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Jiao Z, Yu J. Development and external validation of a nomogram for predicting lymph node metastasis in 1-3 cm lung adenocarcinoma. Future Oncol 2024:1-13. [PMID: 39365105 DOI: 10.1080/14796694.2024.2405457] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2024] [Accepted: 09/13/2024] [Indexed: 10/05/2024] Open
Abstract
Aim: This study aimed to investigate the risk factors for lymph node metastasis in 1-3 cm adenocarcinoma and develop a new nomogram to predict the probability of lymph node metastasis.Materials & methods: This study collected clinical data from 1656 patients for risk factor analysis and an additional 500 patients for external validation. The logistic regression analyses were employed for risk factor analysis. The least absolute shrinkage and selection operator regression was used to select variables, and important variables were used to construct the nomogram and an online calculator.Results: The nomogram for predicting lymph node metastasis comprises six variables: tumor size (mediastinal window), consolidation tumor ratio, tumor location, lymphadenopathy, preoperative serum carcinoembryonic antigen level and pathological grade. According to the predicted results, the risk of lymph node metastasis was divided into low-risk group and high-risk group. We confirmed the exceptional clinical efficacy of the model through multiple evaluation methods.Conclusion: The importance of intraoperative frozen section is increasing. We discussed the risk factors for lymph node metastasis and developed a nomogram to predict the probability of lymph node metastasis in 1-3 cm adenocarcinomas, which can guide lymph node resection strategies during surgery.
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Affiliation(s)
- Zhenhua Jiao
- Department of Thoracic Surgery, Tongji Hospital, Huazhong University of Science & Technology, Wuhan, 430030, China
| | - Jun Yu
- Department of Thoracic Surgery, Tongji Hospital, Huazhong University of Science & Technology, Wuhan, 430030, China
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Tsubokawa N, Mimae T, Saeki A, Miyata Y, Kanno C, Kudo Y, Nagashima T, Ito H, Ikeda N, Okada M. Feasibility and comparative prognosis of segmentectomy versus lobectomy in centrally located small and solid dominant cN0 non-small cell lung cancer. J Thorac Cardiovasc Surg 2024:S0022-5223(24)00538-5. [PMID: 38969057 DOI: 10.1016/j.jtcvs.2024.06.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/24/2024] [Revised: 05/28/2024] [Accepted: 06/17/2024] [Indexed: 07/07/2024]
Abstract
OBJECTIVES To determine the feasibility of segmentectomy in patients with central, whole tumor size ≤2 cm and radiologically solid-dominant cN0 non-small cell lung cancer (NSCLC). METHODS We retrospectively reviewed 1240 patients who underwent lobectomy or segmentectomy for small and radiologically solid-dominant cN0 NSCLC between January 2010 and December 2022. The inclusion criteria encompassed centrally located tumors, defined as tumors located in the inner two-thirds of the pulmonary parenchyma. Propensity score matching was applied to balance the baseline characteristics in the 2 study groups. RESULTS Among the 299 eligible patients, no significant differences in recurrence-free survival (RFS) and overall survival (OS) were observed between the segmentectomy (n = 121) and lobectomy (n = 178) groups (P = .794 and .577, respectively). After propensity score matching, no significant differences in hilar and mediastinal lymph node upstaging were found among the 93 matched patients (P = 1.00), and locoregional recurrence was comparable in the segmentectomy (n = 4) and lobectomy (n = 4) groups. RFS and OS did not differ significantly between the 2 groups (P = .700 and .870, respectively). Propensity score-adjusted multivariable Cox analysis for RFS and OS indicated that segmentectomy was not an independent prognostic factor (RFS: hazard ratio [HR], 0.89; 95% confidence interval [CI], 0.43-1.85; P = .755; OS: HR, 1.09; 95% CI, 0.38-3.14; P = .860). CONCLUSIONS Segmentectomy may be a viable treatment option, with local control and prognosis comparable to that of lobectomy in appropriately selected patients with central, small (≤2 cm), and radiologically solid-dominant NSCLC.
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Affiliation(s)
| | - Takahiro Mimae
- Department of Surgical Oncology, Hiroshima University, Horoshima, Japan
| | - Akira Saeki
- Department of Surgical Oncology, Hiroshima University, Horoshima, Japan
| | - Yoshihiro Miyata
- Department of Surgical Oncology, Hiroshima University, Horoshima, Japan
| | - Chiaki Kanno
- Department of Thoracic Surgery, Tokyo Medical University, Tokyo, Japan
| | - Yujin Kudo
- Department of Thoracic Surgery, Tokyo Medical University, Tokyo, Japan
| | - Takuya Nagashima
- Department of Thoracic Surgery, Kanagawa Cancer Center, Yokohama, Japan
| | - Hiroyuki Ito
- Department of Thoracic Surgery, Kanagawa Cancer Center, Yokohama, Japan
| | - Norihiko Ikeda
- Department of Thoracic Surgery, Tokyo Medical University, Tokyo, Japan
| | - Morihito Okada
- Department of Surgical Oncology, Hiroshima University, Horoshima, Japan.
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Yano K, Yotsukura M, Watanabe H, Akamine T, Yoshida Y, Nakagawa K, Yatabe Y, Kusumoto M, Watanabe SI. Oncological feasibility of segmentectomy for inner-located lung cancer. JTCVS OPEN 2024; 18:261-275. [PMID: 38690420 PMCID: PMC11056493 DOI: 10.1016/j.xjon.2024.02.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Revised: 01/27/2024] [Accepted: 02/11/2024] [Indexed: 05/02/2024]
Abstract
Objective Oncological feasibility of segmentectomy for internal non-small cell lung cancer (NSCLC) has not been assessed adequately. We assessed the oncological feasibility of segmentectomy for inner-located NSCLC by investigating surgical margins and patient prognosis after undergoing the procedure. Methods Of the 3555 patients who underwent resection for lung cancer between 2013 and 2019 at our institution, 659 patients who underwent segmentectomy for clinical stage 0 to stage1A NSCLC were included in this study. Patients were separated into 2 groups according to whether the tumor was in the inner or outer third of the lung area. Clinical characteristics and prognoses were retrospectively compared between the groups. Results Of the included 659 cases, 183 (27.8%) were inner-located, and 476 (72.2%) had outer-located NSCLC. The surgical margin was significantly shorter in the inner-located group than in the outer group (median, 16 vs 25 mm; P < .001). The 5-year recurrence-free survival and overall survival probabilities were 91.1%/91.8% (P = .530) and 94.1%/95.6% (P = .345) for inner/outer-located groups, respectively. Multivariate analysis showed that clinical stage IA2 or 3 (P = .043), lymphovascular invasion (P < .001), and surgical margins <20 mm (P = .017) were independent prognostic factors for recurrence-free survival. The location of the inner or outer tumors was not related to the prognosis. Conclusions For clinical stage 0 to stage1A NSCLC, tumor location in the inner two-thirds of the lung was not associated with prognosis after segmentectomy. Because one of the independent prognostic factors is margin distance, segmentectomy for inner-located NSCLC would be oncologically acceptable when an adequate surgical margin is secured.
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Affiliation(s)
- Kaito Yano
- Department of Thoracic Surgery, National Cancer Center Hospital, Tokyo, Japan
| | - Masaya Yotsukura
- Department of Thoracic Surgery, National Cancer Center Hospital, Tokyo, Japan
| | - Hirokazu Watanabe
- Department of Diagnostic Radiology, National Cancer Center Hospital, Tokyo, Japan
| | - Takaki Akamine
- Department of Thoracic Surgery, National Cancer Center Hospital, Tokyo, Japan
| | - Yukihiro Yoshida
- Department of Thoracic Surgery, National Cancer Center Hospital, Tokyo, Japan
| | - Kazuo Nakagawa
- Department of Thoracic Surgery, National Cancer Center Hospital, Tokyo, Japan
| | - Yasushi Yatabe
- Department of Diagnostic Pathology, National Cancer Center Hospital, Tokyo, Japan
| | - Masahiko Kusumoto
- Department of Diagnostic Radiology, National Cancer Center Hospital, Tokyo, Japan
| | - Shun-ichi Watanabe
- Department of Thoracic Surgery, National Cancer Center Hospital, Tokyo, Japan
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Matsui T, Takahashi Y, Nakada T, Sugita Y, Seto K, Sakakura N, Mizuno K, Haneda H, Okuda K, Kuroda H. Impact of intrapulmonary tumour location of non-small-cell lung cancer on surgical outcomes for segmentectomy. Eur J Cardiothorac Surg 2024; 65:ezae036. [PMID: 38310338 DOI: 10.1093/ejcts/ezae036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/04/2023] [Revised: 01/02/2024] [Accepted: 01/30/2024] [Indexed: 02/05/2024] Open
Abstract
OBJECTIVES While segmentectomy is considered a viable option for small peripheral non-small-cell lung cancer, its efficacy for central lesions remains uncertain. This study aimed to assess the oncological outcomes of segmentectomy for central lesions compared to peripheral ones. METHODS We retrospectively examined 338 clinical stage IA non-small-cell lung cancer patients who underwent thoracoscopic anatomical segmentectomy at our institution from January 2013 to December 2021. Patients were divided into 2 groups based on intrapulmonary tumour location: inner two-thirds (central group, n = 82) and outer one-third (peripheral group, n = 256). RESULTS The gender, body mass index, performance score, smoking, comorbidities and preoperative pulmonary function were similar in both groups. On computed tomography images, tumour diameter and consolidation-to-tumour ratio were comparable between the groups. The central group had significantly greater tumour-to-pleura distances [mm, 23 (18-27) vs 11 (8-14); P < 0.001], shorter margin distances [mm, 20 (15-20) vs 20 (20-20); P < 0.001] and larger resected lung volumes based on subsegment count [4 (3-6) vs 3 (3-5); P = 0.004] than the peripheral group. Surgery duration, bleeding, hospitalization or drainage period, mortality, readmission and pathological stage were equivalent between the groups. The central group showed significantly more postoperative pleural effusions (5% vs 1%; P = 0.03) than the peripheral group, with no adverse impact on postoperative pulmonary functions. During the follow-up period, local-only recurrence rates were 0% and 8% in the respective groups (Gray test P = 0.07), and total recurrence rates were 6% and 11% (Gray test P = 0.70), with no significant differences. Moreover, no significant inter-group difference in overall survival rates was observed (82% vs 93%; P = 0.15). CONCLUSIONS Segmentectomy may be a promising therapeutic option for early-stage non-small-cell lung cancer located in the inner two-thirds of the parenchyma.
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Affiliation(s)
- Takuya Matsui
- Department of Thoracic Surgery, Aichi Cancer Center, Nagoya, Japan
- Department of Thoracic and Pediatric Surgery, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan
| | - Yusuke Takahashi
- Department of Thoracic Surgery, Aichi Cancer Center, Nagoya, Japan
| | - Takeo Nakada
- Department of Thoracic Surgery, Aichi Cancer Center, Nagoya, Japan
| | - Yusuke Sugita
- Department of Thoracic Surgery, Aichi Cancer Center, Nagoya, Japan
| | - Katsutoshi Seto
- Department of Thoracic Surgery, Aichi Cancer Center, Nagoya, Japan
| | - Noriaki Sakakura
- Department of Thoracic Surgery, Aichi Cancer Center, Nagoya, Japan
| | - Kotaro Mizuno
- Department of Thoracic and Pediatric Surgery, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan
| | - Hiroshi Haneda
- Department of Thoracic and Pediatric Surgery, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan
| | - Katsuhiro Okuda
- Department of Thoracic and Pediatric Surgery, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan
| | - Hiroaki Kuroda
- Department of Thoracic Surgery, Aichi Cancer Center, Nagoya, Japan
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Xue M, Liu J, Li Z, Lu M, Zhang H, Liu W, Tian H. The role of adenocarcinoma subtypes and immunohistochemistry in predicting lymph node metastasis in early invasive lung adenocarcinoma. BMC Cancer 2024; 24:139. [PMID: 38287300 PMCID: PMC10823663 DOI: 10.1186/s12885-024-11843-4] [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: 11/07/2023] [Accepted: 01/04/2024] [Indexed: 01/31/2024] Open
Abstract
BACKGROUND Identifying lymph node metastasis areas during surgery for early invasive lung adenocarcinoma remains challenging. The aim of this study was to develop a nomogram mathematical model before the end of surgery for predicting lymph node metastasis in patients with early invasive lung adenocarcinoma. METHODS In this study, we included patients with invasive lung adenocarcinoma measuring ≤ 2 cm who underwent pulmonary resection with definite pathology at Qilu Hospital of Shandong University from January 2020 to January 2022. Preoperative biomarker results, clinical features, and computed tomography characteristics were collected. The enrolled patients were randomized into a training cohort and a validation cohort in a 7:3 ratio. 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. Recipient operating characteristic (ROC) curves were used to assess the discrimination ability of the model. Calibration capability was assessed using the Hosmer-Lemeshow test and calibration curves. The clinical utility of the nomogram was assessed using decision curve analysis (DCA). RESULTS The overall incidence of lymph node metastasis was 13.23% (61/461). Six indicators were finally determined to be independently associated with lymph node metastasis. These six indicators were: age (P < 0.001), serum amyloid (SA) (P = 0.008); carcinoma antigen 125 (CA125) (P = 0. 042); mucus composition (P = 0.003); novel aspartic proteinase of the pepsin family A (Napsin A) (P = 0.007); and cytokeratin 5/6 (CK5/6) (P = 0.042). The area under the ROC curve (AUC) was 0.843 (95% CI: 0.779-0.908) in the training cohort and 0.838 (95% CI: 0.748-0.927) in the validation cohort. the P-value of the Hosmer-Lemeshow test was 0.0613 in the training cohort and 0.8628 in the validation cohort. the bias of the training cohort corrected C-index was 0.8444 and the bias-corrected C-index for the validation cohort was 0.8375. demonstrating that the prediction model has good discriminative power and good calibration. CONCLUSIONS The column line graphs created showed excellent discrimination and calibration to predict lymph node status in patients with ≤ 2 cm invasive lung adenocarcinoma. In addition, the predictive model has predictive potential before the end of surgery and can inform clinical decision making.
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Affiliation(s)
- Mengchao Xue
- Department of Thoracic Surgery, Qilu Hospital, Shandong University, Lixia District, Jinan City, Shandong Province, China
| | - Junjie Liu
- Department of Thoracic Surgery, Qilu Hospital, Shandong University, Lixia District, Jinan City, Shandong Province, China
| | - Zhenyi Li
- Department of Thoracic Surgery, Qilu Hospital, Shandong University, Lixia District, Jinan City, Shandong Province, China
| | - Ming Lu
- Department of Thoracic Surgery, Qilu Hospital, Shandong University, Lixia District, Jinan City, Shandong Province, China
| | - Huiying Zhang
- Department of Thoracic Surgery, Qilu Hospital, Shandong University, Lixia District, Jinan City, Shandong Province, China
| | - Wen Liu
- Department of Thoracic Surgery, Qilu Hospital, Shandong University, Lixia District, Jinan City, Shandong Province, China
| | - Hui Tian
- Department of Thoracic Surgery, Qilu Hospital, Shandong University, Lixia District, Jinan City, Shandong Province, China.
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Karita R, Suzuki H, Onozato Y, Kaiho T, Inage T, Ito T, Tanaka K, Sakairi Y, Yoshino I. A simple nomogram for predicting occult lymph node metastasis of non-small cell lung cancer from preoperative computed tomography findings, including the volume-doubling time. Surg Today 2024; 54:31-40. [PMID: 37129682 DOI: 10.1007/s00595-023-02695-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 04/10/2023] [Indexed: 05/03/2023]
Abstract
PURPOSE Latent lymph node metastasis is a clinical concern in the surgical treatment of non-small cell lung cancer (NSCLC). The present study identified a simple tool, including the volume-doubling time (VDT), for evaluating the risk of nodal metastasis. METHODS We reviewed, retrospectively, 560 patients who underwent radical resection for cN0M0 NSCLC. The whole tumor VDT and solid component VDT (SVDT) for differentiating the histological type and adenocarcinoma subtype were analyzed and a nomogram was constructed using variables selected through a stepwise selection method. The model was assessed through a calibration curve and decision curve analysis (DCA). RESULTS Lymph node metastases were detected in 89 patients (15.9%). The SVDT tended to be longer in patients with adenocarcinoma (294.5 days, p < 0.0001) than in those with other histological types of NSCLC, but was shorter when the solid/micropapillary component was predominant (127.0 days, p < 0.0001). The selected variables (tumor location, solid component diameter, consolidation tumor ratio, SVDT, and carcinoembryonic antigen) demonstrated significant differences and were used for the nomogram. The calibration curve indicated consistency, and the DCA showed validity across most threshold ranges from 0 to 68%. CONCLUSIONS The established nomogram is a useful tool for the preoperative prediction of lymph node metastasis, and the SVDT was the most influential factor in the nomogram.
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Affiliation(s)
- Ryo Karita
- Department of General Thoracic Surgery, Chiba University Graduate School of Medicine, 1-8-1, Inohana, Chuo-Ku, Chiba, 260-8670, Japan
- Department of Thoracic Surgery, International University of Health and Welfare School of Medicine, Chiba, Japan
| | - Hidemi Suzuki
- Department of General Thoracic Surgery, Chiba University Graduate School of Medicine, 1-8-1, Inohana, Chuo-Ku, Chiba, 260-8670, Japan.
| | - Yuki Onozato
- Department of General Thoracic Surgery, Chiba University Graduate School of Medicine, 1-8-1, Inohana, Chuo-Ku, Chiba, 260-8670, Japan
- Department of Thoracic Surgery, International University of Health and Welfare School of Medicine, Chiba, Japan
| | - Taisuke Kaiho
- Department of General Thoracic Surgery, Chiba University Graduate School of Medicine, 1-8-1, Inohana, Chuo-Ku, Chiba, 260-8670, Japan
| | - Terunaga Inage
- Department of General Thoracic Surgery, Chiba University Graduate School of Medicine, 1-8-1, Inohana, Chuo-Ku, Chiba, 260-8670, Japan
| | - Takamasa Ito
- Department of General Thoracic Surgery, Chiba University Graduate School of Medicine, 1-8-1, Inohana, Chuo-Ku, Chiba, 260-8670, Japan
| | - Kazuhisa Tanaka
- Department of General Thoracic Surgery, Chiba University Graduate School of Medicine, 1-8-1, Inohana, Chuo-Ku, Chiba, 260-8670, Japan
| | - Yuichi Sakairi
- Department of General Thoracic Surgery, Chiba University Graduate School of Medicine, 1-8-1, Inohana, Chuo-Ku, Chiba, 260-8670, Japan
| | - Ichiro Yoshino
- Department of General Thoracic Surgery, Chiba University Graduate School of Medicine, 1-8-1, Inohana, Chuo-Ku, Chiba, 260-8670, Japan
- Department of Thoracic Surgery, International University of Health and Welfare School of Medicine, Chiba, Japan
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Jiang C, Zhang Y, Fu F, Deng P, Chen H. A Shift in Paradigm: Selective Lymph Node Dissection for Minimizing Oversurgery in Early Stage Lung Cancer. J Thorac Oncol 2024; 19:25-35. [PMID: 37748691 DOI: 10.1016/j.jtho.2023.09.1443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Revised: 08/29/2023] [Accepted: 09/17/2023] [Indexed: 09/27/2023]
Abstract
Systematic lymph node dissection has been widely accepted and turned into a standard procedure for lung cancer surgery. In recent years, the concept of "minimal invasive surgery (MIS)" has greatly changed the surgical paradigm of lung cancer. Previous studies revealed that excessive dissection of lymph nodes without metastases had uncertain clinical benefit. Meanwhile, it leads to the elevated risk of postoperative complications including chylothorax and laryngeal nerve injury. In addition, dissection of nonmetastatic lymph nodes may disturb systematic immunity, resulting in the secondary effect on primary tumor or latent metastases. The past decades have witnessed the innovative strategies such as lobe-specific lymph node dissection and selective lymph node dissection. On the basis of evolution of lymph node dissection strategy, we discuss the negative effects of excessive nonmetastatic lymph node dissection and summarize the recent advances in the optimized dissection strategies, hoping to provide unique perspectives on the future directions.
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Affiliation(s)
- Chenyu Jiang
- Department of Thoracic Surgery and State Key Laboratory of Genetic Engineering, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China; Institute of Thoracic Oncology, Fudan University, Shanghai, People's Republic of China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China
| | - Yang Zhang
- Department of Thoracic Surgery and State Key Laboratory of Genetic Engineering, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China; Institute of Thoracic Oncology, Fudan University, Shanghai, People's Republic of China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China
| | - Fangqiu Fu
- Department of Thoracic Surgery and State Key Laboratory of Genetic Engineering, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China; Institute of Thoracic Oncology, Fudan University, Shanghai, People's Republic of China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China
| | - Penghao Deng
- Department of Thoracic Surgery and State Key Laboratory of Genetic Engineering, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China; Institute of Thoracic Oncology, Fudan University, Shanghai, People's Republic of China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China
| | - Haiquan Chen
- Department of Thoracic Surgery and State Key Laboratory of Genetic Engineering, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China; Institute of Thoracic Oncology, Fudan University, Shanghai, People's Republic of China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China.
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Zhong Y, Cai C, Chen T, Gui H, Deng J, Yang M, Yu B, Song Y, Wang T, Sun X, Shi J, Chen Y, Xie D, Chen C, She Y. PET/CT based cross-modal deep learning signature to predict occult nodal metastasis in lung cancer. Nat Commun 2023; 14:7513. [PMID: 37980411 PMCID: PMC10657428 DOI: 10.1038/s41467-023-42811-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2023] [Accepted: 10/20/2023] [Indexed: 11/20/2023] Open
Abstract
Occult nodal metastasis (ONM) plays a significant role in comprehensive treatments of non-small cell lung cancer (NSCLC). This study aims to develop a deep learning signature based on positron emission tomography/computed tomography to predict ONM of clinical stage N0 NSCLC. An internal cohort (n = 1911) is included to construct the deep learning nodal metastasis signature (DLNMS). Subsequently, an external cohort (n = 355) and a prospective cohort (n = 999) are utilized to fully validate the predictive performances of the DLNMS. Here, we show areas under the receiver operating characteristic curve of the DLNMS for occult N1 prediction are 0.958, 0.879 and 0.914 in the validation set, external cohort and prospective cohort, respectively, and for occult N2 prediction are 0.942, 0.875 and 0.919, respectively, which are significantly better than the single-modal deep learning models, clinical model and physicians. This study demonstrates that the DLNMS harbors the potential to predict ONM of clinical stage N0 NSCLC.
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Affiliation(s)
- Yifan Zhong
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Chuang Cai
- School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, Jiangsu, China
| | - Tao Chen
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Hao Gui
- Graduate School at Shenzhen, Tsinghua University, Shenzhen, China
| | - Jiajun Deng
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Minglei Yang
- Department of Thoracic Surgery, Ningbo HwaMei Hospital, Chinese Academy of Sciences, Zhejiang, China
| | - Bentong Yu
- Department of Thoracic Surgery, The First Affiliated Hospital of Nanchang University, Jiangxi, China
| | - Yongxiang Song
- Department of Thoracic Surgery, Affiliated Hospital of Zunyi Medical University, Guizhou, China
| | - Tingting Wang
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Xiwen Sun
- Department of Radiology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Jingyun Shi
- Department of Radiology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Yangchun Chen
- Department of Nuclear Medicine, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Dong Xie
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China.
| | - Chang Chen
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China.
| | - Yunlang She
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China.
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Chung HS, Yoon HI, Hwangbo B, Park EY, Choi CM, Park YS, Lee K, Ji W, Park S, Lee GK, Kim TS, Kim HY, Kim MS, Lee JM. Prediction Models for Mediastinal Metastasis and Its Detection by Endobronchial Ultrasound-Guided Transbronchial Needle Aspiration in Potentially Operable Non-Small Cell Lung Cancer: A Prospective Study. Chest 2023; 164:770-784. [PMID: 37019355 DOI: 10.1016/j.chest.2023.03.041] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 03/15/2023] [Accepted: 03/28/2023] [Indexed: 04/05/2023] Open
Abstract
BACKGROUND Prediction models for mediastinal metastasis and its detection by endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) have not been developed using a prospective cohort of potentially operable patients with non-small cell lung cancer (NSCLC). RESEARCH QUESTION Can mediastinal metastasis and its detection by EBUS-TBNA be predicted with prediction models in NSCLC? STUDY DESIGN AND METHODS For the prospective development cohort, 589 potentially operable patients with NSCLC were evaluated (July 2016-June 2019) from five Korean teaching hospitals. Mediastinal staging was performed using EBUS-TBNA (with or without the transesophageal approach). Surgery was performed for patients without clinical N (cN) 2-3 disease by endoscopic staging. The prediction model for lung cancer staging-mediastinal metastasis (PLUS-M) and a model for mediastinal metastasis detection by EBUS-TBNA (PLUS-E) were developed using multivariable logistic regression analyses. Validation was performed using a retrospective cohort (n = 309) from a different period (June 2019-August 2021). RESULTS The prevalence of mediastinal metastasis diagnosed by EBUS-TBNA or surgery and the sensitivity of EBUS-TBNA in the development cohort were 35.3% and 87.0%, respectively. In PLUS-M, younger age (< 60 years and 60-70 years compared with ≥ 70 years), nonsquamous histology (adenocarcinoma and others), central tumor location, tumor size (> 3-5 cm), cN1 or cN2-3 stage by CT, and cN1 or cN2-3 stage by PET-CT were significant risk factors for N2-3 disease. Areas under the receiver operating characteristic curve (AUCs) for PLUS-M and PLUS-E were 0.876 (95% CI, 0.845-0.906) and 0.889 (95% CI, 0.859-0.918), respectively. Model fit was good (PLUS-M: Hosmer-Lemeshow P = .658, Brier score = 0.129; PLUS-E: Hosmer-Lemeshow P = .569, Brier score = 0.118). In the validation cohort, PLUS-M (AUC, 0.859 [95% CI, 0.817-0.902], Hosmer-Lemeshow P = .609, Brier score = 0.144) and PLUS-E (AUC, 0.900 [95% CI, 0.865-0.936], Hosmer-Lemeshow P = .361, Brier score = 0.112) showed good discrimination ability and calibration. INTERPRETATION PLUS-M and PLUS-E can be used effectively for decision-making for invasive mediastinal staging in NSCLC. TRIAL REGISTRY ClinicalTrials.gov; No.: NCT02991924; URL: www. CLINICALTRIALS gov.
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Affiliation(s)
- Hyun Sung Chung
- Division of Pulmonology, National Cancer Center, Goyang, Gyeonggi, Korea
| | - Ho Il Yoon
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Gyeonggi, Korea
| | - Bin Hwangbo
- Division of Pulmonology, National Cancer Center, Goyang, Gyeonggi, Korea.
| | - Eun Young Park
- Biostatistics Collaboration Team, Research Core Center, National Cancer Center, Goyang, Gyeonggi, Korea
| | - Chang-Min Choi
- Department of Pulmonary and Critical Care Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Young Sik Park
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea
| | - Kyungjong Lee
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Wonjun Ji
- Department of Pulmonary and Critical Care Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Sohee Park
- Department of Health Informatics and Biostatistics, Graduate School of Public Health, Yonsei University, Seoul, Korea
| | - Geon Kook Lee
- Department of Pathology, National Cancer Center, Goyang, Gyeonggi, Korea
| | - Tae Sung Kim
- Department of Nuclear Medicine, National Cancer Center, Goyang, Gyeonggi, Korea
| | - Hyae Young Kim
- Department of Radiology, National Cancer Center, Goyang, Gyeonggi, Korea
| | - Moon Soo Kim
- Department of Thoracic Surgery, National Cancer Center, Goyang, Gyeonggi, Korea
| | - Jong Mog Lee
- Department of Thoracic Surgery, National Cancer Center, Goyang, Gyeonggi, Korea
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10
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Zhang Y, Deng C, Zheng Q, Qian B, Ma J, Zhang C, Jin Y, Shen X, Zang Y, Guo Y, Fu F, Li H, Zheng S, Wu H, Huang Q, Wang S, Liu Q, Ye T, Sun Y, Zhang Y, Xiang J, Hu H, Li Y, Chen H. Selective Mediastinal Lymph Node Dissection Strategy for Clinical T1N0 Invasive Lung Cancer: A Prospective, Multicenter, Clinical Trial. J Thorac Oncol 2023; 18:931-939. [PMID: 36841542 DOI: 10.1016/j.jtho.2023.02.010] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 01/30/2023] [Accepted: 02/11/2023] [Indexed: 02/27/2023]
Abstract
INTRODUCTION We aimed to prospectively evaluate our previously proposed selective mediastinal lymph node (LN) dissection strategy for peripheral clinical T1N0 invasive NSCLC. METHODS This is a multicenter, prospective clinical trial in China. We set six criteria for predicting negative LN stations and finally guiding selective LN dissection. Consolidation tumor ratio less than or equal to 0.5, segment location, lepidic-predominant adenocarcinoma (LPA), negative hilar nodes (stations 10-12), and negative visceral pleural invasion (VPI) were used separately or in combination as predictors of negative LN status in the whole, superior, or inferior mediastinal zone. LPA, hilar node involvement, and VPI were diagnosed intraoperatively. All patients actually underwent systematic mediastinal LN dissection. The primary end point was the accuracy of the strategy in predicting LN involvement. If LN metastasis occurred in certain mediastinal zone that was predicted to be negative, it was considered as an "inaccurate" case. RESULTS A total of 720 patients were enrolled. The median number of LN dissected was 15 (interquartile range: 11-20). All negative node status in certain mediastinal zone was correctly predicted by the strategy. Compared with final pathologic findings, the accuracy of frozen section to diagnose LPA, VPI, and hilar node metastasis was 94.0%, 98.9%, and 99.6%, respectively. Inaccurate intraoperative diagnosis of LPA, VPI, or hilar node metastasis did not lead to inaccurate prediction of node-negative status. CONCLUSIONS This is the first prospective trial validating the specific mediastinal LN metastasis pattern in cT1N0 invasive NSCLC, which provides important evidence for clinical applications of selective LN dissection strategy.
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Affiliation(s)
- Yang Zhang
- Department of Thoracic Surgery and State Key Laboratory of Genetic Engineering, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China; Institute of Thoracic Oncology, Fudan University, Shanghai, People's Republic of China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China
| | - Chaoqiang Deng
- Department of Thoracic Surgery and State Key Laboratory of Genetic Engineering, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China; Institute of Thoracic Oncology, Fudan University, Shanghai, People's Republic of China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China
| | - Qiang Zheng
- Institute of Thoracic Oncology, Fudan University, Shanghai, People's Republic of China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China; Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China
| | - Bin Qian
- Department of Thoracic Surgery, Jiang du People's Hospital of Yangzhou City, Jiangsu, People's Republic of China
| | - Junjie Ma
- Department of Thoracic Surgery, The Second People's Hospital of Liaocheng, The Second Hospital of Liaocheng Affiliated to Shandong First Medical University, Shandong, People's Republic of China
| | - Chunyang Zhang
- Department of Thoracic Surgery, Jiang du People's Hospital of Yangzhou City, Jiangsu, People's Republic of China
| | - Yan Jin
- Institute of Thoracic Oncology, Fudan University, Shanghai, People's Republic of China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China; Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China
| | - Xuxia Shen
- Institute of Thoracic Oncology, Fudan University, Shanghai, People's Republic of China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China; Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China
| | - Yibing Zang
- Department of Thoracic Surgery, The Second People's Hospital of Liaocheng, The Second Hospital of Liaocheng Affiliated to Shandong First Medical University, Shandong, People's Republic of China
| | - Yufeng Guo
- Department of Thoracic Surgery, The Second People's Hospital of Liaocheng, The Second Hospital of Liaocheng Affiliated to Shandong First Medical University, Shandong, People's Republic of China
| | - Fangqiu Fu
- Department of Thoracic Surgery and State Key Laboratory of Genetic Engineering, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China; Institute of Thoracic Oncology, Fudan University, Shanghai, People's Republic of China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China
| | - Hang Li
- Department of Thoracic Surgery and State Key Laboratory of Genetic Engineering, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China; Institute of Thoracic Oncology, Fudan University, Shanghai, People's Republic of China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China
| | - Shanbo Zheng
- Department of Thoracic Surgery and State Key Laboratory of Genetic Engineering, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China; Institute of Thoracic Oncology, Fudan University, Shanghai, People's Republic of China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China
| | - Haoxuan Wu
- Department of Thoracic Surgery and State Key Laboratory of Genetic Engineering, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China; Institute of Thoracic Oncology, Fudan University, Shanghai, People's Republic of China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China
| | - Qingyuan Huang
- Department of Thoracic Surgery and State Key Laboratory of Genetic Engineering, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China; Institute of Thoracic Oncology, Fudan University, Shanghai, People's Republic of China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China
| | - Shengping Wang
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China; Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China
| | - Quan Liu
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China; Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China
| | - Ting Ye
- Department of Thoracic Surgery and State Key Laboratory of Genetic Engineering, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China; Institute of Thoracic Oncology, Fudan University, Shanghai, People's Republic of China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China
| | - Yihua Sun
- Department of Thoracic Surgery and State Key Laboratory of Genetic Engineering, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China; Institute of Thoracic Oncology, Fudan University, Shanghai, People's Republic of China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China
| | - Yawei Zhang
- Department of Thoracic Surgery and State Key Laboratory of Genetic Engineering, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China; Institute of Thoracic Oncology, Fudan University, Shanghai, People's Republic of China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China
| | - Jiaqing Xiang
- Department of Thoracic Surgery and State Key Laboratory of Genetic Engineering, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China; Institute of Thoracic Oncology, Fudan University, Shanghai, People's Republic of China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China
| | - Hong Hu
- Department of Thoracic Surgery and State Key Laboratory of Genetic Engineering, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China; Institute of Thoracic Oncology, Fudan University, Shanghai, People's Republic of China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China
| | - Yuan Li
- Institute of Thoracic Oncology, Fudan University, Shanghai, People's Republic of China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China; Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China
| | - Haiquan Chen
- Department of Thoracic Surgery and State Key Laboratory of Genetic Engineering, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China; Institute of Thoracic Oncology, Fudan University, Shanghai, People's Republic of China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, People's Republic of China.
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11
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Huang Y, Jiang X, Xu H, Zhang D, Liu LN, Xia YX, Xu DK, Wu HJ, Cheng G, Shi YH. Preoperative prediction of mediastinal lymph node metastasis in non-small cell lung cancer based on 18F-FDG PET/CT radiomics. Clin Radiol 2023; 78:8-17. [PMID: 36192203 DOI: 10.1016/j.crad.2022.08.140] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 07/14/2022] [Accepted: 08/26/2022] [Indexed: 01/07/2023]
Abstract
AIM To establish and verify a 2-[18F]-fluoro-2-deoxy-d-glucose (FDG) positron-emission tomography (PET)/computed tomography (CT)-based radiomics nomogram to predict mediastinal lymph node metastasis (LNM) in non-small cell lung cancer (NSCLC) patients preoperatively. MATERIALS AND METHODS This retrospective study enrolled 155 NSCLC patients (primary cohort, n=93; validation cohort, n=62). For each patient, 2,704 radiomic features were extracted from the primary lung cancer regions. Four procedures including the Mann-Whitney U-test, Spearman's correlation analysis, minimum redundancy-maximum relevance (mRMR), and least absolute shrinkage and selection operator (LASSO) binary logistic regression were utilised for determining essential features and establishing a radiomics signature. After that, a nomogram was established. The nomogram's potential was assessed based on its discrimination, calibration, and clinical usefulness. The radiomics signature and nomogram predictive performances were evaluated with respect to the area under the receiver operating characteristic curve (AUC), specificity, accuracy, and sensitivity. RESULTS The radiomics signature composed of eight selected features had good discriminatory performance of LNM versus non-LNM groups an AUC of 0.851 and 0.826 in primary and validation cohorts, respectively. The nomogram also indicated good discrimination with an AUC of 0.869 and 0.847 in the primary and validation cohorts, respectively. Furthermore, good calibration was demonstrated utilising the nomogram. CONCLUSIONS An 18F-FDG PET/CT-based radiomics nomogram that integrates the radiomics signature and age was promoted to predict mediastinal LNM within NSCLC patients, which could potentially facilitate individualised therapy for mediastinal LNM before treatment. The nomogram was beneficial in clinical practice, as illustrated by decision curve analysis.
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Affiliation(s)
- Y Huang
- Department of Nuclear Medicine, The Second Affiliated Hospital of Chengdu Medical College, China National Nuclear Corporation 416 Hospital, Chengdu, Sichuan, China
| | - X Jiang
- Department of Nuclear Medicine, The Second Affiliated Hospital of Chengdu Medical College, China National Nuclear Corporation 416 Hospital, Chengdu, Sichuan, China
| | - H Xu
- Department of Radiology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - D Zhang
- Department of Nuclear Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - L-N Liu
- Department of Nuclear Medicine, The Second Affiliated Hospital of Chengdu Medical College, China National Nuclear Corporation 416 Hospital, Chengdu, Sichuan, China
| | - Y-X Xia
- Department of Nuclear Medicine, The Second Affiliated Hospital of Chengdu Medical College, China National Nuclear Corporation 416 Hospital, Chengdu, Sichuan, China
| | - D-K Xu
- Department of Nuclear Medicine, The Second Affiliated Hospital of Chengdu Medical College, China National Nuclear Corporation 416 Hospital, Chengdu, Sichuan, China
| | - H-J Wu
- Department of Nuclear Medicine, The Second Affiliated Hospital of Chengdu Medical College, China National Nuclear Corporation 416 Hospital, Chengdu, Sichuan, China
| | - G Cheng
- Department of Nuclear Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
| | - Y-H Shi
- Department of Nuclear Medicine, The Second Affiliated Hospital of Chengdu Medical College, China National Nuclear Corporation 416 Hospital, Chengdu, Sichuan, China.
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12
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Zhang H, Liao M, Guo Q, Chen J, Wang S, Liu S, Xiao F. Predicting N2 lymph node metastasis in presurgical stage I-II non-small cell lung cancer using multiview radiomics and deep learning method. Med Phys 2022; 50:2049-2060. [PMID: 36563341 DOI: 10.1002/mp.16177] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 11/07/2022] [Accepted: 12/11/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Accurate diagnosis of N2 lymph node status of the resectable stage I-II non-small cell lung cancer (NSCLC) before surgery is crucial, while there is lack of corresponding method clinically. PURPOSE To develop and validate a model to quantitively predict the N2 lymph node metastasis in presurgical clinical stage I-II NSCLC using multiview radiomics and deep learning method. METHODS In this study, 140 NSCLC patients were enrolled and randomly divided into training and test sets. Univariate and multiple analysis method were used step by step to establish the clinical model; Then a multiview radiomics modeling scheme was designed, in which the optimal input feature set was determined by subcategorizing radiomics features (C1: original; C2: LoG and C3: wavelet) and comparison of corresponding radiomics model. The minimum-redundancy maximum-relevance (mRMR) selection and the least absolute shrinkage and selection operator (LASSO) algorithm were used for the feature selection and construction of each radiomics model (Rad). Next, an end-to-end ResNet18 architecture and transfer learning techniques were designed to construct a deep learning model (DL). Subsequently, the screened clinical risk factors and constructed Rad and DL models were combined and compared and a nomogram was constructed. Finally, the diagnostic performance of all constructed models were evaluated and compared using receiver operating characteristic curve (ROC) analysis, Delong test, Calibration analysis, Hosmer-Lemeshow test, and decision curves, respectively. RESULTS Carcinoma embryonic antigen (CEA) level and spiculation were screened to make up the Clinical model, while seven radiomics features in the optimal input feature set C2 + C3 were selected to construct the Rad. DL was constructed by training on 1.8 million natural images and small sample data of our N2 lymph node volume of interest (VOI) images. Except for the Clinical model, all other models showed good predictive accuracy and consistency in both training set and test set. DL (area under curve (AUC): 0.83) was better than Rad (AUC: 0.76) in predictive accuracy, but their difference was not significant (p = 0.45). The combined models showed better diagnostic performance than the model only clinical or image risk factors were used (AUC for Clinical, Rad + DL, Rad + Clinical, DL + Clinical, and Rad + DL + Clinical were respectively 0.66, 0.86, 0.82, 0.86, and 0.88). Finally, the Rad + DL + Clinical model with the best diagnostic performance was selected to draw the final nomogram for clinical use. CONCLUSION This study proposes a nomogram based on multiview radiomics, deep learning, and clinical features that can be efficiently used to quantitively predict presurgical N2 diseases in patients with clinical stage I-II NSCLC.
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Affiliation(s)
- Hanfei Zhang
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Meiyan Liao
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | | | - Jun Chen
- Wuhan GE Healthcare, Wuhan, China
| | - Shan Wang
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Songmei Liu
- Department of Clinical Laboratory, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Feng Xiao
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
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13
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Zhou Y, Du J, Ma C, Zhao F, Li H, Ping G, Wang W, Luo J, Chen L, Zhang K, Zhang S. Mathematical models for intraoperative prediction of metastasis to regional lymph nodes in patients with clinical stage I non-small cell lung cancer. Medicine (Baltimore) 2022; 101:e30362. [PMID: 36281188 PMCID: PMC9592279 DOI: 10.1097/md.0000000000030362] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
It remains challenging to determine the regions of metastasis to lymph nodes during operation for clinical stage I non-small cell lung cancer (NSCLC). This study aimed to establish intraoperative mathematical models with nomograms for predicting the hilar-intrapulmonary node metastasis (HNM) and the mediastinal node metastasis (MNM) in patients with clinical stage I NSCLC. The clinicopathological variables of 585 patients in a derivation cohort who underwent thoracoscopic lobectomy with complete lymph node dissection were retrospectively analyzed for their association with the HNM or the MNM. After analyzing the variables, we developed multivariable logistic models with nomograms to estimate the risk of lymph node metastasis in different regions. The predictive efficacy was then validated in a validation cohort of 418 patients. It was confirmed that carcinoembryonic antigen (>5.75 ng/mL), CYFRA211 (>2.85 ng/mL), the maximum diameter of tumor (>2.75 cm), tumor differentiation (grade III), bronchial mucosa and cartilage invasion, and vascular invasion were predictors of HNM, and carcinoembryonic antigen (>8.25 ng/mL), CYFRA211 (>2.95 ng/mL), the maximum diameter of tumor (>2.75 cm), tumor differentiation (grade III), bronchial mucosa and cartilage invasion, vascular invasion, and visceral pleural invasion were predictors of MNM. The validation of the prediction models based on the above results demonstrated good discriminatory power. Our predictive models are helpful in the decision-making process of specific therapeutic strategies for the regional lymph node metastasis in patients with clinical stage I NSCLC.
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Affiliation(s)
- Yue Zhou
- Department of Thoracic and Cardiovascular Surgery, the First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Junjie Du
- Department of Thoracic and Cardiovascular Surgery, the First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Changhui Ma
- Department of Thoracic and Cardiovascular Surgery, the First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Fei Zhao
- Department of Thoracic and Cardiovascular Surgery, the First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Hai Li
- Department of Pathology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Guoqiang Ping
- Department of Pathology, the First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Wei Wang
- Department of Thoracic and Cardiovascular Surgery, the First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Jinhua Luo
- Department of Thoracic and Cardiovascular Surgery, the First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Liang Chen
- Department of Thoracic and Cardiovascular Surgery, the First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Kai Zhang
- Pancreas Center, the First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Shijiang Zhang
- Department of Thoracic and Cardiovascular Surgery, the First Affiliated Hospital of Nanjing Medical University, Nanjing, China
- *Correspondence: Shijiang Zhang, No. 300 Guangzhou Road, Nanjing 210029, China (e-mail: )
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14
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Yuan H, Zou Y, Gao Y, Zhang S, Zheng X, You X. Correlation analysis between unenhanced and enhanced CT radiomic features of lung cancers presenting as solid nodules and their efficacy for predicting hilar and mediastinal lymph node metastases. FRONTIERS IN RADIOLOGY 2022; 2:911179. [PMID: 37492652 PMCID: PMC10365119 DOI: 10.3389/fradi.2022.911179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Accepted: 09/21/2022] [Indexed: 07/27/2023]
Abstract
Objectives If hilar and mediastinal lymph node metastases occur in solid nodule lung cancer is critical for tumor staging, which determines the treatment strategy and prognosis of patients. We aimed to develop an effective model to predict hilar and mediastinal lymph node metastases by using texture features of solid nodule lung cancer. Methods Two hundred eighteen patients with solid nodules on CT images were analyzed retrospectively. The 3D tumors were delineated using ITK-SNAP software. Radiomics features were extracted from unenhanced and enhanced CT images based on AK software. Correlations between radiomics features of unenhanced and enhanced CT images were analyzed with Spearman rank correlation analysis. According to pathological findings, the patients were divided into no lymph node metastasis group and lymph node metastasis group. All patients were randomly divided into training group and test group at a ratio of 7:3. Valuable features were selected. Multivariate logistic regression was used to build predictive models. Two predictive models were established with unenhanced and enhanced CT images. ROC analysis was used to estimate the predictive efficiency of the models. Results A total of 7 categories of features, including 107 features, were extracted. There was a high correlation between the 7 categories of features from unenhanced CT images and enhanced CT images (all r > 0.7, p < 0.05). Among them, the shape features had the strongest correlation (mean r = 0.98). There were 5 features in the enhanced model and the unenhanced model, which had important predicting significance. The AUCs were 0.811 and 0.803, respectively. There was no significant difference in the predictive performance of the two models (DeLong's test, p = 0.05). Conclusion Our study models achieved higher accuracy for predicting hilar and mediastinal lymph node metastasis of solid nodule lung cancer and have some value in promoting the staging accuracy of lung cancer. Our results show that CT radiomics features have potential to predict hilar and mediastinal lymph node metastases in solid nodular lung cancer. In addition, enhanced and unenhanced CT radiomics models had comparable predictive power in predicting hilar and mediastinal lymph node metastases.
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Affiliation(s)
- Huanchu Yuan
- Department of Radiology, Dongguan People’s Hospital, Dongguan, China
| | - Yujian Zou
- Department of Radiology, Dongguan People’s Hospital, Dongguan, China
| | - Yun Gao
- Department of Radiology, Dongguan People’s Hospital, Dongguan, China
| | - Shihao Zhang
- Department of Pathology, Dongguan People’s Hospital, Dongguan, China
| | - Xiaolin Zheng
- Department of Radiology, Dongguan People’s Hospital, Dongguan, China
| | - Xiaoting You
- Department of Radiology, Dongguan People’s Hospital, Dongguan, China
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15
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Construction and Evaluation of a Preoperative Prediction Model for Lymph Node Metastasis of cIA Lung Adenocarcinoma Using Random Forest. JOURNAL OF ONCOLOGY 2022; 2022:4008113. [PMID: 36199801 PMCID: PMC9527416 DOI: 10.1155/2022/4008113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 08/24/2022] [Accepted: 09/13/2022] [Indexed: 11/18/2022]
Abstract
Background Lymph node metastasis (LNM) is the main route of metastasis in lung adenocarcinoma (LA), and preoperative prediction of LNM in early LA is key for accurate medical treatment. We aimed to establish a preoperative prediction model of LNM of early LA through clinical data mining to reduce unnecessary lymph node dissection, reduce surgical injury, and shorten the operation time. Methods We retrospectively collected imaging data and clinical features of 1121 patients with early LA who underwent video-assisted thoracic surgery at the First Hospital of China Medical University from 2004 to 2021. Logistic regression analysis was used to select variables and establish the preoperative diagnosis model using random forest classifier (RFC). The prediction results from the test set were used to evaluate the prediction performance of the model. Results Combining the results of logistic analysis and practical clinical application experience, nine clinical features were included. In the random forest classifier model, when the number of nodes was three and the n-tree value is 500, we obtained the best prediction model (accuracy = 0.9769), with a positive prediction rate of 90% and a negative prediction rate of 98.69%. Conclusion We established a preoperative prediction model for LNM of early LA using a machine learning random forest method combined with clinical and imaging features. More excellent predictors may be obtained by refining imaging features.
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Huang J, Bian C, Zhang W, Mu G, Chen Z, Xia Y, Yuan M, Ujiie H, Daemen JHT, de Loos ER, Zhu Q, Wu W, Chen L, Wang J. Partitioning the lung field based on the depth ratio in three-dimensional space. Transl Lung Cancer Res 2022; 11:1165-1175. [PMID: 35832440 PMCID: PMC9271427 DOI: 10.21037/tlcr-22-391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 06/17/2022] [Indexed: 11/11/2022]
Abstract
Background To explore the feasibility of the depth ratio method partitioning the lung parenchyma and the depth distribution of lung nodules in pulmonary segmentectomy. Methods Based on the measurement units, patients were allocated to the chest group, the lobar group, and the symmetrical 3 sectors group. In each unit, the center of the respective bronchial cross-section was set as the starting point (O). Connecting the O point with the center of the lesion (A) and extending to the endpoint (B) on the pleural, the radial line (OB) was trisected to divide the outer, middle, and inner regions. The depth ratio and relevant regional distribution were simultaneously verified using 2-dimensional (2D) coronal, sagittal, and axial computed tomography images and 3-dimensional (3D) reconstruction images. Results Two hundred and nine patients were included in this study. The median age was 53 (IQR, 44.5–62) years and 64 were males. The intra-group consistency of the depth ratio region partition was 100%. The consistency of the inter-group region partition differed among the three groups (Kappa values 0.511, 0.517, and 0.923). The chest group, lobar group, and symmetrical 3 sectors group had 69.4%, 26.3%, and 4.8% mediastinum disturbance, respectively (P<0.001). Conclusions The depth ratio method in the symmetrical 3 sectors of the lung maximally eliminated the disturbance of the mediastinal structures and more accurately trisected the lung parenchymal in 3D space. Sublobar resection based on subsegments strategy is feasible for outer 2/3 pulmonary nodules when depth ratio is used as the measurement method.
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Affiliation(s)
- Jingjing Huang
- Department of Thoracic Surgery, Jiangsu Province Hospital and The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Chengyu Bian
- Department of Thoracic Surgery, Jiangsu Province Hospital and The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Wenhao Zhang
- Department of Thoracic Surgery, Jiangsu Province Hospital and The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Guang Mu
- Department of Thoracic Surgery, Jiangsu Province Hospital and The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Zhipeng Chen
- Department of Thoracic Surgery, Jiangsu Province Hospital and The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Yang Xia
- Department of Thoracic Surgery, Jiangsu Province Hospital and The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Mei Yuan
- Department of Radiology, Jiangsu Province Hospital and The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Hideki Ujiie
- Department of Cardiovascular and Thoracic Surgery, Hokkaido University, Hokkaido, Japan
| | - Jean H T Daemen
- Department of Surgery, Division of General Thoracic Surgery, Zuyderland Medical Center, Heerlen, The Netherlands
| | - Erik R de Loos
- Department of Surgery, Division of General Thoracic Surgery, Zuyderland Medical Center, Heerlen, The Netherlands
| | - Quan Zhu
- Department of Thoracic Surgery, Jiangsu Province Hospital and The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Weibing Wu
- Department of Thoracic Surgery, Jiangsu Province Hospital and The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Liang Chen
- Department of Thoracic Surgery, Jiangsu Province Hospital and The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Jun Wang
- Department of Thoracic Surgery, Jiangsu Province Hospital and The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
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Kim H, Choi H, Lee KH, Cho S, Park CM, Kim YT, Goo JM. Definitions of Central Tumors in Radiologically Node-Negative, Early-Stage Lung Cancer for Preoperative Mediastinal Lymph Node Staging: A Dual-Institution, Multireader Study. Chest 2022; 161:1393-1406. [PMID: 34785237 DOI: 10.1016/j.chest.2021.11.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Revised: 10/25/2021] [Accepted: 11/01/2021] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND Definitions for central lung cancer (CLC) have been ambiguous in guidelines, causing difficulty in selecting candidates for invasive mediastinal staging among patients with radiologically node-negative, early-stage lung cancer. RESEARCH QUESTION What is the optimal definition for CLC that is robust to interreader and institutional variation to select candidates for invasive mediastinal staging among those with clinical T1N0M0 lung cancer? STUDY DESIGN AND METHODS Two retrospective cohorts were evaluated for the associations of central lung cancer according to 13 definitions based on chest CT scan with occult nodal metastasis. Univariate and multivariate ordinal logistic regression analyses were performed with the pathologic N category as an ordinal outcome. Robust definitions, which retained statistical significance across multireader, dual-institutional datasets, were identified. For these definitions, binary diagnostic performance and interreader agreement were investigated. RESULTS In the two cohorts, 807 patients (median age, 63 years; interquartile range [IQR], 56-71 years; 410 women; 33 pN1, 48 pN2, and 1 pN3) and 510 patients (median age, 65 years; IQR, 58-71 years; 267 women; 33 pN1, 20 pN2, and no pN3) were included, respectively. Three definitions robust to interreader variation and dataset heterogeneity were identified: definition 7 (concentric lines arising from the midline, inner one-third, medial margin; adjusted OR, 2.01; 95% CI, 1.13-3.51; P = .02), definition 10 (location index-based inner one-third, center; adjusted OR, 3.60; 95% CI, 1.49-8.25; P = .003), and definition 12 (location index-based inner one-third, medial margin; adjusted OR, 3.57; 95% CI, 1.91-6.52; P < .001). Definition 12 showed higher interreader agreement than definition 7 (Cohen κ, 0.80 vs 0.66; P = .005). Nevertheless, the sensitivity and positive predictive value of the three definitions were < 50%. INTERPRETATION Three definitions exhibited robust associations with occult nodal metastasis. However, selecting candidates for invasive mediastinal staging solely based on a central tumor location would be suboptimal.
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Affiliation(s)
- Hyungjin Kim
- Department of Radiology, Seoul National University Hospital, Seoul, South Korea; Department of Radiology, Seoul National University College of Medicine, Seoul, South Korea
| | - Hyewon Choi
- Department of Radiology, Chung-Ang University Hospital, Seoul, South Korea
| | - Kyung Hee Lee
- Department of Radiology, Seoul National University College of Medicine, Seoul, South Korea; Department of Radiology, Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, South Korea.
| | - Sukki Cho
- Department of Thoracic and Cardiovascular Surgery, Seoul National University College of Medicine, Seoul, South Korea; Department of Thoracic and Cardiovascular Surgery, Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, South Korea
| | - Chang Min Park
- Department of Radiology, Seoul National University Hospital, Seoul, South Korea; Department of Radiology, Seoul National University College of Medicine, Seoul, South Korea; Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, South Korea; Cancer Research Institute, Seoul National University, Seoul, South Korea
| | - Young Tae Kim
- Department of Thoracic and Cardiovascular Surgery, Seoul National University Hospital, Seoul, South Korea; Department of Thoracic and Cardiovascular Surgery, Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, South Korea
| | - Jin Mo Goo
- Department of Radiology, Seoul National University Hospital, Seoul, South Korea; Department of Radiology, Seoul National University College of Medicine, Seoul, South Korea; Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, South Korea; Cancer Research Institute, Seoul National University, Seoul, South Korea
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Zhang R, Zhang R, Luan T, Liu B, Zhang Y, Xu Y, Sun X, Xing L. A Radiomics Nomogram for Preoperative Prediction of Clinical Occult Lymph Node Metastasis in cT1-2N0M0 Solid Lung Adenocarcinoma. Cancer Manag Res 2021; 13:8157-8167. [PMID: 34737644 PMCID: PMC8560059 DOI: 10.2147/cmar.s330824] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 09/30/2021] [Indexed: 12/12/2022] Open
Abstract
Background Clinical occult lymph node metastasis (cOLNM) means that the lymph node is negatively diagnosed by preoperative computed tomography (CT), but has been proven to be positive by postoperative pathology. The aim of this study was to establish and validate a nomogram based on radiomics features for the preoperative prediction of cOLNM in early-stage solid lung adenocarcinoma patients. Methods A total of 244 patients with clinical T1-2N0M0 solid lung adenocarcinoma who underwent preoperative contrast-enhanced chest CT were divided into a primary group (n = 160) and an independent validation group from another hospital (n = 84). The records of 851 radiomics features of each primary tumor were extracted. LASSO analysis was used to reduce the data dimensionality and select features. Multivariable logistic regression was utilized to identify independent predictors of cOLNM and develop a predictive nomogram. The performance of the predictive model was assessed by its calibration and discrimination. Decision curve analysis (DCA) was performed to estimate the clinical usefulness of the nomogram. Results The predictive model consisted of a clinical factor (CT-reported tumor size) and a radiomics feature (Rad-score). The nomogram presented good discrimination, with a C-index of 0.782 (95% CI, 0.768–0.796) in the primary cohort and 0.813 (95% CI, 0.787–0.839) in the validation cohort, and good calibration. DCA showed that the radiomics nomogram was clinically useful. Conclusion This study develops and validates a nomogram that incorporates clinical and radiomics factors. It can be tailored for the individualized preoperative prediction of cOLNM in early-stage solid lung adenocarcinoma patients.
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Affiliation(s)
- Ran Zhang
- Department of Radiation Oncology, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, People's Republic of China.,Tongji University, Shanghai, People's Republic of China
| | - Ranran Zhang
- Department of Medical Imaging, Linyi Cancer Hospital, Linyi, Shandong, People's Republic of China
| | - Ting Luan
- Department of Graduate, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, People's Republic of China.,Department of Nuclear Medicine, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, People's Republic of China
| | - Biwei Liu
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, People's Republic of China
| | - Yimei Zhang
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, People's Republic of China
| | - Yaping Xu
- Department of Radiation Oncology, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, People's Republic of China
| | - Xiaorong Sun
- Department of Nuclear Medicine, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, People's Republic of China
| | - Ligang Xing
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, People's Republic of China
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Zhong Y, She Y, Deng J, Chen S, Wang T, Yang M, Ma M, Song Y, Qi H, Wang Y, Shi J, Wu C, Xie D, Chen C. Deep Learning for Prediction of N2 Metastasis and Survival for Clinical Stage I Non-Small Cell Lung Cancer. Radiology 2021; 302:200-211. [PMID: 34698568 DOI: 10.1148/radiol.2021210902] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Background Preoperative mediastinal staging is crucial for the optimal management of clinical stage I non-small cell lung cancer (NSCLC). Purpose To develop a deep learning signature for N2 metastasis prediction and prognosis stratification in clinical stage I NSCLC. Materials and Methods In this retrospective study conducted from May 2020 to October 2020 in a population with clinical stage I NSCLC, an internal cohort was adopted to establish a deep learning signature. Subsequently, the predictive efficacy and biologic basis of the proposed signature were investigated in an external cohort. A multicenter diagnostic trial (registration number: ChiCTR2000041310) was also performed to evaluate its clinical utility. Finally, on the basis of the N2 risk scores, the instructive significance of the signature in prognostic stratification was explored. The diagnostic efficiency was quantified with the area under the receiver operating characteristic curve (AUC), and the survival outcomes were assessed using the Cox proportional hazards model. Results A total of 3096 patients (mean age ± standard deviation, 60 years ± 9; 1703 men) were included in the study. The proposed signature achieved AUCs of 0.82, 0.81, and 0.81 in an internal test set (n = 266), external test cohort (n = 133), and prospective test cohort (n = 300), respectively. In addition, higher deep learning scores were associated with a lower frequency of EGFR mutation (P = .04), higher rate of ALK fusion (P = .02), and more activation of pathways of tumor proliferation (P < .001). Furthermore, in the internal test set and external cohort, higher deep learning scores were predictive of poorer overall survival (adjusted hazard ratio, 2.9; 95% CI: 1.2, 6.9; P = .02) and recurrence-free survival (adjusted hazard ratio, 3.2; 95% CI: 1.4, 7.4; P = .007). Conclusion The deep learning signature could accurately predict N2 disease and stratify prognosis in clinical stage I non-small cell lung cancer. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Park and Lee in this issue.
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Affiliation(s)
- Yifan Zhong
- From the Departments of Thoracic Surgery (Y.Z., Y. She, J.D., D.X., C.C.), Radiology (T.W., J.S.), and Pathology (C.W.), Shanghai Pulmonary Hospital, Tongji University School of Medicine, 507 Zhengmin Rd, Shanghai 200433, China; Department of Computer Science and Technology, College of Electronics and Information Engineering, Tongji University, Shanghai, China (S.C., H.Q., Y.W.); Department of Thoracic Surgery, Ningbo No. 2 Hospital, Chinese Academy of Sciences, Zhejiang, China (M.Y.); Department of Thoracic Surgery, The First Hospital of Lanzhou University, Gansu, China (M.M., C.C.); The International Science and Technology Cooperation Base for Development and Application of Key Technologies in Thoracic Surgery, Gansu Province, China (M.M., C.C.); and Department of Thoracic Surgery, Affiliated Hospital of Zunyi Medical College, Zunyi Medical College, Guizhou, China (Y. Song)
| | - Yunlang She
- From the Departments of Thoracic Surgery (Y.Z., Y. She, J.D., D.X., C.C.), Radiology (T.W., J.S.), and Pathology (C.W.), Shanghai Pulmonary Hospital, Tongji University School of Medicine, 507 Zhengmin Rd, Shanghai 200433, China; Department of Computer Science and Technology, College of Electronics and Information Engineering, Tongji University, Shanghai, China (S.C., H.Q., Y.W.); Department of Thoracic Surgery, Ningbo No. 2 Hospital, Chinese Academy of Sciences, Zhejiang, China (M.Y.); Department of Thoracic Surgery, The First Hospital of Lanzhou University, Gansu, China (M.M., C.C.); The International Science and Technology Cooperation Base for Development and Application of Key Technologies in Thoracic Surgery, Gansu Province, China (M.M., C.C.); and Department of Thoracic Surgery, Affiliated Hospital of Zunyi Medical College, Zunyi Medical College, Guizhou, China (Y. Song)
| | - Jiajun Deng
- From the Departments of Thoracic Surgery (Y.Z., Y. She, J.D., D.X., C.C.), Radiology (T.W., J.S.), and Pathology (C.W.), Shanghai Pulmonary Hospital, Tongji University School of Medicine, 507 Zhengmin Rd, Shanghai 200433, China; Department of Computer Science and Technology, College of Electronics and Information Engineering, Tongji University, Shanghai, China (S.C., H.Q., Y.W.); Department of Thoracic Surgery, Ningbo No. 2 Hospital, Chinese Academy of Sciences, Zhejiang, China (M.Y.); Department of Thoracic Surgery, The First Hospital of Lanzhou University, Gansu, China (M.M., C.C.); The International Science and Technology Cooperation Base for Development and Application of Key Technologies in Thoracic Surgery, Gansu Province, China (M.M., C.C.); and Department of Thoracic Surgery, Affiliated Hospital of Zunyi Medical College, Zunyi Medical College, Guizhou, China (Y. Song)
| | - Shouyu Chen
- From the Departments of Thoracic Surgery (Y.Z., Y. She, J.D., D.X., C.C.), Radiology (T.W., J.S.), and Pathology (C.W.), Shanghai Pulmonary Hospital, Tongji University School of Medicine, 507 Zhengmin Rd, Shanghai 200433, China; Department of Computer Science and Technology, College of Electronics and Information Engineering, Tongji University, Shanghai, China (S.C., H.Q., Y.W.); Department of Thoracic Surgery, Ningbo No. 2 Hospital, Chinese Academy of Sciences, Zhejiang, China (M.Y.); Department of Thoracic Surgery, The First Hospital of Lanzhou University, Gansu, China (M.M., C.C.); The International Science and Technology Cooperation Base for Development and Application of Key Technologies in Thoracic Surgery, Gansu Province, China (M.M., C.C.); and Department of Thoracic Surgery, Affiliated Hospital of Zunyi Medical College, Zunyi Medical College, Guizhou, China (Y. Song)
| | - Tingting Wang
- From the Departments of Thoracic Surgery (Y.Z., Y. She, J.D., D.X., C.C.), Radiology (T.W., J.S.), and Pathology (C.W.), Shanghai Pulmonary Hospital, Tongji University School of Medicine, 507 Zhengmin Rd, Shanghai 200433, China; Department of Computer Science and Technology, College of Electronics and Information Engineering, Tongji University, Shanghai, China (S.C., H.Q., Y.W.); Department of Thoracic Surgery, Ningbo No. 2 Hospital, Chinese Academy of Sciences, Zhejiang, China (M.Y.); Department of Thoracic Surgery, The First Hospital of Lanzhou University, Gansu, China (M.M., C.C.); The International Science and Technology Cooperation Base for Development and Application of Key Technologies in Thoracic Surgery, Gansu Province, China (M.M., C.C.); and Department of Thoracic Surgery, Affiliated Hospital of Zunyi Medical College, Zunyi Medical College, Guizhou, China (Y. Song)
| | - Minglei Yang
- From the Departments of Thoracic Surgery (Y.Z., Y. She, J.D., D.X., C.C.), Radiology (T.W., J.S.), and Pathology (C.W.), Shanghai Pulmonary Hospital, Tongji University School of Medicine, 507 Zhengmin Rd, Shanghai 200433, China; Department of Computer Science and Technology, College of Electronics and Information Engineering, Tongji University, Shanghai, China (S.C., H.Q., Y.W.); Department of Thoracic Surgery, Ningbo No. 2 Hospital, Chinese Academy of Sciences, Zhejiang, China (M.Y.); Department of Thoracic Surgery, The First Hospital of Lanzhou University, Gansu, China (M.M., C.C.); The International Science and Technology Cooperation Base for Development and Application of Key Technologies in Thoracic Surgery, Gansu Province, China (M.M., C.C.); and Department of Thoracic Surgery, Affiliated Hospital of Zunyi Medical College, Zunyi Medical College, Guizhou, China (Y. Song)
| | - Minjie Ma
- From the Departments of Thoracic Surgery (Y.Z., Y. She, J.D., D.X., C.C.), Radiology (T.W., J.S.), and Pathology (C.W.), Shanghai Pulmonary Hospital, Tongji University School of Medicine, 507 Zhengmin Rd, Shanghai 200433, China; Department of Computer Science and Technology, College of Electronics and Information Engineering, Tongji University, Shanghai, China (S.C., H.Q., Y.W.); Department of Thoracic Surgery, Ningbo No. 2 Hospital, Chinese Academy of Sciences, Zhejiang, China (M.Y.); Department of Thoracic Surgery, The First Hospital of Lanzhou University, Gansu, China (M.M., C.C.); The International Science and Technology Cooperation Base for Development and Application of Key Technologies in Thoracic Surgery, Gansu Province, China (M.M., C.C.); and Department of Thoracic Surgery, Affiliated Hospital of Zunyi Medical College, Zunyi Medical College, Guizhou, China (Y. Song)
| | - Yongxiang Song
- From the Departments of Thoracic Surgery (Y.Z., Y. She, J.D., D.X., C.C.), Radiology (T.W., J.S.), and Pathology (C.W.), Shanghai Pulmonary Hospital, Tongji University School of Medicine, 507 Zhengmin Rd, Shanghai 200433, China; Department of Computer Science and Technology, College of Electronics and Information Engineering, Tongji University, Shanghai, China (S.C., H.Q., Y.W.); Department of Thoracic Surgery, Ningbo No. 2 Hospital, Chinese Academy of Sciences, Zhejiang, China (M.Y.); Department of Thoracic Surgery, The First Hospital of Lanzhou University, Gansu, China (M.M., C.C.); The International Science and Technology Cooperation Base for Development and Application of Key Technologies in Thoracic Surgery, Gansu Province, China (M.M., C.C.); and Department of Thoracic Surgery, Affiliated Hospital of Zunyi Medical College, Zunyi Medical College, Guizhou, China (Y. Song)
| | - Haoyu Qi
- From the Departments of Thoracic Surgery (Y.Z., Y. She, J.D., D.X., C.C.), Radiology (T.W., J.S.), and Pathology (C.W.), Shanghai Pulmonary Hospital, Tongji University School of Medicine, 507 Zhengmin Rd, Shanghai 200433, China; Department of Computer Science and Technology, College of Electronics and Information Engineering, Tongji University, Shanghai, China (S.C., H.Q., Y.W.); Department of Thoracic Surgery, Ningbo No. 2 Hospital, Chinese Academy of Sciences, Zhejiang, China (M.Y.); Department of Thoracic Surgery, The First Hospital of Lanzhou University, Gansu, China (M.M., C.C.); The International Science and Technology Cooperation Base for Development and Application of Key Technologies in Thoracic Surgery, Gansu Province, China (M.M., C.C.); and Department of Thoracic Surgery, Affiliated Hospital of Zunyi Medical College, Zunyi Medical College, Guizhou, China (Y. Song)
| | - Yin Wang
- From the Departments of Thoracic Surgery (Y.Z., Y. She, J.D., D.X., C.C.), Radiology (T.W., J.S.), and Pathology (C.W.), Shanghai Pulmonary Hospital, Tongji University School of Medicine, 507 Zhengmin Rd, Shanghai 200433, China; Department of Computer Science and Technology, College of Electronics and Information Engineering, Tongji University, Shanghai, China (S.C., H.Q., Y.W.); Department of Thoracic Surgery, Ningbo No. 2 Hospital, Chinese Academy of Sciences, Zhejiang, China (M.Y.); Department of Thoracic Surgery, The First Hospital of Lanzhou University, Gansu, China (M.M., C.C.); The International Science and Technology Cooperation Base for Development and Application of Key Technologies in Thoracic Surgery, Gansu Province, China (M.M., C.C.); and Department of Thoracic Surgery, Affiliated Hospital of Zunyi Medical College, Zunyi Medical College, Guizhou, China (Y. Song)
| | - Jingyun Shi
- From the Departments of Thoracic Surgery (Y.Z., Y. She, J.D., D.X., C.C.), Radiology (T.W., J.S.), and Pathology (C.W.), Shanghai Pulmonary Hospital, Tongji University School of Medicine, 507 Zhengmin Rd, Shanghai 200433, China; Department of Computer Science and Technology, College of Electronics and Information Engineering, Tongji University, Shanghai, China (S.C., H.Q., Y.W.); Department of Thoracic Surgery, Ningbo No. 2 Hospital, Chinese Academy of Sciences, Zhejiang, China (M.Y.); Department of Thoracic Surgery, The First Hospital of Lanzhou University, Gansu, China (M.M., C.C.); The International Science and Technology Cooperation Base for Development and Application of Key Technologies in Thoracic Surgery, Gansu Province, China (M.M., C.C.); and Department of Thoracic Surgery, Affiliated Hospital of Zunyi Medical College, Zunyi Medical College, Guizhou, China (Y. Song)
| | - Chunyan Wu
- From the Departments of Thoracic Surgery (Y.Z., Y. She, J.D., D.X., C.C.), Radiology (T.W., J.S.), and Pathology (C.W.), Shanghai Pulmonary Hospital, Tongji University School of Medicine, 507 Zhengmin Rd, Shanghai 200433, China; Department of Computer Science and Technology, College of Electronics and Information Engineering, Tongji University, Shanghai, China (S.C., H.Q., Y.W.); Department of Thoracic Surgery, Ningbo No. 2 Hospital, Chinese Academy of Sciences, Zhejiang, China (M.Y.); Department of Thoracic Surgery, The First Hospital of Lanzhou University, Gansu, China (M.M., C.C.); The International Science and Technology Cooperation Base for Development and Application of Key Technologies in Thoracic Surgery, Gansu Province, China (M.M., C.C.); and Department of Thoracic Surgery, Affiliated Hospital of Zunyi Medical College, Zunyi Medical College, Guizhou, China (Y. Song)
| | - Dong Xie
- From the Departments of Thoracic Surgery (Y.Z., Y. She, J.D., D.X., C.C.), Radiology (T.W., J.S.), and Pathology (C.W.), Shanghai Pulmonary Hospital, Tongji University School of Medicine, 507 Zhengmin Rd, Shanghai 200433, China; Department of Computer Science and Technology, College of Electronics and Information Engineering, Tongji University, Shanghai, China (S.C., H.Q., Y.W.); Department of Thoracic Surgery, Ningbo No. 2 Hospital, Chinese Academy of Sciences, Zhejiang, China (M.Y.); Department of Thoracic Surgery, The First Hospital of Lanzhou University, Gansu, China (M.M., C.C.); The International Science and Technology Cooperation Base for Development and Application of Key Technologies in Thoracic Surgery, Gansu Province, China (M.M., C.C.); and Department of Thoracic Surgery, Affiliated Hospital of Zunyi Medical College, Zunyi Medical College, Guizhou, China (Y. Song)
| | - Chang Chen
- From the Departments of Thoracic Surgery (Y.Z., Y. She, J.D., D.X., C.C.), Radiology (T.W., J.S.), and Pathology (C.W.), Shanghai Pulmonary Hospital, Tongji University School of Medicine, 507 Zhengmin Rd, Shanghai 200433, China; Department of Computer Science and Technology, College of Electronics and Information Engineering, Tongji University, Shanghai, China (S.C., H.Q., Y.W.); Department of Thoracic Surgery, Ningbo No. 2 Hospital, Chinese Academy of Sciences, Zhejiang, China (M.Y.); Department of Thoracic Surgery, The First Hospital of Lanzhou University, Gansu, China (M.M., C.C.); The International Science and Technology Cooperation Base for Development and Application of Key Technologies in Thoracic Surgery, Gansu Province, China (M.M., C.C.); and Department of Thoracic Surgery, Affiliated Hospital of Zunyi Medical College, Zunyi Medical College, Guizhou, China (Y. Song)
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- From the Departments of Thoracic Surgery (Y.Z., Y. She, J.D., D.X., C.C.), Radiology (T.W., J.S.), and Pathology (C.W.), Shanghai Pulmonary Hospital, Tongji University School of Medicine, 507 Zhengmin Rd, Shanghai 200433, China; Department of Computer Science and Technology, College of Electronics and Information Engineering, Tongji University, Shanghai, China (S.C., H.Q., Y.W.); Department of Thoracic Surgery, Ningbo No. 2 Hospital, Chinese Academy of Sciences, Zhejiang, China (M.Y.); Department of Thoracic Surgery, The First Hospital of Lanzhou University, Gansu, China (M.M., C.C.); The International Science and Technology Cooperation Base for Development and Application of Key Technologies in Thoracic Surgery, Gansu Province, China (M.M., C.C.); and Department of Thoracic Surgery, Affiliated Hospital of Zunyi Medical College, Zunyi Medical College, Guizhou, China (Y. Song)
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Tane S, Kimura K, Shimizu N, Kitamura Y, Matsumoto G, Uchino K, Nishio W. Segmentectomy for inner location small-sized non-small-cell lung cancer: Is it feasible? Ann Thorac Surg 2021; 114:1918-1924. [PMID: 34563504 DOI: 10.1016/j.athoracsur.2021.08.035] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 08/13/2021] [Accepted: 08/16/2021] [Indexed: 11/30/2022]
Abstract
BACKGROUND The efficacy of segmentectomy for inner small-sized non-small-cell lung cancer (NSCLC) remains unknown. We aimed to elucidate whether segmentectomy for inner small-sized NSCLC, defined using novel three-dimensional measuring method, yields feasible oncological outcomes compared to segmentectomy for outer lesions. METHODS We retrospectively analyzed patients with small-sized (<2cm) cN0 NSCLC who underwent segmentectomy between January 2007 and December 2020. Tumor centrality ratio, which was measured by using three dimensional reconstruction software, was evaluated, with the location of tumor origin confirmed pathologically. Cases with a ratio below and above 2/3 were allocated to the 'Inner group' and 'Outer group', respectively. Oncological outcomes were compared between the two groups. RESULTS Our cohort was divided into the 'Inner group' (n=75) and 'Outer group' (n=127). The proximal distance from a tumor exceeded 20 mm in all cases. Tumor centrality ratio was associated with the pathological origin of a tumor. The rate of unforeseen positive lymph node metastasis was significantly higher in the 'Inner group' (p=0.04). There were no significant differences in the 5-year recurrence free survival (RFS; 91% versus 87%, p=0.67). Univariate analysis identified age, consolidation/tumor ratio, the presence of ground-glass-opacity (GGO) and lymphovascular invasion, but not tumor centrality, as significant prognostic factors for RFS. In the multivariate analysis, the presence of GGO and lymphovascular invasion remained significant. CONCLUSIONS Regarding oncological outcomes, segmentectomy with a safety proximal distance could be feasible, even for inner small-sized NSCLC. Tumor invasiveness, not tumor centrality, may influence tumor recurrence. (242 words).
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Affiliation(s)
- Shinya Tane
- Department of General Thoracic Surgery, Osaka Saiseikai Nakatsu Hospital, 2-10-39, Shibata, kita-ward, Osaka city, Japan.
| | - Kenji Kimura
- Division of Chest Surgery, Hyogo Cancer Center, 13-70, kitaoji-cho, Akashi city, Japan
| | - Nahoko Shimizu
- Division of Chest Surgery, Hyogo Cancer Center, 13-70, kitaoji-cho, Akashi city, Japan
| | - Yoshitaka Kitamura
- Division of Chest Surgery, Hyogo Cancer Center, 13-70, kitaoji-cho, Akashi city, Japan
| | - Gaku Matsumoto
- Department of General Thoracic Surgery, Osaka Saiseikai Nakatsu Hospital, 2-10-39, Shibata, kita-ward, Osaka city, Japan
| | - Kazuya Uchino
- Department of General Thoracic Surgery, Osaka Saiseikai Nakatsu Hospital, 2-10-39, Shibata, kita-ward, Osaka city, Japan
| | - Wataru Nishio
- Division of Chest Surgery, Hyogo Cancer Center, 13-70, kitaoji-cho, Akashi city, Japan
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21
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Aokage K, Suzuki K, Wakabayashi M, Mizutani T, Hattori A, Fukuda H, Watanabe SI. Predicting pathological lymph node status in clinical stage IA peripheral lung adenocarcinoma. Eur J Cardiothorac Surg 2021; 60:64-71. [PMID: 33514999 DOI: 10.1093/ejcts/ezaa478] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Revised: 11/19/2020] [Accepted: 12/02/2020] [Indexed: 11/13/2022] Open
Abstract
OBJECTIVES Even with current diagnostic technology, it is difficult to accurately predict pathological lymph node status (PLNS). This study aimed to develop a prediction model of PLNS in peripheral adenocarcinoma with a dominant solid component, based on clinical and radiological factors on thin-section computed tomography, to identify patients to whom wedge resection or other local therapies could be applied. METHODS Of 811 patients enrolled in a prospective multi-institutional study (JCOG0201), 420 patients with clinical stage IA peripheral lung adenocarcinoma having a dominant solid component were included. Multivariable logistic regression was performed to develop a model based on clinical and centrally reviewed radiological factors. Leave-one-out cross-validation and external validation analyses were performed, using independent data from 221 patients. Sensitivity, specificity and concordance statistics were calculated to evaluate diagnostic performance. RESULTS The formula for calculating the probability of pathological lymph node metastasis included the following variables: tumour diameter (including ground-glass opacity), consolidation-to-tumour ratio and density of solid component. The concordance statistic was 0.8041. When the cut-off value associated with the risk of incorrectly predicting negative pathological lymph node metastasis (pN-) was 4.9%, diagnostic sensitivity and specificity in predicting PLNS were 95.7% and 46.0%, respectively. The concordance statistic for the external validation set was 0.7972, and diagnostic sensitivity and specificity in predicting PLNS were 95.4% and 40.5%, respectively. CONCLUSIONS The proposed model is clinically useful and successfully predicts pN- in patients with clinical stage IA peripheral lung adenocarcinoma with a dominant solid component.
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Affiliation(s)
- Keiju Aokage
- Division of Thoracic Surgery, National Cancer Center Hospital East, Kashiwa, Japan
| | - Kenji Suzuki
- Department of General Thoracic Surgery, Juntendo University School of Medicine, Tokyo, Japan
| | - Masashi Wakabayashi
- JCOG Data Center/Operations Office, National Cancer Center Hospital, Tokyo, Japan
| | - Tomonori Mizutani
- JCOG Data Center/Operations Office, National Cancer Center Hospital, Tokyo, Japan
| | - Aritoshi Hattori
- Department of General Thoracic Surgery, Juntendo University School of Medicine, Tokyo, Japan
| | - Haruhiko Fukuda
- JCOG Data Center/Operations Office, National Cancer Center Hospital, Tokyo, Japan
| | - Shun-Ichi Watanabe
- Division of Thoracic Surgery, National Cancer Center Hospital, Tokyo, Japan
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Inci I, Benker M, Çitak N, Schneiter D, Caviezel C, Hillinger S, Opitz I, Weder W. Complex sleeve lobectomy has the same surgical outcome when compared with conventional lobectomy in patients with lung cancer. Eur J Cardiothorac Surg 2021; 57:860-866. [PMID: 31919516 DOI: 10.1093/ejcts/ezz357] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/01/2019] [Revised: 11/11/2019] [Accepted: 11/29/2019] [Indexed: 12/11/2022] Open
Abstract
OBJECTIVES No significant data are available to assess whether complex sleeve lobectomy (complex-SL) can be considered comparable to conventional lobectomy (CL) in terms of surgical outcome. The purpose of this study was to compare surgical and oncological outcomes of complex-SL with CL in patients with lung cancer. METHODS Between 2000 and 2015, a total of 568 patients who underwent open CL (defined as resection of only 1 lobe) and 187 patients who underwent SL were analysed. The SL group was divided into 2 subgroups: standard-SL (bronchial SL, n = 106) and complex-SL (n = 81) (defined as bronchial sleeve resection together with another surgical intervention: bronchovascular SL, n = 40; vascular SL, n = 26; atypical bronchoplasty with resection of more than 1 lobe, n = 12; bronchial SL + chest wall resection, n = 3). RESULTS The complex-SL group had more patients with chronic obstructive pulmonary disease (COPD) (25.9% vs 12.5%, P = 0.001), neoadjuvant treatment (39.5% vs 12.0%, P < 0.001), advanced-stage non-small-cell lung cancer (53.2% vs 33.1%, P = 0.001) and low preoperative forced expiratory volume in 1 s (77.2% vs 84.3%, P = 0.004) than the CL group. The overall surgical mortality (in-hospital or 30-day) was 2.6% (n = 20); it was 2.8% for CL and 2.8% for complex-SL. Postoperative complications occurred in 34.9% of the CL group and 39.5% of the complex-SL group (P = 0.413). The pulmonary complication rate was similar between the groups (24.1% for CL, 27.2% for complex-SL, P = 0.552). The 5-year survival in the CL group was 57.1%, and in the complex-SL group it was 56.2% (P = 0.888). Multivariate analysis showed that TNM stage (P < 0.001) and N status (P < 0.001) were significant and independent negative prognostic factors for survival. CONCLUSIONS Complex-SL had a comparable outcome to CL, although the complex-SL group had more patients with advanced-stage NSCLC, low preoperative forced expiratory volume in 1 s and COPD.
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Affiliation(s)
- Ilhan Inci
- Department of Thoracic Surgery, University Hospital Zurich, Zurich, Switzerland
| | - Martina Benker
- Department of Thoracic Surgery, University Hospital Zurich, Zurich, Switzerland
| | - Necati Çitak
- Department of Thoracic Surgery, University Hospital Zurich, Zurich, Switzerland.,Department of Thoracic Surgery, Bakırköy Dr. Sadi Konuk Research and Education Hospital, Istanbul, Turkey
| | - Didier Schneiter
- Department of Thoracic Surgery, University Hospital Zurich, Zurich, Switzerland
| | - Claudio Caviezel
- Department of Thoracic Surgery, University Hospital Zurich, Zurich, Switzerland
| | - Sven Hillinger
- Department of Thoracic Surgery, University Hospital Zurich, Zurich, Switzerland
| | - Isabelle Opitz
- Department of Thoracic Surgery, University Hospital Zurich, Zurich, Switzerland
| | - Walter Weder
- Department of Thoracic Surgery, University Hospital Zurich, Zurich, Switzerland
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Guinde J, Bourdages-Pageau E, Collin-Castonguay MM, Laflamme L, Lévesque-Laplante A, Marcoux S, Roy P, Ugalde PA, Lacasse Y, Fortin M. A Prediction Model to Optimize Invasive Mediastinal Staging Procedures for Non-Small Cell Lung Cancer in Patients With a Radiologically Normal Mediastinum: The Quebec Prediction Model. Chest 2021; 160:2283-2292. [PMID: 34119514 DOI: 10.1016/j.chest.2021.05.062] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Revised: 05/04/2021] [Accepted: 05/23/2021] [Indexed: 10/21/2022] Open
Abstract
BACKGROUND Current guideline-recommended criteria for invasive mediastinal staging in patients with a radiologically normal mediastinum fail to identify a significant proportion of patients with occult mediastinal disease (OMD), despite it leading to a large number of invasive staging procedures. RESEARCH QUESTION Which variables available before surgery predict the probability of OMD in patients with a radiologically normal mediastinum? STUDY DESIGN AND METHODS We identified all cTxN0/N1M0 non-small cell lung cancer tumors staged by CT imaging and PET with CT imaging in our institution between 2014 and 2018 who underwent gold standard surgical lymph node dissection or were demonstrated to have OMD before surgery by invasive mediastinal staging techniques and divided them into a derivation and an independent validation cohort to create the Quebec Prediction Model (QPM), which allows calculation of the probability of OMD. RESULTS Eight hundred three patients were identified (development set, n = 502; validation set, n = 301) with a prevalence of OMD of 9.1%. The developed prediction model included largest mediastinal lymph node size (P < .001), tumor centrality (P = .23), presence of cN1 disease (P = .29), and lesion standardized uptake value (P = .09). Using a calculated probability of more than 10% as a threshold to identify OMD, this model had a sensitivity, specificity, positive predictive value, and negative predictive value in the derivation cohort of 73.9% (95% CI, 58.9%-85.7%), 81.1% (95% CI, 77.2%-84.6%), 28.3% (95% CI, 23.4%-33.8%), and 96.8% (95% CI, 95.0%-98.1%), respectively. It performed similarly in the validation cohort (P = .77, Hosmer-Lemeshow test; P = .5163, Pearson χ2 and unweighted sum-of-squares statistics; and P = .0750, Stukel score test) and outperformed current guideline-recommended criteria in identifying patients with OMD (area under the receiver operating characteristic curve [AUC] for American College of Chest Physicians guidelines criteria, 0.65 [95% CI, 0.59-0.71]; AUC for European Society of Thoracic Surgeons guidelines criteria, 0.60 [95% CI, 0.54-0.67]; and AUC for the QPM, 0.85 [95% CI, 0.80-0.90]). INTERPRETATION The QPM allows the clinician to integrate available information from CT and PET imaging to minimize invasive staging procedures that will not modify management, while also minimizing the risk of unforeseen mediastinal disease found at surgery.
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Affiliation(s)
- Julien Guinde
- Department of Pulmonology and Thoracic Surgery, Institut Universitaire de Cardiologie et de Pneumologie de Québec, Laval University, Quebec City, QC, Canada; Department of Thoracic Oncology, Pleural Diseases and Interventional Pulmonology, North University Hospital, Marseille, France
| | - Etienne Bourdages-Pageau
- Department of Pulmonology and Thoracic Surgery, Institut Universitaire de Cardiologie et de Pneumologie de Québec, Laval University, Quebec City, QC, Canada
| | - Marie-May Collin-Castonguay
- Department of Pulmonology and Thoracic Surgery, Institut Universitaire de Cardiologie et de Pneumologie de Québec, Laval University, Quebec City, QC, Canada
| | - Laurie Laflamme
- Department of Pulmonology and Thoracic Surgery, Institut Universitaire de Cardiologie et de Pneumologie de Québec, Laval University, Quebec City, QC, Canada
| | - Alexandra Lévesque-Laplante
- Department of Pulmonology and Thoracic Surgery, Institut Universitaire de Cardiologie et de Pneumologie de Québec, Laval University, Quebec City, QC, Canada
| | - Sabrina Marcoux
- Department of Pulmonology and Thoracic Surgery, Institut Universitaire de Cardiologie et de Pneumologie de Québec, Laval University, Quebec City, QC, Canada
| | - Pascalin Roy
- Department of Pulmonology and Thoracic Surgery, Institut Universitaire de Cardiologie et de Pneumologie de Québec, Laval University, Quebec City, QC, Canada
| | - Paula Antonia Ugalde
- Department of Pulmonology and Thoracic Surgery, Institut Universitaire de Cardiologie et de Pneumologie de Québec, Laval University, Quebec City, QC, Canada
| | - Yves Lacasse
- Department of Pulmonology and Thoracic Surgery, Institut Universitaire de Cardiologie et de Pneumologie de Québec, Laval University, Quebec City, QC, Canada
| | - Marc Fortin
- Department of Pulmonology and Thoracic Surgery, Institut Universitaire de Cardiologie et de Pneumologie de Québec, Laval University, Quebec City, QC, Canada.
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Martínez-Palau M, Trujillo-Reyes JC, Jaen À, Call S, Martínez-Hernández NJ, Provencio M, Vollmer I, Rami-Porta R, Sanz-Santos J. How do we Classify a Central Tumor? Results of a Multidisciplinary Survey from the SEPAR Thoracic Oncology Area. Arch Bronconeumol 2021; 57:359-365. [PMID: 32828588 DOI: 10.1016/j.arbres.2020.06.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Revised: 05/27/2020] [Accepted: 06/15/2020] [Indexed: 10/23/2022]
Abstract
INTRODUCTION In patients with non-small cell lung cancer (NSCLC) and normal mediastinal imaging tests, centrally located tumors have greater occult mediastinal involvement. Clinical guidelines, therefore, recommend invasive mediastinal staging in this situation. However, definitions of centrality in the different guidelines are inconsistent. The SEPAR Thoracic Oncology area aimed to evaluate the degree of familiarity with various concepts related to tumor site among professionals who see patients with NSCLC in Spain. METHODS A questionnaire was distributed to members of Spanish medical societies involved in the management of NSCLC, structured according to the 3 aspects to be evaluated: 1) uniformity in the definition of central tumor location; 2) uniformity in the classification of lesions that extend beyond dividing lines; and 3) ability to delineate lesions in the absence of dividing lines. RESULTS A total of 430 participants responded. The most voted definition of centrality was «lesions in contact with hilar structures» (49.7%). The lines most often chosen to delimit the hemitorax were concentric hilar lines (89%). Most participants (92.8%) classified tumors according to the side of the dividing line that contained most of their volume. Overall, 78.6% were able to correctly classify a central lesion in the absence of dividing lines. CONCLUSIONS In our survey, the most widely accepted definition of centrality is not one of the proposals specified in the clinical guidelines. The results reflect wide variability in the classification of tumor lesions.
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Affiliation(s)
- Mireia Martínez-Palau
- Servicio de Neumología, Hospital Universitari Mútua Terrassa, Barcelona, España; Departament de Medicina, Facultat de Medicina, Universitat de Barcelona, Barcelona, España
| | - Juan Carlos Trujillo-Reyes
- Servicio de Cirugía Torácica, Hospital de la Santa Creu i Sant Pau, Barcelona, España; Departament de Cirurgia, Universitat Autonoma de Barcelona, Barcelona, España; Sociedad Española de Neumología y Cirugía Torácica, Área de Oncología Torácica, Barcelona, España
| | - Àngels Jaen
- Fundació Mútua Terrassa per a la Recerca Biomèdica i Social, Barcelona, España
| | - Sergi Call
- Servicio de Cirugía Torácica. Hospital Universitari Mútua Terrassa, Barcelona, España; Departament de Ciències Morfològiques, Àrea d'anatomia i embriologia humana, Universitat Autònoma de Barcelona, Barcelona, España
| | - Néstor J Martínez-Hernández
- Servicio de Cirugía Torácica. Hospital Universitari de la Ribera, Valencia, España; Sociedad Española de Cirugía Torácica, Comité Científico, Madrid, España
| | - Mariano Provencio
- Servicio de Oncología Médica, Hospital Universitario Puerta de Hierro, Madrid, España; Facultad de Medicina, Universidad Autónoma de Madrid, España; Grupo Español de Cáncer de Pulmón, Barcelona, España
| | - Iván Vollmer
- Servicio de Radiologia, Centre Diagnòstic per la Imatge (CDI), Hospital Clínic, Barcelona, España; Sociedad Española de Imagen Cardiotorácica, Valencia, España
| | - Ramón Rami-Porta
- Servicio de Cirugía Torácica. Hospital Universitari Mútua Terrassa, Barcelona, España
| | - José Sanz-Santos
- Servicio de Neumología, Hospital Universitari Mútua Terrassa, Barcelona, España; Departament de Medicina, Facultat de Medicina, Universitat de Barcelona, Barcelona, España.
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Martinez-Zayas G, Almeida FA, Yarmus L, Steinfort D, Lazarus DR, Simoff MJ, Saettele T, Murgu S, Dammad T, Duong DK, Mudambi L, Filner JJ, Molina S, Aravena C, Thiboutot J, Bonney A, Rueda AM, Debiane LG, Hogarth DK, Bedi H, Deffebach M, Sagar AES, Cicenia J, Yu DH, Cohen A, Frye L, Grosu HB, Gildea T, Feller-Kopman D, Casal RF, Machuzak M, Arain MH, Sethi S, Eapen GA, Lam L, Jimenez CA, Ribeiro M, Noor LZ, Mehta A, Song J, Choi H, Ma J, Li L, Ost DE. Predicting Lymph Node Metastasis in Non-small Cell Lung Cancer: Prospective External and Temporal Validation of the HAL and HOMER Models. Chest 2021; 160:1108-1120. [PMID: 33932466 DOI: 10.1016/j.chest.2021.04.048] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 04/02/2021] [Accepted: 04/08/2021] [Indexed: 12/25/2022] Open
Abstract
BACKGROUND Two models, the Help with the Assessment of Adenopathy in Lung cancer (HAL) and Help with Oncologic Mediastinal Evaluation for Radiation (HOMER), were recently developed to estimate the probability of nodal disease in patients with non-small cell lung cancer (NSCLC) as determined by endobronchial ultrasound-transbronchial needle aspiration (EBUS-TBNA). The objective of this study was to prospectively externally validate both models at multiple centers. RESEARCH QUESTION Are the HAL and HOMER models valid across multiple centers? STUDY DESIGN AND METHODS This multicenter prospective observational cohort study enrolled consecutive patients with PET-CT clinical-radiographic stages T1-3, N0-3, M0 NSCLC undergoing EBUS-TBNA staging. HOMER was used to predict the probability of N0 vs N1 vs N2 or N3 (N2|3) disease, and HAL was used to predict the probability of N2|3 (vs N0 or N1) disease. Model discrimination was assessed using the area under the receiver operating characteristics curve (ROC-AUC), and calibration was assessed using the Brier score, calibration plots, and the Hosmer-Lemeshow test. RESULTS Thirteen centers enrolled 1,799 patients. HAL and HOMER demonstrated good discrimination: HAL ROC-AUC = 0.873 (95%CI, 0.856-0.891) and HOMER ROC-AUC = 0.837 (95%CI, 0.814-0.859) for predicting N1 disease or higher (N1|2|3) and 0.876 (95%CI, 0.855-0.897) for predicting N2|3 disease. Brier scores were 0.117 and 0.349, respectively. Calibration plots demonstrated good calibration for both models. For HAL, the difference between forecast and observed probability of N2|3 disease was +0.012; for HOMER, the difference for N1|2|3 was -0.018 and for N2|3 was +0.002. The Hosmer-Lemeshow test was significant for both models (P = .034 and .002), indicating a small but statistically significant calibration error. INTERPRETATION HAL and HOMER demonstrated good discrimination and calibration in multiple centers. Although calibration error was present, the magnitude of the error is small, such that the models are informative.
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Affiliation(s)
- Gabriela Martinez-Zayas
- Department of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX
| | | | - Lonny Yarmus
- Division of Pulmonary and Critical Care, Johns Hopkins University, Baltimore, MD
| | - Daniel Steinfort
- Department of Respiratory Medicine, Royal Melbourne Hospital, Melbourne, Australia; Department of Medicine, University of Melbourne, Melbourne, Australia
| | - Donald R Lazarus
- Department of Pulmonary, Critical Care, and Sleep Medicine, Baylor College of Medicine, Houston, TX
| | - Michael J Simoff
- Department of Pulmonary and Critical Care Medicine, Henry Ford Hospital, Detroit, MI
| | - Timothy Saettele
- Department of Pulmonary Disease and Critical Care Medicine, Saint Luke's Hospital of Kansas City, Kansas City, MO
| | - Septimiu Murgu
- Division of Pulmonary and Critical Care Medicine, University of Chicago, Chicago, IL
| | - Tarek Dammad
- Department of Pulmonary Medicine, University of New Mexico, Albuquerque, NM; Department of Pulmonary and Critical Care Medicine, CHRISTUS St. Vincent Medical Center, Santa Fe, NM
| | - D Kevin Duong
- Department of Pulmonary, Allergy and Critical Care Medicine, Stanford University Medical Center and School of Medicine, Stanford, CA
| | - Lakshmi Mudambi
- Division of Pulmonary and Critical Care, VA Portland Health Care System, Oregon Health and Science University, Portland, OR
| | - Joshua J Filner
- Department of Pulmonary Medicine, Northwest Permanente and The Center for Health Research, Kaiser Permanente Northwest, Portland, OR
| | - Sofia Molina
- Department of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Carlos Aravena
- Department of Respiratory Diseases, Pontificia Universidad Catolica de Chile, Santiago, Chile
| | - Jeffrey Thiboutot
- Division of Pulmonary and Critical Care, Johns Hopkins University, Baltimore, MD
| | - Asha Bonney
- Department of Respiratory Medicine, Royal Melbourne Hospital, Melbourne, Australia
| | - Adriana M Rueda
- Department of Pulmonary, Critical Care, and Sleep Medicine, Baylor College of Medicine, Houston, TX
| | - Labib G Debiane
- Department of Pulmonary and Critical Care Medicine, Henry Ford Hospital, Detroit, MI
| | - D Kyle Hogarth
- Division of Pulmonary and Critical Care Medicine, University of Chicago, Chicago, IL
| | - Harmeet Bedi
- Department of Pulmonary, Allergy and Critical Care Medicine, Stanford University Medical Center and School of Medicine, Stanford, CA
| | - Mark Deffebach
- Division of Pulmonary and Critical Care, VA Portland Health Care System, Oregon Health and Science University, Portland, OR
| | - Ala-Eddin S Sagar
- Department of Pulmonary Medicine, Banner MD Anderson Cancer Center, Gilbert, AZ
| | - Joseph Cicenia
- Department of Pulmonary Medicine, Cleveland Clinic, Cleveland, OH
| | - Diana H Yu
- Division of Pulmonary, Critical Care and Sleep Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA
| | - Avi Cohen
- Department of Pulmonary and Critical Care Medicine, Henry Ford Hospital, Detroit, MI
| | - Laura Frye
- Division of Allergy, Pulmonary and Critical Care Medicine, University of Wisconsin, Madison, WI
| | - Horiana B Grosu
- Department of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Thomas Gildea
- Department of Pulmonary Medicine, Cleveland Clinic, Cleveland, OH
| | - David Feller-Kopman
- Division of Pulmonary and Critical Care, Johns Hopkins University, Baltimore, MD
| | - Roberto F Casal
- Department of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Michael Machuzak
- Department of Pulmonary Medicine, Cleveland Clinic, Cleveland, OH
| | - Muhammad H Arain
- Department of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Sonali Sethi
- Department of Pulmonary Medicine, Cleveland Clinic, Cleveland, OH
| | - George A Eapen
- Department of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Louis Lam
- Department of Pulmonary Medicine, Cleveland Clinic, Cleveland, OH
| | - Carlos A Jimenez
- Department of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Manuel Ribeiro
- Department of Pulmonary Medicine, Cleveland Clinic, Cleveland, OH
| | - Laila Z Noor
- Department of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Atul Mehta
- Department of Pulmonary Medicine, Cleveland Clinic, Cleveland, OH
| | - Juhee Song
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Humberto Choi
- Department of Pulmonary Medicine, Cleveland Clinic, Cleveland, OH
| | - Junsheng Ma
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Liang Li
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - David E Ost
- Department of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX.
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Kukhon FR, Lan X, Helgeson SA, Arunthari V, Fernandez-Bussy S, Patel NM. Occult lymph node metastasis in radiologic stage I non-small cell lung cancer: The role of endobronchial ultrasound. CLINICAL RESPIRATORY JOURNAL 2021; 15:676-682. [PMID: 33630405 DOI: 10.1111/crj.13344] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Accepted: 02/19/2021] [Indexed: 12/25/2022]
Abstract
RATIONALE The use of endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) is currently recommended for staging non-small cell lung cancer (NSCLC) in centrally located tumors, tumors >3 cm, or with radiologic evidence of lymph node (LN) metastasis. Current guidelines do not recommend staging EBUS-TBNA in patients with stage I NSCLC who do not have any of the aforementioned conditions. OBJECTIVE We hypothesize that using EBUS-TBNA is useful for detecting occult metastasis in radiologic stage I NSCLC. METHODS In this single-center, retrospective study, charts of patients ≥18 years old who underwent staging EBUS-TBNA from January 2005 to May 2019 were reviewed. Only patients with combined positron-emission tomography and computed tomography (PET/CT) scans consistent with radiologic stage I NSCLC were included. Identified variables included: age, gender, personal history of any cancer, smoking history, tumor location, tumor centrality, tumor size, tumor PET activity, histopathologic type of NSCLC, and LN biopsy results. Patients whose LN samples showed a diagnosis other than NSCLC were excluded. The association between LN positivity, and each of the variables was assessed using Pearson's correlation for categorical variables, and logistic regression analysis for continuous variables. RESULTS From the 2,892 initially screened patients, 188 were included. Of those, 13 (6.9%; 95% CI, 4%-11%) had a malignancy-positive LN biopsy. The number needed to test (NNT) in order to detect one case of any occult metastasis was 15. Among the included variables, a significant association was found between LN positivity and tumor centrality, with central tumors found in 61.5% of patients with positive LN (n = 8) (p < 0.01). This association stayed significant after adjusting for age, gender, smoking history, tumor size, tumor location, and PET activity (p = 0.015). Among patients with malignancy-positive LN biopsies, five (38.5%; 95% CI, 17.6%-64.6%) were upstaged to N1, and eight (61.5%; 95% CI, 35.4%-82.4%) were upstaged to N2, with NNT of 23 to detect one case of occult N2 metastasis. Subgroup analysis comparing LN-positive patients based on their N stage did not show statistically significant association with any of the variables. CONCLUSION Based on our results and along with the existing evidence, EBUS-TBNA should be recommended as part of the routine staging in all patients with radiologic stage I NSCLC.
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Affiliation(s)
- Faeq R Kukhon
- Department of Pulmonary Medicine, Mayo Clinic, Jacksonville, FL, USA
| | - Xinyue Lan
- Department of Biology, Zanvyl Krieger School of Arts and Sciences, Johns Hopkins University, Baltimore, MD, USA
| | - Scott A Helgeson
- Department of Pulmonary Medicine, Mayo Clinic, Jacksonville, FL, USA
| | - Vichaya Arunthari
- Department of Pulmonary Medicine, Mayo Clinic, Jacksonville, FL, USA
| | | | - Neal M Patel
- Department of Pulmonary Medicine, Mayo Clinic, Jacksonville, FL, USA
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Lv X, Wu Z, Cao J, Hu Y, Liu K, Dai X, Yuan X, Wang Y, Zhao K, Lv W, Hu J. A nomogram for predicting the risk of lymph node metastasis in T1-2 non-small-cell lung cancer based on PET/CT and clinical characteristics. Transl Lung Cancer Res 2021; 10:430-438. [PMID: 33569324 PMCID: PMC7867781 DOI: 10.21037/tlcr-20-1026] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Background Accurately predicting the risk level for a lymph node metastasis is critical in the treatment of non-small cell lung cancer (NSCLC). This study aimed to construct a novel nomogram to identify patients with a risk of lymph node metastasis in T1–2 NSCLC based on positron emission tomography/computed tomography (PET/CT) and clinical characteristics. Methods From January 2011 to November 2017, the records of 318 consecutive patients who had undergone PET/CT examination within 30 days before surgical resection for clinical T1–2 NSCLC were retrospectively reviewed. A nomogram to predict the risk of lymph node metastasis was constructed. The model was confirmed using bootstrap resampling, and an independent validation cohort contained 156 patients from June 2017 to February 2020 at another institution. Results Six factors [age, tumor location, histology, the lymph node maximum standardized uptake value (SUVmax), the tumor SUVmax and the carcinoembryonic antigen (CEA) value] were identified and entered into the nomogram. The nomogram developed based on the analysis showed robust discrimination, with an area under the receiver operating characteristic curve of 0.858 in the primary cohort and 0.749 in the validation cohort. The calibration curve for the probability of lymph node metastasis showed excellent concordance between the predicted and actual results. Decision curve analysis suggested that the nomogram was clinically useful. Conclusions We set up and validated a novel and effective nomogram that can predict the risk of lymph node metastasis for individual patients with T1–2 NSCLC. This model may help clinicians to make treatment recommendations for individuals.
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Affiliation(s)
- Xiayi Lv
- Department of Thoracic Surgery, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Zhigang Wu
- Department of Thoracic Surgery, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Jinlin Cao
- Department of Thoracic Surgery, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Yeji Hu
- Department of Thoracic Surgery, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Kai Liu
- Department of Thoracic Surgery, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Xiaona Dai
- Department of Quality Management, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Xiaoshuai Yuan
- Department of Thoracic Surgery, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Yiqing Wang
- Department of Thoracic Surgery, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Kui Zhao
- Departments of Radiology, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Wang Lv
- Department of Thoracic Surgery, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Jian Hu
- Department of Thoracic Surgery, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
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Geometrical Measurement of Central Tumor Location in cT1N0M0 NSCLC Predicts N1 but Not N2 Upstaging. Ann Thorac Surg 2020; 111:1190-1197. [PMID: 32853568 DOI: 10.1016/j.athoracsur.2020.06.040] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Revised: 05/20/2020] [Accepted: 06/15/2020] [Indexed: 12/25/2022]
Abstract
BACKGROUND In patients with non-small cell lung cancer (NSCLC) and normal mediastinum, the central tumor location predicts occult nodal disease (both N1 and N2). We evaluated a novel definition of central location based on a geometrical measurement of the tumor location within the lung that could predict N2, N1, or both. METHODS This retrospective study included patients with confirmed NSCLC, radiologically and metabolically staged T1 N0 M0, who underwent invasive mediastinal staging and/or lung resection. The central tumor location was measured considering 2 ratios. The inner margin ratio (IMR) and outer margin ratio (OMR) were both calculated as the distance from the inner margin of the lung to both margins of the tumor (inner [IMR], outer [OMR]) divided by the lung width. Optimal cutoffs for IMR and OMR were calculated. Tumors with values lower than the cutoffs were considered central. Prevalences of N1 and N2 upstaging were estimated and bivariate logistic regression analysis was performed to predict the odds of N1 and N2 upstaging using IMR and OMR cutoffs. RESULTS A total of 209 patients were included. The prevalence of N1 and N2 upstaging was 11% and 5.3%, respectively. Cutoffs of 0.5 for IMR and 0.64 for OMR were estimated. Both ratios predicted N1 upstaging (adjusted odds ratio [95% confidence interval]: 4.2 [1.5-12]; P < .007; area under the curve, 0.65) but did not predict N2 upstaging. CONCLUSIONS Central tumor location can be assessed by means of IMR and OMR and predicts N1 upstaging in patients with radiologically and metabolically T1 N0 M0 tumors. This is important for the selection of patients for therapies that require N0 tumors.
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Recent and Current Advances in FDG-PET Imaging within the Field of Clinical Oncology in NSCLC: A Review of the Literature. Diagnostics (Basel) 2020; 10:diagnostics10080561. [PMID: 32764429 PMCID: PMC7459495 DOI: 10.3390/diagnostics10080561] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Revised: 07/29/2020] [Accepted: 08/03/2020] [Indexed: 02/07/2023] Open
Abstract
Lung cancer is the leading cause of cancer-related deaths around the world, the most common type of which is non-small-cell lung cancer (NSCLC). Computed tomography (CT) is required for patients with NSCLC, but often involves diagnostic issues and large intra- and interobserver variability. The anatomic data obtained using CT can be supplemented by the metabolic data obtained using fluorodeoxyglucose F 18 (FDG) positron emission tomography (PET); therefore, the use of FDG-PET/CT for staging NSCLC is recommended, as it provides more accuracy than either modality alone. Furthermore, FDG-PET/magnetic resonance imaging (MRI) provides useful information on metabolic activity and tumor cellularity, and has become increasingly popular. A number of studies have described FDG-PET/MRI as having a high diagnostic performance in NSCLC staging. Therefore, multidimensional functional imaging using FDG-PET/MRI is promising for evaluating the activity of the intratumoral environment. Radiomics is the quantitative extraction of imaging features from medical scans. The chief advantages of FDG-PET/CT radiomics are the ability to capture information beyond the capabilities of the human eye, non-invasiveness, the (virtually) real-time response, and full-field analysis of the lesion. This review summarizes the recent advances in FDG-PET imaging within the field of clinical oncology in NSCLC, with a focus on surgery and prognostication, and investigates the site-specific strengths and limitations of FDG-PET/CT. Overall, the goal of treatment for NSCLC is to provide the best opportunity for long-term survival; therefore, FDG-PET/CT is expected to play an increasingly important role in deciding the appropriate treatment for such patients.
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Brascia D, De Iaco G, Schiavone M, Panza T, Signore F, Geronimo A, Sampietro D, Montrone M, Galetta D, Marulli G. Resectable IIIA-N2 Non-Small-Cell Lung Cancer (NSCLC): In Search for the Proper Treatment. Cancers (Basel) 2020; 12:cancers12082050. [PMID: 32722386 PMCID: PMC7465235 DOI: 10.3390/cancers12082050] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Revised: 07/18/2020] [Accepted: 07/21/2020] [Indexed: 12/25/2022] Open
Abstract
Locally advanced non-small cell lung cancer accounts for one third of non-small cell lung cancer (NSCLC) at the time of initial diagnosis and presents with a wide range of clinical and pathological heterogeneity. To date, the combined multimodality approach involving both local and systemic control is the gold standard for these patients, since occult distant micrometastatic disease should always be suspected. With the rapid increase in treatment options, the need for an interdisciplinary discussion involving oncologists, surgeons, radiation oncologists and radiologists has become essential. Surgery should be recommended to patients with non-bulky, discrete, or single-level N2 involvement and be included in the multimodality treatment. Resectable stage IIIA patients have been the subject of a number of clinical trials and retrospective analysis, discussing the efficiency and survival benefits on patients treated with the available therapeutic approaches. However, most of them have some limitations due to their retrospective nature, lack of exact pretreatment staging, and the involvement of heterogeneous populations leading to the awareness that each patient should undergo a tailored therapy in light of the nature of his tumor, its extension and his performance status.
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Affiliation(s)
- Debora Brascia
- Thoracic Surgery Unit, Department of Organ Transplantation and Emergency, University Hospital of Bari, 70121 Bari, Italy; (D.B.); (G.D.I.); (M.S.); (T.P.); (F.S.); (A.G.); (D.S.)
| | - Giulia De Iaco
- Thoracic Surgery Unit, Department of Organ Transplantation and Emergency, University Hospital of Bari, 70121 Bari, Italy; (D.B.); (G.D.I.); (M.S.); (T.P.); (F.S.); (A.G.); (D.S.)
| | - Marcella Schiavone
- Thoracic Surgery Unit, Department of Organ Transplantation and Emergency, University Hospital of Bari, 70121 Bari, Italy; (D.B.); (G.D.I.); (M.S.); (T.P.); (F.S.); (A.G.); (D.S.)
| | - Teodora Panza
- Thoracic Surgery Unit, Department of Organ Transplantation and Emergency, University Hospital of Bari, 70121 Bari, Italy; (D.B.); (G.D.I.); (M.S.); (T.P.); (F.S.); (A.G.); (D.S.)
| | - Francesca Signore
- Thoracic Surgery Unit, Department of Organ Transplantation and Emergency, University Hospital of Bari, 70121 Bari, Italy; (D.B.); (G.D.I.); (M.S.); (T.P.); (F.S.); (A.G.); (D.S.)
| | - Alessandro Geronimo
- Thoracic Surgery Unit, Department of Organ Transplantation and Emergency, University Hospital of Bari, 70121 Bari, Italy; (D.B.); (G.D.I.); (M.S.); (T.P.); (F.S.); (A.G.); (D.S.)
| | - Doroty Sampietro
- Thoracic Surgery Unit, Department of Organ Transplantation and Emergency, University Hospital of Bari, 70121 Bari, Italy; (D.B.); (G.D.I.); (M.S.); (T.P.); (F.S.); (A.G.); (D.S.)
| | - Michele Montrone
- Medical Thoracic Oncology Unit, IRCCS Istituto Tumori “Giovanni Paolo II”, 70121 Bari, Italy; (M.M.); (D.G.)
| | - Domenico Galetta
- Medical Thoracic Oncology Unit, IRCCS Istituto Tumori “Giovanni Paolo II”, 70121 Bari, Italy; (M.M.); (D.G.)
| | - Giuseppe Marulli
- Thoracic Surgery Unit, Department of Organ Transplantation and Emergency, University Hospital of Bari, 70121 Bari, Italy; (D.B.); (G.D.I.); (M.S.); (T.P.); (F.S.); (A.G.); (D.S.)
- Correspondence: or
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Age-different extent of resection for clinical IA non-small cell lung cancer: analysis of nodal metastasis. Sci Rep 2020; 10:9587. [PMID: 32533050 PMCID: PMC7293256 DOI: 10.1038/s41598-020-66509-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2020] [Accepted: 05/19/2020] [Indexed: 02/08/2023] Open
Abstract
Whether age has any impact on the risk of lymph node (LN) metastasis in patients with early-stage non-small cell lung cancer (NSCLC) remains controversial. Therefore, we aimed to objectively compare the risk of LN metastasis between elderly and young patients so as to justify for age-different extent of surgical resection for treating these patients. We retrospectively collected clinical data of patients undergoing lobectomy or segmentectomy with systematic hilar and mediastinal LN dissection for clinical stage IA peripheral NSCLC from January 2015 to December 2018. Both multivariate logistic regression analysis and propensity score-matched (PSM) analysis were applied to compare the risk of LN metastasis between elderly (>65 years old) and young (≤65 years old) patients. We finally included a total of 590 patients for analysis (142 elderly patients and 448 young patients). In the analysis of unmatched cohorts, young patients tended to have higher rates of hilar/intrapulmonary LN (13.4% VS 9.2%) and mediastinal LN metastasis (10.5% VS 6.3%) than elderly patients. In the multivariate analysis, age was found to be an independent predictor of both hilar/intrapulmonary (Odds ratio(OR) = 2.065, 95%confidence interval(CI): 1.049–4.064, P = 0.036) and mediastinal (OR = 2.400, 95%CI: 1.083–5.316, P = 0.031) LN metastasis. Moreover, in the analysis of well-matched cohorts generated by PSM analysis, young patients had significantly higher rates of hilar/intrapulmonary (18.8% VS 9.4%, P = 0.039) and mediastinal LN metastasis (17.1% VS 6.0%, P = 0.008) than elderly patients. Therefore, age remains to be an independent predictor of LN metastasis in early-stage NSCLC and age-different extent of surgical resection may be justified for these patients.
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Zhang R, Zhang X, Huang Z, Wang F, Lin Y, Wen Y, Liu L, Li J, Liu X, Xie W, Huang M, Wang G, Yang L, Zhao D, Yu X, Xi K, Wang W, Cai L, Zhang L. Development and validation of a preoperative noninvasive predictive model based on circular tumor DNA for lymph node metastasis in resectable non-small cell lung cancer. Transl Lung Cancer Res 2020; 9:722-730. [PMID: 32676334 PMCID: PMC7354122 DOI: 10.21037/tlcr-20-593] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Background Clinical lymph node staging in resectable non-small cell lung cancer (NSCLC) patients not only indicates prognosis, but also determines primary treatment strategy. The demand of noninvasive tool for preoperative lymph node metastasis prediction remains significant. This study aimed to develop and externally validate a preoperative noninvasive predictive model based on circular tumor DNA (ctDNA) for the lymph node metastasis in resectable NSCLC patients. Methods Resectable NSCLC patients in TRACERx cohort were included as training group. Potential preoperative noninvasively accessible predictors were incorporated into the development of a nomogram via multivariate logistic regression. The predictive model was externally validated by a similar cohort from our hospital. Results Overall, 58 patients from TRACERx cohort were included as training group and 37 patients from our hospital were included as external validation group. Variant allele frequency (VAF) level of ctDNA was significantly associated with lymph node metastasis (OR: 4.89, 95% CI: 1.22–19.54, P=0.03). The predictive model incorporating age, tumor size and VAF demonstrated satisfactory discrimination and calibration in both training group (AUC =0.77, 95% CI: 0.65–0.90, P=0.001) and external validation group (AUC =0.84, 95% CI: 0.70–0.99, P=0.005). Conclusions High VAF level in preoperative ctDNA may indicate lymph node metastasis of resectable NSCLC. And a preoperative noninvasive predictive model based on ctDNA for the lymph node metastasis in resectable NSCLC patients was developed and externally validated with satisfactory discrimination and calibration.
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Affiliation(s)
- Rusi Zhang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China.,Department of Thoracic Surgery, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Xuewen Zhang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China.,Department of Anesthesiology, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Zirui Huang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China.,Department of Thoracic Surgery, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Fang Wang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China.,Department of Molecular Pathology, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Yongbin Lin
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China.,Department of Thoracic Surgery, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Yingsheng Wen
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China.,Department of Thoracic Surgery, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Li Liu
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China.,Department of Thoracic Surgery, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Jinbo Li
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China.,Department of Thoracic Surgery, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Xinyi Liu
- The Medical Department, 3D Medicines Inc., Shanghai 201114, China
| | - Wenzhuan Xie
- The Medical Department, 3D Medicines Inc., Shanghai 201114, China
| | - Mengli Huang
- The Medical Department, 3D Medicines Inc., Shanghai 201114, China
| | - Gongming Wang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China.,Department of Thoracic Surgery, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Longjun Yang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China.,Department of Thoracic Surgery, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Dechang Zhao
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China.,Department of Thoracic Surgery, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Xiangyang Yu
- Department of Thoracic Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Kexing Xi
- Department of Colorectal Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Weidong Wang
- Department of Thoracic Surgery, School of Medicine, The First Affiliated Hospital, Zhejiang University, Hangzhou 310003, China
| | - Ling Cai
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China.,Department of Radiation Oncology, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Lanjun Zhang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China.,Department of Thoracic Surgery, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
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Nakahashi K, Tsunooka N, Hirayama K, Matsuno M, Endo M, Akahira J, Taguri M. Preoperative predictors of lymph node metastasis in clinical T1 adenocarcinoma. J Thorac Dis 2020; 12:2352-2360. [PMID: 32642140 PMCID: PMC7330315 DOI: 10.21037/jtd.2020.03.74] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Background The subcategory “solid component of tumor” is a new criterion of tumor categories in the updated eighth edition of the TNM classification. Nevertheless, the predictors of lymph node metastasis among patients with clinical T1 adenocarcinoma, based on the TNM classification 8th edition, remain unclear. This study aimed to identify the preoperative predictors of lymph node metastasis in clinical T1 adenocarcinoma by comparing clinicopathological characteristics between the groups with and without lymph node metastasis. Methods We performed a retrospective observational single-center study at the Sendai Kousei Hospital. From January 2012 to September 2019, we included 515 patients who underwent curative lobectomy or segmentectomy and mediastinal lymph node dissection among those with clinical T1 adenocarcinoma according to the UICC-TNM staging 8th edition. They were divided into two groups: those with lymph node metastasis (positive group) and those without (negative group). The clinicopathological factors were retrospectively analyzed and compared between the groups. Results In univariate analysis, carcinoembryonic antigen (>5.0 ng/mL) (P=0.0007), maximum standardized uptake (>3.5) (P<0.0001), clinical T factor (T1c) (P<0.0001), and consolidation tumor ratio (>0.85) (P<0.0001) were significant predictors of lymph node metastasis. Multivariate analysis revealed that maximum standardized uptake SUVmax (>3.5) (odds ratio =10.4, P<0.0001) was independently associated with lymph node metastasis. In univariate analysis, carcinoembryonic antigen (>5.0) (P=0.048) was the only predictor of lymph node metastasis among patients of cT1b, while no parameters were identified as significant predictors among patients of cT1c. Conclusions SUVmax and CEA are useful preoperative predictors of lymph node metastases in patients with clinical T1 adenocarcinoma, stratified to T1b and T1c, based on the 8th TNM classification.
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Affiliation(s)
- Kenta Nakahashi
- Department of Thoracic Surgery, Sendai Kousei Hospital, Sendai, Japan
| | - Nobuo Tsunooka
- Department of Thoracic Surgery, Sendai Kousei Hospital, Sendai, Japan
| | - Kyo Hirayama
- Department of Thoracic Surgery, Sendai Kousei Hospital, Sendai, Japan
| | - Masahiro Matsuno
- Department of Thoracic Surgery, Sendai Kousei Hospital, Sendai, Japan
| | - Mareyuki Endo
- Department of Pathology, Sendai Kousei Hospital, Sendai, Japan
| | - Junichi Akahira
- Department of Pathology, Sendai Kousei Hospital, Sendai, Japan
| | - Masataka Taguri
- Department of Data Science, Yokohama City University, School of Data Science, Yokohama, Japan
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Martinez-Zayas G, Almeida FA, Simoff MJ, Yarmus L, Molina S, Young B, Feller-Kopman D, Sagar AES, Gildea T, Debiane LG, Grosu HB, Casal RF, Arain MH, Eapen GA, Jimenez CA, Noor LZ, Baghaie S, Song J, Li L, Ost DE. A Prediction Model to Help with Oncologic Mediastinal Evaluation for Radiation: HOMER. Am J Respir Crit Care Med 2020; 201:212-223. [PMID: 31574238 PMCID: PMC6961739 DOI: 10.1164/rccm.201904-0831oc] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2019] [Accepted: 09/27/2019] [Indexed: 02/06/2023] Open
Abstract
Rationale: When stereotactic ablative radiotherapy is an option for patients with non-small cell lung cancer (NSCLC), distinguishing between N0, N1, and N2 or N3 (N2|3) disease is important.Objectives: To develop a prediction model for estimating the probability of N0, N1, and N2|3 disease.Methods: Consecutive patients with clinical-radiographic stage T1 to T3, N0 to N3, and M0 NSCLC who underwent endobronchial ultrasound-guided staging from a single center were included. Multivariate ordinal logistic regression analysis was used to predict the presence of N0, N1, or N2|3 disease. Temporal validation used consecutive patients from 3 years later at the same center. External validation used three other hospitals.Measurements and Main Results: In the model development cohort (n = 633), younger age, central location, adenocarcinoma, and higher positron emission tomography-computed tomography nodal stage were associated with a higher probability of having advanced nodal disease. Areas under the receiver operating characteristic curve (AUCs) were 0.84 and 0.86 for predicting N1 or higher (vs. N0) disease and N2|3 (vs. N0 or N1) disease, respectively. Model fit was acceptable (Hosmer-Lemeshow, P = 0.960; Brier score, 0.36). In the temporal validation cohort (n = 473), AUCs were 0.86 and 0.88. Model fit was acceptable (Hosmer-Lemeshow, P = 0.172; Brier score, 0.30). In the external validation cohort (n = 722), AUCs were 0.86 and 0.88 but required calibration (Hosmer-Lemeshow, P < 0.001; Brier score, 0.38). Calibration using the general calibration method resulted in acceptable model fit (Hosmer-Lemeshow, P = 0.094; Brier score, 0.34).Conclusions: This prediction model can estimate the probability of N0, N1, and N2|3 disease in patients with NSCLC. The model has the potential to facilitate decision-making in patients with NSCLC when stereotactic ablative radiotherapy is an option.
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Affiliation(s)
- Gabriela Martinez-Zayas
- Escuela de Medicina y Ciencias de la Salud, Tecnologico de Monterrey, Monterrey, Mexico
- Department of Pulmonary Medicine and
| | | | - Michael J. Simoff
- Department of Pulmonary and Critical Care Medicine, Henry Ford Hospital, Detroit, Michigan
| | - Lonny Yarmus
- Division of Pulmonary and Critical Care, Johns Hopkins University, Baltimore, Maryland; and
| | - Sofia Molina
- Escuela de Medicina y Ciencias de la Salud, Tecnologico de Monterrey, Monterrey, Mexico
- Department of Pulmonary Medicine and
| | - Benjamin Young
- Division of Pulmonary and Critical Care Medicine, University Hospitals, Cleveland, Ohio
| | - David Feller-Kopman
- Division of Pulmonary and Critical Care, Johns Hopkins University, Baltimore, Maryland; and
| | | | - Thomas Gildea
- Department of Pulmonary Medicine, Cleveland Clinic, Cleveland, Ohio
| | - Labib G. Debiane
- Division of Pulmonary and Critical Care, Johns Hopkins University, Baltimore, Maryland; and
| | | | | | | | | | | | | | | | - Juhee Song
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Liang Li
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas
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Yang M, She Y, Deng J, Wang T, Ren Y, Su H, Wu J, Sun X, Jiang G, Fei K, Zhang L, Xie D, Chen C. CT-based radiomics signature for the stratification of N2 disease risk in clinical stage I lung adenocarcinoma. Transl Lung Cancer Res 2019; 8:876-885. [PMID: 32010566 DOI: 10.21037/tlcr.2019.11.18] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Background Risk stratification of N2 disease is vital for selecting candidates to receive invasive mediastinal staging modalities. In this study, we aimed to stratify the risk of N2 metastasis in clinical stage I lung adenocarcinoma using radiomics analysis. Methods Two datasets of patients with clinical stage I lung adenocarcinoma who underwent lung resection were included (training dataset, 880; validation dataset, 322). Using PyRadiomics, 1,078 computed tomography (CT)-based radiomics features were extracted after semi-automated lung nodule segmentation. In order to predict N2 status, a radiomics signature was constructed after selecting the optimal radiomics feature subset by sequentially applying minimum-redundancy-maximum-relevance and least absolute shrinkage and selection operator (LASSO) techniques. Its performance was validated in the validation dataset. Results The incidences of N2 metastasis were 8.4% and 7.1% in the training and validation datasets, respectively. Unsupervised cluster analysis revealed that radiomics features significantly correlated with lymph node status and pathological subtypes. For N2 disease prediction, five radiomics features were selected to establish the radiomics signature, which showed a significantly better predictive performance than clinical factors (P<0.001). The area under the receiver operating characteristic curve was 0.81 (0.77-0.86) and 0.69 (0.63-0.75) for radiomics signature and clinical factors, respectively, in the training dataset, and 0.82 (0.71-0.92) and 0.64 (0.52-0.75), respectively, in the validation dataset. Conclusions The established CT-based radiomics signature could stratify the risk of N2 metastasis in clinical stage I lung adenocarcinoma, thus assisting clinicians in making patient-specific mediastinal staging strategy.
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Affiliation(s)
- Minglei Yang
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai 200433, China.,Department of Thoracic Surgery, Ningbo No.2 Hospital, Chinese Academy of Sciences, Ningbo 315010, China
| | - Yunlang She
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai 200433, China
| | - Jiajun Deng
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai 200433, China
| | - Tingting Wang
- Department of Radiology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai 200433, China
| | - Yijiu Ren
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai 200433, China
| | - Hang Su
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai 200433, China
| | - Junqi Wu
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai 200433, China
| | - Xiwen Sun
- Department of Radiology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai 200433, China
| | - Gening Jiang
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai 200433, China
| | - Ke Fei
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai 200433, China
| | - Lei Zhang
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai 200433, China
| | - Dong Xie
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai 200433, China
| | - Chang Chen
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai 200433, China
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Preoperative Risk Assessment of Lymph Node Metastasis in cT1 Lung Cancer: A Retrospective Study from Eastern China. J Immunol Res 2019; 2019:6263249. [PMID: 31886306 PMCID: PMC6914921 DOI: 10.1155/2019/6263249] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2019] [Accepted: 10/28/2019] [Indexed: 12/26/2022] Open
Abstract
Background Lymph node status of clinical T1 (diameter ≤ 3 cm) lung cancer largely affects the treatment strategies in the clinic. In order to assess lymph node status before operation, we aim to develop a noninvasive predictive model using preoperative clinical information. Methods We retrospectively reviewed 924 patients (development group) and 380 patients (validation group) of clinical T1 lung cancer. Univariate analysis followed by polytomous logistic regression was performed to estimate different risk factors of lymph node metastasis between N1 and N2 diseases. A predictive model of N2 metastasis was established with dichotomous logistic regression, externally validated and compared with previous models. Results Consolidation size and clinical N stage based on CT were two common independent risk factors for both N1 and N2 metastases, with different odds ratios. For N2 metastasis, we identified five independent predictors by dichotomous logistic regression: peripheral location, larger consolidation size, lymph node enlargement on CT, no smoking history, and higher levels of serum CEA. The model showed good calibration and discrimination ability in the development data, with the reasonable Hosmer-Lemeshow test (p = 0.839) and the area under the ROC being 0.931 (95% CI: 0.906-0.955). When externally validated, the model showed a great negative predictive value of 97.6% and the AUC of our model was better than other models. Conclusion In this study, we analyzed risk factors for both N1 and N2 metastases and built a predictive model to evaluate possibilities of N2 metastasis of clinical T1 lung cancers before the surgery. Our model will help to select patients with low probability of N2 metastasis and assist in clinical decision to further management.
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Shin SH, Jeong BH, Jhun BW, Yoo H, Lee K, Kim H, Kwon OJ, Han J, Kim J, Lee KS, Um SW. The utility of endosonography for mediastinal staging of non-small cell lung cancer in patients with radiological N0 disease. Lung Cancer 2019; 139:151-156. [PMID: 31805443 DOI: 10.1016/j.lungcan.2019.11.021] [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] [Received: 09/15/2019] [Revised: 11/24/2019] [Accepted: 11/25/2019] [Indexed: 12/25/2022]
Abstract
OBJECTIVES Recent practice guidelines recommend endosonography for patients with radiological N0 non-small cell lung cancer (NSCLC) when the primary tumors are >3 cm in diameter or centrally located. However, any role for endosonography remains debatable. We evaluated the utility of endosonography in patients with radiological N0 NSCLC based on tumor centrality, diameter and histology. MATERIALS AND METHODS Patients who underwent staging endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) with or without transesophageal bronchoscopic ultrasound-guided fine needle aspiration (EUS-B-FNA) for radiological N0 NSCLC were retrospectively investigated using prospectively collected endosonography data. The radiological N0 stage was defined by node diameter as evident on computed tomography images and 18F-FDG uptake using integrated positron emission tomography-computed tomography. RESULTS In total of 168 patients, the median size of the primary tumor was 39 mm, and 41 % of tumors were centrally located. The prevalence of occult mediastinal metastases was 11.3 % (19/168). The sensitivity of endosonography in terms of diagnosing occult mediastinal metastases was only 47 % (9/19); 6 of 10 patients with false-negative endosonography data exhibited metastases in accessible nodes. The diagnostic performance of endosonography did not differ by tumor centrality or diameter. Patients with adenocarcinoma histology showed higher prevalence of occult mediastinal metastases and higher false-negative results in endosonography compared with those with non-adenocarcinoma histology. CONCLUSION Not all patients with radiological N0 NSCLC benefit from endosonography, given the low prevalence of occult mediastinal metastases and the poor sensitivity of endosonography in this population. The strategy of invasive mediastinal staging needs to be tailored considering the histology of the tumor in this population.
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Affiliation(s)
- Sun Hye Shin
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Byeong-Ho Jeong
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Byung Woo Jhun
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Hongseok Yoo
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Kyungjong Lee
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Hojoong Kim
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - O Jung Kwon
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Jungho Han
- Department of Pathology and Translational Genomics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Jhingook Kim
- Department of Thoracic Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Kyung Soo Lee
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Sang-Won Um
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
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Development of a predictive radiomics model for lymph node metastases in pre-surgical CT-based stage IA non-small cell lung cancer. Lung Cancer 2019; 139:73-79. [PMID: 31743889 DOI: 10.1016/j.lungcan.2019.11.003] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2019] [Revised: 11/03/2019] [Accepted: 11/08/2019] [Indexed: 12/25/2022]
Abstract
OBJECTIVES To develop and validate predictive models using clinical parameters, radiomic features and a combination of both for lymph node metastasis (LNM) in pre-surgical CT-based stage IA non-small cell lung cancer (NSCLC) patients. METHODS This retrospective study included 649 pre-surgical CT-based stage IA NSCLC patients from our hospital. One hundred and thirty-eight (21 %) of the 649 patients had LNM after surgery. A total of 396 radiomic features were extracted from the venous phase contrast enhanced computed tomography (CECT). The training group included 455 patients (97 with and 358 without LNM) and the testing group included 194 patients (41 with and 153 without LNM). The least absolute shrinkage and selection operator (LASSO) algorithm was used for radiomic feature selection. The random forest (RF) was used for model development. Three models (a clinical model, a radiomics model, and a combined model) were developed to predict LNM in early stage NSCLC patients. The area under the receiver operating characteristic (ROC) curve (AUC) value and decision curve analysis were used to evaluate the performance in LNM status (with or without LNM) using the three models. RESULTS The ROC analysis (also decision curve analysis) showed predictive performance for LNM of the radiomics model (AUC values for training and testing, respectively 0.898 and 0.851) and of the combined model (0.911 and 0.860, respectively). Both performed better than the clinical model (0.739 and 0.614, respectively; delong test p-values both<0.001). CONCLUSION A radiomics model using the venous phase of CE-CT has potential for predicting LNM in pre-surgical CT-based stage IA NSCLC patients.
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Chen B, Xia W, Wang Z, Zhao H, Li X, Liu L, Liu Y, Hu J, Fu X, Li Y, Xu Y, Liu D, Yang H, Xu L, Jiang F. Risk analyses of N2 lymph-node metastases in patients with T1 non-small cell lung cancer: a multi-center real-world observational study in China. J Cancer Res Clin Oncol 2019; 145:2771-2777. [PMID: 31428933 DOI: 10.1007/s00432-019-03006-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2019] [Accepted: 08/16/2019] [Indexed: 02/05/2023]
Abstract
PURPOSE N2 lymph-node metastases occur in approximately 6-17% of the patients with T1-2 non-small cell lung cancer (NSCLC). However, the clinical characteristics of N2 patients are not fully understood. METHODS This retrospective, multi-center analysis included T1 NSCLC patients receiving surgical resection during a period from Jan 2nd, 2014 to Dec 27th, 2017. The diagnosis was pathologically verified in all cases. Univariate and multivariate logistic regression analyses were conducted to analyze the factors that are associated with pN2 lymph-node metastases. RESULTS A total of 10,885 patients (48.4% men; 84.7% adenocarcinoma) were included in the analysis. The mean age was 59.0 ± 9.9 years. The mean tumor size was 1.8 ± 0.8 cm. Of the patients, 3260 (29.9%) were smokers or ex-smokers. Lymph-node metastases were verified in 1808 (16.6%) patients, and 1167 (10.7%) patients had N2 lymph-node metastases. The multivariate analyses indicated that larger tumor size, lower differentiation, CEA level ≥ 5 ng/mL, vascular invasion (+), and pleural involvement (+) were associated with higher percentages of N2 lymph-node metastases (p < 0.001 for all). CONCLUSIONS This study demonstrated the significant association between N2 lymph-node metastases and tumor size and differentiation, CEA levels, and status of vascular invasion and pleural involvement.
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Affiliation(s)
- Bing Chen
- Department of Thoracic Surgery, Nanjing Medical University Affiliated Cancer Hospital, No.42, Baiziting, Xuanwu District, Nanjing, 210009, Jiangsu, China
- Department of Thoracic Surgery, Jiangsu Cancer Hospital and Jiangsu Institute of Cancer Research, Nanjing, 210009, China
- Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Cancer Institute of Jiangsu Province, Nanjing, 210009, China
| | - Wenjie Xia
- Department of Thoracic Surgery, Nanjing Medical University Affiliated Cancer Hospital, No.42, Baiziting, Xuanwu District, Nanjing, 210009, Jiangsu, China
- Department of Thoracic Surgery, Jiangsu Cancer Hospital and Jiangsu Institute of Cancer Research, Nanjing, 210009, China
- Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Cancer Institute of Jiangsu Province, Nanjing, 210009, China
| | - Zhongqiu Wang
- Department of Thoracic Surgery, Nanjing Medical University Affiliated Cancer Hospital, No.42, Baiziting, Xuanwu District, Nanjing, 210009, Jiangsu, China
- Department of Thoracic Surgery, Jiangsu Cancer Hospital and Jiangsu Institute of Cancer Research, Nanjing, 210009, China
| | - Heng Zhao
- Department of Thoracic Surgery, Shanghai Chest Hospital, Shanghai, 200030, China
| | - Xiaofei Li
- Department of Thoracic Surgery, Tangdu Hospital, Fourth Military Medical University, Xi'an, 710038, China
| | - Lunxu Liu
- Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Yang Liu
- Department of Thoracic Surgery, Chinese People's Liberation Army General Hospital, Beijing, 1000853, China
| | - Jian Hu
- Department of Thoracic Surgery, First Hospital Affiliated to Medical College of Zhejiang University, Hangzhou, 310000, China
| | - Xiangning Fu
- Department of Thoracic Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Yin Li
- Department of Thoracic Surgery, Henan Cancer Hospital, Zhengzhou, 450008, China
| | - Yijun Xu
- Department of Thoracic Surgery, Tianjin Chest Hospital, Tianjin, 300051, China
| | - Deruo Liu
- Department of Thoracic Surgery, China-Japan Friendship Hospital, Beijing, 100029, China
| | - Haiying Yang
- Medical Affairs, Linkdoc Technology Co, Ltd, Beijing, 100080, China
| | - Lin Xu
- Department of Thoracic Surgery, Nanjing Medical University Affiliated Cancer Hospital, No.42, Baiziting, Xuanwu District, Nanjing, 210009, Jiangsu, China.
- Department of Thoracic Surgery, Jiangsu Cancer Hospital and Jiangsu Institute of Cancer Research, Nanjing, 210009, China.
- Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Cancer Institute of Jiangsu Province, Nanjing, 210009, China.
| | - Feng Jiang
- Department of Thoracic Surgery, Nanjing Medical University Affiliated Cancer Hospital, No.42, Baiziting, Xuanwu District, Nanjing, 210009, Jiangsu, China.
- Department of Thoracic Surgery, Jiangsu Cancer Hospital and Jiangsu Institute of Cancer Research, Nanjing, 210009, China.
- Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Cancer Institute of Jiangsu Province, Nanjing, 210009, China.
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Verdial FC, Madtes DK, Hwang B, Mulligan MS, Odem-Davis K, Waworuntu R, Wood DE, Farjah F. Prediction Model for Nodal Disease Among Patients With Non-Small Cell Lung Cancer. Ann Thorac Surg 2019; 107:1600-1606. [PMID: 30710518 PMCID: PMC6535349 DOI: 10.1016/j.athoracsur.2018.12.041] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/06/2018] [Revised: 12/12/2018] [Accepted: 12/17/2018] [Indexed: 12/25/2022]
Abstract
BACKGROUND We characterized the performance characteristics of guideline-recommended invasive mediastinal staging (IMS) for lung cancer and developed a prediction model for nodal disease as a potential alternative approach to staging. METHODS We conducted a prospective cohort study of adults with suspected/confirmed non-small cell lung cancer without evidence of distant metastatic disease (by computed tomography/positron emission tomography) who underwent nodal evaluation by IMS and/or at the time of resection. The true-positive rate was the proportion of patients with true nodal disease selected to undergo IMS based on guideline recommendations, and the false-positive rate was the proportion of patients without true nodal disease selected to undergo IMS. Logistic regression was used to predict nodal disease using radiographic predictors. RESULTS Among 123 eligible subjects, 31 (25%) had pathologically confirmed nodal disease. A guideline-recommended invasive staging strategy had a true-positive rate and false-positive rate of 100% and 65%, respectively. The prediction model fit the data well (goodness-of-fit test, p = 0.55) and had excellent discrimination (optimism corrected c-statistic, 0.78; 95% confidence interval, 0.72 to 0.89). Exploratory analysis revealed that use of the prediction model could achieve a false-positive rate of 44% and a true-positive rate of 97%. CONCLUSIONS A guideline-recommended strategy for IMS selects all patients with true nodal disease and most patients without nodal disease for IMS. Our prediction model appears to maintain (within a margin of error) the sensitivity of a guideline-recommended invasive staging strategy and has the potential to reduce the use of invasive procedures.
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Affiliation(s)
- Francys C Verdial
- Department of Surgery, University of Washington School of Medicine, Seattle, Washington
| | - David K Madtes
- Division of Pulmonary and Critical Care Medicine, University of Washington, Seattle, Washington; Division of Pulmonary and Critical Care Medicine, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | - Billanna Hwang
- Department of Surgery, University of Washington School of Medicine, Seattle, Washington; Center for Lung Biology, University of Washington, Seattle, Washington
| | - Michael S Mulligan
- Department of Surgery, University of Washington School of Medicine, Seattle, Washington; Center for Lung Biology, University of Washington, Seattle, Washington
| | | | - Rachel Waworuntu
- Department of Surgery, University of Washington School of Medicine, Seattle, Washington; Center for Lung Biology, University of Washington, Seattle, Washington
| | - Douglas E Wood
- Department of Surgery, University of Washington School of Medicine, Seattle, Washington
| | - Farhood Farjah
- Department of Surgery, University of Washington School of Medicine, Seattle, Washington.
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Shin SH, Jeong DY, Lee KS, Cho JH, Choi YS, Lee K, Um SW, Kim H, Jeong BH. Which definition of a central tumour is more predictive of occult mediastinal metastasis in nonsmall cell lung cancer patients with radiological N0 disease? Eur Respir J 2019; 53:13993003.01508-2018. [PMID: 30635291 PMCID: PMC6422838 DOI: 10.1183/13993003.01508-2018] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2018] [Accepted: 12/18/2018] [Indexed: 12/25/2022]
Abstract
Background Guidelines recommend invasive mediastinal staging for centrally located tumours, even in radiological N0 nonsmall cell lung cancer (NSCLC). However, there is no uniform definition of a central tumour that is more predictive of occult mediastinal metastasis. Methods A total of 1337 consecutive patients with radiological N0 disease underwent invasive mediastinal staging. Tumours were categorised into central and peripheral by seven different definitions. Results About 7% (93 out of 1337) of patients had occult N2 disease, and they had significantly larger tumour size and more solid tumours on computed tomography. After adjustment for patient- and tumour-related characteristics, only the central tumour definition of the inner one-third of the hemithorax adopted by drawing concentric lines arising from the midline significantly predicted occult N2 disease (adjusted OR 2.13, 95% CI 1.17–3.87; p=0.013). This association was maintained after excluding patients with pure ground-glass nodules (adjusted OR 2.54, 95% CI 1.37–4.71; p=0.003) or only including those with solid tumours (adjusted OR 2.30, 95% CI 1.08–4.88; p=0.030). Conclusions We suggest that a central tumour should be defined using the inner one-third of the hemithorax adopted by drawing concentric lines from the midline. This is particularly useful for predicting occult N2 disease in patients with NSCLC. Central tumours defined as located in the inner one-third of the hemithorax adopted by drawing concentric lines from the midline are associated with occult mediastinal metastasis in patients with NSCLC and radiological N0 diseasehttp://ow.ly/scg630nbRmY
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Affiliation(s)
- Sun Hye Shin
- Division of Pulmonary and Critical Care Medicine, Dept of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.,These two authors contributed equally to this work
| | - Dong Young Jeong
- Dept of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.,These two authors contributed equally to this work
| | - Kyung Soo Lee
- Dept of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Jong Ho Cho
- Dept of Thoracic Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Yong Soo Choi
- Dept of Thoracic Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Kyungjong Lee
- Division of Pulmonary and Critical Care Medicine, Dept of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Sang-Won Um
- Division of Pulmonary and Critical Care Medicine, Dept of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Hojoong Kim
- Division of Pulmonary and Critical Care Medicine, Dept of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Byeong-Ho Jeong
- Division of Pulmonary and Critical Care Medicine, Dept of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
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Casal RF, Sepesi B, Sagar AES, Tschirren J, Chen M, Li L, Sunny J, Williams J, Grosu HB, Eapen GA, Jimenez CA, Ost DE. Centrally located lung cancer and risk of occult nodal disease: an objective evaluation of multiple definitions of tumour centrality with dedicated imaging software. Eur Respir J 2019; 53:13993003.02220-2018. [DOI: 10.1183/13993003.02220-2018] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2018] [Accepted: 02/08/2019] [Indexed: 12/25/2022]
Abstract
IntroductionCurrent guidelines recommend invasive mediastinal staging in patients with centrally located radiographic stage T1N0M0 nonsmall cell lung cancer (NSCLC). The lack of a specific definition of a central tumour has resulted in discrepancies among guidelines and heterogeneity in practice patterns.MethodsOur objective was to study specific definitions of tumour centrality and their association with occult nodal disease. Pre-operative chest computed tomography scans from patients with clinical (c) T1N0M0 NSCLC were processed with a dedicated software system that divides the lungs in thirds following vertical and concentric lines. This software accurately assigns tumours to a specific third based both on the location of the centre of the tumour and its most medial aspect, creating eight possible definitions of central tumours.Results607 patients were included in our study. Surgery was performed for 596 tumours (98%). The overall pathological (p) N disease was: 504 (83%) N0, 56 (9%) N1, 47 (8%) N2 and no N3. The prevalence of N2 disease remained relatively low regardless of tumour location. Central tumours were associated with upstaging from cN0 to any N (pN1/pN2). Two definitions were associated with upstaging to any N: concentric lines, inner one-third, centre of the tumour (OR 3.91, 95% CI 1.85–8.26; p<0.001) and concentric lines, inner two-thirds, most medial aspect of the tumour (OR 1.91, 95% CI 1.23–2.97; p=0.004).ConclusionsWe objectively identified two specific definitions of central tumours. While the rate of occult mediastinal disease was relatively low regardless of tumour location, central tumours were associated with upstaging from cN0 to any N.
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Chen B, Wang X, Yu X, Xia WJ, Zhao H, Li XF, Liu LX, Liu Y, Hu J, Fu XN, Li Y, Xu YJ, Liu DR, Yang HY, Xu L, Jiang F. Lymph node metastasis in Chinese patients with clinical T1 non-small cell lung cancer: A multicenter real-world observational study. Thorac Cancer 2019; 10:533-542. [PMID: 30666800 PMCID: PMC6397906 DOI: 10.1111/1759-7714.12970] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2018] [Revised: 12/17/2018] [Accepted: 12/18/2018] [Indexed: 02/05/2023] Open
Abstract
Background Approximately 8.3–15.9% of patients with clinical stage I non‐small cell lung cancer are subsequently shown to have lymph node metastasis. However, the clinical characteristics of patients with lymph node metastasis in China are not fully understood. Methods This is a multicenter retrospective analysis of pathological T1 non‐small cell lung cancer patients who underwent surgical resection from 2 January 2014 to 27 December 2017. Clinical and pathological information was collected with the assistance of the Large‐scale Data Analysis Center of Cancer Precision Medicine‐LinkDoc database. The clinical and pathological factors associated with lymph node metastasis were analyzed by univariate and multivariate logistic regression. Results A total of 10 885 participants (51.6% women; 15.3% squamous cell carcinoma) were included in the analysis. The median age was 60.0 years (range 12.9–86.6 years). A total of 1159 patients (10.6%) had metastases in mediastinal nodes (N2), and 640 patients (5.9%) had metastasis in pulmonary lymph nodes (N1). Most patients had T1b lung cancer (4766, 43.8%). Of the patients, 3260 (29.9%) were current or former smokers. The univariate and multivariate analyses showed that younger age, squamous cell carcinoma, poor differentiation, larger tumor size, carcinoembryonic antigen level ≥5 ng/mL, and vascular invasion (+) were significantly associated with higher percentages of lymph node metastases (P < 0.001 for all). Conclusion This real‐world study showed the significant association of lymph node metastasis with age, tumor size, histology and differentiation, carcinoembryonic antigen levels, and status of vascular invasion. Female patients with T1a adenocarcinoma in the right upper lobe barely had lymph node metastasis.
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Affiliation(s)
- Bing Chen
- Department of Thoracic Surgery, Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research & The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, China.,Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Cancer Institute of Jiangsu Province, Nanjing, China
| | - Xiaojun Wang
- Department of Thoracic Surgery, Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research & The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, China.,Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Cancer Institute of Jiangsu Province, Nanjing, China
| | - Xinnian Yu
- Department of Oncology, Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research & The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, China
| | - Wen-Jie Xia
- Department of Thoracic Surgery, Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research & The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, China.,Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Cancer Institute of Jiangsu Province, Nanjing, China
| | - Heng Zhao
- Department of Thoracic Surgery, Shanghai Chest Hospital, Shanghai, China
| | - Xiao-Fei Li
- Department of Thoracic Surgery, Tangdu Hospital, Fourth Military Medical University, Xi'an, China
| | - Lun-Xu Liu
- Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Yang Liu
- Department of Thoracic Surgery, Chinese People's Liberation Army General Hospital, Beijing, China
| | - Jian Hu
- Department of Thoracic Surgery, First Hospital Affiliated to Medical College of Zhejiang University, Hangzhou, China
| | - Xiang-Ning Fu
- Department of Thoracic Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yin Li
- Department of Thoracic Surgery, Henan Cancer Hospital, Zhengzhou, China
| | - Yi-Jun Xu
- Department of Thoracic Surgery, Tianjin Chest Hospital, Tianjin, China
| | - De-Ruo Liu
- Department of Thoracic Surgery, China-Japan Friendship Hospital, Beijing, China
| | - Hai-Ying Yang
- Medical Affairs, Linkdoc Technology Co, Ltd, Beijing, China
| | - Lin Xu
- Department of Thoracic Surgery, Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research & The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, China.,Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Cancer Institute of Jiangsu Province, Nanjing, China
| | - Feng Jiang
- Department of Thoracic Surgery, Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research & The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, China.,Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Cancer Institute of Jiangsu Province, Nanjing, China
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Zheng D, Chen H. Lung cancer screening in China: early-stage lung cancer and minimally invasive surgery 3.0. J Thorac Dis 2018; 10:S1677-S1679. [PMID: 30034835 DOI: 10.21037/jtd.2018.05.206] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Affiliation(s)
- Difan Zheng
- Department of Thoracic Surgery, Fudan University Shanghai Cancer Center, Shanghai 200032, China.,Department of Oncology, Fudan University Shanghai Medical College, Shanghai 200032, China
| | - Haiquan Chen
- Department of Thoracic Surgery, Fudan University Shanghai Cancer Center, Shanghai 200032, China.,Department of Oncology, Fudan University Shanghai Medical College, Shanghai 200032, China
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Gu Y, She Y, Xie D, Dai C, Ren Y, Fan Z, Zhu H, Sun X, Xie H, Jiang G, Chen C. A Texture Analysis-Based Prediction Model for Lymph Node Metastasis in Stage IA Lung Adenocarcinoma. Ann Thorac Surg 2018; 106:214-220. [PMID: 29550204 DOI: 10.1016/j.athoracsur.2018.02.026] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/04/2017] [Revised: 02/02/2018] [Accepted: 02/12/2018] [Indexed: 10/17/2022]
Abstract
BACKGROUND Some clinical N0 lung adenocarcinomas have been pathologically diagnosed as N1 or N2. To improve the preoperative diagnostic accuracy of lymph node disease, we developed a prediction model for lymph node metastasis in cT1 N0 M0 lung adenocarcinoma based on computed tomography texture analysis and clinical characteristics to estimate the probability of lymph node metastasis. METHODS The records of 501 consecutive patients with cT1 N0 M0 lung adenocarcinoma who underwent computed tomography scan and pulmonary resection with systematic lymph nodes dissection or lymph nodes sampling were reviewed. Each nodule was manually segmented, and its computerized texture features were extracted. Multivariate logistic regression with fivefold validation was used to estimate independent predictors and build the prediction model. The prediction model was then externally validated. A nomogram was developed based on logistic regression results. RESULTS Among 501 patients, 41 were diagnosed with positive lymph nodes (8.18%). Four independent predictors were identified: the skewness and 90th percentile of computed tomography number, nodule compactness, and carcinoembryonic antigen level. This model showed good calibration (Hosmer-Lemeshow test, p = 0.337), with an area under the curve of 0.883 (95% confidence interval, 0.842 to 0.924; p < 0.001). The area under the curve was 0.808 (95% confidence interval, 0.735 to 0.880) when validated with independent data. CONCLUSIONS A model based on computerized textures and carcinoembryonic antigen level can assess the lymph node status of patients with cT1 N0 M0 lung adenocarcinoma preoperatively, which could assist surgeons in making subsequent clinical decisions.
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Affiliation(s)
- Yawei Gu
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China
| | - Yunlang She
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China
| | - Dong Xie
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China
| | - Chenyang Dai
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China
| | - Yijiu Ren
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China
| | - Ziwen Fan
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China
| | - Huiyuan Zhu
- Department of Radiology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China
| | - Xiwen Sun
- Department of Radiology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China
| | - Huikang Xie
- Department of Pathology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China
| | - Gening Jiang
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China
| | - Chang Chen
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China.
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Decaluwé H, Moons J, Fieuws S, De Wever W, Deroose C, Stanzi A, Depypere L, Nackaerts K, Coolen J, Lambrecht M, Verbeken E, De Ruysscher D, Vansteenkiste J, Van Raemdonck D, De Leyn P, Dooms C. Is central lung tumour location really predictive for occult mediastinal nodal disease in (suspected) non-small-cell lung cancer staged cN0 on 18F-fluorodeoxyglucose positron emission tomography–computed tomography? Eur J Cardiothorac Surg 2018; 54:134-140. [DOI: 10.1093/ejcts/ezy018] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/10/2017] [Accepted: 12/30/2017] [Indexed: 12/25/2022] Open
Affiliation(s)
- Herbert Decaluwé
- Department of Thoracic Surgery, University Hospitals Leuven, Leuven, Belgium
| | - Johnny Moons
- Department of Thoracic Surgery, University Hospitals Leuven, Leuven, Belgium
| | - Steffen Fieuws
- Leuven Biostatistics and Statistical Bioinformatics Centre (L-BioStat), Leuven, Belgium
| | - Walter De Wever
- Department of Radiology, University Hospitals Leuven, Leuven, Belgium
| | - Christophe Deroose
- Department of Nuclear Medicine, University Hospitals Leuven, Leuven, Belgium
| | - Alessia Stanzi
- Department of Thoracic Surgery, University Hospitals Leuven, Leuven, Belgium
| | - Lieven Depypere
- Department of Thoracic Surgery, University Hospitals Leuven, Leuven, Belgium
| | - Kristiaan Nackaerts
- Department of Respiratory Oncology & Pulmonology, University Hospitals Leuven, Leuven, Belgium
| | - Johan Coolen
- Department of Radiology, University Hospitals Leuven, Leuven, Belgium
| | - Maarten Lambrecht
- Department of Radiotherapy, University Hospitals Leuven, Leuven, Belgium
| | - Eric Verbeken
- Department of Pathology, University Hospitals Leuven, Belgium
| | - Dirk De Ruysscher
- Department of Radiotherapy, University Hospitals Leuven, Leuven, Belgium
| | - Johan Vansteenkiste
- Leuven Biostatistics and Statistical Bioinformatics Centre (L-BioStat), Leuven, Belgium
| | - Dirk Van Raemdonck
- Department of Thoracic Surgery, University Hospitals Leuven, Leuven, Belgium
| | - Paul De Leyn
- Department of Thoracic Surgery, University Hospitals Leuven, Leuven, Belgium
| | - Christophe Dooms
- Department of Respiratory Oncology & Pulmonology, University Hospitals Leuven, Leuven, Belgium
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Xia W, Wang A, Jin M, Mao Q, Xia W, Dong G, Chen B, Ma W, Xu L, Jiang F. Young age increases risk for lymph node positivity but decreases risk for non-small cell lung cancer death. Cancer Manag Res 2018; 10:41-48. [PMID: 29386914 PMCID: PMC5764302 DOI: 10.2147/cmar.s152017] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
Non-small cell lung cancer (NSCLC) prognosis and risk of lymph node positivity (LN+) are reference points for reasonable treatments. The aim of the current study was to investigate the effect of age on LN+ and NSCLC death. Data from the Surveillance, Epidemiology, and End Results (SEER) registry were used to identify 82,253 patients with NSCLC diagnosed between 1988 and 2008. All the patients underwent standard lung cancer surgery with lymph node examination. Demographic and clinicopathological parameters were extracted and compared among each age group. Impact of age on LN+ and NSCLC death was evaluated by the Cochran-Armitage trend test and logistic univariate and multivariate analyses for all T stages. Overall, 22,711 (27.60%) patients of the entirety had lymph node metastasis and 28,968 (35.22%) patients died of NSCLC within 5 years. With the increase in age, LN+ rates decreased regardless of T stages (P<0.001), whereas NSCLC-specific mortality increased in stages T1-T3 (P<0.001). Controlling other covariates in multivariable logistic regression, age remained an independent risk factor for LN+ in all T stages (P<0.05) and in stages T1-T3 (P<0.05). Our SEER analysis demonstrated a higher rate of LN+ and lower mortality in younger patients with NSCLC, after accounting for other covariates.
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Affiliation(s)
- Wenjie Xia
- Department of Thoracic Surgery, Nanjing Medical University Affiliated Cancer Hospital & Jiangsu Cancer Hospital, Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Cancer Institute of Jiangsu Province.,Department of Oncology, Fourth Clinical College of Nanjing Medical University, Nanjing
| | - Anpeng Wang
- Department of Thoracic Surgery, Nanjing Medical University Affiliated Cancer Hospital & Jiangsu Cancer Hospital, Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Cancer Institute of Jiangsu Province.,Department of Oncology, Fourth Clinical College of Nanjing Medical University, Nanjing
| | - Meng Jin
- Department of Gastroenterology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing
| | - Qixing Mao
- Department of Thoracic Surgery, Nanjing Medical University Affiliated Cancer Hospital & Jiangsu Cancer Hospital, Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Cancer Institute of Jiangsu Province.,Department of Oncology, Fourth Clinical College of Nanjing Medical University, Nanjing
| | - Wenying Xia
- Department of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, People's Republic of China
| | - Gaochao Dong
- Department of Thoracic Surgery, Nanjing Medical University Affiliated Cancer Hospital & Jiangsu Cancer Hospital, Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Cancer Institute of Jiangsu Province
| | - Bing Chen
- Department of Thoracic Surgery, Nanjing Medical University Affiliated Cancer Hospital & Jiangsu Cancer Hospital, Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Cancer Institute of Jiangsu Province.,Department of Oncology, Fourth Clinical College of Nanjing Medical University, Nanjing
| | - Weidong Ma
- Department of Thoracic Surgery, Nanjing Medical University Affiliated Cancer Hospital & Jiangsu Cancer Hospital, Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Cancer Institute of Jiangsu Province.,Department of Oncology, Fourth Clinical College of Nanjing Medical University, Nanjing
| | - Lin Xu
- Department of Thoracic Surgery, Nanjing Medical University Affiliated Cancer Hospital & Jiangsu Cancer Hospital, Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Cancer Institute of Jiangsu Province
| | - Feng Jiang
- Department of Thoracic Surgery, Nanjing Medical University Affiliated Cancer Hospital & Jiangsu Cancer Hospital, Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Cancer Institute of Jiangsu Province
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48
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Tanner NT, Gould MK. Invasive Mediastinal Staging in Lung Cancer. Use a Prediction Model or Just Do It? Am J Respir Crit Care Med 2017; 195:1556-1558. [PMID: 28617083 DOI: 10.1164/rccm.201702-0397ed] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Affiliation(s)
- Nichole T Tanner
- 1 Health Equity and Rural Outreach Innovation Center Ralph H. Johnson Veterans Affairs Hospital Charleston, South Carolina.,2 Division of Pulmonary and Critical Care Medical University of South Carolina Charleston, South Carolina and
| | - Michael K Gould
- 3 Kaiser Permanente Southern California Pasadena, California
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49
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Bille A, Woo KM, Ahmad U, Rizk NP, Jones DR. Incidence of occult pN2 disease following resection and mediastinal lymph node dissection in clinical stage I lung cancer patients. Eur J Cardiothorac Surg 2017; 51:674-679. [PMID: 28200091 DOI: 10.1093/ejcts/ezw400] [Citation(s) in RCA: 71] [Impact Index Per Article: 10.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/23/2016] [Accepted: 10/10/2016] [Indexed: 12/22/2022] Open
Abstract
Objectives Early clinical stage (T1 and T2) non-small cell lung cancer (NSCLC) is commonly treated with anatomic lung resection and lymph node sampling or dissection. The aims of this study were to evaluate the incidence and the distribution of occult N2 disease according to tumour location and the short- and long-term outcomes. Methods We performed a retrospective review of patients with clinical stage I NSCLC who underwent anatomic lung resection and lymphadenectomy. Mediastinal lymphadenectomy (ML) was defined as resection of at least 2 mediastinal stations, always including station 7 lymph nodes. Patients who had a lobe-specific lymphadenectomy were excluded. Results One thousand six hundred and sixty-seven consecutive patients met inclusion criteria and were included. Overall, 9% (146/1667) of the patients had occult pN2 disease. At multivariable analysis, adenocarcinoma histology and vascular invasion were independently associated with greater risk of occult pN2 disease. In left and right upper lobe tumours, station 7 nodes were involved in 5 and 13% of pN2 positive cases, respectively. Station 5 and station 2/4 nodes were involved in 29 and 18% of left and right lower lobe pN2 tumours, respectively. There was no postoperative mortality, and postoperative morbidity was 28%. The median overall survival was 77.4 months. N0 patients had a median overall survival of 83.7 months vs 48.0 months and 37.9 months in N1 and N2 populations, respectively ( P < 0.001). Conclusions Sixteen percent of pN2 patients had mediastinal lymph node metastasis beyond the lobe-specific lymphatic drainage. We recommend a complete lymphadenectomy be performed, even in clinical stage I NSCLC.
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Affiliation(s)
- Andrea Bille
- Department of Thoracic Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Kaitlin M Woo
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Usman Ahmad
- Department of Thoracic Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Nabil P Rizk
- Department of Thoracic Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - David R Jones
- Department of Thoracic Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
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50
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O'Connell OJ, Almeida FA, Simoff MJ, Yarmus L, Lazarus R, Young B, Chen Y, Semaan R, Saettele TM, Cicenia J, Bedi H, Kliment C, Li L, Sethi S, Diaz-Mendoza J, Feller-Kopman D, Song J, Gildea T, Lee H, Grosu HB, Machuzak M, Rodriguez-Vial M, Eapen GA, Jimenez CA, Casal RF, Ost DE. A Prediction Model to Help with the Assessment of Adenopathy in Lung Cancer: HAL. Am J Respir Crit Care Med 2017; 195:1651-1660. [PMID: 28002683 DOI: 10.1164/rccm.201607-1397oc] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
RATIONALE Estimating the probability of finding N2 or N3 (prN2/3) malignant nodal disease on endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) in patients with non-small cell lung cancer (NSCLC) can facilitate the selection of subsequent management strategies. OBJECTIVES To develop a clinical prediction model for estimating the prN2/3. METHODS We used the AQuIRE (American College of Chest Physicians Quality Improvement Registry, Evaluation, and Education) registry to identify patients with NSCLC with clinical radiographic stage T1-3, N0-3, M0 disease that had EBUS-TBNA for staging. The dependent variable was the presence of N2 or N3 disease (vs. N0 or N1) as assessed by EBUS-TBNA. Univariate followed by multivariable logistic regression analysis was used to develop a parsimonious clinical prediction model to estimate prN2/3. External validation was performed using data from three other hospitals. MEASUREMENTS AND MAIN RESULTS The model derivation cohort (n = 633) had a 25% prevalence of malignant N2 or N3 disease. Younger age, central location, adenocarcinoma histology, and higher positron emission tomography-computed tomography N stage were associated with a higher prN2/3. Area under the receiver operating characteristic curve was 0.85 (95% confidence interval, 0.82-0.89), model fit was acceptable (Hosmer-Lemeshow, P = 0.62; Brier score, 0.125). We externally validated the model in 722 patients. Area under the receiver operating characteristic curve was 0.88 (95% confidence interval, 0.85-0.90). Calibration using the general calibration model method resulted in acceptable goodness of fit (Hosmer-Lemeshow test, P = 0.54; Brier score, 0.132). CONCLUSIONS Our prediction rule can be used to estimate prN2/3 in patients with NSCLC. The model has the potential to facilitate clinical decision making in the staging of NSCLC.
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Affiliation(s)
| | | | - Michael J Simoff
- 3 Department of Pulmonary and Critical Care Medicine, Henry Ford Hospital, Detroit, Michigan; and
| | - Lonny Yarmus
- 4 Division of Pulmonary and Critical Care Medicine, Johns Hopkins University, Baltimore, Maryland
| | | | - Benjamin Young
- 2 Department of Pulmonary Medicine, Cleveland Clinic, Cleveland, Ohio
| | - Yu Chen
- 3 Department of Pulmonary and Critical Care Medicine, Henry Ford Hospital, Detroit, Michigan; and
| | - Roy Semaan
- 3 Department of Pulmonary and Critical Care Medicine, Henry Ford Hospital, Detroit, Michigan; and
| | | | - Joseph Cicenia
- 2 Department of Pulmonary Medicine, Cleveland Clinic, Cleveland, Ohio
| | - Harmeet Bedi
- 3 Department of Pulmonary and Critical Care Medicine, Henry Ford Hospital, Detroit, Michigan; and
| | - Corrine Kliment
- 4 Division of Pulmonary and Critical Care Medicine, Johns Hopkins University, Baltimore, Maryland
| | - Liang Li
- 5 Department of Biostatistics, MD Anderson Cancer Center, Houston, Texas
| | - Sonali Sethi
- 2 Department of Pulmonary Medicine, Cleveland Clinic, Cleveland, Ohio
| | - Javier Diaz-Mendoza
- 3 Department of Pulmonary and Critical Care Medicine, Henry Ford Hospital, Detroit, Michigan; and
| | - David Feller-Kopman
- 4 Division of Pulmonary and Critical Care Medicine, Johns Hopkins University, Baltimore, Maryland
| | - Juhee Song
- 5 Department of Biostatistics, MD Anderson Cancer Center, Houston, Texas
| | - Thomas Gildea
- 2 Department of Pulmonary Medicine, Cleveland Clinic, Cleveland, Ohio
| | - Hans Lee
- 4 Division of Pulmonary and Critical Care Medicine, Johns Hopkins University, Baltimore, Maryland
| | | | - Michael Machuzak
- 2 Department of Pulmonary Medicine, Cleveland Clinic, Cleveland, Ohio
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