two groups according to intraoperative frozen results: the minimally invasive adenocarcinoma group and the at least minimally invasive adenocarcinoma group. A variety of clinical features were collected. Chi-square tests and multiple regression logistic analysis were used to screen out independent risk factors related to pathological upstage, and then ROC curves were established. In addition, an independent validation set included 1164 cases was collected. RESULTS Independent risk factors related to pathological upstage were CT value, maximum tumor diameter, and frozen result of AL-MIA. The AUC of diagnostic mode was 71.1% [95%CI: 60.8-81.3%]. The independent validation included 1164 patients, 417 (35.8%) patients had paraffin-based pathology of invasive adenocarcinoma. The AUC of diagnostic mode was 75.7% [95%CI: 72.9-78.4%]. CONCLUSIONS The intraoperative frozen diagnosis was AL-MIA, maximum tumor diameter larger than 15 mm and CT value is more than - 450Hu, highly suggesting that the lung GGO was invasive adenocarcinoma which represent a higher risk to recurrence. For these patients, sublobectomy would be insufficient, lobectomy or complementary treatment is encouraged.
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Affiliation(s)
- Yida Liao
- Department of Thoracic Surgery, School of Medicine, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
| | - Zhixin Li
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, 200433, P.R. China
| | - Linhong Song
- Department of Pathology, School of Medicine, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Yang Xue
- Department of Thoracic Surgery, School of Medicine, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiangru Chen
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, 200433, P.R. China
| | - Gang Feng
- Department of Thoracic Surgery, School of Medicine, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
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Liu Q, Lv X, Zhou D, Yu N, Hong Y, Zeng Y. Establishment and validation of multiclassification prediction models for pulmonary nodules based on machine learning. THE CLINICAL RESPIRATORY JOURNAL 2024; 18:e13769. [PMID: 38736274 PMCID: PMC11089274 DOI: 10.1111/crj.13769] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/26/2023] [Revised: 03/29/2024] [Accepted: 04/12/2024] [Indexed: 05/14/2024]
Abstract
BACKGROUND Lung cancer is the leading cause of cancer-related death worldwide. This study aimed to establish novel multiclassification prediction models based on machine learning (ML) to predict the probability of malignancy in pulmonary nodules (PNs) and to compare with three published models. METHODS Nine hundred fourteen patients with PNs were collected from four medical institutions (A, B, C and D), which were organized into tables containing clinical features, radiologic features and laboratory test features. Patients were divided into benign lesion (BL), precursor lesion (PL) and malignant lesion (ML) groups according to pathological diagnosis. Approximately 80% of patients in A (total/male: 632/269, age: 57.73 ± 11.06) were randomly selected as a training set; the remaining 20% were used as an internal test set; and the patients in B (total/male: 94/53, age: 60.04 ± 11.22), C (total/male: 94/47, age: 59.30 ± 9.86) and D (total/male: 94/61, age: 62.0 ± 11.09) were used as an external validation set. Logical regression (LR), decision tree (DT), random forest (RF) and support vector machine (SVM) were used to establish prediction models. Finally, the Mayo model, Peking University People's Hospital (PKUPH) model and Brock model were externally validated in our patients. RESULTS The AUC values of RF model for MLs, PLs and BLs were 0.80 (95% CI: 0.73-0.88), 0.90 (95% CI: 0.82-0.99) and 0.75 (95% CI: 0.67-0.88), respectively. The weighted average AUC value of the RF model for the external validation set was 0.71 (95% CI: 0.67-0.73), and its AUC values for MLs, PLs and BLs were 0.71 (95% CI: 0.68-0.79), 0.98 (95% CI: 0.88-1.07) and 0.68 (95% CI: 0.61-0.74), respectively. The AUC values of the Mayo model, PKUPH model and Brock model were 0.68 (95% CI: 0.62-0.74), 0.64 (95% CI: 0.58-0.70) and 0.57 (95% CI: 0.49-0.65), respectively. CONCLUSIONS The RF model performed best, and its predictive performance was better than that of the three published models, which may provide a new noninvasive method for the risk assessment of PNs.
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Affiliation(s)
- Qiao Liu
- Department of RadiologyThe Third Affiliated Hospital of Chongqing Medical UniversityChongqingChina
| | - Xue Lv
- Department of RadiologyThe Third Affiliated Hospital of Chongqing Medical UniversityChongqingChina
| | - Daiquan Zhou
- Department of RadiologyThe Third Affiliated Hospital of Chongqing Medical UniversityChongqingChina
| | - Na Yu
- Department of RadiologyThe Third Affiliated Hospital of Chongqing Medical UniversityChongqingChina
| | - Yuqin Hong
- Department of RadiologyThe Third Affiliated Hospital of Chongqing Medical UniversityChongqingChina
| | - Yan Zeng
- Department of RadiologyThe Third Affiliated Hospital of Chongqing Medical UniversityChongqingChina
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Ye Y, Sun Y, Hu J, Ren Z, Chen X, Chen C. A clinical-radiological predictive model for solitary pulmonary nodules and the relationship between radiological features and pathological subtype. Clin Radiol 2024; 79:e432-e439. [PMID: 38097460 DOI: 10.1016/j.crad.2023.11.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 11/09/2023] [Accepted: 11/13/2023] [Indexed: 02/15/2024]
Abstract
AIM To develop a clinical-radiological model to predict the malignancy of solitary pulmonary nodules (SPNs) and to evaluate the accuracy of chest computed tomography imaging characteristics of SPN in diagnosing pathological type. MATERIALS AND METHODS The predictive model was developed using a retrospective cohort of 601 SPN patients (Group A) between July 2015 and July 2020. The established model was tested using a second retrospective cohort of 124 patients between August 2020 and August 2021 (Group B). The radiological characteristics of all adenocarcinomas in two groups were analysed to determine the correlation between radiological and pathological characteristics. RESULTS Malignant nodules were found in 78.87% of cases and benign in 21.13%. Two clinical characteristics (age and gender) and four radiological characteristics (calcification, vascular convergence, pleural retraction sign, and density) were identified as independent predictors of malignancy in patients with SPN using logistic regression analysis. The area under the receiver operating characteristic curve (0.748) of the present model was greater than the other two reported models. Diameter, spiculation, lobulation, vascular convergence, and pleural retraction signs differed significantly among pre-invasive lesions, minimally invasive adenocarcinoma, and invasive adenocarcinoma. Only diameter and density were significantly different among invasive adenocarcinoma subtypes. CONCLUSIONS Older age, male gender, no calcification, vascular convergence, pleural contraction sign, and lower density were independent malignancy predictors of SPNs. Furthermore, the pathological classification can be clarified based on the radiological characteristics of SPN, providing a new option for the prevention and treatment of early lung cancer.
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Affiliation(s)
- Y Ye
- Cancer Center, Department of Pulmonary and Critical Care Medicine, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, 310014, China
| | - Y Sun
- Cancer Center, Department of Pulmonary and Critical Care Medicine, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, 310014, China
| | - J Hu
- General Surgery, Cancer Center, Department of Gastrointestinal and Pancreatic Surgery, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, 310014, China
| | - Z Ren
- Cancer Center, Department of Pulmonary and Critical Care Medicine, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, 310014, China
| | - X Chen
- Cancer Center, Department of Medical Oncology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, 310014, China
| | - C Chen
- Cancer Center, Department of Pulmonary and Critical Care Medicine, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, 310014, China.
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Zhu J, Qu Y, Lu M, Ma A, Mo J, Wen Z. CT-based radiomics for prediction of pulmonary haemorrhage after percutaneous CT-guided transthoracic lung biopsy of pulmonary nodules. Clin Radiol 2023; 78:e993-e1000. [PMID: 37726191 DOI: 10.1016/j.crad.2023.08.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 08/14/2023] [Accepted: 08/23/2023] [Indexed: 09/21/2023]
Abstract
AIM To evaluate the feasibility of intranodular and perinodular computed tomography (CT) radiomics features for predicting the occurrence of pulmonary haemorrhage after percutaneous CT-guided transthoracic lung biopsy (PCTLB) in pulmonary nodules. MATERIALS AND METHODS The data for 332 patients with pulmonary nodules who underwent PCTLB were reviewed retrospectively. Pulmonary haemorrhage after PCTLB was evaluated using CT (144 cases occurred). Radiomics features based on gross nodular (GNV) and perinodular volumes (PNV) were extracted from pre-biopsy CT images and features selection using least absolute shrinkage and selection operator (LASSO) regression, and three radiomics scores (rad-scores) were built. Rad-scores, clinical, and clinical-radiomic models were developed and evaluated to predict the occurrence of pulmonary haemorrhage. RESULTS Five, five, and six significant features were selected for prediction of pulmonary haemorrhage based on GNV, PNV, and GNV + PNV, respectively. Lesion depth was the only clinical characteristics related to pulmonary haemorrhage. Lesion depth and rad-score based on GNV, PNV, and GNV + PNV for predicting the pulmonary haemorrhage achieved areas under the curves (AUCs) of 0.656, 0.645, 0.651, and 0.635 in the validation group, respectively. Three clinical-radiomic models improved the AUCs to 0.743, 0.723, and 0.748. The performance of rad-score_GNV + PNV combined with lesion depth outperformed the clinical model (p=0.024) and the radiomics signature (p=0.038). In addition, the radiomics signatures were significantly associated with higher-grade pulmonary haemorrhage (p<0.05). CONCLUSIONS Radiomics features from intranodular and perinodular regions of pulmonary nodules have good predictive ability for pulmonary haemorrhage after PCTLB, which may provide additional predictive value for clinical practice.
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Affiliation(s)
- J Zhu
- Department of Radiology, Zhujiang Hospital, Southern Medical University, 253 Gongye Middle Avenue, Haizhu District, Guangzhou, Guangdong, 510282, China
| | - Y Qu
- Department of Radiology, Zhujiang Hospital, Southern Medical University, 253 Gongye Middle Avenue, Haizhu District, Guangzhou, Guangdong, 510282, China
| | - M Lu
- Department of Radiology, Zhujiang Hospital, Southern Medical University, 253 Gongye Middle Avenue, Haizhu District, Guangzhou, Guangdong, 510282, China
| | - A Ma
- Department of Radiology, Zhujiang Hospital, Southern Medical University, 253 Gongye Middle Avenue, Haizhu District, Guangzhou, Guangdong, 510282, China
| | - J Mo
- Department of Radiology, Zhujiang Hospital, Southern Medical University, 253 Gongye Middle Avenue, Haizhu District, Guangzhou, Guangdong, 510282, China
| | - Z Wen
- Department of Radiology, Zhujiang Hospital, Southern Medical University, 253 Gongye Middle Avenue, Haizhu District, Guangzhou, Guangdong, 510282, China.
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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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Xue M, Li R, Wang K, Liu W, Liu J, Li Z, Ma Z, Zhang H, Tian H, Tian Y. Nomogram combining clinical and radiological characteristics for predicting the malignant probability of solitary pulmonary nodules measuring ≤ 2 cm. Front Oncol 2023; 13:1196778. [PMID: 37795448 PMCID: PMC10545867 DOI: 10.3389/fonc.2023.1196778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Accepted: 09/04/2023] [Indexed: 10/06/2023] Open
Abstract
Background At present, how to identify the benign or malignant nature of small (≤ 2 cm) solitary pulmonary nodules (SPN) are an urgent clinical challenge. This retrospective study aimed to develop a clinical prediction model combining clinical and radiological characteristics for assessing the probability of malignancy in SPNs measuring ≤ 2 cm. Method In this study, we included patients with SPNs measuring ≤ 2 cm who underwent pulmonary resection with definite pathology at Qilu Hospital of Shandong University from January 2020 to December 2021. Clinical features, preoperative biomarker results, and computed tomography characteristics were collected. The enrolled patients were randomized at a ratio of 7:3 into a training cohort of 775 and a validation cohort of 331. The training cohort was used to construct the predictive model, while the validation cohort was used to test the model independently. Univariate and multivariate logistic regression analyses were performed to identify independent risk factors. The prediction model and nomogram were established based on the independent risk factors. The receiver operating characteristic (ROC) curve was used to evaluate the identification ability of the model. The calibration power was evaluated using the Hosmer-Lemeshow test and calibration curve. The clinical utility of the nomogram was also assessed by decision curve analysis (DCA). Result A total of 1,106 patients were included in this study. Among them, the malignancy rate of SPNs was 85.08% (941/1,106). We finally identified the following six independent risk factors by logistic regression: age, carcinoembryonic antigen, nodule shape, calcification, maximum diameter, and consolidation-to-tumor ratio. The area under the ROC curve (AUC) for the training cohort was 0.764 (95% confidence interval [CI]: 0.714-0.814), and the AUC for the validation cohort was 0.729 (95% CI: 0.647-0.811), indicating that the prediction accuracy of nomogram was relatively good. The calibration curve of the predictive model also demonstrated a good calibration in both cohorts. DCA proved that the clinical prediction model was useful in clinical practice. Conclusion We developed and validated a predictive model and nomogram for estimating the probability of malignancy in SPNs measuring ≤ 2 cm. With the application of predictive models, thoracic surgeons can make more rational clinical decisions while avoiding overtreatment and wasting medical resources.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Hui Tian
- Department of Thoracic Surgery, Qilu Hospital of Shandong University, Jinan, China
| | - Yu Tian
- Department of Thoracic Surgery, Qilu Hospital of Shandong University, Jinan, China
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Li Y, Jiang G, Wu W, Yang H, Jin Y, Wu M, Liu W, Yang A, Chervova O, Zhang S, Zheng L, Zhang X, Du F, Kanu N, Wu L, Yang F, Wang J, Chen K. Multi-omics integrated circulating cell-free DNA genomic signatures enhanced the diagnostic performance of early-stage lung cancer and postoperative minimal residual disease. EBioMedicine 2023; 91:104553. [PMID: 37027928 PMCID: PMC10102814 DOI: 10.1016/j.ebiom.2023.104553] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2022] [Revised: 03/17/2023] [Accepted: 03/19/2023] [Indexed: 04/08/2023] Open
Abstract
BACKGROUND Liquid biopsy is a promising non-invasive alternative for cancer screening and minimal residual disease (MRD) detection, although there are some concerns regarding its clinical applications. We aimed to develop an accurate detection platform based on liquid biopsy for both cancer screening and MRD detection in patients with lung cancer (LC), which is also applicable to clinical use. METHODS We applied a modified whole-genome sequencing (WGS) -based High-performance Infrastructure For MultIomics (HIFI) method for LC screening and postoperative MRD detection by combining the hyper-co-methylated read approach and the circulating single-molecule amplification and resequencing technology (cSMART2.0). FINDINGS For early screening of LC, the LC score model was constructed using the support vector machine, which showed sensitivity (51.8%) at high specificity (96.3%) and achieved an AUC of 0.912 in the validation set prospectively enrolled from multiple centers. The screening model achieved detection efficiency with an AUC of 0.906 in patients with lung adenocarcinoma and outperformed other clinical models in solid nodule cohort. When applied the HIFI model to real social population, a negative predictive value (NPV) of 99.92% was achieved in Chinese population. Additionally, the MRD detection rate improved significantly by combining results from WGS and cSMART2.0, with sensitivity of 73.7% at specificity of 97.3%. INTERPRETATION In conclusion, the HIFI method is promising for diagnosis and postoperative monitoring of LC. FUNDING This study was supported by CAMS Innovation Fund for Medical Sciences, Chinese Academy of Medical Sciences, National Natural Science Foundation of China, Beijing Natural Science Foundation and Peking University People's Hospital.
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Lin RY, Zheng YN, Lv FJ, Fu BJ, Li WJ, Liang ZR, Chu ZG. A combined non-enhanced CT radiomics and clinical variable machine learning model for differentiating benign and malignant sub-centimeter pulmonary solid nodules. Med Phys 2023; 50:2835-2843. [PMID: 36810703 DOI: 10.1002/mp.16316] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 02/08/2023] [Accepted: 02/08/2023] [Indexed: 02/24/2023] Open
Abstract
BACKGROUND Radiomics has been used to predict pulmonary nodule (PN) malignancy. However, most of the studies focused on pulmonary ground-glass nodules. The use of computed tomography (CT) radiomics in pulmonary solid nodules, particularly sub-centimeter solid nodules, is rare. PURPOSE This study aims to develop a radiomics model based on non-enhanced CT images that can distinguish between benign and malignant sub-centimeter pulmonary solid nodules (SPSNs, <1 cm). METHODS The clinical and CT data of 180 SPSNs confirmed by pathology were analyzed retrospectively. All SPSNs were divided into two groups: training set (n = 144) and testing set (n = 36). From non-enhanced chest CT images, over 1000 radiomics features were extracted. Radiomics feature selection was performed using the analysis of variance and principal component analysis. The selected radiomics features were fed into a support vector machine (SVM) to develop a radiomics model. The clinical and CT characteristics were used to develop a clinical model. Associating non-enhanced CT radiomics features with clinical factors were used to develop a combined model using SVM. The performance was evaluated using the area under the receiver-operating characteristic curve (AUC). RESULTS The radiomics model performed well in distinguishing between benign and malignant SPSNs, with an AUC of 0.913 (95% confidence interval [CI], 0.862-0.954) in the training set and an AUC of 0.877 (95% CI, 0.817-0.924) in the testing set. The combined model outperformed the clinical and radiomics models with an AUC of 0.940 (95% CI, 0.906-0.969) in the training set and an AUC of 0.903 (95% CI, 0.857-0.944) in the testing set. CONCLUSIONS Radiomics features based on non-enhanced CT images can be used to differentiate SPSNs. The combined model, which included radiomics and clinical factors, had the best discrimination power between benign and malignant SPSNs.
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Affiliation(s)
- Rui-Yu Lin
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yi-Neng Zheng
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Fa-Jin Lv
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Bin-Jie Fu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Wang-Jia Li
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Zhang-Rui Liang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Zhi-Gang Chu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
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Wang Y, Li J, Zhang X, Liu M, Ji L, Yang T, Wang K, Song C, Wang P, Ye H, Shi J, Dai L. Autoantibody signatures discovered by HuProt protein microarray to enhance the diagnosis of lung cancer. Clin Immunol 2023; 246:109206. [PMID: 36528251 DOI: 10.1016/j.clim.2022.109206] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 10/27/2022] [Accepted: 11/28/2022] [Indexed: 12/23/2022]
Abstract
This study aims to discover novel autoantibodies against tumor-associated antigens (TAAs) and establish diagnostic models for assisting in the diagnosis of lung cancer and discrimination of pulmonary nodules (PNs). Ten autoantibodies to TAAbs (TAAbs) were discovered by means of protein microarray and their serum level was also higher in 212 LC patients than that in 212 NC of validation cohort 1 (P < 0.05). The model 1 comprising 4 TAAbs and CEA reached an AUC of 0.813 (95%CI: 0.762-0.864) for diagnosing LC from normal individuals. Five TAAbs existed a significant difference between 105 malignant pulmonary nodules (MPNs) and 105 benign pulmonary nodules (BPNs) patients in validation cohort 2 (P < 0.05). Model 2 could distinguish MPNs from BPNs with an AUC of 0.845. High-throughput protein microarray is an efficient approach in discovering novel TAAbs which could be used as biomarkers in lung cancer diagnosis.
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Affiliation(s)
- Yulin Wang
- Henan Institute of Medical and Pharmaceutical Sciences, Academy of Medical Science, Zhengzhou University, Zhengzhou 450052, Henan, China
| | - Jiaqi Li
- Henan Institute of Medical and Pharmaceutical Sciences, Academy of Medical Science, Zhengzhou University, Zhengzhou 450052, Henan, China; Henan Key Laboratory of Tumor Molecular Biomarkers, Zhengzhou University, Zhengzhou 450052, Henan, China; Henan Key Laboratory of Tumor Epidemiology & State Key Laboratory of Esophageal Cancer Prevention, Zhengzhou University, Zhengzhou 450052, Henan, China
| | - Xue Zhang
- Henan Institute of Medical and Pharmaceutical Sciences, Academy of Medical Science, Zhengzhou University, Zhengzhou 450052, Henan, China; Henan Key Laboratory of Tumor Molecular Biomarkers, Zhengzhou University, Zhengzhou 450052, Henan, China; Henan Key Laboratory of Tumor Epidemiology & State Key Laboratory of Esophageal Cancer Prevention, Zhengzhou University, Zhengzhou 450052, Henan, China
| | - Man Liu
- Henan Institute of Medical and Pharmaceutical Sciences, Academy of Medical Science, Zhengzhou University, Zhengzhou 450052, Henan, China; Henan Key Laboratory of Tumor Molecular Biomarkers, Zhengzhou University, Zhengzhou 450052, Henan, China; Henan Key Laboratory of Tumor Epidemiology & State Key Laboratory of Esophageal Cancer Prevention, Zhengzhou University, Zhengzhou 450052, Henan, China; Laboratory of Molecular Biology, Henan Luoyang Orthopedic Hospital (Henan Provincial Orthopedic Hospital), Zhengzhou, China
| | - Longtao Ji
- Henan Institute of Medical and Pharmaceutical Sciences, Academy of Medical Science, Zhengzhou University, Zhengzhou 450052, Henan, China; Henan Key Laboratory of Tumor Molecular Biomarkers, Zhengzhou University, Zhengzhou 450052, Henan, China; BGI College, Zhengzhou University, Zhengzhou 450052, Henan, China
| | - Ting Yang
- Henan Institute of Medical and Pharmaceutical Sciences, Academy of Medical Science, Zhengzhou University, Zhengzhou 450052, Henan, China; Henan Key Laboratory of Tumor Molecular Biomarkers, Zhengzhou University, Zhengzhou 450052, Henan, China; BGI College, Zhengzhou University, Zhengzhou 450052, Henan, China
| | - Kaijuan Wang
- Henan Key Laboratory of Tumor Epidemiology & State Key Laboratory of Esophageal Cancer Prevention, Zhengzhou University, Zhengzhou 450052, Henan, China
| | - Chunhua Song
- Henan Key Laboratory of Tumor Epidemiology & State Key Laboratory of Esophageal Cancer Prevention, Zhengzhou University, Zhengzhou 450052, Henan, China
| | - Peng Wang
- Henan Key Laboratory of Tumor Epidemiology & State Key Laboratory of Esophageal Cancer Prevention, Zhengzhou University, Zhengzhou 450052, Henan, China
| | - Hua Ye
- Henan Key Laboratory of Tumor Epidemiology & State Key Laboratory of Esophageal Cancer Prevention, Zhengzhou University, Zhengzhou 450052, Henan, China
| | - Jianxiang Shi
- Henan Institute of Medical and Pharmaceutical Sciences, Academy of Medical Science, Zhengzhou University, Zhengzhou 450052, Henan, China; Henan Key Laboratory of Tumor Molecular Biomarkers, Zhengzhou University, Zhengzhou 450052, Henan, China; Henan Key Laboratory of Tumor Epidemiology & State Key Laboratory of Esophageal Cancer Prevention, Zhengzhou University, Zhengzhou 450052, Henan, China
| | - Liping Dai
- Henan Institute of Medical and Pharmaceutical Sciences, Academy of Medical Science, Zhengzhou University, Zhengzhou 450052, Henan, China; Henan Key Laboratory of Tumor Molecular Biomarkers, Zhengzhou University, Zhengzhou 450052, Henan, China; Henan Key Laboratory of Tumor Epidemiology & State Key Laboratory of Esophageal Cancer Prevention, Zhengzhou University, Zhengzhou 450052, Henan, China; BGI College, Zhengzhou University, Zhengzhou 450052, Henan, China.
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12
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Artificial Intelligence assisted discrimination between pulmonary tuberculous nodules and solid lung cancer nodules. CLINICAL EHEALTH 2022. [DOI: 10.1016/j.ceh.2022.12.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
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13
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Liu M, Zhou Z, Liu F, Wang M, Wang Y, Gao M, Sun H, Zhang X, Yang T, Ji L, Li J, Si Q, Dai L, Ouyang S. CT and CEA-based machine learning model for predicting malignant pulmonary nodules. Cancer Sci 2022; 113:4363-4373. [PMID: 36056603 DOI: 10.1111/cas.15561] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 08/22/2022] [Accepted: 08/25/2022] [Indexed: 12/15/2022] Open
Abstract
Computed tomography (CT), an efficient radiological technology, is used to detect lung cancer in the clinic. Carcinoembryonic antigen (CEA), a common tumor biomarker, is applied in the detection of various tumors. To highlight the advantages of two-dimensional techniques and assist clinicians in optimizing lung cancer diagnostic schemes, we established a favorable model combining CT and CEA. In the study, univariate analysis was performed to screen independent predictors in a training cohort of 271 patients with malignant pulmonary nodules (MPNs) and 92 with benign pulmonary nodules (BPNs). Six machine learning-based models involving five CT predictors (mediastinal lymph node enlargement, lobulation, vascular notch sign, spiculation, and nodule number) and lnCEA were constructed and validated in an independent cohort of 129 participants (92 MPNs and 37 BPNs) by SPSS Modeler. A nomogram and the Delong test were generated by R software. Finally, the model established by logistic regression had highest diagnostic efficiency (area under the curve [AUC] = 0.912). Moreover, the diagnostic ability of the logistic model in the validation cohort (AUC = 0.882, 80.4% sensitivity, 75.7% specificity) was higher than that of the Peking University model (AUC = 0.712, 68.5% sensitivity, 70.3% specificity) and the Mayo model (AUC = 0.745, 62.0% sensitivity, 75.7% specificity). Interestingly, for the participants with intermediate (10-30 mm) and CEA-negative nodule, the model reached an AUC of 0.835 (72.3% sensitivity, 83.3% specificity). The AUC for the early lung cancer was as high as 0.822 with 67.3% sensitivity and 78.9% specificity. As a conclusion, this promising model presents a new diagnostic strategy for the clinic to distinguish MPNs from BPNs.
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Affiliation(s)
- Man Liu
- Department of Respiratory and Sleep Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Henan Institute of Medical and Pharmaceutical Sciences & Henan Key Medical Laboratory of Tumor Molecular Biomarkers, Zhengzhou University, Zhengzhou, China
| | - Zhigang Zhou
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Fenghui Liu
- Department of Respiratory and Sleep Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Meng Wang
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yulin Wang
- Henan Institute of Medical and Pharmaceutical Sciences & Henan Key Medical Laboratory of Tumor Molecular Biomarkers, Zhengzhou University, Zhengzhou, China
| | - Mengyu Gao
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Huifang Sun
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xue Zhang
- Henan Institute of Medical and Pharmaceutical Sciences & Henan Key Medical Laboratory of Tumor Molecular Biomarkers, Zhengzhou University, Zhengzhou, China
| | - Ting Yang
- Henan Institute of Medical and Pharmaceutical Sciences & Henan Key Medical Laboratory of Tumor Molecular Biomarkers, Zhengzhou University, Zhengzhou, China.,BGI College, Zhengzhou University, Zhengzhou, China
| | - Longtao Ji
- Henan Institute of Medical and Pharmaceutical Sciences & Henan Key Medical Laboratory of Tumor Molecular Biomarkers, Zhengzhou University, Zhengzhou, China.,BGI College, Zhengzhou University, Zhengzhou, China
| | - Jiaqi Li
- Henan Institute of Medical and Pharmaceutical Sciences & Henan Key Medical Laboratory of Tumor Molecular Biomarkers, Zhengzhou University, Zhengzhou, China
| | - Qiufang Si
- Henan Institute of Medical and Pharmaceutical Sciences & Henan Key Medical Laboratory of Tumor Molecular Biomarkers, Zhengzhou University, Zhengzhou, China.,BGI College, Zhengzhou University, Zhengzhou, China
| | - Liping Dai
- Henan Institute of Medical and Pharmaceutical Sciences & Henan Key Medical Laboratory of Tumor Molecular Biomarkers, Zhengzhou University, Zhengzhou, China.,BGI College, Zhengzhou University, Zhengzhou, China
| | - Songyun Ouyang
- Department of Respiratory and Sleep Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
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Li X, Chen K, Yang F, Wang J. Perspectives on early-stage lung cancer identification and challenges to thoracic surgery. Chronic Dis Transl Med 2022; 8:79-82. [PMID: 35774430 DOI: 10.1002/cdt3.28] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 04/12/2022] [Accepted: 04/20/2022] [Indexed: 12/17/2022] Open
Affiliation(s)
- Xiao Li
- Department of Thoracic Surgery Peking University People's Hospital Beijing 100044 China
| | - Kezhong Chen
- Department of Thoracic Surgery Peking University People's Hospital Beijing 100044 China
| | - Fan Yang
- Department of Thoracic Surgery Peking University People's Hospital Beijing 100044 China
| | - Jun Wang
- Department of Thoracic Surgery Peking University People's Hospital Beijing 100044 China
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15
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Li L, Guo C, Wan JL, Fan QS, Xu XL, Fu YF. The use of carcinoembryonic antigen levels to predict lung nodule malignancy: a meta-analysis. Acta Clin Belg 2022; 77:227-232. [PMID: 32703103 DOI: 10.1080/17843286.2020.1797330] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
OBJECTIVES To assess the diagnostic value of serum carcinoembryonic antigen (CEA) as a diagnostic biomarker that can be used to differentiate between benign and malignant lung nodules (LNs). METHODS PubMed, Cochrane Library, and Embase were reviewed from January 2000 to April 2020 for eligible studies. Stata v12.0 was used to conduct this meta-analysis. RESULTS Our initial literature search identified 511 potentially relevant studies, of which 11 were ultimately included in the present meta-analysis. Ten studies were retrospective and only 1 study was prospective. Overall these studies incorporated 2760 patients and 2760 total LNs (1733 malignant, 1027 benign). Pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR) values for these studies were 0.33 (95% CI: 0.20-0.49), 0.92 (95% CI: 0.85-0.96), 3.96 (95% CI: 2.84-5.54), 0.73 (95% CI: 0.62-0.87), and 5.42 (95% CI: 3.77-7.78), respectively. The area under curve (AUC) value was 0.77, consistent with moderate diagnostic accuracy. We detected significant heterogeneity when calculating pooled sensitivity (I2 = 95.9%, P = 0.00), specificity (I2 = 92.0%, P = 0.00), PLR (I2 = 61.7%, P = 0.00), NLR (I2 = 92.8%, P = 0.00), and DOR (I2 = 93.8%, P = 0.00). No significant evidence of publication bias was detected via Deeks' funnel plot asymmetry test (P = 0.371). Meta-regression analysis revealed different reference standards to be closely associated with both sensitivity and specificity. CONCLUSIONS Serum CEA can achieve moderate diagnostic performance as a means of differentiating between malignant and benign LNs.
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Affiliation(s)
- Lei Li
- Department of Oncology, Binzhou Medical University Hospital, Binzhou, China
| | - Chen Guo
- Department of Oncology, Binzhou Medical University Hospital, Binzhou, China
| | - Jin-Liang Wan
- Department of Oncology, Binzhou Medical University Hospital, Binzhou, China
| | - Qing-Shuai Fan
- Department of Oncology, Binzhou Medical University Hospital, Binzhou, China
| | - Xiao-Liang Xu
- Department of Pediatric Surgery, Binzhou Medical University Hospital, Binzhou, China
| | - Yu-Fei Fu
- Department of Radiology, Xuzhou Central Hospital, Xuzhou, 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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Sun K, Chen S, Zhao J, Wang B, Yang Y, Wang Y, Wu C, Sun X. Convolutional Neural Network-Based Diagnostic Model for a Solid, Indeterminate Solitary Pulmonary Nodule or Mass on Computed Tomography. Front Oncol 2021; 11:792062. [PMID: 34993146 PMCID: PMC8724915 DOI: 10.3389/fonc.2021.792062] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2021] [Accepted: 11/19/2021] [Indexed: 12/26/2022] Open
Abstract
PURPOSE To establish a non-invasive diagnostic model based on convolutional neural networks (CNNs) to distinguish benign from malignant lesions manifesting as a solid, indeterminate solitary pulmonary nodule (SPN) or mass (SPM) on computed tomography (CT). METHOD A total of 459 patients with solid indeterminate SPNs/SPMs on CT were ultimately included in this retrospective study and assigned to the train (n=366), validation (n=46), and test (n=47) sets. Histopathologic analysis was available for each patient. An end-to-end CNN model was proposed to predict the natural history of solid indeterminate SPN/SPMs on CT. Receiver operating characteristic curves were plotted to evaluate the predictive performance of the proposed CNN model. The accuracy, sensitivity, and specificity of diagnoses by radiologists alone were compared with those of diagnoses by radiologists by using the CNN model to assess its clinical utility. RESULTS For the CNN model, the AUC was 91% (95% confidence interval [CI]: 0.83-0.99) in the test set. The diagnostic accuracy of radiologists with the CNN model was significantly higher than that without the model (89 vs. 66%, P<0.01; 87 vs. 61%, P<0.01; 85 vs. 66%, P=0.03, in the train, validation, and test sets, respectively). In addition, while there was a slight increase in sensitivity, the specificity improved significantly by an average of 42% (the corresponding improvements in the three sets ranged from 43, 33, and 42% to 82, 78, and 84%, respectively; P<0.01 for all). CONCLUSION The CNN model could be a valuable tool in non-invasively differentiating benign from malignant lesions manifesting as solid, indeterminate SPNs/SPMs on CT.
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Affiliation(s)
- Ke Sun
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
- Department of Radiology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Shouyu Chen
- Department of Computer Science and Technology, College of Electronics and Information Engineering, Tongji University, Shanghai, China
| | - Jiabi Zhao
- Department of Radiology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Bin Wang
- Department of Radiology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Yang Yang
- Department of Radiology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Yin Wang
- Department of Computer Science and Technology, College of Electronics and Information Engineering, Tongji University, Shanghai, China
| | - Chunyan Wu
- Department of Pathology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Xiwen Sun
- Department of Radiology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
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Xu QS, Wang T, Cao W, Rong PH. Coil localization assisted wedge resection for pulmonary nodules in patients with malignant history. Medicine (Baltimore) 2021; 100:e28025. [PMID: 34964799 PMCID: PMC8615322 DOI: 10.1097/md.0000000000028025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/18/2020] [Accepted: 11/11/2021] [Indexed: 01/05/2023] Open
Abstract
We describe the clinical efficacy of coil localization (CL) assisted video-assisted thoracoscopic surgery (VATS) wedge resection (WR) for pulmonary nodules (PNs) in patients having a history of malignancy.In a total of 16 patients having PNs and malignant history, treatment was carried out using computed tomography (CT)-guided CL and subsequent VATS-guided WR procedures from November 2015 to December 2019. Technical success of CL, WR, and long-term outcomes was analyzed.A total of 21 PNs were localized (1.3 PNs per patient). A 100% technical success rate was achieved in this study for CT-guided CL. Each PN was localized with 1 coil. Two and 2 patients experienced pneumothorax and hemoptysis, respectively. VATS-guided WR also achieved a 100% technical success rate. Additional lobectomy was performed in 2 patients due to the invasive adenocarcinoma. The final diagnoses of these 21 PNs were adenocarcinoma (T1N0M0, n = 8), adenocarcinoma in situ (n = 2), pre-cancerosis (n = 1), metastasis (n = 2), and benign (n = 8). All patients underwent CT follow-up for 6 to 48 months. All patients were alive during the follow-up. The cumulative 6-, 12, and 24-month disease-free survival rates were 100%, 92.9%, and 47.3%, respectively. The median disease-free survival was 27.9 months.Pre-operative CT-guided CL can be safely and conveniently used to facilitate a high success rate of VATS-guided WR for PNs in patients with a malignant history. Among the PNs in patients with malignant history, primary lung cancer also occupied approximately half of the PNs.
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Zhou C, Liu XB, Gan XJ, Li X. Calcification sign for prediction of benignity in pulmonary nodules: A meta-analysis. THE CLINICAL RESPIRATORY JOURNAL 2021; 15:1073-1080. [PMID: 34142452 DOI: 10.1111/crj.13410] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Revised: 04/13/2021] [Accepted: 06/08/2021] [Indexed: 11/28/2022]
Abstract
BACKGROUND The calcification sign assessed by computed tomography appears to be a potential marker for benignities among patients diagnosed with pulmonary nodules (PNs). The following meta-analysis has been purposefully designed to figure-out the diagnostic value of the calcification signature as a way of identifying benignities from PNs. METHODS Cochrane Library, Embase and PubMed were considered as a reference to obtain the required data from January 2000 until October 2020. Stata v12.0 was used as a standard tool for statistical assessment. RESULTS Eleven retrospective studies were assessed via this meta-analysis, which included 6136 PNs (1827 benign and 4309 malignant). The pooled diagnostic odd ratios, positive likelihood ratio (PLR), negative likelihood ratio (NLR), sensitivity and specificity were 6.79, 6.06, 0.89, 13% and 98%, respectively. The value obtained for the area under the curve was 0.65, showing moderate overall diagnostic accuracy. A significant heterogeneity was found while calculating the pooled sensitivity (I2 = 85.5%), specificity (I2 = 75.0%), PLR (I2 = 59.0%), NLR (I2 = 79.5%) and DOR (I2 = 100.0%) in the current analysis. Sub-group analyses presented better PLR and specificity values for the study with a sample size ≥ 400. Deeks' funnel plot asymmetry test detected no potential evidence of significant publication bias (p = 0.091). CONCLUSIONS Calcification signs have been identified as moderate regulators corresponding to overall diagnostic performance (via marking a distinct differentiation between malignant and benign) for PNs. However, the manifestation of the calcification sign had a good directive property for benign PNs.
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Affiliation(s)
- Cheng Zhou
- CT Department, The Third Affiliated Hospital of Xinjiang Medical University, Urumqi, China
| | - Xiao-Bei Liu
- Imaging Center, The Fifth Affiliated Hospital of Xinjiang Medical University, Urumqi, China
| | - Xiao-Jing Gan
- CT Department, The Third Affiliated Hospital of Xinjiang Medical University, Urumqi, China
| | - Xing Li
- Department of Radiology, Xuzhou Infectious Hospital, Xuzhou, China
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Zhao HC, Xu QS, Shi YB, Ma XJ. Clinical-radiological predictive model in differential diagnosis of small (≤ 20 mm) solitary pulmonary nodules. BMC Pulm Med 2021; 21:281. [PMID: 34482833 PMCID: PMC8419959 DOI: 10.1186/s12890-021-01651-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Accepted: 09/01/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND There is a lack of clinical-radiological predictive models for the small (≤ 20 mm) solitary pulmonary nodules (SPNs). We aim to establish a clinical-radiological predictive model for differentiating malignant and benign small SPNs. MATERIALS AND METHODS Between January 2013 and December 2018, a retrospective cohort of 250 patients with small SPNs was used to construct the predictive model. A second retrospective cohort of 101 patients treated between January 2019 and December 2020 was used to independently test the model. The model was also compared to two other models that had previously been identified. RESULTS In the training group, 250 patients with small SPNs including 156 (62.4%) malignant SPNs and 94 (37.6%) benign SPNs patients were included. Multivariate logistic regression analysis indicated that older age, pleural retraction sign, CT bronchus sign, and higher CEA level were the risk factors of malignant small SPNs. The predictive model was established as: X = - 10.111 + [0.129 × age (y)] + [1.214 × pleural retraction sign (present = 1; no present = 0)] + [0.985 × CT bronchus sign (present = 1; no present = 0)] + [0.21 × CEA level (ug/L)]. Our model had a significantly higher region under the receiver operating characteristic (ROC) curve (0.870; 50% CI: 0.828-0.913) than the other two models. CONCLUSIONS We established and validated a predictive model for estimating the pre-test probability of malignant small SPNs, that can help physicians to choose and interpret the outcomes of subsequent diagnostic tests.
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Affiliation(s)
- Hai-Cheng Zhao
- Shuanggou Hospital Department, Xuzhou Central Hospital, 199 South Jiefang Road, Xuzhou, China
| | - Qing-Song Xu
- Department of Radiology, Xuzhou Central Hospital, 199 South Jiefang Road, Xuzhou, China
| | - Yi-Bing Shi
- Department of Radiology, Xuzhou Central Hospital, 199 South Jiefang Road, Xuzhou, China
| | - Xi-Juan Ma
- Department of Radiology, Xuzhou Central Hospital, 199 South Jiefang Road, Xuzhou, China.
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Martins Jarnalo CO, Linsen PVM, Blazís SP, van der Valk PHM, Dickerscheid DBM. Clinical evaluation of a deep-learning-based computer-aided detection system for the detection of pulmonary nodules in a large teaching hospital. Clin Radiol 2021; 76:838-845. [PMID: 34404517 DOI: 10.1016/j.crad.2021.07.012] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Accepted: 07/15/2021] [Indexed: 12/17/2022]
Abstract
AIM To evaluate a deep-learning-based computer-aided detection (DL-CAD) software system for pulmonary nodule detection on computed tomography (CT) images and assess its added value in the clinical practice of a large teaching hospital. MATERIALS AND METHODS A retrospective analysis was performed of 145 chest CT examinations by comparing the output of the DL-CAD software with a reference standard based on the consensus reading of three radiologists. For every nodule in each scan, the location, composition, and maximum diameter in the axial plane were recorded. The subgroup of chest CT examinations (n = 97) without any nodules was used to determine the negative predictive value at the given clinical sensitivity threshold setting. RESULTS The radiologists found 91 nodules and the CAD system 130 nodules of which 80 were true positive. The measured sensitivity was 88% and the mean false-positive rate was 1.04 false positives/scan. The negative predictive value was 95%. For 23 nodules, there was a size discrepancy of which 19 (83%) were measured smaller by the radiologist. The agreement of nodule composition between the CAD results and the reference standard was 95%. CONCLUSIONS The present study found a sensitivity of 88% and a false-positive rate of 1.04 false positives/scan, which match the vendor specification. Together with the measured negative predictive value of 95% the system performs very well; however, these rates are still not good enough to replace the radiologist, even for the specific task of nodule detection. Furthermore, a surprisingly high rate of overestimation of nodule size was observed, which can lead to too many follow-up examinations.
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Affiliation(s)
- C O Martins Jarnalo
- Department of Radiology, Albert Schweitzer Ziekenhuis, Dordrecht, the Netherlands.
| | - P V M Linsen
- Department of Radiology, Albert Schweitzer Ziekenhuis, Dordrecht, the Netherlands
| | - S P Blazís
- Department of Clinical Physics, FP, the Netherlands
| | - P H M van der Valk
- Department of Radiology, Albert Schweitzer Ziekenhuis, Dordrecht, the Netherlands
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22
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Abstract
The increasing use of low-dose CT for screening for lung cancer will inevitably identify many small, asymptomatic lung nodules and ground-glass opacities (GGOs). Current guidelines for the management of screening-detected lesions tend to advise a conservative approach based on serial imaging and intervention only if ‘suspicious’ features emerge. However, more recent developments in thoracic surgery and in the understanding of the screening-detected lesions themselves prompt some pertinent questions over this conservatism. Is CT surveillance sufficiently reliable to exclude malignancy? Is it really necessary to hold back on operative biopsy and resection given modern surgical safety and efficacy? Is the option for early surgical therapy a viable one—especially with the availability of sublobar resection today? Modern data suggests that the risk of inaction for some screening-detected lesions may be higher than expected, whereas the potential harm of surgical intervention may be substantially reduced by sublobar resection and the latest minimally invasive surgical techniques. A more pro-active approach towards offering surgery for screening-detected lesions should now be considered.
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Affiliation(s)
- Alan D L Sihoe
- Gleneagles Hong Kong Hospital, Hong Kong, China.,International Medical Centre, Hong Kong, China
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Chen K, Nie Y, Park S, Zhang K, Zhang Y, Liu Y, Hui B, Zhou L, Wang X, Qi Q, Li H, Kang G, Huang Y, Chen Y, Liu J, Cui J, Li M, Park IK, Kang CH, Shen H, Yang Y, Guan T, Zhang Y, Yang F, Kim YT, Wang J. Development and Validation of Machine Learning-based Model for the Prediction of Malignancy in Multiple Pulmonary Nodules: Analysis from Multicentric Cohorts. Clin Cancer Res 2021; 27:2255-2265. [PMID: 33627492 DOI: 10.1158/1078-0432.ccr-20-4007] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2020] [Revised: 12/08/2020] [Accepted: 01/21/2021] [Indexed: 11/16/2022]
Abstract
PURPOSE Nodule evaluation is challenging and critical to diagnose multiple pulmonary nodules (MPNs). We aimed to develop and validate a machine learning-based model to estimate the malignant probability of MPNs to guide decision-making. EXPERIMENTAL DESIGN A boosted ensemble algorithm (XGBoost) was used to predict malignancy using the clinicoradiologic variables of 1,739 nodules from 520 patients with MPNs at a Chinese center. The model (PKU-M model) was trained using 10-fold cross-validation in which hyperparameters were selected and fine-tuned. The model was validated and compared with solitary pulmonary nodule (SPN) models, clinicians, and a computer-aided diagnosis (CADx) system in an independent transnational cohort and a prospective multicentric cohort. RESULTS The PKU-M model showed excellent discrimination [area under the curve; AUC (95% confidence interval (95% CI)), 0.909 (0.854-0.946)] and calibration (Brier score, 0.122) in the development cohort. External validation (583 nodules) revealed that the AUC of the PKU-M model was 0.890 (0.859-0.916), higher than those of the Brock model [0.806 (0.771-0.838)], PKU model [0.780 (0.743-0.817)], Mayo model [0.739 (0.697-0.776)], and VA model [0.682 (0.640-0.722)]. Prospective comparison (200 nodules) showed that the AUC of the PKU-M model [0.871 (0.815-0.915)] was higher than that of surgeons [0.790 (0.711-0.852), 0.741 (0.662-0.804), and 0.727 (0.650-0.788)], radiologist [0.748 (0.671-0.814)], and the CADx system [0.757 (0.682-0.818)]. Furthermore, the model outperformed the clinicians with an increase of 14.3% in sensitivity and 7.8% in specificity. CONCLUSIONS After its development using machine learning algorithms, validation using transnational multicentric cohorts, and prospective comparison with clinicians and the CADx system, this novel prediction model for MPNs presented solid performance as a convenient reference to help decision-making.
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Affiliation(s)
- Kezhong Chen
- Department of Thoracic Surgery, Peking University People's Hospital, Beijing, China.
| | - Yuntao Nie
- Department of Thoracic Surgery, Peking University People's Hospital, Beijing, China
| | - Samina Park
- Department of Thoracic and Cardiovascular Surgery, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Kai Zhang
- Department of Thoracic Surgery, Peking University People's Hospital, Beijing, China
| | - Yangming Zhang
- Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Yuan Liu
- Synthetic and Functional Biomolecules Center, Beijing National Laboratory for Molecular Sciences, Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, College of Chemistry and Molecular Engineering, Peking University, Beijing, China
| | - Bengang Hui
- Department of Thoracic Surgery, Tangdu Hospital, Air Force Medical University, Xi'an, China
| | - Lixin Zhou
- Department of Thoracic Surgery, Peking University People's Hospital, Beijing, China
| | - Xun Wang
- Department of Thoracic Surgery, Peking University People's Hospital, Beijing, China
| | - Qingyi Qi
- Department of Radiology, Peking University People's Hospital, Beijing, China
| | - Hao Li
- Department of Thoracic Surgery, Peking University People's Hospital, Beijing, China
| | - Guannan Kang
- Department of Thoracic Surgery, Peking University People's Hospital, Beijing, China
| | - Yuqing Huang
- Department of Thoracic Surgery, Beijing Haidian Hospital, Beijing, China
| | - Yingtai Chen
- Department of Thoracic Surgery, Beijing Aerospace General Hospital, Beijing, China
| | - Jiabao Liu
- Department of Thoracic Surgery, First Hospital of Shijiazhuang, Shijiazhuang, China
| | - Jian Cui
- Department of Thoracic Surgery, Beijing Chuiyangliu Hospital, Beijing, China
| | - Mingru Li
- Department of Thoracic Surgery, Aerospace 731 Hospital, Beijing, China
| | - In Kyu Park
- Department of Thoracic and Cardiovascular Surgery, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Chang Hyun Kang
- Department of Thoracic and Cardiovascular Surgery, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Haifeng Shen
- Department of Thoracic Surgery, Peking University People's Hospital, Beijing, China
| | - Yingshun Yang
- Department of Thoracic Surgery, Beijing Haidian Hospital, Beijing, China
| | - Tian Guan
- Department of Thoracic Surgery, Peking University People's Hospital, Beijing, China
| | - Yaxiao Zhang
- Department of Thoracic Surgery, First Hospital of Shijiazhuang, Shijiazhuang, China
| | - Fan Yang
- Department of Thoracic Surgery, Peking University People's Hospital, Beijing, China
| | - Young Tae Kim
- Department of Thoracic and Cardiovascular Surgery, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea.
| | - Jun Wang
- Department of Thoracic Surgery, Peking University People's Hospital, Beijing, China.
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Tu Y, Wu Y, Lu Y, Bi X, Chen T. Development of risk prediction models for lung cancer based on tumor markers and radiological signs. J Clin Lab Anal 2020; 35:e23682. [PMID: 33325592 PMCID: PMC7957970 DOI: 10.1002/jcla.23682] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2020] [Revised: 11/21/2020] [Accepted: 11/29/2020] [Indexed: 12/19/2022] Open
Abstract
Background Accurate prediction of malignancy risk for pulmonary lesions with pleural effusion improves early diagnosis of lung cancer. This study aimed to develop and validate a model to predict lung cancer. Methods Clinical data of 536 patients with pulmonary diseases were collected. The risk factors were identified by regression analysis. Three prediction models were developed. The predictive performances of the models were measured by the area under the curves (AUCs) and calibrated with 1000 bootstrap samples to minimize the over‐fitting bias. The net benefits of the models were evaluated by decision curve analysis. Finally, a separate cohort of 134 patients was used to validate the models externally. Results Seven independent risk factors were identified from 18 clinical variables, which included the pleural fluid carcinoembryonic antigen (CEA), serum cytokeratin‐19 fragment (CYFRA 21‐1), the ratio of CEA in the pleural fluid to serum, extrathoracic cancer history (>5 years), tumor size, vessel convergence, and lobulation. The AUCs of the three models were 0.976, 0.927, and 0.944 in the training set and 0.930, 0.845, and 0.944 in the external set, respectively. The accuracies of the three models were 89.6%, 81.4%, and 88.8%. Model 1 showed the best iteration fit (R2 = 0.84, 0.68, and 0.73) and a higher net benefit on decision curve analysis when compared to the other two models. Conclusion The advantageous model could assess the risk of lung cancer in patients with pleural effusion and act as a useful tool for early identification of lung cancer.
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Affiliation(s)
- Yuqin Tu
- Department of Medical Laboratory, The First Affiliated Hospital, Chongqing Medical University, Chongqing, China
| | - Yan Wu
- Department of Blood Transfusion, The First Affiliated Hospital, Chongqing Medical University, Chongqing, China
| | - Yunfeng Lu
- Department of Radiology, The First Affiliated Hospital, Chongqing Medical University, Chongqing, China
| | - Xiaoyun Bi
- Department of Medical Laboratory, The First Affiliated Hospital, Chongqing Medical University, Chongqing, China
| | - Te Chen
- Department of Medical Laboratory, The First Affiliated Hospital, Chongqing Medical University, Chongqing, China
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Computed Tomography-Guided Biopsy for Small (≤20 mm) Lung Nodules: A Meta-Analysis. J Comput Assist Tomogr 2020; 44:841-846. [PMID: 32976266 DOI: 10.1097/rct.0000000000001071] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
PURPOSE This study was designed to evaluate the diagnostic accuracy of computed tomography (CT)-guided biopsy for small lung nodules (SLNs) (≤20 mm) and to assess related complication rates. METHODS We reviewed the Pubmed, Embase, and Cochrane Library databases to identify all relevant studies published as of April 2020. Random effects modeling were then used to evaluate pooled data pertaining to technical success rates, diagnostic accuracy, pneumothorax rates, and rates of hemoptysis. The meta-analysis was conducted using Stata v12.0. RESULTS In total, we identified 25 relevant studies for incorporation into this meta-analysis, incorporating 2922 total CT-guided lung biopsy. Pooled technical success rates, diagnostic accuracy, pneumothorax rates, and hemoptysis rates were 94% (95% confidential interval [CI], 0.91-0.98), 90% (95% CI, 0.88-0.93), 19% (95% CI:, 0.15-0.24), and 12% (95% CI, 0.08-0.15), respectively. We observed significant heterogeneity among these studies for all 4 of these parameters (I = 90.0%, 82.7%, 88.6%, and 88.4%, respectively). When we conducted a meta-regression analysis, we did not identify any variables that influenced diagnostic accuracy or technical success, pneumothorax, or hemoptysis rates. Publication bias risk analyses suggested that there was relatively little risk of publication bias pertaining to pneumothorax rates (P = 0.400) or hemoptysis rates (P = 0.377). In contrast, we detected a high risk of publication bias pertaining to reported technical success rates (P = 0.007) and diagnostic accuracy (P = 0.000). CONCLUSIONS A CT-guided biopsy can be safely and effectively used to diagnose SLNs.
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Wei Q, Fang W, Chen X, Yuan Z, Du Y, Chang Y, Wang Y, Chen S. Establishment and validation of a mathematical diagnosis model to distinguish benign pulmonary nodules from early non-small cell lung cancer in Chinese people. Transl Lung Cancer Res 2020; 9:1843-1852. [PMID: 33209606 PMCID: PMC7653141 DOI: 10.21037/tlcr-20-460] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Background In this study, we aimed to establish and validate a mathematical diagnosis model to distinguish benign pulmonary nodules (BPNs) from early non-small cell lung cancer (eNSCLC) based on clinical characteristics, radiomics features, and hematological biomarkers. Methods Medical records from 81 patients (27 BPNs, 54 eNSCLC) were used to establish a novel mathematical diagnosis model and an additional 61 patients (21 BPNs, 40 eNSCLC) were used to validate this new model. To establish a clinical diagnosis model, a least absolute shrinkage and selection operator (LASSO) regression was applied to select predictors for eNSCLC, then multivariate logistic regression analysis was performed to determine independent predictors of the probability of eNSCLC, and to establish a clinical diagnosis model. The diagnostic accuracy and discriminative ability of our model were compared with the PKUPH and Mayo models using the following 4 indices: area under the receiver-operating characteristics curve (ROC), net reclassification improvement index (NRI), integrated discrimination improvement index (IDI), and decision curve analysis (DCA). Results Multivariate logistic regression analysis identified age, border, and albumin (ALB) as independent diagnostic markers of eNSCLC. In the training cohort, the AUC of our model was 0.740, which was larger than the AUCs for the PKUPH model (0.717, P=0.755) and the Mayo model (0.652, P=0.275). Compared with the PKUPH and Mayo models, the NRI of our model increased by 3.7% (P=0.731) and 27.78% (P=0.008), respectively, while the IDI changed −4.77% (P=0.437) and 11.67% (P=0.015), respectively. Moreover, the DCA demonstrated that our model had a higher overall net benefit compared to previously published models. Importantly, similar findings were confirmed in the validation cohort. Conclusions Age, border, and serum ALB levels were independent diagnostic markers of eNSCLC. Thus, our model could more accurately distinguish BPNs from eNSCLC and outperformed previously published models.
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Affiliation(s)
- Qiang Wei
- Department of Laboratory Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Weizhen Fang
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Department of Laboratory Medicine, Sun Yat-sen Memorial Hospital, Guangzhou, China
| | - Xi Chen
- Department of Laboratory Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Zhongzhen Yuan
- Department of Pharmacy, Chongqing University Cancer Hospital & Chongqing Cancer Institute & Chongqing Cancer Hospital, Chongqing, China
| | - Yumei Du
- School of Public Health and Management of Chongqing Medical University, Chongqing, China
| | - Yanbin Chang
- Department of Laboratory Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yonghong Wang
- Department of Laboratory Medicine, Chongqing Qianjiang Central Hospital, Chongqing, China
| | - Shulin Chen
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
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Lung cancer LDCT screening and mortality reduction - evidence, pitfalls and future perspectives. Nat Rev Clin Oncol 2020; 18:135-151. [PMID: 33046839 DOI: 10.1038/s41571-020-00432-6] [Citation(s) in RCA: 204] [Impact Index Per Article: 51.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/04/2020] [Indexed: 12/17/2022]
Abstract
In the past decade, the introduction of molecularly targeted agents and immune-checkpoint inhibitors has led to improved survival outcomes for patients with advanced-stage lung cancer; however, this disease remains the leading cause of cancer-related mortality worldwide. Two large randomized controlled trials of low-dose CT (LDCT)-based lung cancer screening in high-risk populations - the US National Lung Screening Trial (NLST) and NELSON - have provided evidence of a statistically significant mortality reduction in patients. LDCT-based screening programmes for individuals at a high risk of lung cancer have already been implemented in the USA. Furthermore, implementation programmes are currently underway in the UK following the success of the UK Lung Cancer Screening (UKLS) trial, which included the Liverpool Health Lung Project, Manchester Lung Health Check, the Lung Screen Uptake Trial, the West London Lung Cancer Screening pilot and the Yorkshire Lung Screening trial. In this Review, we focus on the current evidence on LDCT-based lung cancer screening and discuss the clinical developments in high-risk populations worldwide; additionally, we address aspects such as cost-effectiveness. We present a framework to define the scope of future implementation research on lung cancer screening programmes referred to as Screening Planning and Implementation RAtionale for Lung cancer (SPIRAL).
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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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29
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Li Y, Wang T, Fu YF, Shi YB. Computed tomography-based spiculated sign for prediction of malignancy in lung nodules: A meta-analysis. CLINICAL RESPIRATORY JOURNAL 2020; 14:1113-1121. [PMID: 32790919 DOI: 10.1111/crj.13258] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/26/2020] [Revised: 08/04/2020] [Accepted: 08/10/2020] [Indexed: 12/12/2022]
Abstract
BACKGROUND Computed tomography (CT)-based spiculated sign is a risk factor for malignancy in patients with lung nodules (LNs). The present meta-analysis aimed to evaluate the diagnostic utility of CT-based spiculated sign as a means of differentiating between malignant and benign LNs. METHODS PubMed, Cochrane Library and Embase were reviewed from January 2000 to March 2020 for eligible studies. Stata v12.0 was used to conduct this meta-analysis. RESULTS We identified 19 retrospective studies for inclusion in this meta-analysis. These studies compiled data pertaining to 8549 LNs (5547 malignant and 3003 benign). Pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR) and diagnostic odds ratios (DOR) were 0.51 (95% CI: 0.36-0.65), 0.84 (95% CI: 0.74-0.91), 3.15 (95% CI: 2.34-4.23), 0.59 (95% CI: 0.47-0.73) and 5.36 (95% CI: 3.93-7.31), respectively. The area under curve (AUC) was 0.76. Significant heterogeneity was detected among these studies with respect to sensitivity (I2 = 98.4%, P = .00), specificity (I2 = 95.8%, P = .00), PLR (I2 = 78.9%, P = .00), NLR (I2 = 99.3%, P = .00) and DOR (I2 = 100%, P = .00). A meta-regression analysis revealed that the country in which a study was conducted (China vs Not China) had a strong influence on reported sensitivity and specificity. No significant publication bias was detected via Deeks' funnel plot asymmetry test (P = .191). CONCLUSIONS CT-based spiculated sign can achieve moderate diagnostic performance as a means of differentiating between malignant and benign LNs.
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Affiliation(s)
- Yu Li
- Department of Radiology, Xuzhou Central Hospital, Xuzhou, China
| | - Tao Wang
- Department of Radiology, Xuzhou Central Hospital, Xuzhou, China
| | - Yu-Fei Fu
- Department of Radiology, Xuzhou Central Hospital, Xuzhou, China
| | - Yi-Bing Shi
- Department of Radiology, Xuzhou Central Hospital, Xuzhou, China
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Jacob M, Romano J, Araújo D, Pereira JM, Ramos I, Hespanhol V. Predicting lung nodules malignancy. Pulmonology 2020; 28:454-460. [PMID: 32739327 DOI: 10.1016/j.pulmoe.2020.06.011] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Revised: 06/26/2020] [Accepted: 06/29/2020] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND It is critical to developing an accurate method for differentiating between malignant and benign solitary pulmonary nodules. This study aimed was to establish a predicting model of lung nodules malignancy in a real-world setting. METHODS The authors retrospectively analysed the clinical and computed tomography (CT) data of 121 patients with lung nodules, submitted to percutaneous CT-guided transthoracic biopsy, between 2014 and 2015. Multiple logistic regression was used to screen independent predictors for malignancy and to establish a clinical prediction model to evaluate the probability of malignancy. RESULTS From a total of 121 patients, 75 (62%) were men and with a mean age of 64.7 years old. Multivariate logistic regression analysis identified six independent predictors of malignancy: age, gender, smoking status, current extra-pulmonary cancer, air bronchogram and nodule size (p<0.05). The area under the curve (AUC) was 0.8573. CONCLUSIONS The prediction model established in this study can be used to assess the probability of malignancy in the Portuguese population, thereby providing help for the diagnosis of lung nodules and the selection of follow-up interventions.
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Affiliation(s)
- M Jacob
- Pulmonology Department, Centro Hospitalar Universitário de São João, Porto, Portugal.
| | - J Romano
- Physical Medicine and Rehabilitation Department, Unidade de Saúde Local de Matosinhos, Porto, Portugal
| | - D Araújo
- Pulmonology Department, Centro Hospitalar Universitário de São João, Porto, Portugal
| | - J M Pereira
- Radiology Department, Centro Hospitalar Universitário de São João, Porto, Portugal; Faculty of Medicine of Porto University, Porto, Portugal
| | - I Ramos
- Radiology Department, Centro Hospitalar Universitário de São João, Porto, Portugal; Faculty of Medicine of Porto University, Porto, Portugal
| | - V Hespanhol
- Pulmonology Department, Centro Hospitalar Universitário de São João, Porto, Portugal; Faculty of Medicine of Porto University, Porto, Portugal
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Chen X, Feng B, Chen Y, Liu K, Li K, Duan X, Hao Y, Cui E, Liu Z, Zhang C, Long W, Liu X. A CT-based radiomics nomogram for prediction of lung adenocarcinomas and granulomatous lesions in patient with solitary sub-centimeter solid nodules. Cancer Imaging 2020; 20:45. [PMID: 32641166 PMCID: PMC7346427 DOI: 10.1186/s40644-020-00320-3] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Accepted: 06/25/2020] [Indexed: 01/15/2023] Open
Abstract
PURPOSE To develop a radiomics nomogram based on computed tomography (CT) images that can help differentiate lung adenocarcinomas and granulomatous lesions appearing as sub-centimeter solid nodules (SCSNs). MATERIALS AND METHODS The records of 214 consecutive patients with SCSNs that were surgically resected and histologically confirmed as lung adenocarcinomas (n = 112) and granulomatous lesions (n = 102) from 2 medical institutions between October 2011 and June 2019 were retrospectively analyzed. Patients from center 1 ware enrolled as training cohort (n = 150) and patients from center 2 were included as external validation cohort (n = 64), respectively. Radiomics features were extracted from non-contrast chest CT images preoperatively. The least absolute shrinkage and selection operator (LASSO) regression model was used for radiomics feature extraction and radiomics signature construction. Clinical characteristics, subjective CT findings, and radiomics signature were used to develop a predictive radiomics nomogram. The performance was examined by assessment of the area under the receiver operating characteristic curve (AUC). RESULTS Lung adenocarcinoma was significantly associated with an irregular margin and lobulated shape in the training set (p = 0.001, < 0.001) and external validation set (p = 0.016, = 0.018), respectively. The radiomics signature consisting of 22 features was significantly associated with lung adenocarcinomas of SCSNs (p < 0.001). The radiomics nomogram incorporated the radiomics signature, gender and lobulated shape. The AUCs of combined model in the training and external validation dataset were 0.885 (95% confidence interval [CI]: 0.823-0.931), 0.808 (95% CI: 0.690-0.896), respectively. Decision curve analysis (DCA) demonstrated that the radiomics nomogram was clinically useful. CONCLUSION A radiomics signature based on non-enhanced CT has the potential to differentiate between lung adenocarcinomas and granulomatous lesions. The radiomics nomogram incorporating the radiomics signature and subjective findings may facilitate the individualized, preoperative treatment in patients with SCSNs.
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Affiliation(s)
- Xiangmeng Chen
- Department of Radiology, Jiangmen Central Hospital, 23#, North Road, Pengjiang Zone, Jiangmen, Guangdong Province 529030 People’s Republic of China
| | - Bao Feng
- Department of Radiology, Jiangmen Central Hospital, 23#, North Road, Pengjiang Zone, Jiangmen, Guangdong Province 529030 People’s Republic of China
- School of electronic information and automation, Guilin University of Aerospace Technology, Guilin City, Guangxi Province 541004 People’s Republic of China
| | - Yehang Chen
- School of electronic information and automation, Guilin University of Aerospace Technology, Guilin City, Guangxi Province 541004 People’s Republic of China
| | - Kunfeng Liu
- Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong Province 519000 People’s Republic of China
| | - Kunwei Li
- Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong Province 519000 People’s Republic of China
| | - Xiaobei Duan
- Department of Nuclear Medicine, Jiangmen Central Hospital, Jiangmen, Guangdong Province 529030 People’s Republic of China
| | - Yixiu Hao
- Department of Radiology, Guangzhou First People’s Hospital, Guangzhou City, Guangdong Province 510180 People’s Republic of China
| | - Enming Cui
- Department of Radiology, Jiangmen Central Hospital, 23#, North Road, Pengjiang Zone, Jiangmen, Guangdong Province 529030 People’s Republic of China
| | - Zhuangsheng Liu
- Department of Radiology, Jiangmen Central Hospital, 23#, North Road, Pengjiang Zone, Jiangmen, Guangdong Province 529030 People’s Republic of China
| | - Chaotong Zhang
- Department of Radiology, Jiangmen Central Hospital, 23#, North Road, Pengjiang Zone, Jiangmen, Guangdong Province 529030 People’s Republic of China
| | - Wansheng Long
- Department of Radiology, Jiangmen Central Hospital, 23#, North Road, Pengjiang Zone, Jiangmen, Guangdong Province 529030 People’s Republic of China
| | - Xueguo Liu
- Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guangdong Province 519000 People’s Republic of China
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Liu Q, Huang Y, Chen H, Liu Y, Liang R, Zeng Q. The development and validation of a radiomic nomogram for the preoperative prediction of lung adenocarcinoma. BMC Cancer 2020; 20:533. [PMID: 32513144 PMCID: PMC7278188 DOI: 10.1186/s12885-020-07017-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2019] [Accepted: 05/28/2020] [Indexed: 12/12/2022] Open
Abstract
Background Accurate diagnosis of early lung cancer from small pulmonary nodules (SPN) is challenging in clinical setting. We aimed to develop a radiomic nomogram to differentiate lung adenocarcinoma from benign SPN. Methods This retrospective study included a total of 210 pathologically confirmed SPN (≤ 10 mm) from 197 patients, which were randomly divided into a training dataset (n = 147; malignant nodules, n = 94) and a validation dataset (n = 63; malignant nodules, n = 39). Radiomic features were extracted from the cancerous volumes of interest on contrast-enhanced CT images. The least absolute shrinkage and selection operator (LASSO) regression was used for data dimension reduction, feature selection, and radiomic signature building. Using multivariable logistic regression analysis, a radiomic nomogram was developed incorporating the radiomic signature and the conventional CT signs observed by radiologists. Discrimination and calibration of the radiomic nomogram were evaluated. Results The radiomic signature consisting of five radiomic features achieved an AUC of 0.853 (95% confidence interval [CI]: 0.735–0.970), accuracy of 81.0%, sensitivity of 82.9%, and specificity of 77.3%. The two conventional CT signs achieved an AUC of 0.833 (95% CI: 0.707–0.958), accuracy of 65.1%, sensitivity of 53.7%, and specificity of 86.4%. The radiomic nomogram incorporating the radiomic signature and conventional CT signs showed an improved AUC of 0.857 (95% CI: 0.723–0.991), accuracy of 84.1%, sensitivity of 85.4%, and specificity of 81.8%. The radiomic nomogram had good calibration power. Conclusion The radiomic nomogram might has the potential to be used as a non-invasive tool for individual prediction of SPN preoperatively. It might facilitate decision-making and improve the management of SPN in the clinical setting.
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Affiliation(s)
- Qin Liu
- Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, 151 Yanjiang Road, Guangzhou, Guangdong, 510120, People's Republic of China
| | - Yan Huang
- Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, 151 Yanjiang Road, Guangzhou, Guangdong, 510120, People's Republic of China
| | - Huai Chen
- Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, 151 Yanjiang Road, Guangzhou, Guangdong, 510120, People's Republic of China
| | - Yanwen Liu
- Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, 151 Yanjiang Road, Guangzhou, Guangdong, 510120, People's Republic of China
| | - Ruihong Liang
- Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, 151 Yanjiang Road, Guangzhou, Guangdong, 510120, People's Republic of China
| | - Qingsi Zeng
- Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, 151 Yanjiang Road, Guangzhou, Guangdong, 510120, People's Republic of China.
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González Maldonado S, Delorme S, Hüsing A, Motsch E, Kauczor HU, Heussel CP, Kaaks R. Evaluation of Prediction Models for Identifying Malignancy in Pulmonary Nodules Detected via Low-Dose Computed Tomography. JAMA Netw Open 2020; 3:e1921221. [PMID: 32058555 DOI: 10.1001/jamanetworkopen.2019.21221] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
IMPORTANCE Malignancy prediction models based on participant-related characteristics and imaging parameters from low-dose computed tomography (CT) may improve decision-making regarding nodule management and diagnosis in lung cancer screening. OBJECTIVE To externally validate 5 malignancy prediction models that were developed in screening settings, compared with 3 models that were developed in clinical settings, in terms of discrimination and absolute risk calibration among participants in the German Lung Cancer Screening Intervention trial. DESIGN, SETTING, AND PARTICIPANTS In this population-based diagnostic study, malignancy probabilities were estimated by applying 8 prediction models to data from 1159 participants in the intervention arm of the Lung Cancer Screening Intervention trial, a randomized clinical trial conducted from October 23, 2007, to April 30, 2016, with ongoing follow-up. This analysis considers end points up to 1 year after individuals' last screening visit. Inclusion criteria for participants were at least 1 noncalcified pulmonary nodule detected on any of 5 annual screening visits, receiving a lung cancer diagnosis within the active screening phase of the Lung Cancer Screening Intervention trial, and an unequivocal identification of the malignant nodules. Data analysis was performed from February 1, 2019, through December 5, 2019. INTERVENTIONS Five annual rounds of low-dose multislice CT. MAIN OUTCOMES AND MEASURES Discrimination ability and calibration of malignancy probabilities estimated by 5 models developed in data from screening studies (4 Pan-Canadian Early Detection of Lung Cancer Study [PanCan] models using a parsimonious approach including nodule spiculation [PanCan-1b] or a comprehensive approach including nodule spiculation [PanCan-2b], and PanCan-2b replacing the nodule diameter variable with mean diameter [PanCan-MD] or volume [PanCan-VOL], as well as a model developed by the UK Lung Cancer Screening trial) and 3 models developed in clinical settings (US Department of Veterans Affairs, Mayo Clinic, and Peking University People's Hospital). RESULTS A total of 1159 participants (median [range] age, 57.63 [50.34-71.89] years; 763 [65.8%] men) with 3903 pulmonary nodules were included in this study. For nodules detected in the prevalence round of CT, the PanCan models showed excellent discrimination (PanCan-1b: area under the curve [AUC], 0.93 [95% CI, 0.87-0.99]; PanCan-2b: AUC, 0.94 [95% CI, 0.89-0.99]; PanCan-MD: AUC, 0.94 [95% CI, 0.91-0.98]; PanCan-VOL: AUC, 0.94 [95% CI, 0.90-0.98]), and all of the screening models except PanCan-MD and PanCan-VOL showed acceptable calibration (PanCan-1b: Spiegelhalter z = -1.081; P = .28; PanCan-2b: Spiegelhalter z = 0.436; P = .67; PanCan-MD: Spiegelhalter z = 3.888; P < .001; PanCan-VOL: Spiegelhalter z = 1.978; P = .05; UK Lung Cancer Screening trial: Spiegelhalter z = -1.076; P = .28), whereas the other models showed worse discrimination and calibration, from an AUC of 0.58 (95% CI, 0.46-0.70) for the UK Lung Cancer Screening trial model to an AUC of 0.89 (95% CI, 0.82-0.97) for the Mayo Clinic model. CONCLUSIONS AND RELEVANCE This diagnostic study found that PanCan models showed excellent discrimination and calibration in prevalence screenings, confirming their ability to improve nodule management in screening settings, although calibration to nodules detected in follow-up scans should be improved. The models developed by the Mayo Clinic, Peking University People's Hospital, Department of Veterans Affairs, and UK Lung Cancer Screening Trial did not perform as well.
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Affiliation(s)
- Sandra González Maldonado
- Division of Cancer Epidemiology, German Cancer Research Center, Heidelberg, Germany
- Translational Lung Research Center Heidelberg, German Center for Lung Research, Heidelberg, Germany
| | - Stefan Delorme
- Division of Radiology, German Cancer Research Center, Heidelberg, Germany
| | - Anika Hüsing
- Division of Cancer Epidemiology, German Cancer Research Center, Heidelberg, Germany
- Translational Lung Research Center Heidelberg, German Center for Lung Research, Heidelberg, Germany
| | - Erna Motsch
- Division of Cancer Epidemiology, German Cancer Research Center, Heidelberg, Germany
- Translational Lung Research Center Heidelberg, German Center for Lung Research, Heidelberg, Germany
| | - Hans-Ulrich Kauczor
- Translational Lung Research Center Heidelberg, German Center for Lung Research, Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology, University Hospital Heidelberg, Heidelberg, Germany
| | - Claus-Peter Heussel
- Translational Lung Research Center Heidelberg, German Center for Lung Research, Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik-Heidelberg GmbH, Heidelberg, Germany
| | - Rudolf Kaaks
- Division of Cancer Epidemiology, German Cancer Research Center, Heidelberg, Germany
- Translational Lung Research Center Heidelberg, German Center for Lung Research, Heidelberg, Germany
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Chen XB, Yan RY, Zhao K, Zhang DF, Li YJ, Wu L, Dong XX, Chen Y, Gao DP, Ding YY, Wang XC, Li ZH. Nomogram For The Prediction Of Malignancy In Small (8-20 mm) Indeterminate Solid Solitary Pulmonary Nodules In Chinese Populations. Cancer Manag Res 2019; 11:9439-9448. [PMID: 31807073 PMCID: PMC6842752 DOI: 10.2147/cmar.s225739] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2019] [Accepted: 10/04/2019] [Indexed: 12/18/2022] Open
Abstract
Purpose This study aimed to develop and validate a nomogram for predicting the malignancy of small (8–20 mm) solid indeterminate solitary pulmonary nodules (SPNs) in a Chinese population by using routine clinical and computed tomography data. Methods The prediction model was developed using a retrospective cohort that comprised 493 consecutive patients with small indeterminate SPNs who were treated between December 2012 and December 2016. The model was independently validated using a second retrospective cohort comprising 216 consecutive patients treated between January 2017 and May 2018. The investigated variables included patient characteristics (e.g., age and smoking history), nodule parameters (e.g., marginal spiculation and significant enhancement), and tumor biomarker levels (e.g., carcinoembryonic antigen). A prediction model was developed by using multivariable logistic regression analysis, and the model’s performance was presented as a nomogram. The model was evaluated based on its discriminative ability, calibration, and clinical usefulness. Results The developed nomogram was ultimately based on age, marginal spiculation, significant enhancement, and pleural indentation. The Harrell concordance index values were 0.869 in the training cohort (95% confidence interval: 0.837–0.901) and 0.847 in the validation cohort (95% confidence interval: 0.792–0.902). The Hosmer-Lemeshow test revealed good calibration in each of the training and validation cohorts. Decision curve analysis confirmed that the nomogram was clinically useful (risk threshold from 0.10 to 0.85). Conclusion Patient age, marginal spiculation, significant enhancement, and pleural indentation are independent predictors of malignancy in small indeterminate solid SPNs. The developed nomogram is easy-to-use and may allow the accurate prediction of malignancy in small indeterminate solid SPNs among Chinese patients.
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Affiliation(s)
- Xiao-Bo Chen
- First Department of Thoracic Surgery, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming 650118, People's Republic of China
| | - Rui-Ying Yan
- Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming 650118, People's Republic of China
| | - Ke Zhao
- Department of Radiology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, People's Republic of China.,School of Medicine, South China University of Technology, Guangzhou 510641, People's Republic of China
| | - Da-Fu Zhang
- Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming 650118, People's Republic of China
| | - Ya-Jun Li
- Department of Radiology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, People's Republic of China.,School of Medicine, South China University of Technology, Guangzhou 510641, People's Republic of China
| | - Lin Wu
- Department of Pathology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming 650118, People's Republic of China
| | - Xing-Xiang Dong
- Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming 650118, People's Republic of China
| | - Ying Chen
- First Department of Thoracic Surgery, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming 650118, People's Republic of China
| | - De-Pei Gao
- Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming 650118, People's Republic of China
| | - Ying-Ying Ding
- Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming 650118, People's Republic of China
| | - Xi-Cai Wang
- Cancer Research Institute, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming 650118, People's Republic of China
| | - Zhen-Hui Li
- Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming 650118, People's Republic of China
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Liu XL, Li W, Yang WX, Rui MP, Li Z, Lv L, Yang LP. Computed tomography-guided biopsy of small lung nodules: diagnostic accuracy and analysis for true negatives. J Int Med Res 2019; 48:300060519879006. [PMID: 31601137 PMCID: PMC7783288 DOI: 10.1177/0300060519879006] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023] Open
Abstract
Objective We evaluated the diagnostic accuracy of computed tomography (CT)-guided
transthoracic core needle biopsy (TCNB) for small (≤20-mm) lung nodules and
identified predictive factors for true negatives among benign biopsy
results. Methods From March 2010 to June 2015, 222 patients with small lung nodules underwent
CT-guided TCNB. We retrospectively analysed data regarding technical
success, diagnostic accuracy, and predictors of true negatives. Results The technical success rate was 100%. The TCNB results of the 222 lung nodules
included malignancy (n = 136), suspected malignancy (n = 8), specific benign
lesion (n = 17), and nonspecific benign lesion (n = 61). The final diagnosis
of 222 lung nodules included malignant (n = 160), benign (n = 60), and
nondiagnostic lesions (n = 2). The sensitivity, specificity, and overall
diagnostic accuracy of CT-guided TCNB for small lung nodules were 90.0%,
100%, and 92.7%, respectively. Pneumothorax and haemoptysis occurred in 23
and 41 patients, respectively. Based on the Cox regression analysis, the
significant independent predictive factor for true negatives was a biopsy
result of chronic inflammation with fibroplasia. Conclusions CT-guided TCNB offers high diagnostic accuracy for small lung nodules, and a
biopsy result of chronic inflammation with fibroplasia can predict a
true-negative result.
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Affiliation(s)
- Xing-Li Liu
- Department of Radiology, First People's Hospital of Yunnan Province, Kunming, Yunnan, China.,Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan, China
| | - Wei Li
- Department of Radiology, First People's Hospital of Yunnan Province, Kunming, Yunnan, China.,Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan, China
| | - Wei-Xin Yang
- Department of Radiology, First People's Hospital of Yunnan Province, Kunming, Yunnan, China.,Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan, China
| | - Mao-Ping Rui
- Department of Radiology, First People's Hospital of Yunnan Province, Kunming, Yunnan, China.,Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan, China
| | - Zhi Li
- Department of Radiology, First People's Hospital of Yunnan Province, Kunming, Yunnan, China.,Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan, China
| | - Liang Lv
- Department of Radiology, First People's Hospital of Yunnan Province, Kunming, Yunnan, China.,Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan, China
| | - Li-Peng Yang
- Department of Radiology, First People's Hospital of Yunnan Province, Kunming, Yunnan, China.,Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan, China
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Development and Validation of a 18F-FDG PET/CT-Based Clinical Prediction Model for Estimating Malignancy in Solid Pulmonary Nodules Based on a Population With High Prevalence of Malignancy. Clin Lung Cancer 2019; 21:47-55. [PMID: 31474376 DOI: 10.1016/j.cllc.2019.07.014] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2019] [Revised: 07/27/2019] [Accepted: 07/31/2019] [Indexed: 02/07/2023]
Abstract
PURPOSE To develop a prediction model based on 18F-fludeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) for solid pulmonary nodules (SPNs) with high malignant probability. PATIENTS AND METHODS We retrospectively reviewed the records of CT-undetermined SPNs, which were further evaluated by PET/CT between January 2008 and December 2015. A total of 312 cases were included as a training set and 159 as a validation set. Logistic regression was applied to determine independent predictors, and a mathematical model was deduced. The area under the receiver operating characteristic curve (AUC) was compared to other models. Model fitness was assessed based on the American College of Chest Physicians guidelines. RESULTS There were 215 (68.9%) and 127 (79.9%) malignant lesions in the training and validation sets, respectively. Eight independent predictors were identified: age [odds ratio (OR) = 1.030], male gender (OR = 0.268), smoking history (OR = 2.719), lesion diameter (OR = 1.067), spiculation (OR = 2.530), lobulation (OR = 2.614), cavity (OR = 2.847), and standardized maximum uptake value of SPNs (OR = 1.229). Our AUCs (training set, 0.858; validation set, 0.809) was better than those of previous models (Mayo: 0.685, P = .0061; Peking University People's Hospital: 0.646, P = .0180; Herder: 0.708, P = .0203; Zhejiang University: 0.757, P = .0699). The C index of the nomogram was 0.858. Our model reduced the diagnosis of indeterminate nodules (26.4% vs. 79.2%, 53.5%, 39.6%, and 34.0%, respectively) while improved sensitivity (81.3% vs. 16.4%, 49.2%, 62.5%, and 68.0%, respectively) and accuracy (65.4% vs. 16.4%, 39.6%, 52.8%, and 58.5%, respectively). CONCLUSION Our model could permit accurate diagnoses and may be recommended to identify malignant SPNs with high malignant probability, as our data pertain to a very high-prevalence cohort only.
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Chen K, Kang G, Zhao H, Zhang K, Zhang J, Yang F, Wang J. Liquid biopsy in newly diagnosed patients with locoregional (I-IIIA) non-small cell lung cancer. Expert Rev Mol Diagn 2019; 19:419-427. [PMID: 30905203 DOI: 10.1080/14737159.2019.1599717] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
INTRODUCTION Liquid biopsy is a promising method for the management of lung cancer, but previous studies focused mainly on patients with advanced-stage disease. As the methodology has progressed for the detection of circulating tumor DNA (ctDNA) and its aberrant methylation, researchers are gradually investigating the utility of liquid biopsy in early-stage patients. As a result, liquid biopsy has shown its potential for the application in patients with early- and locally advanced-stage non-small cell lung cancer (NSCLC). Areas covered: This review summarizes the utility of liquid biopsy in NSCLC and provide an outlook for future development. We focus on the role of ctDNA and its aberrant methylation in patients with stage IA to stageⅢA NSCLC, in the field of early detection and screening, perioperative management, and postoperative surveillance. Expert opinion: Liquid biopsy has shown the potential for clinical application of early-stage patients but has not been routinely applied yet. The utilization of liquid biopsy will be promoted by improved detection methods and data from well-designed clinical trials. With the development of precision medicine, liquid biopsy will likely play an increasingly important clinical role.
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Affiliation(s)
- Kezhong Chen
- a Department of Thoracic Surgery , Peking University People's Hospital , Beijing , P.R. China
| | - Guannan Kang
- a Department of Thoracic Surgery , Peking University People's Hospital , Beijing , P.R. China
| | - Heng Zhao
- a Department of Thoracic Surgery , Peking University People's Hospital , Beijing , P.R. China
| | - Kai Zhang
- a Department of Thoracic Surgery , Peking University People's Hospital , Beijing , P.R. China
| | - Jian Zhang
- a Department of Thoracic Surgery , Peking University People's Hospital , Beijing , P.R. China
| | - Fan Yang
- a Department of Thoracic Surgery , Peking University People's Hospital , Beijing , P.R. China
| | - Jun Wang
- a Department of Thoracic Surgery , Peking University People's Hospital , Beijing , P.R. China
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Xiang Y, Sun Y, Liu Y, Han B, Chen Q, Ye X, Zhu L, Gao W, Fang W. Development and validation of a predictive model for the diagnosis of solid solitary pulmonary nodules using data mining methods. J Thorac Dis 2019; 11:950-958. [PMID: 31019785 DOI: 10.21037/jtd.2019.01.90] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Background The purpose of this study is to develop a predictive model to accurately predict the malignancy of solid solitary pulmonary nodule (SPN) by data mining methods. Methods A training cohort of 388 consecutive patients with solid SPNs was used to develop a predictive model to evaluate the malignancy of solid SPNs. By using SPSS Modeler, we utilized logistic regression (LR), artificial neural network (ANN), k-nearest neighbor (KNN), random forest (RF), and support vector machines (SVM) classifiers to build predictive models. Another cohort of 200 consecutive patients with solid SPNs was used to verify the accuracy of the predictive model. Predictive performance was evaluated using the area under the receiver operating characteristic curve (AUC). Results There was no significant difference in patients' characteristics between the training cohort and the validation cohort. The AUCs of LR, ANN, KNN, RF, and SVM models for the validation cohort were 0.874±0.0280 (P=0.605), 0.833±0.0351 (P=0.104), 0.792±0.0418 (P=0.014), 0.775±0.0400 (P=0.013), and 0.890±0.0323 (reference), respectively. The SVM algorithm had the highest AUC, and the best sensitivity (90.3%), specificity (80.4%), positive predictive value (93.9%), negative predictive value (71.2%) and accuracy (88.0%) for the validation cohort among the five models. Conclusions Data mining by SVM might be a useful auxiliary algorithm in predicting malignancy of solid SPNs.
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Affiliation(s)
- Yangwei Xiang
- Department of Thoracic Surgery, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai 200030, China
| | - Yifeng Sun
- Department of Thoracic Surgery, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai 200030, China
| | - Yuan Liu
- Department of Statistics Cente, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai 200030, China
| | - Baohui Han
- Department of Pulmonary Medicine, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai 200030, China
| | - Qunhui Chen
- Department of Radiology, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai 200030, China
| | - Xiaodan Ye
- Department of Radiology, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai 200030, China
| | - Li Zhu
- Department of Radiology, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai 200030, China
| | - Wen Gao
- Department of Thoracic Surgery, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai 200030, China.,Department of Thoracic Surgery, Shanghai Huadong Hospital, Fudan University School of Medicine, Shanghai 200030, China
| | - Wentao Fang
- Department of Thoracic Surgery, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai 200030, China
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Patro M, Gothi D, Sah RB, Vaidya S. An interesting case of incidental solitary pulmonary nodule. Breathe (Sheff) 2019; 14:e128-e133. [PMID: 30820253 PMCID: PMC6388654 DOI: 10.1183/20734735.019018] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
Doubling time, clinical prediction models of malignancy and positive bronchus sign are useful in stepwise evaluation of SPN to avoid thoracotomy. GeneXpert can be used as initial diagnostic test for tuberculosis and detection of rifampicin resistance.
http://ow.ly/N37030mB8Fi.
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Affiliation(s)
- Mahismita Patro
- Department of pulmonary medicine, ESI PGIMSR, New Delhi, India
| | - Dipti Gothi
- Department of pulmonary medicine, ESI PGIMSR, New Delhi, India
| | - Ram Babu Sah
- Department of pulmonary medicine, ESI PGIMSR, New Delhi, India
| | - Sameer Vaidya
- Department of pulmonary medicine, ESI PGIMSR, New Delhi, India
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Loverdos K, Fotiadis A, Kontogianni C, Iliopoulou M, Gaga M. Lung nodules: A comprehensive review on current approach and management. Ann Thorac Med 2019; 14:226-238. [PMID: 31620206 PMCID: PMC6784443 DOI: 10.4103/atm.atm_110_19] [Citation(s) in RCA: 73] [Impact Index Per Article: 14.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
In daily clinical practice, radiologists and pulmonologists are faced with incidental radiographic findings of pulmonary nodules. Deciding how to manage these findings is very important as many of them may be benign and require no further action, but others may represent early disease and importantly early-stage lung cancer and require prompt diagnosis and definitive treatment. As the diagnosis of pulmonary nodules includes invasive procedures which can be relatively minimal, such as bronchoscopy or transthoracic aspiration or biopsy, but also more invasive procedures such as thoracic surgical biopsies, and as these procedures are linked to anxiety and to cost, it is important to have clearly defined algorithms for the description, management, and follow-up of these nodules. Clear algorithms for the imaging protocols and the management of positive findings should also exist in lung cancer screening programs, which are already established in the USA and which will hopefully be established worldwide. This article reviews current knowledge on nodule definition, diagnostic evaluation, and management based on literature data and mainly recent guidelines.
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Affiliation(s)
| | - Andreas Fotiadis
- 7th Respiratory Medicine Department, Athens Chest Hospital, Athens, Greece
| | | | | | - Mina Gaga
- 7th Respiratory Medicine Department, Athens Chest Hospital, Athens, Greece
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Toyokawa G, Yamada Y, Tagawa T, Kamitani T, Yamasaki Y, Shimokawa M, Oda Y, Maehara Y. Computed tomography features of resected lung adenocarcinomas with spread through air spaces. J Thorac Cardiovasc Surg 2018; 156:1670-1676.e4. [DOI: 10.1016/j.jtcvs.2018.04.126] [Citation(s) in RCA: 47] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/26/2018] [Revised: 04/24/2018] [Accepted: 04/28/2018] [Indexed: 11/28/2022]
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Fang R, Yang Y, Han H, Fu X, Dong L, Xie B, Lu W, Ma C, Cui F, Hu J, Wang J. Analysis of risk factors for stage I lung adenocarcinoma using low-dose high-resolution computed tomography. Oncol Lett 2018; 16:2483-2489. [PMID: 30013641 PMCID: PMC6036570 DOI: 10.3892/ol.2018.8921] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2018] [Accepted: 05/26/2018] [Indexed: 12/02/2022] Open
Abstract
Risk factors for stage I lung adenocarcinoma were analyzed using low-dose high-resolution computed tomography (CT). The patients were divided into case group (stage I lung adenocarcinoma patients) and control group (benign pulmonary nodules patients). All patients were subjected to low-dose high-resolution CT. Multiple linear regression was performed to analyze the CT imaging features of the two groups. Stage I lung adenocarcinoma patients were significantly associated with nodular site (X3, upper left lobe) [95% CI (1.796, 54.695), p=0.008], nodule type (X4) (p<0.001), nodule size (X5) [95% CI (0.614, 0.803), p<0.001], spicule sign (X7) [95% CI (0.029, 0.580), p=0.008], lobulation sign (X8) [95% CI (0.048, 0.673), p=0.011]. The stepwise regression equation is: Logistic (p) =-12.009 + 2.294X3 - 0.327X4 - 0.354X5 - 2.042X7 - 1.713X8. Risk factors of low-dose and high-resolution CT imaging for patients with stage I lung adenocarcinoma are nodular site (upper left lobe), nodule type, nodule size, spicule sign, and lobulation sign.
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Affiliation(s)
- Rui Fang
- Guangxing Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, Zhejiang 310007, P.R. China
- School of Medicine, Hangzhou Normal University, Hangzhou, Zhejiang 310018, P.R. China
| | - Yong Yang
- Guangxing Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, Zhejiang 310007, P.R. China
| | - Haicheng Han
- School of Medicine, Hangzhou Normal University, Hangzhou, Zhejiang 310018, P.R. China
| | - Xiaoqing Fu
- Guangxing Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, Zhejiang 310007, P.R. China
| | - Liwen Dong
- Guangxing Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, Zhejiang 310007, P.R. China
| | - Baisheng Xie
- Guangxing Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, Zhejiang 310007, P.R. China
| | - Wei Lu
- The Third Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, Zhejiang 310053, P.R. China
| | - Chenyang Ma
- The Third Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, Zhejiang 310053, P.R. China
| | - Feng Cui
- Guangxing Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, Zhejiang 310007, P.R. China
| | - Jian Hu
- The First Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang 310003, P.R. China
| | - Jun Wang
- Peking University People's Hospital, Beijing 100044, P.R. China
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Oke JL, Pickup LC, Declerck J, Callister ME, Baldwin D, Gustafson J, Peschl H, Ather S, Tsakok M, Exell A, Gleeson F. Development and validation of clinical prediction models to risk stratify patients presenting with small pulmonary nodules: a research protocol. Diagn Progn Res 2018; 2:22. [PMID: 31093569 PMCID: PMC6460802 DOI: 10.1186/s41512-018-0044-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/11/2018] [Accepted: 11/13/2018] [Indexed: 01/13/2023] Open
Abstract
INTRODUCTION Lung cancer is a common cancer, with over 1.3 million cases worldwide each year. Early diagnosis using computed tomography (CT) screening has been shown to reduce mortality but also detect non-malignant nodules that require follow-up scanning or alternative methods of investigation. Practical and accurate tools that can predict the probability that a lung nodule is benign or malignant will help reduce costs and the risk of morbidity and mortality associated with lung cancer. METHODS Retrospectively collected data from 1500 patients with pulmonary nodule(s) of up to 15 mm detected on routinely performed CT chest scans aged 18 years old or older from three academic centres in the UK will be used to to develop risk stratification models. Radiological, clinical and patient characteristics will be combined in multivariable logistic regression models to predict nodule malignancy. Data from over 1000 participants recruited in a prospective phase of the study will be used to evaluate model performance. Discrimination, calibration and clinical utility measures will be presented.
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Affiliation(s)
- Jason L Oke
- 1Nuffield Department of Primary Care Health Sciences, University of Oxford, Woodstock Road, OX2 6GG, Oxford, UK
| | | | | | | | | | | | - Heiko Peschl
- 2Oxford University Hospitals NHS Foundation Trust, Oxford, Oxford, UK
| | - Sarim Ather
- 2Oxford University Hospitals NHS Foundation Trust, Oxford, Oxford, UK
| | - Maria Tsakok
- 2Oxford University Hospitals NHS Foundation Trust, Oxford, Oxford, UK
| | - Alan Exell
- 2Oxford University Hospitals NHS Foundation Trust, Oxford, Oxford, UK
| | - Fergus Gleeson
- 2Oxford University Hospitals NHS Foundation Trust, Oxford, Oxford, UK
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Fu YF, Zhang M, Wu WB, Wang T. Coil Localization-Guided Video-Assisted Thoracoscopic Surgery for Lung Nodules. J Laparoendosc Adv Surg Tech A 2017; 28:292-297. [PMID: 29135327 DOI: 10.1089/lap.2017.0484] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Abstract
PURPOSE To determine the clinical efficacy of preoperative coil localization-guided video-assisted thoracoscopic surgery (VATS) for lung nodules. MATERIALS AND METHODS Between November 2015 and July 2017, 56 patients with lung nodules underwent coil localization-guided VATS procedure. The coil implantation was performed under the guidance of computed tomography (CT). The end tail of the coil remained above the visceral pleura. The target lung nodules were removed by VATS wedge resection. Data on the technical success of coil localization and wedge resection, procedure-related complications, and pathological results were collected and analyzed. RESULTS Sixty-seven lung nodules in 56 patients (1.2 nodules/case) were localized. The technical success rate of coil localization was 89.6% (60/67). Sixty-three nodules were localized with one coil and four nodules with two coils. The mean time taken to perform CT-guided coil implantation was 15.7 ± 5.3 (range: 8-40) minutes. Six patients (9.0%) experienced pneumothorax after coil implantation. The technical success rate of wedge resection was 97.0% (65/67). Two nodules were removed directly by video-assisted lobectomy. Nine patients with multiple target lung nodules underwent single-stage resection. The mean total operating time was 147.2 ± 79.1 (range: 50-360) minutes. The mean volume of blood loss was 113.2 ± 113.0 (range: 10-700) mL. Postoperative complications included prolonged air leak (n = 2) and pleural effusion (n = 5). CONCLUSIONS Preoperative coil localization is a safe and effective method to facilitate a high successful rate of VATS wedge-resection for lung nodules.
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Affiliation(s)
- Yu-Fei Fu
- 1 Department of Radiology, Xuzhou Central Hospital , Xuzhou, China
| | - Miao Zhang
- 2 Department of Thoracic Surgery, Xuzhou Central Hospital , Xuzhou, China
| | - Wen-Bin Wu
- 2 Department of Thoracic Surgery, Xuzhou Central Hospital , Xuzhou, China
| | - Tao Wang
- 1 Department of Radiology, Xuzhou Central Hospital , Xuzhou, China
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Li GC, Fu YF, Cao W, Shi YB, Wang T. Computed tomography-guided percutaneous cutting needle biopsy for small (≤ 20 mm) lung nodules. Medicine (Baltimore) 2017; 96:e8703. [PMID: 29145307 PMCID: PMC5704852 DOI: 10.1097/md.0000000000008703] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
The goal of this study is to determine the feasibility, diagnostic accuracy, and risk factor of complications of computed tomography (CT)-guided percutaneous cutting needle biopsy (PCNB) for small lung nodules.From January 2014 to May 2015, 141 patients with small lung nodule were performed with CT-guided PCNB procedure. Data on technical success, diagnostic accuracy, and complication were collected and analyzed.Technical success of CT-guided PCNB for small lung nodules was 100%. A total of 141 nodules were punctured. The mean time of the procedure was 15.7 ± 4.3 minutes. The PCNB results included malignancy (n = 79), suspected malignancy (n = 6), specific benign lesion (n = 8), nonspecific benign lesion (n = 47), and invalid diagnosis (n = 1). The final diagnosis of the 141 nodules included malignancy (n = 90), benign (n = 37), and nondiagnostic lesion (n = 14). The nondiagnostic nodules were not included for calculating the diagnostic accuracy. The sensitivity, specificity, and overall diagnostic accuracy of CT-guided PCNB for small lung nodule were 94.4% (85/90), 100% (37/37), and 96.1% (122/127), respectively. Pneumothorax and lung hemorrhage (≥ grade 2) occurred in 17 (12.1%) and 22 (15.6%) patients, respectively. Based on the univariate and multivariate logistic analyses, the risk factors of pneumothorax included nonprone position (P = .019) and longer procedure time (P = .018). The independent risk factor of lung hemorrhage (≥ grade 2) was deeper lesion distance from pleura along needle path (P = .024).This study demonstrates that CT-guided PCNB can provide a high diagnostic accuracy for small lung nodule with acceptable complications.
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Sakoda LC, Henderson LM, Caverly TJ, Wernli KJ, Katki HA. Applying Risk Prediction Models to Optimize Lung Cancer Screening: Current Knowledge, Challenges, and Future Directions. CURR EPIDEMIOL REP 2017. [PMID: 29531893 DOI: 10.1007/s40471-017-0126-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Purpose of review Risk prediction models may be useful for facilitating effective and high-quality decision-making at critical steps in the lung cancer screening process. This review provides a current overview of published lung cancer risk prediction models and their applications to lung cancer screening and highlights both challenges and strategies for improving their predictive performance and use in clinical practice. Recent findings Since the 2011 publication of the National Lung Screening Trial results, numerous prediction models have been proposed to estimate the probability of developing or dying from lung cancer or the probability that a pulmonary nodule is malignant. Respective models appear to exhibit high discriminatory accuracy in identifying individuals at highest risk of lung cancer or differentiating malignant from benign pulmonary nodules. However, validation and critical comparison of the performance of these models in independent populations are limited. Little is also known about the extent to which risk prediction models are being applied in clinical practice and influencing decision-making processes and outcomes related to lung cancer screening. Summary Current evidence is insufficient to determine which lung cancer risk prediction models are most clinically useful and how to best implement their use to optimize screening effectiveness and quality. To address these knowledge gaps, future research should be directed toward validating and enhancing existing risk prediction models for lung cancer and evaluating the application of model-based risk calculators and its corresponding impact on screening processes and outcomes.
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Affiliation(s)
- Lori C Sakoda
- Division of Research, Kaiser Permanente Northern California, Oakland, CA USA
| | - Louise M Henderson
- Department of Radiology, University of North Carolina School of Medicine, Chapel Hill, NC USA
| | - Tanner J Caverly
- Center for Clinical Management Research, Veteran Affairs Ann Arbor Healthcare System, Ann Arbor, MI USA
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI USA
| | - Karen J Wernli
- Kaiser Permanente Washington Health Research Institute, Seattle, WA USA
| | - Hormuzd A Katki
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD USA
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Li Q, Balagurunathan Y, Liu Y, Qi J, Schabath MB, Ye Z, Gillies RJ. Comparison Between Radiological Semantic Features and Lung-RADS in Predicting Malignancy of Screen-Detected Lung Nodules in the National Lung Screening Trial. Clin Lung Cancer 2017; 19:148-156.e3. [PMID: 29137847 DOI: 10.1016/j.cllc.2017.10.002] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2017] [Revised: 08/16/2017] [Accepted: 10/04/2017] [Indexed: 01/10/2023]
Abstract
RATIONALE Lung computed tomography (CT) Screening Reporting and Data System (lung-RADS) has standardized follow-up and management decisions in lung cancer screening. To date, little is known how lung-RADS classification compares with radiological semantic features in risk prediction and diagnostic discrimination. OBJECTIVES To compare the performance of radiological semantic features and lung-RADS in predicting nodule malignancy in lung cancer screening. METHODS We used data and low-dose CT (LDCT) images from the National Lung Screening Trial (NLST). The training cohort contained 60 patients with screen-detected incident lung cancers who had a positive baseline screen (T0) that was not diagnosed and then was diagnosed at second follow-up (T2), and 139 nodule-positive controls who had 3 consecutive positive screens (T0 to T2) that were not diagnosed as lung cancer. The testing cohort included 40 patients with incident lung cancers that were diagnosed at first follow-up (T1) and 40 nodule-positive controls. Twenty-four semantic features were scored on a point scale from the LDCT images. Multivariable linear predictor model was built on the semantic features and the performances were compared with lung-RADS in 3 screening rounds. We also combined non-size-based semantic features with lung-RADS to improve malignancy detection. RESULTS At T0, the average area under the receiver operating characteristic curve (AUROC) for border definition in risk prediction was 0.72. The average AUROC for contour at T1 in risk prediction and T2 in diagnostic discrimination was 0.82 and 0.88, respectively. By comparison, the average AUROC of lung-RADS at T0, T1 and T2 were 0.60, 0.76 and 0.87, respectively. The combined model of the semantic features and lung-RADS shows improvement with AUROCs of 0.74, 0.88 and 0.96 at T0, T1, and T2, respectively, achieved by adding border definition (at T0) or contour (at T1 and T2). CONCLUSION We find semantic features defined by border definition and contour performed similar to lung-RADS at follow-up time point and outperformed lung-RADS at baseline. These semantics alongside of lung-RADS shows improved performance to detect malignancy.
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Affiliation(s)
- Qian Li
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center of Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China; Department of Cancer Imaging and Metabolism, H. Lee Moffitt Cancer Center, Tampa, FL
| | | | - Ying Liu
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center of Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Jin Qi
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center of Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China; Department of Cancer Imaging and Metabolism, H. Lee Moffitt Cancer Center, Tampa, FL
| | - Matthew B Schabath
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center, Tampa, FL
| | - Zhaoxiang Ye
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center of Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China.
| | - Robert J Gillies
- Department of Cancer Imaging and Metabolism, H. Lee Moffitt Cancer Center, Tampa, FL.
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Lin Y, Leng Q, Jiang Z, Guarnera MA, Zhou Y, Chen X, Wang H, Zhou W, Cai L, Fang H, Li J, Jin H, Wang L, Yi S, Lu W, Evers D, Fowle CB, Su Y, Jiang F. A classifier integrating plasma biomarkers and radiological characteristics for distinguishing malignant from benign pulmonary nodules. Int J Cancer 2017; 141:1240-1248. [PMID: 28580707 PMCID: PMC5526452 DOI: 10.1002/ijc.30822] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2017] [Revised: 05/09/2017] [Accepted: 05/22/2017] [Indexed: 12/21/2022]
Abstract
Lung cancer is primarily caused by cigarette smoking and the leading cancer killer in the USA and across the world. Early detection of lung cancer by low-dose CT (LDCT) can reduce the mortality. However, LDCT dramatically increases the number of indeterminate pulmonary nodules (PNs), leading to overdiagnosis. Having a definitive preoperative diagnosis of malignant PNs is clinically important. Using microarray and droplet digital PCR to directly profile plasma miRNA expressions of 135 patients with PNs, we identified 11 plasma miRNAs that displayed a significant difference between patients with malignant versus benign PNs. Using multivariate logistic regression analysis of the molecular results and clinical/radiological characteristics, we developed an integrated classifier comprising two miRNA biomarkers and one radiological characteristic for distinguishing malignant from benign PNs. The classifier had 89.9% sensitivity and 90.9% specificity, being significantly higher compared with the biomarkers or clinical/radiological characteristics alone (all p < 0.05). The classifier was validated in two independent sets of patients. We have for the first time shown that the integration of plasma biomarkers and radiological characteristics could more accurately identify lung cancer among indeterminate PNs. Future use of the classifier could spare individuals with benign growths from the harmful diagnostic procedures, while allowing effective treatments to be immediately initiated for lung cancer, thereby reduces the mortality and cost. Nevertheless, further prospective validation of this classifier is warranted.
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Affiliation(s)
- Yanli Lin
- Department of Pathology, University of Maryland School of Medicine, Baltimore. MD. USA
| | - Qixin Leng
- Department of Pathology, University of Maryland School of Medicine, Baltimore. MD. USA
| | - Zhengran Jiang
- Department of Pathology, University of Maryland School of Medicine, Baltimore. MD. USA
- The F. Edward Hébert School of Medicine at the Uniformed Services University of the Health Sciences, Bethesda, MD. USA
| | - Maria A. Guarnera
- Department of Pathology, University of Maryland School of Medicine, Baltimore. MD. USA
| | - Yun Zhou
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore. MD. USA
| | - Xueqi Chen
- Department of Nuclear Medicine, Peking University First Hospital, Beijing. China
| | - Heping Wang
- Department of Biostatistics, Bioinformatics and Biomathematics, Georgetown University Medical Center, Washington D.C. USA
| | - Wenxian Zhou
- Department of Biostatistics, Bioinformatics and Biomathematics, Georgetown University Medical Center, Washington D.C. USA
| | - Ling Cai
- Department of Biostatistics, Bioinformatics and Biomathematics, Georgetown University Medical Center, Washington D.C. USA
| | - HongBin Fang
- Department of Biostatistics, Bioinformatics and Biomathematics, Georgetown University Medical Center, Washington D.C. USA
| | - Jie Li
- Department of thoracic surgery, the general hospital of PLA, Beijing. China
| | - Hairong Jin
- Department of thoracic surgery, the general hospital of PLA, Beijing. China
| | - Linghui Wang
- Department of thoracic surgery, the general hospital of PLA, Beijing. China
| | - Shaoqiong Yi
- Department of thoracic surgery, the general hospital of PLA, Beijing. China
| | - Wei Lu
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, NY. USA
| | - David Evers
- VA Maryland Health Care System, Baltimore VA Medical Center, Baltimore, MD. USA
| | - Carol B Fowle
- VA Maryland Health Care System, Baltimore VA Medical Center, Baltimore, MD. USA
| | - Yun Su
- Department of Surgery, Jiangsu Province Hospital of Traditional Chinese Medicine (TCM), Affiliated Hospital of Nanjing University of TCM. Nanjing. China
| | - Feng Jiang
- Department of Pathology, University of Maryland School of Medicine, Baltimore. MD. USA
- VA Maryland Health Care System, Baltimore VA Medical Center, Baltimore, MD. USA
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She Y, Zhao L, Dai C, Ren Y, Jiang G, Xie H, Zhu H, Sun X, Yang P, Chen Y, Shi S, Shi W, Yu B, Xie D, Chen C. Development and validation of a nomogram to estimate the pretest probability of cancer in Chinese patients with solid solitary pulmonary nodules: A multi-institutional study. J Surg Oncol 2017; 116:756-762. [PMID: 28570780 DOI: 10.1002/jso.24704] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2017] [Accepted: 05/11/2017] [Indexed: 12/19/2022]
Abstract
OBJECTIVES To develop and validate a nomogram to estimate the pretest probability of malignancy in Chinese patients with solid solitary pulmonary nodule (SPN). MATERIALS AND METHODS A primary cohort of 1798 patients with pathologically confirmed solid SPNs after surgery was retrospectively studied at five institutions from January 2014 to December 2015. A nomogram based on independent prediction factors of malignant solid SPN was developed. Predictive performance also was evaluated using the calibration curve and the area under the receiver operating characteristic curve (AUC). RESULTS The mean age of the cohort was 58.9 ± 10.7 years. In univariate and multivariate analysis, age; history of cancer; the log base 10 transformations of serum carcinoembryonic antigen value; nodule diameter; the presence of spiculation, pleural indentation, and calcification remained the predictive factors of malignancy. A nomogram was developed, and the AUC value (0.85; 95%CI, 0.83-0.88) was significantly higher than other three models. The calibration cure showed optimal agreement between the malignant probability as predicted by nomogram and the actual probability. CONCLUSIONS We developed and validated a nomogram that can estimate the pretest probability of malignant solid SPNs, which can assist clinical physicians to select and interpret the results of subsequent diagnostic tests.
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Affiliation(s)
- Yunlang She
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, P. R. China
| | - Lilan Zhao
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, P. R. China
| | - Chenyang Dai
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, P. R. China
| | - Yijiu Ren
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, P. R. China
| | - Gening Jiang
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, P. R. China
| | - Huikang Xie
- Department of Pathology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, P. R. China
| | - Huiyuan Zhu
- Department of Radiology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, P. R. China
| | - Xiwen Sun
- Department of Radiology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, P. R. China
| | - Ping Yang
- Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota
| | - Yongbing Chen
- Department of Thoracic Surgery, The Second Affiliated Hospital of Soochow University, Jiangsu, P. R. China
| | - Shunbin Shi
- Department of Thoracic Surgery, The Affiliated Wujiang Hospital of Nantong University, Jiangsu, P. R. China
| | - Weirong Shi
- Department of Thoracic Surgery, Nantong Sixth People's Hospital, Jiangsu, P. R. China
| | - Bing Yu
- Department of Thoracic Surgery, Fenghua People's Hospital, Zhejiang, P. R. China
| | - Dong Xie
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, P. R. China
| | - Chang Chen
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, P. R. China
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Abstract
OBJECTIVE Noninvasive liquid biopsies of circulating tumor DNA (ctDNA) can be used to assess non-small cell lung cancer (NSCLC), but previous work focused on patients with advanced-stage cancer. Thus, we evaluated the feasibility and their potential clinical application of circulating tumor DNA approached for surgical patients with NSCLC. METHOD Consecutive patients with suspected lung cancer who underwent curative-intent lung resection were enrolled prospectively in this study. Targeted DNA sequencing with a next-generation sequencing platform was used to identify a series of somatic mutations in matched tumor tissue DNA (tDNA) and plasma ctDNA samples. Plasma was collected before, during, and after surgery. Concordance was defined as matched tDNA and ctDNA with the same identified mutations or with no mutations. RESULTS In the enrolled 76 patients with lung cancer who were included, 31 had concordant mutations and 21 had no mutation in both ctDNA and tDNA, yielding an overall concordance of 68.4%. ctDNA samples obtained before and during surgery had the same mutations with a low variance in mutation frequency (1.2%) that was reduced to an average of 0.28% after surgery (P < .001). More patients were positive as assayed by ctDNA (48; 63.2%) than with serum tumor protein markers (36; 49.3%). The area under the curve was greater in ctDNA (0.887, 95% confidence interval [CI], 0.788-0.986) than for the 2 prediction models (0.803, 95% CI, 0.647-0.959; 0.69, 95% CI, 0.512-0.869) for estimating malignancy of solitary pulmonary nodules. CONCLUSION ctDNA mutation analysis for stage I-III surgical patients with NSCLC is feasible. More studies are needed to investigate its clinical application.
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