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Park J, Kim S, Lim JH, Kim CH, You S, Choi JS, Lim JH, Chang JW, Park D, Lee MW, Lee BJ, Shin SC, Cheon YI, Park IS, Han SH, Youn D, Lee HS, Heo J. Development of a multi-modal learning-based lymph node metastasis prediction model for lung cancer. Clin Imaging 2024; 114:110254. [PMID: 39153380 DOI: 10.1016/j.clinimag.2024.110254] [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: 10/18/2023] [Revised: 08/05/2024] [Accepted: 08/08/2024] [Indexed: 08/19/2024]
Abstract
PURPOSE This study proposed a three-dimensional (3D) multi-modal learning-based model for the automated prediction and classification of lymph node metastasis in patients with non-small cell lung cancer (NSCLC) using computed tomography (CT) images and clinical information. METHODS We utilized clinical information and CT image data from 4239 patients with NSCLC across multiple institutions. Four deep learning algorithm-based multi-modal models were constructed and evaluated for lymph node classification. To further enhance classification performance, a soft-voting ensemble technique was applied to integrate the outcomes of multiple multi-modal models. RESULTS A comparison of the classification performance revealed that the multi-modal model, which integrated CT images and clinical information, outperformed the single-modal models. Among the four multi-modal models, the Xception model demonstrated the highest classification performance, with an area under the curve (AUC) of 0.756 for the internal test dataset and 0.736 for the external validation dataset. The ensemble model (SEResNet50_DenseNet121_Xception) exhibited even better performance, with an AUC of 0.762 for the internal test dataset and 0.751 for the external validation dataset, surpassing the multi-modal model's performance. CONCLUSIONS Integrating CT images and clinical information improved the performance of the lymph node metastasis prediction models in patients with NSCLC. The proposed 3D multi-modal lymph node prediction model can serve as an auxiliary tool for evaluating lymph node metastasis in patients with non-pretreated NSCLC, aiding in patient screening and treatment planning.
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Affiliation(s)
- Jeongmin Park
- Department of Radiation Oncology, Ajou University School of Medicine, Suwon, Republic of Korea
| | - Seonhwa Kim
- Department of Radiation Oncology, Ajou University School of Medicine, Suwon, Republic of Korea
| | - June Hyuck Lim
- Department of Radiation Oncology, Ajou University School of Medicine, Suwon, Republic of Korea
| | - Chul-Ho Kim
- Department of Otolaryngology, Ajou University School of Medicine, Suwon, Republic of Korea
| | - Seulgi You
- Department of Radiology, Ajou University School of Medicine, Suwon, Republic of Korea
| | - Jeong-Seok Choi
- Department of Otorhinolaryngology-Head and Neck Surgery Inha University College of Medicine, Incheon, Republic of Korea
| | - Jun Hyeok Lim
- Division of Pulmonology, Department of Internal Medicine, Inha University College of Medicine, Incheon, Republic of Korea
| | - Jae Won Chang
- Department of Otolaryngology-Head and Neck Surgery, Chungnam National University Hospital, Daejeon, Republic of Korea
| | - Dongil Park
- Division of Pulmonary, Allergy and Critical Care Medicine, Critical Care Medicine, Department of Internal Medicine, Chungnam National University Hospital, Daejeon, Republic of Korea
| | - Myung-Won Lee
- Division of Hematology and Oncology, Department of Internal Medicine, Chungnam National University Hospital, Daejeon, Republic of Korea
| | - Byung-Joo Lee
- Department of Otorhinolaryngology - Head and Neck Surgery, College of Medicine, Pusan National University and Biomedical Research Institute, Pusan National University Hospital, Busan, Republic of Korea
| | - Sung-Chan Shin
- Department of Otorhinolaryngology - Head and Neck Surgery, College of Medicine, Pusan National University and Biomedical Research Institute, Pusan National University Hospital, Busan, Republic of Korea
| | - Yong-Il Cheon
- Department of Otorhinolaryngology - Head and Neck Surgery, College of Medicine, Pusan National University and Biomedical Research Institute, Pusan National University Hospital, Busan, Republic of Korea
| | - Il-Seok Park
- Department of Otorhinolaryngology-Head and Neck Surgery, Hallym University Dontan Sacred Heart Hospital, Hallym University College of Medicine, Republic of Korea
| | - Seung Hoon Han
- Department of Otorhinolaryngology-Head and Neck Surgery, Hallym University Dontan Sacred Heart Hospital, Hallym University College of Medicine, Republic of Korea
| | | | | | - Jaesung Heo
- Department of Radiation Oncology, Ajou University School of Medicine, Suwon, Republic of Korea.
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Yan HJ, Zhao JS, Zuo HD, Zhang JJ, Deng ZQ, Yang C, Luo X, Wan JX, Zheng XY, Chen WY, Li SP, Tian D. Dual-Region Computed Tomography Radiomics-Based Machine Learning Predicts Subcarinal Lymph Node Metastasis in Patients with Non-small Cell Lung Cancer. Ann Surg Oncol 2024; 31:5011-5020. [PMID: 38520581 DOI: 10.1245/s10434-024-15197-w] [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] [Accepted: 03/04/2024] [Indexed: 03/25/2024]
Abstract
BACKGROUND Noninvasively and accurately predicting subcarinal lymph node metastasis (SLNM) for patients with non-small cell lung cancer (NSCLC) remains challenging. This study was designed to develop and validate a tumor and subcarinal lymph nodes (tumor-SLNs) dual-region computed tomography (CT) radiomics model for predicting SLNM in NSCLC. METHODS This retrospective study included NSCLC patients who underwent lung resection and SLNs dissection between January 2017 and December 2020. The radiomic features of the tumor and SLNs were extracted from preoperative CT, respectively. Ninety machine learning (ML) models were developed based on tumor region, SLNs region, and tumor-SLNs dual-region. The model performance was assessed by the area under the curve (AUC) and validated internally by fivefold cross-validation. RESULTS In total, 202 patients were included in this study. ML models based on dual-region radiomics showed good performance for SLNM prediction, with a median AUC of 0.794 (range, 0.686-0.880), which was superior to those of models based on tumor region (median AUC, 0.746; range, 0.630-0.811) and SLNs region (median AUC, 0.700; range, 0.610-0.842). The ML model, which is developed by using the naive Bayes algorithm and dual-region features, had the highest AUC of 0.880 (range of cross-validation, 0.825-0.937) among all ML models. The optimal logistic regression model was inferior to the optimal ML model for predicting SLNM, with an AUC of 0.727. CONCLUSIONS The CT radiomics showed the potential for accurately predicting SLNM in NSCLC patients. The ML model with dual-region radiomic features has better performance than the logistic regression or single-region models.
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Affiliation(s)
- Hao-Ji Yan
- Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, China
- Department of General Thoracic Surgery, Juntendo University School of Medicine, Tokyo, Japan
| | - Jia-Sheng Zhao
- College of Clinical Medicine, North Sichuan Medical College, Nanchong, China
| | - Hou-Dong Zuo
- Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Jun-Jie Zhang
- College of Medical Imaging, North Sichuan Medical College, Nanchong, China
| | - Zhi-Qiang Deng
- College of Medical Imaging, North Sichuan Medical College, Nanchong, China
| | - Chen Yang
- College of Medical Imaging, North Sichuan Medical College, Nanchong, China
| | - Xi Luo
- College of Clinical Medicine, North Sichuan Medical College, Nanchong, China
| | - Jia-Xin Wan
- College of Clinical Medicine, North Sichuan Medical College, Nanchong, China
| | - Xiang-Yun Zheng
- Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Wei-Yang Chen
- Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Su-Ping Li
- Department of Nuclear Medicine, Affiliated Hospital of North Sichuan Medical College, North Sichuan Medical College, Nanchong, China.
| | - Dong Tian
- Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, China.
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Onodera K, Aokage K, Wakabayashi M, Ikeno T, Morita T, Ohashi S, Miyoshi T, Tane K, Samejima J, Tsuboi M. An accurate prediction of negative lymph node metastasis with consideration of glucose metabolism in early-stage non-small cell lung cancer. Gen Thorac Cardiovasc Surg 2024; 72:24-30. [PMID: 37268869 DOI: 10.1007/s11748-023-01946-3] [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: 03/02/2023] [Accepted: 05/26/2023] [Indexed: 06/04/2023]
Abstract
OBJECTIVE We aimed to identify risk factors in lymph node metastasis in early-stage non-small cell lung cancer (NSCLC) and predict lymph node metastasis. METHODS A total of 416 patients with clinical stage IA2-3 NSCLC who underwent lobectomy and lymph node dissection between July 2016 and December 2020 at National Cancer Center Hospital East were included. Multivariable logistic regression was performed to develop a model for predicting lymph node metastasis. Leave-one-out cross-validation was performed to evaluate the developing prediction model, and sensitivity, specificity, and concordance statistics were calculated to evaluate its diagnostic performance. RESULTS The formula for calculating the probability of pathological lymph node metastasis included SUVmax of the primary tumor and serum CEA level. The concordance statistics was 0.7452. When the cutoff value associated with the risk of incorrectly predicting pathological lymph node metastasis was 7.2%, the diagnostic sensitivity and specificity for predicting metastasis were 96.4% and 38.6%, respectively. CONCLUSIONS We created a prediction model for lymph node metastasis in NSCLC by combining the SUVmax of the primary tumor and serum CEA levels, which showed a particularly strong association. This model is clinically useful as it successfully predicts negative lymph node metastasis in patients with clinical stage IA2-3 NSCLC.
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Affiliation(s)
- Ken Onodera
- Division of Thoracic Surgery, National Cancer Center Hospital East, Kashiwanoha 6-5-1, Kashiwa, Chiba, 277-8577, Japan.
| | - Keiju Aokage
- Division of Thoracic Surgery, National Cancer Center Hospital East, Kashiwanoha 6-5-1, Kashiwa, Chiba, 277-8577, Japan
| | - Masashi Wakabayashi
- Biostastics Division, Center for Research Administration and Support, National Cancer Center Hospital East, Kashiwa, Japan
| | - Takashi Ikeno
- Clinical Research Support Office, National Cancer Center Hospital East, Kashiwa, Japan
| | - Takahiro Morita
- Division of Diagnostic Radiology, National Cancer Center Hospital East, Kashiwa, Japan
| | - Shuhei Ohashi
- Division of Radiation Technology, National Cancer Center Hospital East, Kashiwa, Japan
| | - Tomohiro Miyoshi
- Division of Thoracic Surgery, National Cancer Center Hospital East, Kashiwanoha 6-5-1, Kashiwa, Chiba, 277-8577, Japan
| | - Kenta Tane
- Division of Thoracic Surgery, National Cancer Center Hospital East, Kashiwanoha 6-5-1, Kashiwa, Chiba, 277-8577, Japan
| | - Joji Samejima
- Division of Thoracic Surgery, National Cancer Center Hospital East, Kashiwanoha 6-5-1, Kashiwa, Chiba, 277-8577, Japan
| | - Masahiro Tsuboi
- Division of Thoracic Surgery, National Cancer Center Hospital East, Kashiwanoha 6-5-1, Kashiwa, Chiba, 277-8577, Japan
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Liang S, Huang YY, Liu X, Wu LL, Hu Y, Ma G. Risk profiles and a concise prediction model for lymph node metastasis in patients with lung adenocarcinoma. J Cardiothorac Surg 2023; 18:195. [PMID: 37340322 DOI: 10.1186/s13019-023-02288-0] [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: 09/08/2022] [Accepted: 04/15/2023] [Indexed: 06/22/2023] Open
Abstract
BACKGROUND Lung cancer is the second most commonly diagnosed cancer and ranks the first in mortality. Pathological lymph node status(pN) of lung cancer affects the treatment strategy after surgery while systematic lymph node dissection(SLND) is always unsatisfied. METHODS We reviewed the clinicopathological features of 2,696 patients with LUAD and one single lesion ≤ 5 cm who underwent SLND in addition to lung resection at the Sun Yat-Sen University Cancer Center. The relationship between the pN status and all other clinicopathological features was assessed. All participants were stochastically divided into development and validation cohorts; the former was used to establish a logistic regression model based on selected factors from stepwise backward algorithm to predict pN status. C-statistics, accuracy, sensitivity, and specificity were calculated for both cohorts to test the model performance. RESULTS Nerve tract infiltration (NTI), visceral pleural infiltration (PI), lymphovascular infiltration (LVI), right upper lobe (RUL), low differentiated component, tumor size, micropapillary component, lepidic component, and micropapillary predominance were included in the final model. Model performance in the development and validation cohorts was as follows: 0.861 (95% CI: 0.842-0.883) and 0.840 (95% CI: 0.804-0.876) for the C-statistics and 0.803 (95% CI: 0.784-0.821) and 0.785 (95% CI: 0.755-0.814) for accuracy, and 0.754 (95% CI: 0.706-0.798) and 0.686 (95% CI: 0.607-0.757) for sensitivity and 0.814 (95% CI: 0.794-0.833) and 0.811 (95% CI: 0.778-0.841) for specificity, respectively. CONCLUSION Our study showed an easy and credible tool with good performance in predicting pN in patients with LUAD with a single tumor ≤ 5.0 cm without SLND and it is valuable to adjust the treatment strategy.
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Affiliation(s)
- Shenhua Liang
- Department of Thoracic Surgery, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Sun Yat-sen University, Guangzhou, P. R. China
| | - Yang-Yu Huang
- Department of Thoracic Surgery, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Sun Yat-sen University, Guangzhou, P. R. China
| | - Xuan Liu
- Department of Thoracic Surgery, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Sun Yat-sen University, Guangzhou, P. R. China
| | - Lei-Lei Wu
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, P. R. China
| | - Yu Hu
- Department of Thoracic Surgery, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Sun Yat-sen University, Guangzhou, P. R. China
| | - Guowei Ma
- Department of Thoracic Surgery, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Sun Yat-sen University, Guangzhou, P. R. China.
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Ma X, Xia L, Chen J, Wan W, Zhou W. Development and validation of a deep learning signature for predicting lymph node metastasis in lung adenocarcinoma: comparison with radiomics signature and clinical-semantic model. Eur Radiol 2023; 33:1949-1962. [PMID: 36169691 DOI: 10.1007/s00330-022-09153-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 07/23/2022] [Accepted: 09/08/2022] [Indexed: 11/04/2022]
Abstract
OBJECTIVE To develop and validate a deep learning (DL) signature for predicting lymph node (LN) metastasis in patients with lung adenocarcinoma. METHODS A total of 612 patients with pathologically-confirmed lung adenocarcinoma were retrospectively enrolled and were randomly divided into training cohort (n = 489) and internal validation cohort (n = 123). Besides, 108 patients were enrolled and constituted an independent test cohort (n = 108). Patients' clinical characteristics and CT semantic features were collected. The radiomics features were derived from contrast-enhanced CT images. The clinical-semantic model and radiomics signature were built to predict LN metastasis. Furthermore, Swin Transformer was adopted to develop a DL signature predictive of LN metastasis. Model performance was evaluated by area under the receiver operating characteristic curve (AUC), sensitivity, specificity, calibration curve, and decision curve analysis. The comparisons of AUC were conducted by the DeLong test. RESULTS The proposed DL signature yielded an AUC of 0.948-0.961 across all three cohorts, significantly superior to both clinical-semantic model and radiomics signature (all p < 0.05). The calibration curves show that DL signature predicted probabilities fit well the actual observed probabilities of LN metastasis. DL signature gained a higher net benefit than both clinical-semantic model and radiomics signature. The incorporation of radiomics signature or clinical-semantic risk predictors failed to reveal an incremental value over the DL signature. CONCLUSIONS The proposed DL signature based on Swin Transformer achieved a promising performance in predicting LN metastasis and could confer important information in noninvasive mediastinal LN staging and individualized therapeutic options. KEY POINTS • Accurate prediction for lymph node metastasis is crucial to formulate individualized therapeutic options for patients with lung adenocarcinoma. • The deep learning signature yielded an AUC of 0.948-0.961 across all three cohorts in predicting lymph node metastasis, superior to both radiomics signature and clinical-semantic model. • The incorporation of radiomics signature or clinical-semantic risk predictors into deep learning signature failed to reveal an incremental value over deep learning signature.
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Affiliation(s)
- Xiaoling Ma
- Medical Imaging Center, People's Hospital of Ningxia Hui Autonomous Region, Yinchuan, China
| | - Liming Xia
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Road, Qiaokou District, Wuhan, 430030, Hubei, China.
| | | | - Weijia Wan
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Road, Qiaokou District, Wuhan, 430030, Hubei, China
| | - Wen Zhou
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Road, Qiaokou District, Wuhan, 430030, Hubei, 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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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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Lee J, Hong YS, Cho J, Lee J, Kim HK. Comment on "Is It Time for a Specific Nodal Assessment for Every NSCLC Stage?". J Thorac Oncol 2022; 17:e74-e75. [PMID: 36031291 DOI: 10.1016/j.jtho.2022.06.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 06/28/2022] [Indexed: 10/15/2022]
Affiliation(s)
- Junghee Lee
- Department of Thoracic and Cardiovascular Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Yun Soo Hong
- Department of Epidemiology and Medicine, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland; Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland
| | - Juhee Cho
- Department of Epidemiology and Medicine, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland; Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland; Department of Clinical Research Design and Evaluation, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea; Center for Clinical Epidemiology, Sungkyunkwan University, Samsung Medical Center, Seoul, Republic of Korea; Patient-Centered Outcomes Research Institute, Samsung Medical Center, Seoul, Republic of Korea
| | - Jin Lee
- Department of Clinical Research Design and Evaluation, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea; Center for Clinical Epidemiology, Sungkyunkwan University, Samsung Medical Center, Seoul, Republic of Korea
| | - Hong Kwan Kim
- Department of Thoracic and Cardiovascular Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea; Patient-Centered Outcomes Research Institute, Samsung Medical Center, Seoul, Republic of Korea.
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Aokage K, Miyoshi T, Wakabayashi M, Ikeno T, Suzuki J, Tane K, Samejima J, Tsuboi M. Prognostic influence of epidermal growth factor receptor mutation and radiological ground glass appearance in patients with early-stage lung adenocarcinoma. Lung Cancer 2021; 160:8-16. [PMID: 34365179 DOI: 10.1016/j.lungcan.2021.07.018] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 06/30/2021] [Accepted: 07/29/2021] [Indexed: 11/27/2022]
Abstract
OBJECTIVES The ADAURA demonstrated the efficacy of osimertinib as adjuvant therapy in patients with resected stage IB-IIIA adenocarcinoma harboring epidermal growth factor receptor (EGFR) mutations. However, it is controversial whether adjuvant therapy should be applied to all these patients because of their heterogeneities. This study aimed to examine the influence of GGO and EGFR mutations on the prognosis and to identify optimal targets for the development of perioperative therapy. MATERIAL AND METHODS Among the patients who underwent complete resection between 2003 and 2014 and had pathological stage IA3-IIA adenocarcinoma, 505 consecutive patients were examined for EGFR mutation status. The prognosis was analyzed among the clinicopathological factors including EGFR status and presence or absence of GGO. RESULTS Of the 489 patients, 193 (39.5%) showed EGFR mutations. The recurrence-free survival (RFS) and overall survival (OS) of the EGFR mutant were slightly better than those of the EGFR wild type. There was no difference in RFS and OS between EGFR mutant and wild type in patients with GGO; however, EGFR mutant showed better OS than EGFR wild type in patients without GGO. The presence of GGO was a strong independent prognostic predictor in OS and RFS, but EGFR mutations was not predictors. In patients without GGO, EGFR mutants showed slightly higher recurrence, especially with a hazard ratio of 1.427 in stage IB. CONCLUSIONS Adenocarcinoma with GGO show a very good prognosis, so may not require adjuvant therapy. It will be necessary to further develop perioperative therapy in patients with poor prognosis.
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Affiliation(s)
- Keiju Aokage
- Division of Thoracic Surgery, National Cancer Center Hospital East, Kashiwa, Japan.
| | - Tomohiro Miyoshi
- Division of Thoracic Surgery, National Cancer Center Hospital East, Kashiwa, Japan
| | - Masashi Wakabayashi
- Biostatistics Division, Center for Research Administration and Support, National Cancer Center Hospital East, Kashiwa, Japan
| | - Takashi Ikeno
- Clinical Research Support Office, National Cancer Center Hospital East, Kashiwa, Japan
| | - Jun Suzuki
- Division of Thoracic Surgery, National Cancer Center Hospital East, Kashiwa, Japan
| | - Kenta Tane
- Division of Thoracic Surgery, National Cancer Center Hospital East, Kashiwa, Japan
| | - Joji Samejima
- Division of Thoracic Surgery, National Cancer Center Hospital East, Kashiwa, Japan
| | - Masahiro Tsuboi
- Division of Thoracic Surgery, National Cancer Center Hospital East, Kashiwa, Japan
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Thomas PA. Development of radiomics models to predict lymph node metastasis and de-escalated non-small-cell lung cancer surgery: a word of caution. Eur J Cardiothorac Surg 2021; 60:72-73. [PMID: 33523235 DOI: 10.1093/ejcts/ezab021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Pascal Alexandre Thomas
- Department of Thoracic Surgery, North Hospital, Aix-Marseille University & Assistance Publique-Hôpitaux de Marseille, Marseille, France.,Predictive Oncology Laboratory, CRCM, Inserm UMR 1068, CNRS, UMR 7258, Aix-Marseille University UM105, Marseille, France
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Xu L, Yan HJ, Tian D. Prediction model of lymph node metastases for lung adenocarcinoma: increased applicability of this model‡. Eur J Cardiothorac Surg 2021; 60:1007-1008. [PMID: 33890061 DOI: 10.1093/ejcts/ezab191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Accepted: 03/20/2021] [Indexed: 11/14/2022] Open
Affiliation(s)
- Lin Xu
- Department of Thoracic Surgery, Affiliated Hospital of North Sichuan Medical College, Nanchong, China.,College of Clinical Medicine, North Sichuan Medical College, Nanchong, China
| | - Hao-Ji Yan
- College of Medical Imaging, North Sichuan Medical College, Nanchong, China
| | - Dong Tian
- Department of Thoracic Surgery, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
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