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Li C, Hu M, Cai S, Yang G, Yang L, Jing H, Xing L, Sun X. Dysfunction of CD8 + T cells around tumor cells leads to occult lymph node metastasis in NSCLC patients. Cancer Sci 2024; 115:2528-2539. [PMID: 38720474 PMCID: PMC11309950 DOI: 10.1111/cas.16206] [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: 01/04/2024] [Revised: 04/16/2024] [Accepted: 04/19/2024] [Indexed: 08/10/2024] Open
Abstract
Occult lymph node metastasis (OLNM) is one of the main causes of regional recurrence in inoperable N0 non-small cell lung cancer (NSCLC) patients following stereotactic ablation body radiotherapy (SABR) treatment. The integration of immunotherapy and SABR (I-SABR) has shown preliminary efficiency in mitigating this recurrence. Therefore, it is necessary to explore the functional dynamics of critical immune effectors, particularly CD8+ T cells in the development of OLNM. In this study, tissue microarrays (TMAs) and multiplex immunofluorescence (mIF) were used to identify CD8+ T cells and functional subsets (cytotoxic CD8+ T cells/predysfunctional CD8+ T cells (CD8+ Tpredys)/dysfunctional CD8+ T cells (CD8+ Tdys)/other CD8+ T cells) among the no lymph node metastasis, OLNM, and clinically evident lymph node metastasis (CLNM) groups. As the degree of lymph node metastasis escalated, the density of total CD8+ T cells and CD8+ Tdys cells, as well as their proximity to tumor cells, increased progressively and remarkably in the invasive margin (IM). In the tumor center (TC), both the density and proximity of CD8+ Tpredys cells to tumor cells notably decreased in the OLNM group compared with the group without metastasis. Furthermore, positive correlations were found between the dysfunction of CD8+ T cells and HIF-1α+CD8 and cancer microvessels (CMVs). In conclusion, the deterioration in CD8+ T cell function and interactive dynamics between CD8+ T cells and tumor cells play a vital role in the development of OLNM in NSCLC. Strategies aimed at improving hypoxia or targeting CMVs could potentially enhance the efficacy of I-SABR.
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Affiliation(s)
- Chaozhuo Li
- School of Clinical MedicineShandong Second Medical UniversityWeifangChina
- Department of Radiation Oncology, Shandong Cancer Hospital and InstituteShandong First Medical University and Shandong Academy of Medical SciencesJinanChina
| | - Mengyu Hu
- Department of Radiation Oncology, Shandong Cancer Hospital and InstituteShandong First Medical University and Shandong Academy of Medical SciencesJinanChina
- Shandong Cancer Hospital and InstituteShandong First Medical University and Shandong Academy of Medical SciencesJinanChina
| | - Siqi Cai
- Department of Radiation Oncology, Shandong Cancer Hospital and InstituteShandong First Medical University and Shandong Academy of Medical SciencesJinanChina
- Cheeloo College of MedicineShandong UniversityJinanChina
| | - Guanqun Yang
- Department of Radiation Oncology, Shandong Cancer Hospital and InstituteShandong First Medical University and Shandong Academy of Medical SciencesJinanChina
- Cheeloo College of MedicineShandong UniversityJinanChina
| | - Liying Yang
- Department of Radiation Oncology, Shandong Cancer Hospital and InstituteShandong First Medical University and Shandong Academy of Medical SciencesJinanChina
- Cheeloo College of MedicineShandong UniversityJinanChina
| | - Hongbiao Jing
- Department of Pathology, Shandong Cancer Hospital and InstituteShandong First Medical University and Shandong Academy of Medical SciencesJinanChina
| | - Ligang Xing
- Department of Radiation Oncology, Shandong Cancer Hospital and InstituteShandong First Medical University and Shandong Academy of Medical SciencesJinanChina
| | - Xiaorong Sun
- Department of Nuclear Medicine, Shandong Cancer Hospital and InstituteShandong First Medical University and Shandong Academy of Medical SciencesJinanChina
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Ye G, Zhang C, Zhuang Y, Liu H, Song E, Li K, Liao Y. An advanced nomogram model using deep learning radiomics and clinical data for predicting occult lymph node metastasis in lung adenocarcinoma. Transl Oncol 2024; 44:101922. [PMID: 38554572 PMCID: PMC10998193 DOI: 10.1016/j.tranon.2024.101922] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 12/01/2023] [Accepted: 02/23/2024] [Indexed: 04/01/2024] Open
Abstract
PURPOSE To evaluate the effectiveness of deep learning radiomics nomogram in distinguishing the occult lymph node metastasis (OLNM) status in clinical stage IA lung adenocarcinoma. METHODS A cohort of 473 cases of lung adenocarcinomas from two hospitals was included, with 404 cases allocated to the training cohort and 69 cases to the testing cohort. Clinical characteristics and semantic features were collected, and radiomics features were extracted from the computed tomography (CT) images. Additionally, deep transfer learning (DTL) features were generated using RseNet50. Predictive models were developed using the logistic regression (LR) machine learning algorithm. Moreover, gene analysis was conducted on RNA sequencing data from 14 patients to explore the underlying biological basis of deep learning radiomics scores. RESULT The training and testing cohorts achieved AUC values of 0.826 and 0.775 for the clinical model, 0.865 and 0.801 for the radiomics model, 0.927 and 0.885 for the DTL-radiomics model, and 0.928 and 0.898 for the nomogram model. The nomogram model demonstrated superiority over the clinical model. The decision curve analysis (DCA) revealed a net benefit in predicting OLNM for all models. The investigation into the biological basis of deep learning radiomics scores identified an association between high scores and pathways related to tumor proliferation and immune cell infiltration in the microenvironment. CONCLUSIONS The nomogram model, incorporating clinical-semantic features, radiomics, and DTL features, exhibited promising performance in predicting OLNM. It has the potential to provide valuable information for non-invasive lymph node staging and individualized therapeutic approaches.
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Affiliation(s)
- Guanchao Ye
- Department of Thoracic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Chi Zhang
- Department of Thoracic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yuzhou Zhuang
- School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Hong Liu
- School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Enmin Song
- School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Kuo Li
- Department of Thoracic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
| | - Yongde Liao
- Department of Thoracic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
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Jiang X, Luo C, Peng X, Zhang J, Yang L, Liu LZ, Cui YF, Liu MW, Miao L, Jiang JM, Ren JL, Yang XT, Li M, Zhang L. Incidence rate of occult lymph node metastasis in clinical T 1-2N 0M 0 small cell lung cancer patients and radiomic prediction based on contrast-enhanced CT imaging: a multicenter study : Original research. Respir Res 2024; 25:226. [PMID: 38811960 PMCID: PMC11138070 DOI: 10.1186/s12931-024-02852-9] [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: 01/03/2024] [Accepted: 05/16/2024] [Indexed: 05/31/2024] Open
Abstract
BACKGROUND This study aimed to explore the incidence of occult lymph node metastasis (OLM) in clinical T1 - 2N0M0 (cT1 - 2N0M0) small cell lung cancer (SCLC) patients and develop machine learning prediction models using preoperative intratumoral and peritumoral contrast-enhanced CT-based radiomic data. METHODS By conducting a retrospective analysis involving 242 eligible patients from 4 centeres, we determined the incidence of OLM in cT1 - 2N0M0 SCLC patients. For each lesion, two ROIs were defined using the gross tumour volume (GTV) and peritumoral volume 15 mm around the tumour (PTV). By extracting a comprehensive set of 1595 enhanced CT-based radiomic features individually from the GTV and PTV, five models were constucted and we rigorously evaluated the model performance using various metrics, including the area under the curve (AUC), accuracy, sensitivity, specificity, calibration curve, and decision curve analysis (DCA). For enhanced clinical applicability, we formulated a nomogram that integrates clinical parameters and the rad_score (GTV and PTV). RESULTS The initial investigation revealed a 33.9% OLM positivity rate in cT1 - 2N0M0 SCLC patients. Our combined model, which incorporates three radiomic features from the GTV and PTV, along with two clinical parameters (smoking status and shape), exhibited robust predictive capabilities. With a peak AUC value of 0.772 in the external validation cohort, the model outperformed the alternative models. The nomogram significantly enhanced diagnostic precision for radiologists and added substantial value to the clinical decision-making process for cT1 - 2N0M0 SCLC patients. CONCLUSIONS The incidence of OLM in SCLC patients surpassed that in non-small cell lung cancer patients. The combined model demonstrated a notable generalization effect, effectively distinguishing between positive and negative OLMs in a noninvasive manner, thereby guiding individualized clinical decisions for patients with cT1 - 2N0M0 SCLC.
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Affiliation(s)
- Xu Jiang
- Department of Diagnostic Radiology,National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Chao Luo
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, China
| | - Xin Peng
- Department of Radiology, The Third People's Hospital of Chengdu, Chengdu, 610031, China
- Department of Radiology, The First Hospital of China Medical University, Shenyang, 110001, China
| | - Jing Zhang
- Department of Radiology, Shanxi Cancer Hospital, Shanxi Medical University, Taiyuan, 030013, China
| | - Lin Yang
- Department of Pathology, National Clinical Research Center for Cancer/Cancer Hospital, National Cancer Center, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Li-Zhi Liu
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, China
| | - Yan-Fen Cui
- Department of Radiology, Shanxi Cancer Hospital, Shanxi Medical University, Taiyuan, 030013, China
| | - Meng-Wen Liu
- Department of Diagnostic Radiology,National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Lei Miao
- Department of Diagnostic Radiology,National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Jiu-Ming Jiang
- Department of Diagnostic Radiology,National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Jia-Liang Ren
- Department of Pharmaceuticals Diagnostics, GE HealthCare, Beijing, 100176, China
| | - Xiao-Tang Yang
- Department of Radiology, Shanxi Cancer Hospital, Shanxi Medical University, Taiyuan, 030013, China.
| | - Meng Li
- Department of Diagnostic Radiology,National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
| | - Li Zhang
- Department of Diagnostic Radiology,National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
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Shao L, Yang X, Sun Z, Tan X, Lu Z, Hu S, Dou W, Duan S. Three-dimensional pseudo-continuous arterial spin-labelled perfusion imaging for diagnosing upper cervical lymph node metastasis in patients with nasopharyngeal carcinoma: a whole-node histogram analysis. Clin Radiol 2024; 79:e736-e743. [PMID: 38341343 DOI: 10.1016/j.crad.2024.01.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 01/11/2024] [Accepted: 01/16/2024] [Indexed: 02/12/2024]
Abstract
AIM To evaluate whole-node histogram parameters of blood flow (BF) maps derived from three-dimensional pseudo-continuous arterial spin-labelled (3D pCASL) imaging in discriminating metastatic from benign upper cervical lymph nodes (UCLNs) for nasopharyngeal carcinoma (NPC) patients. MATERIALS AND METHODS Eighty NPC patients with a total of 170 histologically confirmed UCLNs (67 benign and 103 metastatic) were included retrospectively. Pre-treatment 3D pCASL imaging was performed and whole-node histogram analysis was then applied. Histogram parameters and morphological features, such as minimum axis diameter (MinAD), maximum axis diameter (MaxAD), and location of UCLNs, were assessed and compared between benign and metastatic lesions. Predictors were identified and further applied to establish a combined model by multivariate logistic regression in predicting the probability of metastatic UCLNs. Receiver operating characteristic (ROC) curves were used to analyse the diagnostic performance. RESULTS Metastatic UCLNs had larger MinAD and MinAD/MaxAD ratio, greater energy and entropy values, and higher incidence of level II (upper jugular group), but lower BF10th value than benign nodes (all p<0.05). MinAD, BF10th, energy, and entropy were validated as independent predictors in diagnosing metastatic UCLNs. The combined model yielded an area under the curve (AUC) of 0.932, accuracy of 84.42 %, sensitivity of 80.6 %, and specificity of 90.29 %. CONCLUSIONS Whole-node histogram analysis on BF maps is a feasible tool to differentiate metastatic from benign UCLNs in NPC patients, and the combined model can further improve the diagnostic efficacy.
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Affiliation(s)
- L Shao
- Department of Radiology, Affiliated Hospital of Jiangnan University, Wuxi City, Jiangsu, China
| | - X Yang
- Department of Radiology, Affiliated Hospital of Jiangnan University, Wuxi City, Jiangsu, China
| | - Z Sun
- Department of Radiology, Affiliated Hospital of Jiangnan University, Wuxi City, Jiangsu, China.
| | - X Tan
- Department of Radiology, Affiliated Hospital of Jiangnan University, Wuxi City, Jiangsu, China
| | - Z Lu
- Department of Radiology, Affiliated Hospital of Jiangnan University, Wuxi City, Jiangsu, China
| | - S Hu
- Department of Radiology, Affiliated Hospital of Jiangnan University, Wuxi City, Jiangsu, China
| | - W Dou
- General Electric (GE) Healthcare, MR Research China, Beijing, China
| | - S Duan
- General Electric (GE) Healthcare China, Shanghai, China
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Liu M, Yang L, Sun X, Liang X, Li C, Feng Q, Li M, Zhang L. Evaluation of Prognosis in Patients with Lung Adenocarcinoma with Atypical Solid Nodules on Thin-Section CT Images. Radiol Cardiothorac Imaging 2024; 6:e220234. [PMID: 38206165 PMCID: PMC10912885 DOI: 10.1148/ryct.220234] [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: 10/13/2022] [Revised: 04/03/2023] [Accepted: 08/23/2023] [Indexed: 01/12/2024]
Abstract
Purpose To evaluate the clinicopathologic characteristics and prognosis of patients with clinical stage IA lung adenocarcinoma with atypical solid nodules (ASNs) on thin-section CT images. Materials and Methods Data from patients with clinical stage IA lung adenocarcinoma who underwent resection between January 2005 and December 2012 were retrospectively reviewed. According to their manifestations on thin-section CT images, nodules were classified as ASNs, subsolid nodules (SSNs), and typical solid nodules (TSNs). The clinicopathologic characteristics of the ASNs were investigated, and the differences across the three groups were analyzed. The Kaplan-Meier method and multivariable Cox analysis were used to evaluate survival differences among patients with ASNs, SSNs, and TSNs. Results Of the 254 patients (median age, 58 years [IQR, 53-66]; 152 women) evaluated, 49 had ASNs, 123 had SSNs, and 82 had TSNs. Compared with patients with SSNs, those with ASNs were more likely to have nonsmall adenocarcinoma (P < .001), advanced-stage adenocarcinoma (P = .004), nonlepidic growth adenocarcinoma (P < .001), and middle- or low-grade differentiation tumors (P < .001). Compared with patients with TSNs, those with ASNs were more likely to have no lymph node involvement (P = .009) and epidermal growth factor receptor mutation positivity (P = .018). Average disease-free survival in patients with ASNs was significantly longer than that in patients with TSNs (P < .001) but was not distinguishable from that in patients with SSNs (P = .051). Conclusion ASNs were associated with better clinical outcomes than TSNs in patients with clinical stage IA lung adenocarcinoma. Keywords: Adenocarcinoma, Atypical Solid Nodules, CT, Disease-free Survival, Lung, Prognosis, Pulmonary Supplemental material is available for this article. Published under a CC BY 4.0 license.
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Affiliation(s)
- Mengwen Liu
- From the Department of Diagnostic Radiology (M. Liu, Q.F., M. Li,
L.Z.), Department of Pathology (L.Y., X.S.), Medical Statistics Office (X.L.),
and Medical Records Room (C.L.), National Cancer Center/National Clinical
Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences
and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District,
Beijing 100021, China
| | - Lin Yang
- From the Department of Diagnostic Radiology (M. Liu, Q.F., M. Li,
L.Z.), Department of Pathology (L.Y., X.S.), Medical Statistics Office (X.L.),
and Medical Records Room (C.L.), National Cancer Center/National Clinical
Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences
and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District,
Beijing 100021, China
| | - Xujie Sun
- From the Department of Diagnostic Radiology (M. Liu, Q.F., M. Li,
L.Z.), Department of Pathology (L.Y., X.S.), Medical Statistics Office (X.L.),
and Medical Records Room (C.L.), National Cancer Center/National Clinical
Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences
and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District,
Beijing 100021, China
| | - Xin Liang
- From the Department of Diagnostic Radiology (M. Liu, Q.F., M. Li,
L.Z.), Department of Pathology (L.Y., X.S.), Medical Statistics Office (X.L.),
and Medical Records Room (C.L.), National Cancer Center/National Clinical
Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences
and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District,
Beijing 100021, China
| | - Cong Li
- From the Department of Diagnostic Radiology (M. Liu, Q.F., M. Li,
L.Z.), Department of Pathology (L.Y., X.S.), Medical Statistics Office (X.L.),
and Medical Records Room (C.L.), National Cancer Center/National Clinical
Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences
and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District,
Beijing 100021, China
| | - Qianqian Feng
- From the Department of Diagnostic Radiology (M. Liu, Q.F., M. Li,
L.Z.), Department of Pathology (L.Y., X.S.), Medical Statistics Office (X.L.),
and Medical Records Room (C.L.), National Cancer Center/National Clinical
Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences
and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District,
Beijing 100021, China
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Zhou Y, Cao X, Gu H, Gao S, Wu Y, Li H, Xiong B, Dong H, Lv Y, Yang R, Wu Y. Establishing and validation of the VBV score for assessing Lung ground-glass nodules based on high-resolution computed tomography. J Cardiothorac Surg 2024; 19:17. [PMID: 38263113 PMCID: PMC10804577 DOI: 10.1186/s13019-024-02487-3] [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: 07/06/2023] [Accepted: 01/14/2024] [Indexed: 01/25/2024] Open
Abstract
BACKGROUND The widespread utilization of chest High-resolution Computed Tomography (HRCT) has prompted detection of pulmonary ground-glass nodules (GGNs) in otherwise asymptomatic individuals. We aimed to establish a simple clinical risk score model for assessing GGNs based on HRCT. METHODS We retrospectively analyzed 574 GGNs in 574 patients undergoing HOOK-WIRE puncture and pulmonary nodule surgery from January 2014 to November 2018. Clinical characteristics and imaging features of the GGNs were assessed. We analyzed the differences between malignant and benign nodules using binary logistic regression analysis and constructed a simple risk score model, the VBV Score, for predicting the malignancy status of GGNs. Then, we validated this model via other 1200 GGNs in 1041 patients collected from three independent clinical centers in 2022. RESULTS For the exploratory phase of this study, out of the 574 GGNs, 481 were malignant and 93 were benign. Vacuole sign, air bronchogram, and intra-nodular vessel sign were important indicators of malignancy in GGNs. Then, we derived a VBV Score = vacuole sign + air bronchogram + intra-nodular vessel sign, to predict the malignancy of GGNs, with a sensitivity, specificity, and accuracy of 95.6%, 80.6%, and 93.2%, respectively. We also validated it on other 1200 GGNs, with a sensitivity, specificity, and accuracy of 96.0%, 82.6%, and 95.0%, respectively. CONCLUSIONS Vacuole sign, air bronchogram, and intra-nodular vessel sign were important indicators of malignancy in GGNs. VBV Score showed good sensitivity, specificity, and accuracy for differentiating benign and malignant pulmonary GGNs.
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Affiliation(s)
- Yuwei Zhou
- Department of Thoracic Surgery, the First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Road, Hangzhou, 310003, China
- Department of Thoracic Surgery, Ningbo Medical Center Lihuili Hospital, Ningbo, Zhejiang, China
| | - Xiaoqing Cao
- Department of Thoracic Surgery, Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China
| | - Haiyong Gu
- Department of Thoracic Surgery, Shanghai Chest Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Shenhu Gao
- Department of Thoracic Surgery, the First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Road, Hangzhou, 310003, China
| | - Yuxuan Wu
- Department of Thoracic Surgery, the First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Road, Hangzhou, 310003, China
| | - Haoyang Li
- Department of Thoracic Surgery, the First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Road, Hangzhou, 310003, China
| | - Bing Xiong
- Department of Radiology, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Haiyang Dong
- Department of Radiology, Shanghai Chest Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Yan Lv
- Department of Medical Imaging, Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China
| | - Rong Yang
- Department of Radiology, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yihe Wu
- Department of Thoracic Surgery, the First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Road, Hangzhou, 310003, China.
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Zhang W, Mu G, Huang J, Bian C, Wang H, Gu Y, Xia Y, Chen L, Yuan M, Wang J. Lymph node metastasis and its risk factors in T1 lung adenocarcinoma. Thorac Cancer 2023; 14:2993-3000. [PMID: 37667435 PMCID: PMC10599970 DOI: 10.1111/1759-7714.15088] [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: 07/09/2023] [Revised: 08/15/2023] [Accepted: 08/16/2023] [Indexed: 09/06/2023] Open
Abstract
BACKGROUND In this study, the focus was primarily on examining the occurrence of lymph node metastasis in T1 lung adenocarcinoma, while also analyzing the relationship between clinical variables such as imaging characteristics, pathological classifications, and lymph node metastasis. METHODS We retrospectively analyzed data from patients with T1 lung adenocarcinoma who underwent lobectomy and lymph node dissection between January 2016 and December 2019. Utilizing univariate and multivariate analyses, we assessed the associations between lymph node metastasis and various clinical factors, including imaging characteristics, lesion location and depth, and pathological subtypes. RESULTS Of the 433 patients with T1 lung adenocarcinoma, 139 had lymph node metastasis. Moreover, the incidence of node 1 (N1) lymph node, sequential, and node 2 (N2) skip metastases were 12.2%, 12.7%, and 7.2%, respectively. Univariate analysis revealed that tumor diameter, depth ratio, sex, invasive imaging features, and pathological subtype were significantly associated with lymph node metastasis. Multivariate analysis revealed that the tumor depth ratio, tumor diameter, pleural indentation or traction sign, nonvascular penetration sign, solid component, nonadherence, and micropapillary pathological subtype were risk factors for lymph node metastasis. In the multivariate analysis, the micropapillary pathological subtype was an independent risk factor for N2 skip metastasis. CONCLUSIONS In patients with clinical stage T1 lung adenocarcinoma, the risk of lymph node metastasis is higher for tumors located deep within the lung tissue with solid components, invasive preoperative imaging features, and larger diameters. For N2 skip lymph node metastasis, the micropapillary pathological subtype represents a significant high-risk factor.
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Affiliation(s)
- Wenhao Zhang
- Department of Thoracic SurgeryJiangsu Province Hospital and The First Affiliated Hospital of Nanjing Medical UniversityNanjingChina
| | - Guang Mu
- Department of Thoracic SurgeryJiangsu Province Hospital and The First Affiliated Hospital of Nanjing Medical UniversityNanjingChina
| | - Jingjing Huang
- Department of Thoracic SurgeryJiangsu Province Hospital and The First Affiliated Hospital of Nanjing Medical UniversityNanjingChina
| | - Chengyu Bian
- Department of Thoracic SurgeryJiangsu Province Hospital and The First Affiliated Hospital of Nanjing Medical UniversityNanjingChina
| | - Hongchang Wang
- Department of Thoracic SurgeryJiangsu Province Hospital and The First Affiliated Hospital of Nanjing Medical UniversityNanjingChina
| | - Yan Gu
- Department of Thoracic SurgeryJiangsu Province Hospital and The First Affiliated Hospital of Nanjing Medical UniversityNanjingChina
| | - Yang Xia
- Department of Thoracic SurgeryJiangsu Province Hospital and The First Affiliated Hospital of Nanjing Medical UniversityNanjingChina
| | - Liang Chen
- Department of Thoracic SurgeryJiangsu Province Hospital and The First Affiliated Hospital of Nanjing Medical UniversityNanjingChina
| | - Mei Yuan
- Department of RadiologyJiangsu Province Hospital and The First Affiliated Hospital of Nanjing Medical UniversityNanjingChina
| | - Jun Wang
- Department of Thoracic SurgeryJiangsu Province Hospital and The First Affiliated Hospital of Nanjing Medical UniversityNanjingChina
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Cai JS, Yang F, Wang X. Occult lymph node metastasis is not a favorable factor for resected NSCLC patients. BMC Cancer 2023; 23:822. [PMID: 37667180 PMCID: PMC10476354 DOI: 10.1186/s12885-023-11189-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 07/18/2023] [Indexed: 09/06/2023] Open
Abstract
BACKGROUND This study was to compare the clinical presentations and survivals between the non-small cell lung cancer (NSCLC) patients with occult lymph node metastasis (OLNM) and those with evident lymph node metastasis (ELNM). We also intended to analyze the predictive factors for OLNM. METHODS Kaplan-Meier method with log-rank test was used to compare survivals between groups. Propensity score matching (PSM) was used to reduce bias. The least absolute shrinkage and selection operator (LASSO)-penalized Cox multivariable analysis was used to identify the prognostic factors. Random forest was used to determine the predictive factors for OLNM. RESULTS A total of 2,067 eligible cases (N0: 1,497 cases; occult N1: 165 cases; evident N1: 54 cases; occult N2: 243 cases; evident N2: 108 cases) were included. The rate of OLNM was 21.4%. Patients with OLNM were tend to be female, non-smoker, adenocarcinoma and had smaller-sized tumors when compared with the patients with ELNM. Survival curves showed that the survivals of the patients with OLNM were similar to those of the patients with ELNM both before and after PSM. Multivariable Cox analysis suggested that positive lymph nodes (PLN) was the only prognostic factor for the patients with OLNM. Random forest showed that clinical tumor size was an important predictive factor for OLNM. CONCLUSIONS OLNM was not rare. OLNM was not a favorable sign for resected NSCLC patients with lymph node metastasis. PLN determined the survivals of the patients with OLNM. Clinical tumor size was a strong predictive factor for OLNM.
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Affiliation(s)
- Jing-Sheng Cai
- Department of Thoracic Surgery, Peking University People's Hospital, No. 11 Xizhimen South Street, Xicheng District, Beijing, 100044, P.R. China
- Thoracic Oncology Institute, Peking University People's Hospital, No. 11 Xizhimen South Street, Xicheng District, Beijing, 100044, P.R. China
| | - Fan Yang
- Department of Thoracic Surgery, Peking University People's Hospital, No. 11 Xizhimen South Street, Xicheng District, Beijing, 100044, P.R. China.
- Thoracic Oncology Institute, Peking University People's Hospital, No. 11 Xizhimen South Street, Xicheng District, Beijing, 100044, P.R. China.
| | - Xun Wang
- Department of Thoracic Surgery, Peking University People's Hospital, No. 11 Xizhimen South Street, Xicheng District, Beijing, 100044, P.R. China.
- Thoracic Oncology Institute, Peking University People's Hospital, No. 11 Xizhimen South Street, Xicheng District, Beijing, 100044, P.R. China.
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Tumour-pleura relationship on CT is a risk factor for occult lymph node metastasis in peripheral clinical stage IA solid adenocarcinoma. Eur Radiol 2023; 33:3083-3091. [PMID: 36806570 DOI: 10.1007/s00330-023-09476-5] [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: 04/29/2022] [Revised: 12/30/2022] [Accepted: 01/31/2023] [Indexed: 02/21/2023]
Abstract
OBJECTIVES To investigate whether the tumour-pleura relationship on computed tomography (CT) is a risk factor for occult lymph node metastasis (OLNM) in peripheral clinical stage IA solid adenocarcinoma. METHODS A total of 232 patients were included in the study. The tumour-pleura relationship was divided into four types: type 1, the tumour was unrelated to the pleura; type 2, the tumour was not in contact with the pleura, and one or more linear or striated pleural tags were visible; type 3, the tumour was not in contact with the pleura, and one or more linear or striated pleural tags with soft tissue component at the pleural end were visible; and type 4, the tumour was in contact with the pleura. Univariate and multivariate logistic regression analyses were used to identify the predictive factors, including the tumour-pleura relationship, clinical factors, conventional CT findings, and pathology-reported visceral pleural invasion, for OLNM. RESULTS Type 3 and 4 tumour-pleura relationships were more likely to have visceral pleural invasion than type 1 and 2 tumour-pleura relationships (p < 0.001). Univariate and multivariate logistic regression analyses revealed that the type 3 or 4 tumour-pleura relationship (OR: 3.261, p = 0.026), carcinoembryonic antigen level (OR: 3.361, p = 0.006), cytokeratin 19 fragments level (OR: 2.539, p = 0.025), and mediastinal window tumour size (OR: 1.078, p = 0.020) were predictive factors for OLNM. CONCLUSIONS The type 3 or 4 tumour-pleura relationship is correlated with a greater risk of OLNM in peripheral clinical stage IA solid adenocarcinoma. KEY POINTS • The tumour-pleura relationship on CT is a risk factor for occult lymph node metastasis in peripheral clinical stage IA solid adenocarcinoma. • Other risk factors for OLNM include CEA level, CYFRA level, and mediastinal window tumour size. • Pathology-reported visceral pleural invasion is not a risk factor for OLNM.
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Wang K, Xue M, Qiu J, Liu L, Wang Y, Li R, Qu C, Yue W, Tian H. Genomics Analysis and Nomogram Risk Prediction of Occult Lymph Node Metastasis in Non-Predominant Micropapillary Component of Lung Adenocarcinoma Measuring ≤ 3 cm. Front Oncol 2022; 12:945997. [PMID: 35912197 PMCID: PMC9326108 DOI: 10.3389/fonc.2022.945997] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Accepted: 06/21/2022] [Indexed: 11/22/2022] Open
Abstract
Background The efficacy of sublobar resection and selective lymph node dissection is gradually being accepted by thoracic surgeons for patients within early-stage non-small cell lung cancer (NSCLC). Nevertheless, there are still some NSCLC patients develop lymphatic metastasis at clinical T1 stage. Lung adenocarcinoma with a micropapillary (MP) component poses a higher risk of lymph node metastasis and recurrence even when the MP component is not predominant. Our study aimed to explore the genetic features and occult lymph node metastasis (OLNM) risk factors in patients with a non-predominant micropapillary component (NP-MPC) in a large of patient’s cohort with surgically resected lung adenocarcinoma. Methods Between January 2019 and December 2021, 6418 patients who underwent complete resection for primary lung adenocarcinoma at the Qilu Hospital of Shandong University. In our study, 442 patients diagnosed with lung adenocarcinoma with NP-MPC with a tumor size ≤3 cm were included. Genetic alterations were analyzed using amplification refractory mutation system-polymerase chain reaction (ARMS-PCR). Abnormal protein expression of gene mutations was validated using immunohistochemistry. A nomogram risk model based on clinicopathological parameters was developed to predict OLNM. This model was invalidated using the calibration plot and concordance index. Results In our retrospective cohort, the incidence rate of the micropapillary component was 11.17%, and OLNM was observed in 20.13% of the patients in our study. ARMS-PCR suggested that EGFR exon 19 del was the most frequent alteration in NP-MCP patients compared with other gene mutations (frequency: 21.2%, P<0.001). Patients harboring exon 19 del showed significantly higher risk of OLNM (P< 0.001). A nomogram was developed based on five risk parameters, which showed good calibration and reliable discrimination ability (C-index = 0.84) for evaluating OLNM risk. Conclusions. Intense expression of EGFR exon 19 del characterizes lung adenocarcinoma in patients with NP-MCP and it’s a potential risk factor for OLNM. We firstly established a nomogram based on age, CYFRA21-1 level, tumor size, micropapillary and solid composition, that was effective in predicting OLNM among NP-MCP of lung adenocarcinoma measuring ≤ 3 cm.
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Affiliation(s)
- Kun Wang
- Department of Thoracic Surgery, Qilu Hospital of Shandong University, Jinan, China
| | - Mengchao Xue
- Department of Thoracic Surgery, Qilu Hospital of Shandong University, Jinan, China
| | - Jianhao Qiu
- Department of Thoracic Surgery, Qilu Hospital of Shandong University, Jinan, China
| | - Ling Liu
- Department of Pathology, Qilu Hospital of Shandong University, Jinan, China
| | - Yueyao Wang
- Department of Pathology, Qilu Hospital of Shandong University, Jinan, China
| | - Rongyang Li
- Department of Thoracic Surgery, Qilu Hospital of Shandong University, Jinan, China
| | - Chenghao Qu
- Department of Thoracic Surgery, Qilu Hospital of Shandong University, Jinan, China
| | - Weiming Yue
- Department of Thoracic Surgery, Qilu Hospital of Shandong University, Jinan, China
| | - Hui Tian
- Department of Thoracic Surgery, Qilu Hospital of Shandong University, Jinan, China
- *Correspondence: Hui Tian,
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Fang C, Xiang Y, Han W. Preoperative risk factors of lymph node metastasis in clinical N0 lung adenocarcinoma of 3 cm or less in diameter. BMC Surg 2022; 22:153. [PMID: 35488235 PMCID: PMC9052540 DOI: 10.1186/s12893-022-01605-z] [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: 01/04/2022] [Accepted: 04/14/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Lung adenocarcinoma is the most common subtype of non-small cell lung cancer. The surgical strategy of lymph node dissection is controversial because many more patients are diagnosed at an early stage in clinical practice. METHODS We retrospectively reviewed 622 clinical N0 lung adenocarcinoma patients with 3 cm or less in tumor size who underwent lobectomy or segmentectomy combined with lymph node dissection in our hospital from January 2017 to December 2019. We performed univariate and multivariate analyses to identify preoperative risk factors of lymph node metastasis. RESULTS Lymph node metastasis was found in 60 out of 622 patients. On univariate analysis, lymph node metastasis was linked to smoking history, preoperative CEA level, tumor size, tumor location (peripheral or central), consolidation/tumor ratio, pleural invasion, and pathologic type. However, only the preoperative CEA level, tumor size, and consolidation/tumor ratio were independent risk factors in multivariate analysis. The ROC curve showed that the cutoff value of tumor size was 1.7 cm. There was no lymph node metastasis in patients without risk factors. CONCLUSIONS The preoperative CEA level, tumor size, and consolidation/tumor ratio were independent risk factors of lymph node metastasis in clinical N0 lung adenocarcinoma with tumor size ≤ 3 cm. The lymph node metastasis rate was extremely low in clinical N0 lung adenocarcinoma patients without risk factors and lymph node dissection should be avoided in these patients to reduce surgical trauma.
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Affiliation(s)
- Cheng Fang
- Department of Lung Transplantation, The First Affiliated Hospital, Zhejiang University School of Medicine, No.79 Qingchun Road, Hangzhou, 310003, Zhejiang Province, China
| | - Yangwei Xiang
- Department of Lung Transplantation, The First Affiliated Hospital, Zhejiang University School of Medicine, No.79 Qingchun Road, Hangzhou, 310003, Zhejiang Province, China
| | - Weili Han
- Department of Lung Transplantation, The First Affiliated Hospital, Zhejiang University School of Medicine, No.79 Qingchun Road, Hangzhou, 310003, Zhejiang Province, China.
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