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Wang Z, Zhang Q, Wang C, Herth FJF, Guo Z, Zhang X. Multiple primary lung cancer: Updates and perspectives. Int J Cancer 2024; 155:785-799. [PMID: 38783577 DOI: 10.1002/ijc.34994] [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: 10/18/2023] [Revised: 02/14/2024] [Accepted: 03/28/2024] [Indexed: 05/25/2024]
Abstract
Management of multiple primary lung cancer (MPLC) remains challenging, partly due to its increasing incidence, especially with the significant rise in cases of multiple lung nodules caused by low-dose computed tomography screening. Moreover, the indefinite pathogenesis, diagnostic criteria, and treatment selection add to the complexity. In recent years, there have been continuous efforts to dissect the molecular characteristics of MPLC and explore new diagnostic approaches as well as treatment modalities, which will be reviewed here, with a focus on newly emerging evidence and future perspectives, hope to provide new insights into the management of MPLC and serve as inspiration for future research related to MPLC.
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Affiliation(s)
- Ziqi Wang
- Department of Respiratory and Critical Care Medicine, Zhengzhou University People's Hospital, Henan Provincial People's Hospital, Zhengzhou, Henan, China
- Henan International Joint Laboratory of Diagnosis and Treatment for Pulmonary Nodules, Zhengzhou, Henan, China
| | - Quncheng Zhang
- Department of Respiratory and Critical Care Medicine, Zhengzhou University People's Hospital, Henan Provincial People's Hospital, Zhengzhou, Henan, China
- Henan International Joint Laboratory of Diagnosis and Treatment for Pulmonary Nodules, Zhengzhou, Henan, China
| | - Chaoyang Wang
- Department of Respiratory and Critical Care Medicine, Zhengzhou University People's Hospital, Henan Provincial People's Hospital, Zhengzhou, Henan, China
- Henan International Joint Laboratory of Diagnosis and Treatment for Pulmonary Nodules, Zhengzhou, Henan, China
| | - Felix J F Herth
- Department of Respiratory and Critical Care Medicine, Zhengzhou University People's Hospital, Henan Provincial People's Hospital, Zhengzhou, Henan, China
- Henan International Joint Laboratory of Diagnosis and Treatment for Pulmonary Nodules, Zhengzhou, Henan, China
- Department of Pneumology and Critical Care Medicine Thoraxklinik, University of Heidelberg, Heidelberg, Germany
| | - Zhiping Guo
- Department of Health Management, Zhengzhou University People's Hospital, Henan Provincial People's Hospital, Zhengzhou, Henan, China
- Henan Provincial Key Laboratory of Chronic Diseases and Health Management, Zhengzhou, Henan, China
| | - Xiaoju Zhang
- Department of Respiratory and Critical Care Medicine, Zhengzhou University People's Hospital, Henan Provincial People's Hospital, Zhengzhou, Henan, China
- Henan International Joint Laboratory of Diagnosis and Treatment for Pulmonary Nodules, Zhengzhou, Henan, China
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Mantilla JG, Moreira AL. The Grading System for Lung Adenocarcinoma: Brief Review of its Prognostic Performance and Future Directions. Adv Anat Pathol 2024; 31:283-288. [PMID: 38666775 DOI: 10.1097/pap.0000000000000452] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/09/2024]
Abstract
Histologic grading of tumors is associated with prognosis in many organs. In the lung, the most recent grading system proposed by International association for the Study of Lung Cancer (IASLC) and adopted by the World Health Organization (WHO) incorporates the predominant histologic pattern, as well as the presence of high-grade architectural patterns (solid, micropapillary, and complex glandular pattern) in proportions >20% of the tumor surface. This system has shown improved prognostic ability when compared with the prior grading system based on the predominant pattern alone, across different patient populations. Interobserver agreement is moderate to excellent, depending on the study. IASLC/WHO grading system has been shown to correlate with molecular alterations and PD-L1 expression in tumor cells. Recent studies interrogating gene expression has shown correlation with tumor grade and molecular alterations in the tumor microenvironment that can further stratify risk of recurrence. The use of machine learning algorithms to grade nonmucinous adenocarcinoma under this system has shown accuracy comparable to that of expert pulmonary pathologists. Future directions include evaluation of tumor grade in the context of adjuvant and neoadjuvant therapies, as well as the development of better prognostic indicators for mucinous adenocarcinoma.
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Affiliation(s)
- Jose G Mantilla
- Department of Pathology, New York University Grossman School of Medicine, New York, NY
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Qu H, Li J, Zeng R, Du M. The presence of a cribriform pattern is related to poor prognosis in lung adenocarcinoma after surgical resection: A meta-analysis. Gen Thorac Cardiovasc Surg 2024; 72:553-561. [PMID: 38801566 DOI: 10.1007/s11748-024-02044-8] [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: 02/19/2024] [Accepted: 05/18/2024] [Indexed: 05/29/2024]
Abstract
OBJECTIVE Previous studies reported that the cribriform pattern (CP) was associated with poor prognosis in lung adenocarcinoma (ADC) patients; therefore, a meta-analysis was performed to thoroughly evaluate the prognostic impact of cribriform pattern in postoperative ADC patients. METHODS Eligible studies were retrieved from PubMed, Embase databases, and Web of Science until April 2023. Studies evaluating the effect of the cribriform pattern on the prognosis of postoperative ADC patients were included. Subsequently, subgroup analysis was conducted according to the proportion of the cribriform pattern, with disease-free survival (DFS) and/or overall survival (OS) as outcomes. Hazard ratios (HRs) and 95% confidence intervals (CIs) were used as effect estimates in the meta-analyses, which were performed with a random-effects model despite the heterogeneity. RESULTS Nine studies published between 2015 and 2022 were included, with 4,289 ADC patients in total. The pooled results revealed a significantly poorer DFS (HR1.56, 95%CI 1.18-2.06, P = 0.11, I2 = 45%) and OS (HR2.11, 95%CI 1.63-2.72, P = 0.01, I2 = 56%) in patients with the cribriform pattern. Furthermore, the subgroup analysis showed that patients with a cribriform pattern (DFS: HR1.32, 95% CI 1.04-1.68 OS:HR2.30, 95% CI 1.55-3.39) and patients with a predominantly cribriform pattern (DFS:HR2.04, 95% CI 1.32--3.15 OS: HR1.92, 95% CI 1.41-2.61) were associated with poor prognosis. CONCLUSIONS The presence of a cribriform pattern is related to poor prognosis in postoperative ADC patients, despite not being a main tumor component. However, the results should be confirmed by large-scale and prospective studies owing to the small sample and potential heterogeneity.
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Affiliation(s)
- Haoran Qu
- Department of Cardiothoracic Surgery, the First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Jianfeng Li
- Department of Cardiothoracic Surgery, the First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Rui Zeng
- Department of Cardiothoracic Surgery, the First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Ming Du
- Department of Cardiothoracic Surgery, the First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China.
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Wang Q, Song X, Zhang Y, Liang S, Zhang M, Wang H, Feng Y, Li R, Ding H, Chen Y, Xia W, Dong G, Xu L, Mao Q, Jiang F. A pro-metastatic tRNA fragment drives aldolase A oligomerization to enhance aerobic glycolysis in lung adenocarcinoma. Cell Rep 2024; 43:114550. [PMID: 39058593 DOI: 10.1016/j.celrep.2024.114550] [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: 03/26/2024] [Revised: 06/19/2024] [Accepted: 07/12/2024] [Indexed: 07/28/2024] Open
Abstract
Despite being the leading cause of lung cancer-related deaths, the underlying molecular mechanisms driving metastasis progression are still not fully understood. Transfer RNA-derived fragments (tRFs) have been implicated in various biological processes in cancer. However, the role of tRFs in lung adenocarcinoma (LUAD) remains unclear. Our study identified a tRF, tRF-Val-CAC-024, associated with the high-risk component of LUAD, through validation using 3 cohorts. Our findings demonstrated that tRF-Val-CAC-024 acts as an oncogene in LUAD. Mechanistically, tRF-Val-CAC-024 was revealed to bind to aldolase A (ALDOA) dependent on Q125/E224 and promote the oligomerization of ALDOA, resulting in increased enzyme activity and enhanced aerobic glycolysis in LUAD cells. Additionally, we provide preliminary evidence of its potential clinical value by investigating the therapeutic effects of tRF-Val-CAC-024 antagomir-loaded lipid nanoparticles (LNPs) in cell-line-derived xenograft models. These results could enhance our understanding of the regulatory mechanisms of tRFs in LUAD and provide a potential therapeutic target.
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Affiliation(s)
- Qinglin Wang
- Department of Thoracic Surgery, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Nanjing 210009, China; Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Cancer Institute of Jiangsu Province, Nanjing 210009, China
| | - Xuming Song
- Department of Thoracic Surgery, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Nanjing 210009, China; Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Cancer Institute of Jiangsu Province, Nanjing 210009, China
| | - Yijian Zhang
- Department of Thoracic Surgery, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Nanjing 210009, China; Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Cancer Institute of Jiangsu Province, Nanjing 210009, China
| | - Si Liang
- Department of Thoracic Surgery, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Nanjing 210009, China; Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Cancer Institute of Jiangsu Province, Nanjing 210009, China
| | - Minhao Zhang
- Department of Anesthesiology, Jiangsu Cancer Hospital and Jiangsu Institute of Cancer Research and The Affiliated Cancer Hospital of Nanjing Medical University, No. 42, Baiziting, Nanjing 210000, Jiangsu, China
| | - Hui Wang
- Department of Thoracic Surgery, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Nanjing 210009, China; Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Cancer Institute of Jiangsu Province, Nanjing 210009, China
| | - Yipeng Feng
- Department of Thoracic Surgery, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Nanjing 210009, China; Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Cancer Institute of Jiangsu Province, Nanjing 210009, China
| | - Rutao Li
- Department of Thoracic Surgery, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Nanjing 210009, China; Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Cancer Institute of Jiangsu Province, Nanjing 210009, China
| | - Hanlin Ding
- Department of Thoracic Surgery, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Nanjing 210009, China; Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Cancer Institute of Jiangsu Province, Nanjing 210009, China
| | - Yuzhong Chen
- Department of Thoracic Surgery, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Nanjing 210009, China; Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Cancer Institute of Jiangsu Province, Nanjing 210009, China
| | - Wenjie Xia
- Department of Thoracic Surgery, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Nanjing 210009, China; Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Cancer Institute of Jiangsu Province, Nanjing 210009, China
| | - Gaochao Dong
- Department of Thoracic Surgery, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Nanjing 210009, China; Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Cancer Institute of Jiangsu Province, Nanjing 210009, China
| | - Lin Xu
- Department of Thoracic Surgery, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Nanjing 210009, China; Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Cancer Institute of Jiangsu Province, Nanjing 210009, China.
| | - Qixing Mao
- Department of Thoracic Surgery, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Nanjing 210009, China; Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Cancer Institute of Jiangsu Province, Nanjing 210009, China.
| | - Feng Jiang
- Department of Thoracic Surgery, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Nanjing 210009, China; Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Cancer Institute of Jiangsu Province, Nanjing 210009, China.
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Xing X, Li L, Sun M, Yang J, Zhu X, Peng F, Du J, Feng Y. Deep-learning-based 3D super-resolution CT radiomics model: Predict the possibility of the micropapillary/solid component of lung adenocarcinoma. Heliyon 2024; 10:e34163. [PMID: 39071606 PMCID: PMC11279278 DOI: 10.1016/j.heliyon.2024.e34163] [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: 01/30/2024] [Revised: 07/03/2024] [Accepted: 07/04/2024] [Indexed: 07/30/2024] Open
Abstract
Objective Invasive lung adenocarcinoma(ILA) with micropapillary (MPP)/solid (SOL) components has a poor prognosis. Preoperative identification is essential for decision-making for subsequent treatment. This study aims to construct and evaluate a super-resolution(SR) enhanced radiomics model designed to predict the presence of MPP/SOL components preoperatively to provide more accurate and individualized treatment planning. Methods Between March 2018 and November 2023, patients who underwent curative intent ILA resection were included in the study. We implemented a deep transfer learning network on CT images to improve their resolution, resulting in the acquisition of preoperative super-resolution CT (SR-CT) images. Models were developed using radiomic features extracted from CT and SR-CT images. These models employed a range of classifiers, including Logistic Regression (LR), Support Vector Machines (SVM), k-Nearest Neighbors (KNN), Random Forest, Extra Trees, Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Multilayer Perceptron (MLP). The diagnostic performance of the models was assessed by measuring the area under the curve (AUC). Result A total of 245 patients were recruited, of which 109 (44.5 %) were diagnosed with ILA with MPP/SOL components. In the analysis of CT images, the SVM model exhibited outstanding effectiveness, recording AUC scores of 0.864 in the training group and 0.761 in the testing group. When this SVM approach was used to develop a radiomics model with SR-CT images, it recorded AUCs of 0.904 in the training and 0.819 in the test cohorts. The calibration curves indicated a high goodness of fit, while decision curve analysis (DCA) highlighted the model's clinical utility. Conclusion The study successfully constructed and evaluated a deep learning(DL)-enhanced SR-CT radiomics model. This model outperformed conventional CT radiomics models in predicting MPP/SOL patterns in ILA. Continued research and broader validation are necessary to fully harness and refine the clinical potential of radiomics when combined with SR reconstruction technology.
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Affiliation(s)
- Xiaowei Xing
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital, (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Liangping Li
- Department of Radiology, Zhejiang Hospital, Hangzhou, Zhejiang, China
| | - Mingxia Sun
- Department of Radiology, Zhejiang Hospital, Hangzhou, Zhejiang, China
| | - Jiahu Yang
- Department of Radiology, Zhejiang Hospital, Hangzhou, Zhejiang, China
| | - Xinhai Zhu
- Department of Thoracic Surgery, Zhejiang Hospital, Hangzhou, Zhejiang, China
| | - Fang Peng
- Department of Pathology, Zhejiang Hospital, Hangzhou, Zhejiang, China
| | - Jianzong Du
- Department of Respiratory Medicine, Zhejiang Hospital, Hangzhou, Zhejiang, China
| | - Yue Feng
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital, (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
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Tan KS, Reiner A, Emoto K, Eguchi T, Takahashi Y, Aly RG, Rekhtman N, Adusumilli PS, Travis WD. Novel Insights Into the International Association for the Study of Lung Cancer Grading System for Lung Adenocarcinoma. Mod Pathol 2024; 37:100520. [PMID: 38777035 PMCID: PMC11260232 DOI: 10.1016/j.modpat.2024.100520] [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: 03/01/2024] [Revised: 04/29/2024] [Accepted: 05/14/2024] [Indexed: 05/25/2024]
Abstract
The new grading system for lung adenocarcinoma proposed by the International Association for the Study of Lung Cancer (IASLC) defines prognostic subgroups on the basis of histologic patterns observed on surgical specimens. This study sought to provide novel insights into the IASLC grading system, with particular focus on recurrence-specific survival (RSS) and lung cancer-specific survival among patients with stage I adenocarcinoma. Under the IASLC grading system, tumors were classified as grade 1 (lepidic predominant with <20% high-grade patterns [micropapillary, solid, and complex glandular]), grade 2 (acinar or papillary predominant with <20% high-grade patterns), or grade 3 (≥20% high-grade patterns). Kaplan-Meier survival estimates, pathologic features, and genomic profiles were investigated for patients whose disease was reclassified into a higher grade under the IASLC grading system on the basis of the hypothesis that they would strongly resemble patients with predominant high-grade tumors. Overall, 423 (29%) of 1443 patients with grade 1 or 2 tumors classified based on the predominant pattern-based grading system had their tumors upgraded to grade 3 based on the IASLC grading system. The RSS curves for patients with upgraded tumors were significantly different from those for patients with grade 1 or 2 tumors (log-rank P < .001) but not from those for patients with predominant high-grade patterns (P = .3). Patients with upgraded tumors had a similar incidence of visceral pleural invasion and spread of tumor through air spaces as patients with predominant high-grade patterns. In multivariable models, the IASLC grading system remained significantly associated with RSS and lung cancer-specific survival after adjustment for aggressive pathologic features such as visceral pleural invasion and spread of tumor through air spaces. The IASLC grading system outperforms the predominant pattern-based grading system and appropriately reclassifies tumors into higher grades with worse prognosis, even after other pathologic features of aggressiveness are considered.
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Affiliation(s)
- Kay See Tan
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York.
| | - Allison Reiner
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Katsura Emoto
- Department of Pathology, Keio University School of Medicine, Tokyo, Japan
| | - Takashi Eguchi
- Division of General Thoracic Surgery, Department of Surgery, Shinshu University School of Medicine, Matsumoto, Japan
| | - Yusuke Takahashi
- Division of Thoracic Surgery, Jikei Medical University, Tokyo, Japan
| | - Rania G Aly
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Natasha Rekhtman
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Prasad S Adusumilli
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York; Center for Cell Engineering, Memorial Sloan Kettering Cancer Center, New York, New York
| | - William D Travis
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York
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Lee JH, Kang Y, Kim S, Jung Y, Chung JH, Lee S, Yi E. Clinical Importance of Grading Tumor Spread through Air Spaces in Early-Stage Small-Lung Adenocarcinoma. Cancers (Basel) 2024; 16:2218. [PMID: 38927923 PMCID: PMC11201625 DOI: 10.3390/cancers16122218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2024] [Revised: 06/11/2024] [Accepted: 06/12/2024] [Indexed: 06/28/2024] Open
Abstract
This study aimed to identify the clinical manifestation and implications according to the grading of tumor spread through air spaces in early-stage small (≤2 cm) pathological stage I non-mucinous lung adenocarcinomas. Medical records of patients with pathological stage I tumors sized ≤2 cm were retrospectively reviewed and analyzed. The furthest distance of the spread through air spaces from the tumor margin was measured on a standard-length scale (mm). Enrolled patients were categorized into spread through air spaces (STAS) (-) and STAS (+), and STAS (+) was subdivided according to its furthest distance as follows: STAS (+)-L (<2 mm) and STAS (+)-H (≥2 mm). Risk factors for STAS (+) included papillary predominant subtype (p = 0.027), presence of micropapillary patterns (p < 0.001), and EGFR (p = 0.039). The overall survival of the three groups did not differ significantly (p = 0.565). The recurrence-free survival of STAS (+)-H groups was significantly lower than those of STAS (-) and STAS (+)-L (p < 0.001 and p = 0.039, respectively). A number of alveolar spaces were definite risk factors for STAS (+)-H groups (p < 0.001), and male gender could be one (p = 0.054). In the patient group with small (≤2 cm) pathological stage I lung adenocarcinomas, the presence of STAS ≥ 2 mm was related to significantly lower recurrence-free survival. For identifying definite risk factors for the presence of farther STAS, more precise analysis from a larger study population should be undertaken.
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Affiliation(s)
- Jeong Hyeon Lee
- Department of Pathology, Korea University Anam Hospital, Seoul 02841, Republic of Korea; (J.H.L.); (Y.K.); (S.K.)
| | - Younggjn Kang
- Department of Pathology, Korea University Anam Hospital, Seoul 02841, Republic of Korea; (J.H.L.); (Y.K.); (S.K.)
| | - Seojin Kim
- Department of Pathology, Korea University Anam Hospital, Seoul 02841, Republic of Korea; (J.H.L.); (Y.K.); (S.K.)
| | - Youggi Jung
- Department of Thoracic and Cardiovascular Surgery, Korea University Anam Hospital, Seoul 02841, Republic of Korea; (Y.J.); (J.H.C.)
| | - Jae Ho Chung
- Department of Thoracic and Cardiovascular Surgery, Korea University Anam Hospital, Seoul 02841, Republic of Korea; (Y.J.); (J.H.C.)
| | - Sungho Lee
- Department of Thoracic and Cardiovascular Surgery, Korea University Anam Hospital, Seoul 02841, Republic of Korea; (Y.J.); (J.H.C.)
| | - Eunjue Yi
- Department of Thoracic and Cardiovascular Surgery, Korea University Anam Hospital, Seoul 02841, Republic of Korea; (Y.J.); (J.H.C.)
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Moreira AL, Zhou F. Invasion and Grading of Pulmonary Non-Mucinous Adenocarcinoma. Surg Pathol Clin 2024; 17:271-285. [PMID: 38692810 DOI: 10.1016/j.path.2023.11.009] [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] [Indexed: 05/03/2024]
Abstract
Lung adenocarcinoma staging and grading were recently updated to reflect the link between histologic growth patterns and outcomes. The lepidic growth pattern is regarded as "in-situ," whereas all other patterns are regarded as invasive, though with stratification. Solid, micropapillary, and complex glandular patterns are associated with worse prognosis than papillary and acinar patterns. These recent changes have improved prognostic stratification. However, multiple pitfalls exist in measuring invasive size and in classifying lung adenocarcinoma growth patterns. Awareness of these limitations and recommended practices will help the pathology community achieve consistent prognostic performance and potentially contribute to improved patient management.
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Affiliation(s)
- Andre L Moreira
- Department of Pathology, New York University Grossman School of Medicine, 560 First Avenue, New York, NY 10016, USA.
| | - Fang Zhou
- Department of Pathology, New York University Grossman School of Medicine, 560 First Avenue, New York, NY 10016, USA
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Ninomiya K, Yanagawa M, Tsubamoto M, Sato Y, Suzuki Y, Hata A, Kikuchi N, Yoshida Y, Yamagata K, Doi S, Ogawa R, Tokuda Y, Kido S, Tomiyama N. Prediction of solid and micropapillary components in lung invasive adenocarcinoma: radiomics analysis from high-spatial-resolution CT data with 1024 matrix. Jpn J Radiol 2024; 42:590-598. [PMID: 38413550 PMCID: PMC11139717 DOI: 10.1007/s11604-024-01534-2] [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: 11/12/2023] [Accepted: 01/13/2024] [Indexed: 02/29/2024]
Abstract
PURPOSE To predict solid and micropapillary components in lung invasive adenocarcinoma using radiomic analyses based on high-spatial-resolution CT (HSR-CT). MATERIALS AND METHODS For this retrospective study, 64 patients with lung invasive adenocarcinoma were enrolled. All patients were scanned by HSR-CT with 1024 matrix. A pathologist evaluated subtypes (lepidic, acinar, solid, micropapillary, or others). Total 61 radiomic features in the CT images were calculated using our modified texture analysis software, then filtered and minimized by least absolute shrinkage and selection operator (LASSO) regression to select optimal radiomic features for predicting solid and micropapillary components in lung invasive adenocarcinoma. Final data were obtained by repeating tenfold cross-validation 10 times. Two independent radiologists visually predicted solid or micropapillary components on each image of the 64 nodules with and without using the radiomics results. The quantitative values were analyzed with logistic regression models. The receiver operating characteristic curves were generated to predict of solid and micropapillary components. P values < 0.05 were considered significant. RESULTS Two features (Coefficient Variation and Entropy) were independent indicators associated with solid and micropapillary components (odds ratio, 30.5 and 11.4; 95% confidence interval, 5.1-180.5 and 1.9-66.6; and P = 0.0002 and 0.0071, respectively). The area under the curve for predicting solid and micropapillary components was 0.902 (95% confidence interval, 0.802 to 0.962). The radiomics results significantly improved the accuracy and specificity of the prediction of the two radiologists. CONCLUSION Two texture features (Coefficient Variation and Entropy) were significant indicators to predict solid and micropapillary components in lung invasive adenocarcinoma.
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Affiliation(s)
- Keisuke Ninomiya
- Department of Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Masahiro Yanagawa
- Department of Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan.
| | - Mitsuko Tsubamoto
- Nishinomiya Municipal Central Hospital, 8-24 Hayashidacho, Nishinomiya, Hyogo, 663-8014, Japan
| | - Yukihisa Sato
- Suita Municipal Hospital, 5-7 Kishibeshinmachi, Suita, Osaka, 564-0018, Japan
| | - Yuki Suzuki
- Department of Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Akinori Hata
- Department of Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Noriko Kikuchi
- Department of Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Yuriko Yoshida
- Department of Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Kazuki Yamagata
- Department of Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Shuhei Doi
- Department of Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Ryo Ogawa
- Department of Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Yukiko Tokuda
- Department of Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Shoji Kido
- Department of Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Noriyuki Tomiyama
- Department of Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan
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Pan C, Wang Q, Wang H, Deng X, Chen L, Li Z. LncRNA CARD8-AS1 suppresses lung adenocarcinoma progression by enhancing TRIM25-mediated ubiquitination of TXNRD1. Carcinogenesis 2024; 45:311-323. [PMID: 38153696 DOI: 10.1093/carcin/bgad097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2023] [Revised: 12/06/2023] [Accepted: 12/27/2023] [Indexed: 12/29/2023] Open
Abstract
Long non-coding RNAs (lncRNAs) play crucial roles in the tumorigenesis and progression of lung adenocarcinoma (LUAD). However, little was known about the role of lncRNAs in high-risk LUAD subtypes: micropapillary-predominant adenocarcinoma (MPA) and solid-predominant adenocarcinoma (SPA). In this study, we conducted a systematic screening of differentially expressed lncRNAs using RNA sequencing in 10 paired MPA/SPA tumor tissues and adjacent normal tissues. Consequently, 110 significantly up-regulated lncRNAs and 288 aberrantly down-regulated lncRNAs were identified (|Log2 Foldchange| ≥ 1 and corrected P < 0.05). The top 10 lncRNAs were further analyzed in 89 MPA/SPA tumor tissues and 59 normal tissues from The Cancer Genome Atlas database. Among them, CARD8-AS1 showed the most significant differential expression, and decreased expression of CARD8-AS1 was significantly associated with a poorer prognosis. Functionally, CARD8-AS1 overexpression remarkably suppressed the proliferation, migration and invasion of LUAD cells both in vitro and in vivo. Conversely, inhibition of CARD8-AS1 yielded opposite effects. Mechanistically, CARD8-AS1 acted as a scaffold to facilitate the interaction between TXNRD1 and E3 ubiquitin ligase TRIM25, thereby promoting the degradation of TXNRD1 through the ubiquitin-proteasome pathway. Additionally, TXNRD1 was found to promote LUAD cell proliferation, migration and invasion in vitro. Furthermore, the suppressed progression of LUAD cells resulting from CARD8-AS1 overexpression could be significantly reversed by simultaneous overexpression of TXNRD1. In conclusion, this study revealed that the lncRNA CARD8-AS1 played a suppressive role in the progression of LUAD by enhancing TRIM25-mediated ubiquitination of TXNRD1. The CARD8-AS1-TRIM25-TXNRD1 axis may represent a promising therapeutic target for LUAD.
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Affiliation(s)
- Cheng Pan
- Department of Thoracic Surgery, First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China
| | - Qi Wang
- Department of Thoracic Surgery, First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China
| | - Hongshun Wang
- Department of Thoracic Surgery, First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China
| | - Xiaheng Deng
- Department of Thoracic Surgery, First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China
| | - Liang Chen
- Department of Thoracic Surgery, First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China
| | - Zhihua Li
- Department of Thoracic Surgery, First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China
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Rusch VW. Wedge Resection vs Segmentectomy for Clinical Stage IA Non-small Cell Lung Cancer: Are They Truly Oncologically Equivalent? Ann Thorac Surg 2024:S0003-4975(24)00359-X. [PMID: 38734401 DOI: 10.1016/j.athoracsur.2024.04.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/18/2024] [Accepted: 04/20/2024] [Indexed: 05/13/2024]
Affiliation(s)
- Valerie W Rusch
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York.
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12
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Huo J, Min X, Luo T, Lv F, Feng Y, Fan Q, Wang D, Ma D, Li Q. Computed tomography-based 3D convolutional neural network deep learning model for predicting micropapillary or solid growth pattern of invasive lung adenocarcinoma. LA RADIOLOGIA MEDICA 2024; 129:776-784. [PMID: 38512613 PMCID: PMC11088553 DOI: 10.1007/s11547-024-01800-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 02/14/2024] [Indexed: 03/23/2024]
Abstract
PURPOSE To investigate the value of a computed tomography (CT)-based deep learning (DL) model to predict the presence of micropapillary or solid (M/S) growth pattern in invasive lung adenocarcinoma (ILADC). MATERIALS AND METHODS From June 2019 to October 2022, 617 patients with ILADC who underwent preoperative chest CT scans in our institution were randomly placed into training and internal validation sets in a 4:1 ratio, and 353 patients with ILADC from another institution were included as an external validation set. Then, a self-paced learning (SPL) 3D Net was used to establish two DL models: model 1 was used to predict the M/S growth pattern in ILADC, and model 2 was used to predict that pattern in ≤ 2-cm-diameter ILADC. RESULTS For model 1, the training cohort's area under the curve (AUC), accuracy, recall, precision, and F1-score were 0.924, 0.845, 0.851, 0.842, and 0.843; the internal validation cohort's were 0.807, 0.744, 0.756, 0.750, and 0.743; and the external validation cohort's were 0.857, 0.805, 0.804, 0.806, and 0.804, respectively. For model 2, the training cohort's AUC, accuracy, recall, precision, and F1-score were 0.946, 0.858, 0.881,0.844, and 0.851; the internal validation cohort's were 0.869, 0.809, 0.786, 0.794, and 0.790; and the external validation cohort's were 0.831, 0.792, 0.789, 0.790, and 0.790, respectively. The SPL 3D Net model performed better than the ResNet34, ResNet50, ResNeXt50, and DenseNet121 models. CONCLUSION The CT-based DL model performed well as a noninvasive screening tool capable of reliably detecting and distinguishing the subtypes of ILADC, even in small-sized tumors.
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Affiliation(s)
- Jiwen Huo
- Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yu Zhong District, Chongqing, 400016, China
| | - Xuhong Min
- Anhui Chest Hospital, 397 Jixi Road, Hefei, 230022, Anhui Province, China
| | - Tianyou Luo
- Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yu Zhong District, Chongqing, 400016, China
| | - Fajin Lv
- Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yu Zhong District, Chongqing, 400016, China
| | - Yibo Feng
- Institute of Research, Infervision Medical Technology Co., Ltd, 25F Building E, Yuanyang International Center, Chaoyang District, Beijing, 100025, China
| | - Qianrui Fan
- Institute of Research, Infervision Medical Technology Co., Ltd, 25F Building E, Yuanyang International Center, Chaoyang District, Beijing, 100025, China
| | - Dawei Wang
- Institute of Research, Infervision Medical Technology Co., Ltd, 25F Building E, Yuanyang International Center, Chaoyang District, Beijing, 100025, China
| | - Dongchun Ma
- Anhui Chest Hospital, 397 Jixi Road, Hefei, 230022, Anhui Province, China.
| | - Qi Li
- Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yu Zhong District, Chongqing, 400016, China.
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13
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Chen C, Chen ZJ, Li WJ, Deng T, Le HB, Zhang YK, Zhang BJ. Evaluation of the preoperative neutrophil-to-lymphocyte ratio as a predictor of the micropapillary component of stage IA lung adenocarcinoma. J Int Med Res 2024; 52:3000605241245016. [PMID: 38661098 PMCID: PMC11047232 DOI: 10.1177/03000605241245016] [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: 12/28/2023] [Accepted: 03/18/2024] [Indexed: 04/26/2024] Open
Abstract
OBJECTIVE To assess the ability of markers of inflammation to identify the solid or micropapillary components of stage IA lung adenocarcinoma and their effects on prognosis. METHODS We performed a retrospective study of clinicopathologic data from 654 patients with stage IA lung adenocarcinoma collected between 2013 and 2019. Logistic regression analysis was used to identify independent predictors of these components, and we also evaluated the relationship between markers of inflammation and recurrence. RESULTS Micropapillary-positive participants had high preoperative neutrophil-to-lymphocyte ratios. There were no significant differences in the levels of markers of systemic inflammation between the participants with or without a solid component. Multivariate analysis showed that preoperative neutrophil-to-lymphocyte ratio (odds ratio [OR] = 2.094; 95% confidence interval [CI], 1.668-2.628), tumor size (OR = 1.386; 95% CI, 1.044-1.842), and carcinoembryonic antigen concentration (OR = 1.067; 95% CI, 1.017-1.119) were independent predictors of a micropapillary component. There were no significant correlations between markers of systemic inflammation and the recurrence of stage IA lung adenocarcinoma. CONCLUSIONS Preoperative neutrophil-to-lymphocyte ratio independently predicts a micropapillary component of stage IA lung adenocarcinoma. Therefore, the potential use of preoperative neutrophil-to-lymphocyte ratio in the optimization of surgical strategies for the treatment of stage IA lung adenocarcinoma should be further studied.
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Affiliation(s)
- Cheng Chen
- Department of Cardiothoracic Surgery, Zhoushan Hospital, Zhejiang, P.R. China
| | - Zhi-Jun Chen
- Department of Cardiothoracic Surgery, Zhoushan Hospital, Zhejiang, P.R. China
| | - Wu-Jun Li
- Department of Cardiothoracic Surgery, Zhoushan Hospital, Zhejiang, P.R. China
| | - Tao Deng
- Department of Pathology, Zhoushan Hospital, Zhejiang, P.R. China
| | - Han-Bo Le
- Department of Cardiothoracic Surgery, Zhoushan Hospital, Zhejiang, P.R. China
| | - Yong-Kui Zhang
- Department of Cardiothoracic Surgery, Zhoushan Hospital, Zhejiang, P.R. China
| | - Bin-Jie Zhang
- Department of Cardiothoracic Surgery, Zhoushan Hospital, Zhejiang, P.R. China
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Ye G, Wu G, Li K, Zhang C, Zhuang Y, Liu H, Song E, Qi Y, Li Y, Yang F, Liao Y. Development and Validation of a Deep Learning Radiomics Model to Predict High-Risk Pathologic Pulmonary Nodules Using Preoperative Computed Tomography. Acad Radiol 2024; 31:1686-1697. [PMID: 37802672 DOI: 10.1016/j.acra.2023.08.040] [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: 08/05/2023] [Revised: 08/27/2023] [Accepted: 08/29/2023] [Indexed: 10/08/2023]
Abstract
RATIONALE AND OBJECTIVES To accurately identify the high-risk pathological factors of pulmonary nodules, our study constructed a model combined with clinical features, radiomics features, and deep transfer learning features to predict high-risk pathological pulmonary nodules. MATERIALS AND METHODS The study cohort consisted of 469 cases of lung adenocarcinoma patients, divided into a training cohort (n = 400) and an external validation cohort (n = 69). We obtained computed tomography (CT) semantic features and clinical characteristics, as well as extracted radiomics and deep transfer learning (DTL) features from the CT images. Selected features were used for constructing prediction models using the logistic regression (LR) algorithm. The performance of the models was evaluated through metrics including the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, calibration curve, and decision curve analysis. RESULTS The clinical model achieved an AUC of 0.774 (95% CI: 0.728-0.821) in the training cohort and 0.762 (95% confidence interval [CI]: 0.650-0.873) in the external validation cohort. The radiomics model demonstrated an AUC of 0.847 (95% CI: 0.810-0.884) in the training cohort and 0.800 (95% CI: 0.693-0.907) in the external validation cohort. The radiomics-DTL (Rad-DTL) model showed an AUC of 0.871 (95% CI: 0.838-0.905) in the training cohort and 0.806 (95% CI: 0.698-0.914) in the external validation cohort. The proposed combined model yielded AUC values of 0.872 and 0.814 in the training and external validation cohorts, respectively. The combined model demonstrated superiority over both the clinical model and the Rad-DTL model. There were no statistically significant differences observed in the comparison between the combined model incorporating clinical features and the Rad-DTL model. Decision curve analysis (DCA) indicated that the models provided a net benefit in predicting high-risk pathologic pulmonary nodules. CONCLUSION Rad-DTL signature is a potential biomarker for predicting high-risk pathologic pulmonary nodules using preoperative CT, determining the appropriate surgical strategy, and guiding the extent of resection.
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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 (G.Y., K.L., C.Z., Y.L.)
| | - Guangyao Wu
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China (G.W., F.Y.)
| | - Kuo Li
- Department of Thoracic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China (G.Y., K.L., C.Z., Y.L.)
| | - Chi Zhang
- Department of Thoracic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China (G.Y., K.L., C.Z., Y.L.)
| | - Yuzhou Zhuang
- School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China (Y.Z., H.L., E.S.)
| | - Hong Liu
- School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China (Y.Z., H.L., E.S.)
| | - Enmin Song
- School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China (Y.Z., H.L., E.S.)
| | - Yu Qi
- Department of Thoracic Surgery of the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China (Y.Q.)
| | - Yiying Li
- Department of Breast Surgery of the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China (Y.L.)
| | - Fan Yang
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China (G.W., F.Y.)
| | - Yongde Liao
- Department of Thoracic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China (G.Y., K.L., C.Z., Y.L.).
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Li X, Gao Z, Diao H, Guo C, Yu Y, Liu S, Feng Z, Peng Z. Lung adenocarcinoma: selection of surgical approaches in solid adenocarcinoma from the viewpoint of clinicopathologic features and tumor microenvironmental heterogeneity. Front Oncol 2024; 14:1326626. [PMID: 38505588 PMCID: PMC10949368 DOI: 10.3389/fonc.2024.1326626] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Accepted: 02/19/2024] [Indexed: 03/21/2024] Open
Abstract
Introduction Solid adenocarcinoma represents a notably aggressive subtype of lung adenocarcinoma. Amidst the prevailing inclination towards conservative surgical interventions for diminutive lung cancer lesions, the critical evaluation of this subtype's malignancy and heterogeneity stands as imperative for the formulation of surgical approaches and the prognostication of long-term patient survival. Methods A retrospective dataset, encompassing 2406 instances of non-solid adenocarcinoma (comprising lepidic, acinar, and papillary adenocarcinoma) and 326 instances of solid adenocarcinoma, was analyzed to ascertain the risk factors concomitant with diverse histological variants of lung adenocarcinoma. Concurrently, RNA-sequencing data delineating explicit pathological subtypes were extracted from 261 cases in the TCGA database and 188 cases in the OncoSG database. This data served to illuminate the heterogeneity across lung adenocarcinoma (LUAD) specimens characterized by differential histological features. Results Solid adenocarcinoma is associated with an elevated incidence of pleural invasion, microscopic vessel invasion, and lymph node metastasis, relative to other subtypes of lung adenocarcinoma. Furthermore, the tumor microenvironment (TME) in solid pattern adenocarcinoma displayed suboptimal oxygenation and acidic conditions, concomitant with augmented tumor cell proliferation and invasion capacities. Energy and metabolic activities were significantly upregulated in tumor cells of the solid pattern subtype. This subtype manifested robust immune tolerance and capabilities for immune evasion. Conclusion This present investigation identifies multiple potential metrics for evaluating the invasive propensity, metastatic likelihood, and immune resistance of solid pattern adenocarcinoma. These insights may prove instrumental in devising surgical interventions that are tailored to patients diagnosed with disparate histological subtypes of LUAD, thereby offering valuable directional guidance.
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Affiliation(s)
- Xiao Li
- Department of Thoracic Surgery, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, China
| | - Zhen Gao
- Department of Thoracic Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Shandong First Medical University, Jinan, Shandong, China
| | - Haixiao Diao
- Department of Thoracic Surgery, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, China
| | - Chenran Guo
- Department of Thoracic Surgery, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, China
| | - Yue Yu
- Department of Thoracic Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Shandong First Medical University, Jinan, Shandong, China
| | - Shang Liu
- Department of Thoracic Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Shandong First Medical University, Jinan, Shandong, China
| | - Zhen Feng
- Department of Thoracic Surgery, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, China
| | - Zhongmin Peng
- Department of Thoracic Surgery, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, China
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16
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Wei B, Zhang Y, Shi K, Jin X, Qian K, Zhang P, Zhao T. Predictive value of systemic immune-inflammation index in the high-grade subtypes components of small-sized lung adenocarcinoma. J Cardiothorac Surg 2024; 19:39. [PMID: 38303053 PMCID: PMC10832140 DOI: 10.1186/s13019-024-02528-x] [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: 04/07/2023] [Accepted: 01/28/2024] [Indexed: 02/03/2024] Open
Abstract
BACKGROUND Identification of micropapillary and solid subtypes components in small-sized (≤ 2 cm) lung adenocarcinoma plays a crucial role in determining optimal surgical procedures. This study aims to propose a straightforward prediction method utilizing preoperative available indicators. METHODS From January 2019 to July 2022, 341 consecutive patients with small-sized lung adenocarcinoma who underwent curative resection in thoracic surgery department of Xuanwu Hospital, Capital Medical University were retrospectively analyzed. The patients were divided into two groups based on whether solid or micropapillary components ≥ 5% or not (S/MP5+ and S/MP5-). Univariate analysis and multivariate logistic regression analysis were utilized to identify independent predictors of S/MP5+. Then a nomogram was constructed to intuitively show the results. Finally, the calibration curve with a 1000 bootstrap resampling and the receiver operating characteristic (ROC) curve were depicted to evaluate its performance. RESULTS According to postoperative pathological results, 79 (23.2%) patients were confirmed as S/MP5+ while 262 (76.8%) patients were S/MP5-. Based on multivariate analysis, maximum diameter (p = 0.010), consolidation tumor ratio (CTR) (p < 0.001) and systemic immune-inflammation index (SII) (p < 0.001) were identified as three independent risk factors and incorporated into the nomogram. The calibration curve showed good concordance between the predicted and actual probability of S/MP5+. Besides, the model showed certain discrimination, with an area under ROC curve of 0.893. CONCLUSIONS The model constructed based on SII is a practical tool to predict high-grade subtypes components of small-sized lung adenocarcinoma preoperatively and contribute to determine the optimal surgical approach.
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Affiliation(s)
- BoHua Wei
- Department of Thoracic Surgery, Xuanwu Hospital, Capital Medical University, No. 45 Changchun Street, Beijing, China
| | - Yi Zhang
- Department of Thoracic Surgery, Xuanwu Hospital, Capital Medical University, No. 45 Changchun Street, Beijing, China.
| | - Kejian Shi
- Department of Thoracic Surgery, Xuanwu Hospital, Capital Medical University, No. 45 Changchun Street, Beijing, China
| | - Xin Jin
- Laboratory of Respiratory Disease and Thoracic Surgery, KU Leuven, 3000, Leuven, Belgium
| | - Kun Qian
- Department of Thoracic Surgery, Xuanwu Hospital, Capital Medical University, No. 45 Changchun Street, Beijing, China
| | - Peilong Zhang
- Department of Thoracic Surgery, Xuanwu Hospital, Capital Medical University, No. 45 Changchun Street, Beijing, China
| | - Teng Zhao
- Department of Thoracic Surgery, Xuanwu Hospital, Capital Medical University, No. 45 Changchun Street, Beijing, China
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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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Zhou L, Sun J, Long H, Zhou W, Xia R, Luo Y, Fang J, Wang Y, Chen X. Imaging phenotyping using 18F-FDG PET/CT radiomics to predict micropapillary and solid pattern in lung adenocarcinoma. Insights Imaging 2024; 15:5. [PMID: 38185779 PMCID: PMC10772036 DOI: 10.1186/s13244-023-01573-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Accepted: 11/22/2023] [Indexed: 01/09/2024] Open
Abstract
OBJECTIVES To develop and validate a machine learning model using 18F-FDG PET/CT radiomics signature and clinical features to predict the presence of micropapillary and solid (MP/S) components in lung adenocarcinoma. METHODS Eight hundred and forty-six patients who underwent preoperative PET/CT with pathologically confirmed adenocarcinoma were enrolled. After segmentation, 1688 radiomics features were extracted from PET/CT and selected to construct predictive models. Then, we developed a nomogram based on PET/CT radiomics integrated with clinical features. Receiver operating curves, calibration curves, and decision curve analysis (DCA) were performed for diagnostics assessment and test of the developed models for distinguishing patients with MP/S components from the patients without. RESULTS PET/CT radiomics-clinical combined model could well distinguish patients with MP/S components from those without MP/S components (AUC = 0.87), which performed better than PET (AUC = 0.829, p < 0.05) or CT (AUC = 0.827, p < 0.05) radiomics models in the training cohort. In test cohorts, radiomics-clinical combined model outperformed the PET radiomics model in test cohort 1 (AUC = 0.859 vs 0.799, p < 0.05) and the CT radiomics model in test cohort 2 (AUC = 0.880 vs 0.829, p < 0.05). Calibration curve indicated good coherence between all model prediction and the actual observation in training and test cohorts. DCA revealed PET/CT radiomics-clinical model exerted the highest clinical benefit. CONCLUSION 18F-FDG PET/CT radiomics signatures could achieve promising prediction efficiency to identify the presence of MP/S components in adenocarcinoma patients to help the clinician decide on personalized treatment and surveillance strategies. The PET/CT radiomics-clinical combined model performed best. CRITICAL RELEVANCE STATEMENT: 18F-FDG PET/CT radiomics signatures could achieve promising prediction efficiency to identify the presence of micropapillary and solid components in adenocarcinoma patients to help the clinician decide on personalized treatment and surveillance strategies.
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Affiliation(s)
- Linyi Zhou
- Department of Nuclear Medicine, Daping Hospital, Army Medical University, Chongqing, China
| | - Jinju Sun
- Department of Nuclear Medicine, Daping Hospital, Army Medical University, Chongqing, China
| | - He Long
- Department of Nuclear Medicine, Daping Hospital, Army Medical University, Chongqing, China
| | - Weicheng Zhou
- Department of Nuclear Medicine, Daping Hospital, Army Medical University, Chongqing, China
| | - Renxiang Xia
- Department of Nuclear Medicine, Daping Hospital, Army Medical University, Chongqing, China
| | - Yi Luo
- Department of Nuclear Medicine, Daping Hospital, Army Medical University, Chongqing, China
| | - Jingqin Fang
- Department of Ultrasound, Daping Hospital, Army Medical University, Chongqing, China.
| | - Yi Wang
- Department of Nuclear Medicine, Daping Hospital, Army Medical University, Chongqing, China.
| | - Xiao Chen
- Department of Nuclear Medicine, Daping Hospital, Army Medical University, Chongqing, China.
- Chongqing Clinical Research Center for Imaging and Nuclear Medicine, Chongqing, China.
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Willner J, Narula N, Moreira AL. Updates on lung adenocarcinoma: invasive size, grading and STAS. Histopathology 2024; 84:6-17. [PMID: 37872108 DOI: 10.1111/his.15077] [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: 07/24/2023] [Revised: 09/29/2023] [Accepted: 10/04/2023] [Indexed: 10/25/2023]
Abstract
Advancements in the classification of lung adenocarcinoma have resulted in significant changes in pathological reporting. The eighth edition of the tumour-node-metastasis (TNM) staging guidelines calls for the use of invasive size in staging in place of total tumour size. This shift improves prognostic stratification and requires a more nuanced approach to tumour measurements in challenging situations. Similarly, the adoption of new grading criteria based on the predominant and highest-grade pattern proposed by the International Association for the Study of Lung Cancer (IASLC) shows improved prognostication, and therefore clinical utility, relative to previous grading systems. Spread through airspaces (STAS) is a form of tumour invasion involving tumour cells spreading through the airspaces, which has been highly researched in recent years. This review discusses updates in pathological T staging, adenocarcinoma grading and STAS and illustrates the utility and limitations of current concepts in lung adenocarcinoma.
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Affiliation(s)
- Jonathan Willner
- Department of Pathology, New York University Grossman School of Medicine, New York, NY, USA
| | - Navneet Narula
- Department of Pathology, New York University Grossman School of Medicine, New York, NY, USA
| | - Andre L Moreira
- Department of Pathology, New York University Grossman School of Medicine, New York, NY, USA
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Zhong Y, Cai C, Chen T, Gui H, Chen C, Deng J, Yang M, Yu B, Song Y, Wang T, Chen Y, Shi H, Xie D, Chen C, She Y. PET/CT-based deep learning grading signature to optimize surgical decisions for clinical stage I invasive lung adenocarcinoma and biologic basis under its prediction: a multicenter study. Eur J Nucl Med Mol Imaging 2024; 51:521-534. [PMID: 37725128 DOI: 10.1007/s00259-023-06434-7] [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: 04/26/2023] [Accepted: 09/06/2023] [Indexed: 09/21/2023]
Abstract
PURPOSE No consensus on a grading system for invasive lung adenocarcinoma had been built over a long period of time. Until October 2020, a novel grading system was proposed to quantify the whole landscape of histologic subtypes and proportions of pulmonary adenocarcinomas. This study aims to develop a deep learning grading signature (DLGS) based on positron emission tomography/computed tomography (PET/CT) to personalize surgical treatments for clinical stage I invasive lung adenocarcinoma and explore the biologic basis under its prediction. METHODS A total of 2638 patients with clinical stage I invasive lung adenocarcinoma from 4 medical centers were retrospectively included to construct and validate the DLGS. The predictive performance of the DLGS was evaluated by the area under the receiver operating characteristic curve (AUC), its potential to optimize surgical treatments was investigated via survival analyses in risk groups defined by the DLGS, and its biological basis was explored by comparing histologic patterns, genotypic alternations, genetic pathways, and infiltration of immune cells in microenvironments between risk groups. RESULTS The DLGS to predict grade 3 achieved AUCs of 0.862, 0.844, and 0.851 in the validation set (n = 497), external cohort (n = 382), and prospective cohort (n = 600), respectively, which were significantly better than 0.814, 0.810, and 0.806 of the PET model, 0.813, 0.795, and 0.824 of the CT model, and 0.762, 0.734, and 0.751 of the clinical model. Additionally, for DLGS-defined high-risk population, lobectomy yielded an improved prognosis compared to sublobectomy p = 0.085 for overall survival [OS] and p = 0.038 for recurrence-free survival [RFS]) and systematic nodal dissection conferred a superior prognosis to limited nodal dissection (p = 0.001 for OS and p = 0.041 for RFS). CONCLUSION The DLGS harbors the potential to predict the histologic grade and personalize the surgical treatments for clinical stage I invasive lung adenocarcinoma. Its applicability to other territories should be further validated by a larger international study.
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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
| | - Cheng Chen
- Department of Thoracic Surgery, Affiliated Hospital of Zunyi Medical College, Zunyi Medical College, Guizhou, 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 College, Zunyi Medical College, Guizhou, China
| | - Tingting Wang
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Yangchun Chen
- Department of Nuclear Medicine, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Huazheng Shi
- Shanghai Universal Cloud Medical Imaging Diagnostic Center, 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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Lee JS, Kim EK, Kim KA, Shim HS. Clinical Impact of Genomic and Pathway Alterations in Stage I EGFR-Mutant Lung Adenocarcinoma. Cancer Res Treat 2024; 56:104-114. [PMID: 37499696 PMCID: PMC10789943 DOI: 10.4143/crt.2023.728] [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: 06/06/2023] [Accepted: 07/21/2023] [Indexed: 07/29/2023] Open
Abstract
PURPOSE We investigated the clinical impact of genomic and pathway alterations in stage I epidermal growth factor receptor (EGFR)-mutant lung adenocarcinomas, which have a high recurrence rate despite complete surgical resection. MATERIALS AND METHODS Out of the initial cohort of 257 patients with completely resected stage I EGFR-mutant lung adenocarcinoma, tumor samples from 105 patients were subjected to analysis using large-panel next-generation sequencing. We analyzed 11 canonical oncogenic pathways and determined the number of pathway alterations (NPA). Survival analyses were performed based on co-occurring alterations and NPA in three patient groups: all patients, patients with International Association for the Study of Lung Cancer (IASLC) pathology grade 2, and patients with recurrent tumors treated with EGFR-tyrosine kinase inhibitor (TKI). RESULTS In the univariate analysis, pathological stage, IASLC grade, TP53 mutation, NPA, phosphoinositide 3-kinase pathway, p53 pathway, and cell cycle pathway exhibited significant associations with worse recurrence-free survival (RFS). Moreover, RPS6KB1 or EGFR amplifications were linked to a poorer RFS. Multivariate analysis revealed that pathologic stage, IASLC grade, and cell cycle pathway alteration were independent poor prognostic factors for RFS (p=0.002, p < 0.001, and p=0.006, respectively). In the grade 2 subgroup, higher NPA was independently associated with worse RFS (p=0.003). Additionally, in patients with recurrence treated with EGFR-TKIs, co-occurring TP53 mutations were linked to shorter progression-free survival (p=0.025). CONCLUSION Genomic and pathway alterations, particularly cell cycle alterations, high NPA, and TP53 mutations, were associated with worse clinical outcomes in stage I EGFR-mutant lung adenocarcinoma. These findings may have implications for risk stratification and the development of new therapeutic strategies in early-stage EGFR-mutant lung cancer patients.
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Affiliation(s)
- Jae Seok Lee
- Department of Pathology, Samsung Changwon Hospital, Sungkyunkwan University School of Medicine, Changwon, Korea
| | - Eun Kyung Kim
- Department of Pathology, National Health Insurance Service Ilsan Hospital, Goyang, Korea
| | - Kyung A Kim
- Department of Pathology, Yonsei University College of Medicine, Seoul, Korea
| | - Hyo Sup Shim
- Department of Pathology, Yonsei University College of Medicine, Seoul, Korea
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Xing X, Li L, Sun M, Zhu X, Feng Y. A combination of radiomic features, clinic characteristics, and serum tumor biomarkers to predict the possibility of the micropapillary/solid component of lung adenocarcinoma. Ther Adv Respir Dis 2024; 18:17534666241249168. [PMID: 38757628 PMCID: PMC11102675 DOI: 10.1177/17534666241249168] [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: 10/19/2023] [Accepted: 04/05/2024] [Indexed: 05/18/2024] Open
Abstract
BACKGROUND Invasive lung adenocarcinoma with MPP/SOL components has a poor prognosis and often shows a tendency to recurrence and metastasis. This poor prognosis may require adjustment of treatment strategies. Preoperative identification is essential for decision-making for subsequent treatment. OBJECTIVE This study aimed to preoperatively predict the probability of MPP/SOL components in lung adenocarcinomas by a comprehensive model that includes radiomics features, clinical characteristics, and serum tumor biomarkers. DESIGN A retrospective case control, diagnostic accuracy study. METHODS This study retrospectively recruited 273 patients (males: females, 130: 143; mean age ± standard deviation, 63.29 ± 10.03 years; range 21-83 years) who underwent resection of invasive lung adenocarcinoma. Sixty-one patients (22.3%) were diagnosed with lung adenocarcinoma with MPP/SOL components. Radiomic features were extracted from CT before surgery. Clinical, radiomic, and combined models were developed using the logistic regression algorithm. The clinical and radiomic signatures were integrated into a nomogram. The diagnostic performance of the models was evaluated using the area under the curve (AUC). Studies were scored according to the Radiomics Quality Score and Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis guidelines. RESULTS The radiomics model achieved the best AUC values of 0.858 and 0.822 in the training and test cohort, respectively. Tumor size (T_size), solid tumor size (ST_size), consolidation-to-tumor ratio (CTR), years of smoking, CYFRA 21-1, and squamous cell carcinoma antigen were used to construct the clinical model. The clinical model achieved AUC values of 0.741 and 0.705 in the training and test cohort, respectively. The nomogram showed higher AUCs of 0.894 and 0.843 in the training and test cohort, respectively. CONCLUSION This study has developed and validated a combined nomogram, a visual tool that integrates CT radiomics features with clinical indicators and serum tumor biomarkers. This innovative model facilitates the differentiation of micropapillary or solid components within lung adenocarcinoma and achieves a higher AUC, indicating superior predictive accuracy.
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Affiliation(s)
- Xiaowei Xing
- Cancer Center, Department of Radiology, Zhejiang Provincial People’s Hospital, (Affiliated People’s Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Liangping Li
- Department of Radiology, Zhejiang Hospital, Hangzhou, Zhejiang, China
| | - Mingxia Sun
- Department of Radiology, Zhejiang Hospital, Hangzhou, Zhejiang, China
| | - Xinhai Zhu
- Department of Thoracic Surgery, Zhejiang Hospital, Hangzhou, Zhejiang, China
| | - Yue Feng
- Cancer Center, Department of Radiology, Zhejiang Provincial People’s Hospital, (Affiliated People’s Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
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23
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Drake L, Adusumilli PS. Commentary: Preoperative identification of spread through air spaces (STAS): An elusive biomarker. J Thorac Cardiovasc Surg 2023:S0022-5223(23)01198-4. [PMID: 38128644 DOI: 10.1016/j.jtcvs.2023.12.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Accepted: 12/14/2023] [Indexed: 12/23/2023]
Affiliation(s)
- Lauren Drake
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Prasad S Adusumilli
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY.
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Guo C, Xu L, Li X, Fu Y, Wang H, Han R, Li G, Feng Z, Li M, Ren W, Peng Z. Computed tomography imaging and clinical characteristics of pulmonary ground-glass nodules ≤2 cm with micropapillary pattern. Thorac Cancer 2023; 14:3433-3444. [PMID: 37876115 PMCID: PMC10719660 DOI: 10.1111/1759-7714.15136] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 09/28/2023] [Accepted: 10/03/2023] [Indexed: 10/26/2023] Open
Abstract
BACKGROUND The aim of this study was to investigate the imaging features, lymph node metastasis, and genetic mutations in micropapillary lung adenocarcinoma (imaging with mixed ground-glass nodules) ≤2 cm, to provide a more precise and refined basis for the selection of lung segment resection. METHODS A retrospective analysis of 162 patients with surgically resected pathologically confirmed cancers ≤2.0 cm in diameter (50 cases of micropapillary mixed ground-glass nodules [mGGNs], 50 cases of nonmicropapillary mGGNs, and 62 cases of micropapillary SNs [solid nodules]) was performed. mGGNs were classified into five categories according to imaging features. The distribution of these five morphologies in micropapillary with mGGN and nonmicropapillary with mGGN was analyzed. The postoperative pathology and prognosis of lymph node metastasis were also compared between micropapillary mGGNs and micropapillary with SNs. After searching the TCGA database, we demonstrated heterogeneity, high malignancy and high risk of microcapillary lung cancer cancers. RESULTS Different pathological subtypes of mGGN differed in morphological features (p < 0.05). The rate of lymph node metastasis was significantly higher in micropapillary mGGNs than in nonmicropapillary mGGNs. In the TCGA database samples, lactate transmembrane protein activity, collagen transcription score, and fibroblast EMT score were remarkably higher in micropapillary adenocarcinoma. Other pathological subtypes had a better survival prognosis and longer disease-free survival compared with micropapillary adenocarcinoma. CONCLUSION mGGNs ≤2 cm with a micropapillary pattern have a higher risk of lymph node metastasis compared with SNs, and computed tomography (CT) imaging features can assist in their diagnosis.
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Affiliation(s)
- Chen‐ran Guo
- Department of Thoracic Surgery, Shandong Provincial HospitalShandong UniversityJinanChina
| | - Lin Xu
- Department of Thoracic Surgery, Shandong Provincial HospitalShandong UniversityJinanChina
| | - Xiao Li
- Department of Thoracic Surgery, Shandong Provincial HospitalShandong UniversityJinanChina
| | - Yi‐lin Fu
- Department of Thoracic SurgeryShandong Provincial HospitalJinanChina
| | - Hui Wang
- Department of Thoracic SurgeryShandong Provincial HospitalJinanChina
| | - Rui Han
- Peking Union Medical CollegeBeijingChina
| | - Geng‐sheng Li
- Department of AnesthesiologyShandong Provincial HospitalJinanChina
| | - Zhen Feng
- Department of Thoracic SurgeryShandong Provincial HospitalJinanChina
| | - Meng Li
- Department of Thoracic SurgeryShandong Provincial HospitalJinanChina
| | - Wan‐gang Ren
- Department of Thoracic SurgeryShandong Provincial HospitalJinanChina
| | - Zhong‐min Peng
- Department of Thoracic Surgery, Shandong Provincial HospitalShandong UniversityJinanChina
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Yoshida C, Kadota K, Yamada K, Fujimoto S, Ibuki E, Ishikawa R, Haba R, Yajima T. CD44v6 downregulation as a prognostic factor for distant recurrence in resected stage I lung adenocarcinomas. Clin Exp Med 2023; 23:5191-5200. [PMID: 37743425 DOI: 10.1007/s10238-023-01185-z] [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: 03/11/2023] [Accepted: 08/30/2023] [Indexed: 09/26/2023]
Abstract
CD44 and CD44 variant isoforms have been reported as contributing factors to cancer progression. In this study, we aimed to assess whether CD44 and its variant isoforms were correlated with the prognostic factors for distant metastasis in stage I lung adenocarcinomas using tissue microarray and immunohistochemistry. In this single-center retrospective study, we analyzed the data of 490 patients with stage I lung adenocarcinoma resected between 1999 and 2016. We constructed tissue microarrays and performed immunohistochemistry for CD44s, CD44v6, and CD44v9. The risk of disease recurrence and its associations with clinicopathological risk factors were assessed. CD44v6 expression was significantly associated with recurrence. Patients with CD44v6-negative tumors had a significantly increased risk of developing distant recurrence than patients with CD44v6-positive tumors (5-year cumulative incidence of recurrence (CIR), 10.7% vs. 4.6%; P = 0.009). However, CD44v6-negative tumors were not associated with an increased risk of locoregional recurrence compared to CD44v6-positive tumors (5-year CIR, 6.0% vs. 4.0%; P = 0.39). The overall survival (OS) of patients with CD44v6-negative tumors was significantly lower than that of patients with CD44v6-positive tumors (5-year OS: 87% vs. 94%, P = 0.016). CD44v6-negative tumors were also associated with invasive tumor size and lymphovascular invasion. Even in stage I disease, tumors with negative-CD44v6 expression had more distant recurrences than those with positive-CD44v6 expression and were associated with poor prognosis in resected stage I lung adenocarcinomas. Thus, CD44v6 downregulation may be a prognostic factor for distant metastasis in stage I lung adenocarcinomas.
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Affiliation(s)
- Chihiro Yoshida
- Department of General Thoracic Surgery, Faculty of Medicine, Kagawa University, Kagawa, Japan
- Department of General Thoracic Surgery, Kochi Health Sciences Center, Kochi, Japan
| | - Kyuichi Kadota
- Department of Pathology, Faculty of Medicine, Shimane University, Shimane, Japan.
| | - Kaede Yamada
- Department of General Thoracic Surgery, Faculty of Medicine, Kagawa University, Kagawa, Japan
| | - Syusuke Fujimoto
- Department of General Thoracic Surgery, Faculty of Medicine, Kagawa University, Kagawa, Japan
| | - Emi Ibuki
- Department of Diagnostic Pathology, Faculty of Medicine, Kagawa University, Kagawa, Japan
| | - Ryou Ishikawa
- Department of Diagnostic Pathology, Faculty of Medicine, Kagawa University, Kagawa, Japan
| | - Reiji Haba
- Department of Diagnostic Pathology, Faculty of Medicine, Kagawa University, Kagawa, Japan
| | - Toshiki Yajima
- Department of General Thoracic Surgery, Faculty of Medicine, Kagawa University, Kagawa, Japan
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Ma L, Sullivan TB, Rieger-Christ KM, Yambayev I, Zhao Q, Higgins SE, Yilmaz OH, Sultan L, Servais EL, Suzuki K, Burks EJ. Vascular invasion predicts the subgroup of lung adenocarcinomas ≤2.0 cm at risk of poor outcome treated by wedge resection compared to lobectomy. JTCVS OPEN 2023; 16:938-947. [PMID: 38204657 PMCID: PMC10775162 DOI: 10.1016/j.xjon.2023.11.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 10/10/2023] [Accepted: 11/06/2023] [Indexed: 01/12/2024]
Abstract
Background Recent randomized control trials (JCOG0802 and CALGB140503) have shown sublobar resection to be noninferior to lobectomy for non-small cell lung cancer (NSCLC) ≤2.0 cm. We have previously proposed histologic criteria stratifying lung adenocarcinoma into indolent low malignant potential (LMP) and aggressive angioinvasive adenocarcinomas, resulting in better prognostication than provided by World Health Organization grade. Here we determine whether pathologic classification is reproducible and whether subsets of adenocarcinomas predict worse outcomes when treated by wedge resection compared to lobectomy. Methods A retrospective cohort of 108 recipients of wedge resection and 187 recipients of lobectomy for stage I/0 lung adenocarcinomas ≤2.0 cm was assembled from 2 institutions. All tumors were classified by a single pathologist, and interobserver reproducibility was assessed in a subset (n = 92) by 5 pathologists. Results Angioinvasive adenocarcinoma (21%-27% of cases) was associated with worse outcomes when treated with wedge resection compared to lobectomy (5-year recurrence-free survival, 57% vs 85% [P = .007]; 5-year disease-free survival [DSS], 70% vs 90% [P = .043]; 7-year overall survival, 37% vs 58% [P = .143]). Adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and LMP exhibited 100% 5-year DSS regardless of the surgical approach. Multivariable analysis showed that angioinvasion, tumor size, margin status, and extent of nodal sampling were significantly associated with recurrence but not with surgical procedure. There was substantial interobserver reproducibility among the pathologists for the diagnosis of angioinvasive adenocarcinoma (κ = 0.71) and the combined indolent AIS/MIA/LMP group (κ = 0.74). Conclusions The majority (∼75%) of lung adenocarcinomas ≤2 cm are adequately managed with wedge resection; however, angioinvasive adenocarcinomas (∼25%) treated by wedge resection with suboptimal nodal sampling exhibit poor outcomes, with a 40% to 45% rate of recurrence within 5 years and 60% to 65% overall mortality at 7 years.
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Affiliation(s)
- Lina Ma
- Department of Pathology & Laboratory Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston Medical Center, Boston, Mass
| | - Travis B. Sullivan
- Department of Translational Research, Ian C. Summerhayes Cell and Molecular Biology Laboratory, Lahey Hospital & Medical Center, Burlington, Mass
| | - Kimberly M. Rieger-Christ
- Department of Translational Research, Ian C. Summerhayes Cell and Molecular Biology Laboratory, Lahey Hospital & Medical Center, Burlington, Mass
| | - Ilyas Yambayev
- Department of Pathology & Laboratory Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston Medical Center, Boston, Mass
| | - Qing Zhao
- Department of Pathology & Laboratory Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston Medical Center, Boston, Mass
| | - Sara E. Higgins
- Department of Pathology & Laboratory Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston Medical Center, Boston, Mass
| | - Osman H. Yilmaz
- Department of Pathology & Laboratory Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston Medical Center, Boston, Mass
| | - Lila Sultan
- Department of Pathology & Laboratory Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston Medical Center, Boston, Mass
| | - Elliot L. Servais
- Department of Surgery, Lahey Hospital & Medical Center, Burlington, Mass
| | - Kei Suzuki
- Division of Thoracic Surgery, Department of Surgery, INOVA, Falls Church, Va
| | - Eric J. Burks
- Department of Pathology & Laboratory Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston Medical Center, Boston, Mass
- Department of Translational Research, Ian C. Summerhayes Cell and Molecular Biology Laboratory, Lahey Hospital & Medical Center, Burlington, Mass
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Tasnim S, Raja S, Mukhopadhyay S, Blackstone EH, Toth AJ, Barron JO, Raymond DP, Bribriesco AC, Schraufnagel DP, Murthy SC, Sudarshan M. Preoperative predictors of spread through air spaces in lung cancer. J Thorac Cardiovasc Surg 2023:S0022-5223(23)01106-6. [PMID: 38006997 DOI: 10.1016/j.jtcvs.2023.11.030] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Revised: 11/09/2023] [Accepted: 11/16/2023] [Indexed: 11/27/2023]
Abstract
OBJECTIVE Spread through air spaces (STAS) is a new histologic feature of invasion of non-small cell lung cancer that lacks sensitivity and specificity on frozen sections and is associated with higher recurrence and worse survival with sublobar resections. Our objective is to identify preoperative characteristics that are predictive of STAS to guide operative decisions. METHODS From January 2018 through December 2021, 439 cT1-3N0 M0 patients with non-small cell lung cancer and a median age of 68 years, 255 (58%) women, who underwent primary surgery at our institution were included. Patients who received neoadjuvant therapy and whose STAS status was not documented were excluded. Age, sex, smoking status, tumor size, ground-glass opacities, maximum standardized uptake values, and molecular markers on preoperative biopsy were evaluated as preoperative markers. Comparisons between groups were conducted using standardized mean differences and random forest classification was used for prediction modeling. RESULTS Of the 439 patients, 177 had at least 1 STAS-positive tumor, and 262 had no STAS-positive tumors. Overall, 179 STAS tumors and 293 non-STAS tumors were evaluated. Younger age (50 years or younger), solid tumor, size ≥2 cm, and maximum standardized uptake value ≥2.5 were independently predictive of STAS with prediction probabilities of 50%, 40%, 38%, and 40%, respectively. STAS tumors were more likely to harbor KRAS mutations and be PD-L1 negative. CONCLUSIONS Young age (50 years or younger), larger (≥2 cm) solid tumors, high maximum standardized uptake values, and presence of KRAS mutation, are risk factors for STAS and can be considered for lobectomy. Smoking status and gender are still controversial risk factors for STAS.
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Affiliation(s)
- Sadia Tasnim
- Department of Thoracic and Cardiovascular Surgery, Cleveland Clinic Foundation, Cleveland, Ohio
| | - Siva Raja
- Department of Thoracic and Cardiovascular Surgery, Cleveland Clinic Foundation, Cleveland, Ohio
| | | | - Eugene H Blackstone
- Department of Thoracic and Cardiovascular Surgery, Cleveland Clinic Foundation, Cleveland, Ohio
| | - Andrew J Toth
- Department of Thoracic and Cardiovascular Surgery, Cleveland Clinic Foundation, Cleveland, Ohio
| | - John O Barron
- Department of Thoracic and Cardiovascular Surgery, Cleveland Clinic Foundation, Cleveland, Ohio
| | - Daniel P Raymond
- Department of Thoracic and Cardiovascular Surgery, Cleveland Clinic Foundation, Cleveland, Ohio
| | - Alejandro C Bribriesco
- Department of Thoracic and Cardiovascular Surgery, Cleveland Clinic Foundation, Cleveland, Ohio
| | - Dean P Schraufnagel
- Department of Thoracic and Cardiovascular Surgery, Cleveland Clinic Foundation, Cleveland, Ohio
| | - Sudish C Murthy
- Department of Thoracic and Cardiovascular Surgery, Cleveland Clinic Foundation, Cleveland, Ohio
| | - Monisha Sudarshan
- Department of Thoracic and Cardiovascular Surgery, Cleveland Clinic Foundation, Cleveland, Ohio.
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Wang Z, Zhang N, Liu J, Liu J. Predicting micropapillary or solid pattern of lung adenocarcinoma with CT-based radiomics, conventional radiographic and clinical features. Respir Res 2023; 24:282. [PMID: 37964254 PMCID: PMC10647174 DOI: 10.1186/s12931-023-02592-2] [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: 08/09/2023] [Accepted: 11/01/2023] [Indexed: 11/16/2023] Open
Abstract
BACKGROUND To build prediction models with radiomics features, clinical/conventional radiographic signs and combined scores for the discrimination of micropapillary or solid subtypes (high-risk subtypes) of lung adenocarcinoma. METHODS This retrospective study enrolled 351 patients with and without high-risk subtypes. Least Absolute Shrinkage and Selection Operator (LASSO) regression with cross-validation was performed to determine the optimal features of radiomics model. Missing clinical data were imputed by Multiple Imputation with Chain Equations (MICE). Clinical model with radiographic signs was built and scores of both models were integrated to establish combined model. Receiver operating characteristics (ROC) curves, area under ROC curves and decision curve analysis (DCA) were plotted to evaluate the model performance and clinical application. RESULTS Stratified splitting allocated 246 patients into training set. MICE for missing values obtained complete and unbiased data for the following analysis. Ninety radiomic features and four clinical/conventional radiographic signs were used to predict the high-risk subtypes. The radiomic model, clinical model and combined model achieved AUCs of 0.863 (95%CI: 0.817-0.909), 0.771 (95%CI: 0.713-0.713) and 0.872 (95%CI: 0.829-0.916) in the training set, and 0.849 (95%CI: 0.774-0.924), 0.778 (95%CI: 0.687-0.868) and 0.853 (95%CI: 0.782-0.925) in the test set. Decision curve showed that the radiomic and combined models were more clinically useful when the threshold reached 37.5%. CONCLUSIONS Radiomics features could facilitate the prediction of subtypes of lung adenocarcinoma. A simple combination of radiomics and clinical scores generated a robust model with high performance for the discrimination of micropapillary or solid subtype of lung adenocarcinoma.
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Affiliation(s)
- Zhe Wang
- Hebei Medical University Fourth Hospital, Thoracic Surgery. 12 Jiankang Road, Shijiazhuang, China
| | - Ning Zhang
- Department of Radiology, Hebei Medical University Fourth Hospital, 12 Jiankang Road, Shijiazhuang, China
| | - Junhong Liu
- Hebei Medical University Fourth Hospital, Thoracic Surgery. 12 Jiankang Road, Shijiazhuang, China
| | - Junfeng Liu
- Hebei Medical University Fourth Hospital, Thoracic Surgery. 12 Jiankang Road, Shijiazhuang, China.
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Choi W, Liu CJ, Alam SR, Oh JH, Vaghjiani R, Humm J, Weber W, Adusumilli PS, Deasy JO, Lu W. Preoperative 18F-FDG PET/CT and CT radiomics for identifying aggressive histopathological subtypes in early stage lung adenocarcinoma. Comput Struct Biotechnol J 2023; 21:5601-5608. [PMID: 38034400 PMCID: PMC10681940 DOI: 10.1016/j.csbj.2023.11.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 11/02/2023] [Accepted: 11/03/2023] [Indexed: 12/02/2023] Open
Abstract
Lung adenocarcinoma (ADC) is the most common non-small cell lung cancer. Surgical resection is the primary treatment for early-stage lung ADC while lung-sparing surgery is an alternative for non-aggressive cases. Identifying histopathologic subtypes before surgery helps determine the optimal surgical approach. Predominantly solid or micropapillary (MIP) subtypes are aggressive and associated with a higher likelihood of recurrence and metastasis and lower survival rates. This study aims to non-invasively identify these aggressive subtypes using preoperative 18F-FDG PET/CT and diagnostic CT radiomics analysis. We retrospectively studied 119 patients with stage I lung ADC and tumors ≤ 2 cm, where 23 had aggressive subtypes (18 solid and 5 MIPs). Out of 214 radiomic features from the PET/CT and CT scans and 14 clinical parameters, 78 significant features (3 CT and 75 PET features) were identified through univariate analysis and hierarchical clustering with minimized feature collinearity. A combination of Support Vector Machine classifier and Least Absolute Shrinkage and Selection Operator built predictive models. Ten iterations of 10-fold cross-validation (10 ×10-fold CV) evaluated the model. A pair of texture feature (PET GLCM Correlation) and shape feature (CT Sphericity) emerged as the best predictor. The radiomics model significantly outperformed the conventional predictor SUVmax (accuracy: 83.5% vs. 74.7%, p = 9e-9) and identified aggressive subtypes by evaluating FDG uptake in the tumor and tumor shape. It also demonstrated a high negative predictive value of 95.6% compared to SUVmax (88.2%, p = 2e-10). The proposed radiomics approach could reduce unnecessary extensive surgeries for non-aggressive subtype patients, improving surgical decision-making for early-stage lung ADC patients.
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Affiliation(s)
- Wookjin Choi
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- Department of Radiation Oncology, Thomas Jefferson University, Philadelphia, PA 19107, USA
| | - Chia-Ju Liu
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Sadegh Riyahi Alam
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Jung Hun Oh
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Raj Vaghjiani
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - John Humm
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Wolfgang Weber
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Prasad S. Adusumilli
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Joseph O. Deasy
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Wei Lu
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
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Kamigaichi A, Hamada A, Tsutani Y. Segmentectomy for patients with early-stage pure-solid non-small cell lung cancer. Front Oncol 2023; 13:1287088. [PMID: 38023140 PMCID: PMC10644359 DOI: 10.3389/fonc.2023.1287088] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 10/19/2023] [Indexed: 12/01/2023] Open
Abstract
For decades, lobectomy has been the recommended surgical procedure for non-small cell lung cancer (NSCLC), including for small-sized lesions. However, two recent pivotal clinical trials conducted by the Japanese Clinical Oncology Group/West Japan Oncology Group (JCOG0802/WJOG4607L) and the Cancer and Leukemia Group B (CALGB140503), which compared the survival outcomes between lobectomy and sublobar resection (the JCOG0802/WJOG4607L included only segmentectomy, not wedge resection), demonstrated the efficacy of sublobar resection in patients with early-stage peripheral lung cancer measuring ≤ 2 cm. The JCOG0802/WJOG4607L demonstrated the superiority of segmentectomy over lobectomy with respect to overall survival, implying the survival benefit conferred by preservation of the lung parenchyma. Subsequently, the JCOG1211 also demonstrated the efficacy of segmentectomy, even for NSCLC, measuring up to 3 cm with the predominant ground-glass opacity phenotype. Segmentectomy has become the standard of care for early-stage NSCLC and its indications are expected to be further expanded to include solid lung cancers > 2 cm. However, local control is still a major concern for segmentectomy for higher-grade malignant tumors. Thus, the indications of segmentectomy, especially for patients with radiologically pure-solid NSCLC, remain controversial due to the aggressive nature of the malignancy. In this study, we reviewed previous studies and discussed the efficacy of segmentectomy for patients with such tumors.
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Affiliation(s)
| | - Akira Hamada
- Division of Thoracic Surgery, Department of Surgery, Kindai University, Osaka, Japan
| | - Yasuhiro Tsutani
- Division of Thoracic Surgery, Department of Surgery, Kindai University, Osaka, Japan
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Wang K, Liu X, Ding Y, Sun S, Li J, Geng H, Xu M, Wang M, Li X, Sun D. A pretreatment prediction model of grade 3 tumors classed by the IASLC grading system in lung adenocarcinoma. BMC Pulm Med 2023; 23:377. [PMID: 37805451 PMCID: PMC10559613 DOI: 10.1186/s12890-023-02690-3] [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: 10/06/2022] [Accepted: 09/28/2023] [Indexed: 10/09/2023] Open
Abstract
PURPOSE The new grading system for invasive nonmucinous lung adenocarcinoma (LUAD) in the 2021 World Health Organization Classification of Thoracic Tumors was based on a combination of histologically predominant subtypes and high-grade components. In this study, a model for the pretreatment prediction of grade 3 tumors was established according to new grading standards. METHODS We retrospectively collected 399 cases of clinical stage I (cStage-I) LUAD surgically treated in Tianjin Chest Hospital from 2015 to 2018 as the training cohort. Besides, the validation cohort consists of 216 patients who were collected from 2019 to 2020. These patients were also diagnosed with clinical cStage-I LUAD and underwent surgical treatment at Tianjin Chest Hospital. Univariable and multivariable logistic regression analyses were used to select independent risk factors for grade 3 adenocarcinomas in the training cohort. The nomogram prediction model of grade 3 tumors was established by R software. RESULTS In the training cohort, there were 155 grade 3 tumors (38.85%), the recurrence-free survival of which in the lobectomy subgroup was better than that in the sublobectomy subgroup (P = 0.034). After univariable and multivariable analysis, four predictors including consolidation-to-tumor ratio, CEA level, lobulation, and smoking history were incorporated into the model. A nomogram was established and internally validated by bootstrapping. The Hosmer-Lemeshow test result was χ2 = 7.052 (P = 0.531). The C-index and area under the receiver operating characteristic curve were 0.708 (95% CI: 0.6563-0.7586) for the training cohort and 0.713 (95% CI: 0.6426-0.7839) for the external validation cohort. CONCLUSIONS The nomogram prediction model of grade 3 LUAD was well fitted and can be used to assist in surgical or adjuvant treatment decision-making.
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Affiliation(s)
- Kai Wang
- Clinical School of Thoracic, Tianjin Medical University, Tianjin, China
- Department of Thoracic Surgery, Tianjin Chest Hospital, Jinnan District, No. 261, Taierzhuang South Road, Tianjin, 300222, China
| | - Xin Liu
- Clinical School of Thoracic, Tianjin Medical University, Tianjin, China
- Department of Thoracic Surgery, Tianjin Chest Hospital, Jinnan District, No. 261, Taierzhuang South Road, Tianjin, 300222, China
| | - Yun Ding
- Clinical School of Thoracic, Tianjin Medical University, Tianjin, China
| | - Shuai Sun
- Clinical School of Thoracic, Tianjin Medical University, Tianjin, China
| | - Jiuzhen Li
- Department of Thoracic Surgery, Tianjin Chest Hospital, Jinnan District, No. 261, Taierzhuang South Road, Tianjin, 300222, China
| | - Hua Geng
- Clinical School of Thoracic, Tianjin Medical University, Tianjin, China
- Department of Pathology, Tianjin Chest Hospital of Tianjin University, Tianjin, China
| | - Meilin Xu
- Clinical School of Thoracic, Tianjin Medical University, Tianjin, China
- Department of Pathology, Tianjin Chest Hospital of Tianjin University, Tianjin, China
| | - Meng Wang
- Clinical School of Thoracic, Tianjin Medical University, Tianjin, China
- Department of Thoracic Surgery, Tianjin Chest Hospital, Jinnan District, No. 261, Taierzhuang South Road, Tianjin, 300222, China
| | - Xin Li
- Clinical School of Thoracic, Tianjin Medical University, Tianjin, China
- Department of Thoracic Surgery, Tianjin Chest Hospital, Jinnan District, No. 261, Taierzhuang South Road, Tianjin, 300222, China
| | - Daqiang Sun
- Clinical School of Thoracic, Tianjin Medical University, Tianjin, China.
- Department of Thoracic Surgery, Tianjin Chest Hospital, Jinnan District, No. 261, Taierzhuang South Road, Tianjin, 300222, China.
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Zhang Y, Qu H, Tian Y, Na F, Yan J, Wu Y, Cui X, Li Z, Zhao M. PB-LNet: a model for predicting pathological subtypes of pulmonary nodules on CT images. BMC Cancer 2023; 23:936. [PMID: 37789252 PMCID: PMC10548640 DOI: 10.1186/s12885-023-11364-6] [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: 11/28/2022] [Accepted: 09/04/2023] [Indexed: 10/05/2023] Open
Abstract
OBJECTIVE To investigate the correlation between CT imaging features and pathological subtypes of pulmonary nodules and construct a prediction model using deep learning. METHODS We collected information of patients with pulmonary nodules treated by surgery and the reference standard for diagnosis was post-operative pathology. After using elastic distortion for data augmentation, the CT images were divided into a training set, a validation set and a test set in a ratio of 6:2:2. We used PB-LNet to analyze the nodules in pre-operative CT and predict their pathological subtypes. Accuracy was used as the model evaluation index and Class Activation Map was applied to interpreting the results. Comparative experiments with other models were carried out to achieve the best results. Finally, images from the test set without data augmentation were analyzed to judge the clinical utility. RESULTS Four hundred seventy-seven patients were included and the nodules were divided into six groups: benign lesions, precursor glandular lesions, minimally invasive adenocarcinoma, invasive adenocarcinoma Grade 1, Grade 2 and Grade 3. The accuracy of the test set was 0.84. Class Activation Map confirmed that PB-LNet classified the nodules mainly based on the lungs in CT images, which is in line with the actual situation in clinical practice. In comparative experiments, PB-LNet obtained the highest accuracy. Finally, 96 images from the test set without data augmentation were analyzed and the accuracy was 0.89. CONCLUSIONS In classifying CT images of lung nodules into six categories based on pathological subtypes, PB-LNet demonstrates satisfactory accuracy without the need of delineating nodules, while the results are interpretable. A high level of accuracy was also obtained when validating on real data, therefore demonstrates its usefulness in clinical practice.
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Affiliation(s)
- Yuchong Zhang
- Department of Medical Oncology, the First Hospital of China Medical University, NO.155, North Nanjing Street, Heping District, Shenyang, Liaoning Province, 110001, China
| | - Hui Qu
- College of Medicine and Biological Information Engineering, Northeastern University, NO. 3-11, Wenhua Road, Heping District, Shenyang, 110819, Liaoning Province, China
| | - Yumeng Tian
- Department of Medical Oncology, the First Hospital of China Medical University, NO.155, North Nanjing Street, Heping District, Shenyang, Liaoning Province, 110001, China
| | - Fangjian Na
- Network Information Center, China Medical University, NO.77 Puhe Road, Shenbei New District, Shenyang, Liaoning Province, 110122, China
| | - Jinshan Yan
- Department of Medical Oncology, the First Hospital of China Medical University, NO.155, North Nanjing Street, Heping District, Shenyang, Liaoning Province, 110001, China
| | - Ying Wu
- Phase I Clinical Trails Center, the First Hospital of China Medical University, 210 1st Baita Street, Hunnan Distriction, Shenyang, Liaoning Province, 110101, China
| | - Xiaoyu Cui
- College of Medicine and Biological Information Engineering, Northeastern University, NO. 3-11, Wenhua Road, Heping District, Shenyang, 110819, Liaoning Province, China.
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Shenyang, China.
| | - Zhi Li
- Department of Medical Oncology, the First Hospital of China Medical University, NO.155, North Nanjing Street, Heping District, Shenyang, Liaoning Province, 110001, China.
| | - Mingfang Zhao
- Department of Medical Oncology, the First Hospital of China Medical University, NO.155, North Nanjing Street, Heping District, Shenyang, Liaoning Province, 110001, China.
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Takeno N, Tarumi S, Abe M, Suzuki Y, Kinoshita I, Kato T. Lung adenocarcinoma with micropapillary and solid patterns: Recurrence rate and trends. Thorac Cancer 2023; 14:2987-2992. [PMID: 37658844 PMCID: PMC10599975 DOI: 10.1111/1759-7714.15087] [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: 06/22/2023] [Revised: 08/13/2023] [Accepted: 08/14/2023] [Indexed: 09/05/2023] Open
Abstract
BACKGROUND Lung adenocarcinomas with micropapillary pattern (MP) or solid pattern (SP) have poor prognosis with frequent postoperative recurrence. However, treatment strategies for these histological subtypes have not been established. This study examined the recurrence rates and patterns in patients with these histological subtypes. METHODS Overall, 238 patients with lung adenocarcinoma who underwent radical resection were included. According to the histological subtypes, the patients were classified into three groups: neither MP nor SP (MP-/SP-), MP (MP+), and SP (SP+). The clinical and pathological characteristics and recurrence-free survival (RFS) were examined in each group. In addition, univariate and multivariate analyses were performed to investigate the recurrence factors. The site of recurrence, PD-L1 expression, and driver mutations were examined in patients with postoperative recurrence. RESULTS The recurrence rates were significantly higher in the MP+ and SP+ groups (p = 0.01). The RFS was significantly shorter in the MP+ and SP+ groups (p < 0.001) than in the MP-/SP- group, especially in pStage 1A (p = 0.001). The relationship between recurrence and pathologic factors was significant for pleural, lymphatic, and vascular invasion, as well as MP in univariate analysis and only for MP in multivariate analysis. Most recurrences were distant metastases in the MP+ and SP+ groups. PD-L1 was highly expressed in recurrent SP+ cases. CONCLUSIONS Early-stage lung adenocarcinoma with MP or SP frequently has postoperative recurrence. Prevention of distant metastases is important in these patients to improve prognosis, and aggressive postoperative chemotherapy may be considered.
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Affiliation(s)
- Naoki Takeno
- Department of Thoracic SurgeryKeiyukai Sapporo HospitalSapporoJapan
| | - Shintaro Tarumi
- Department of Thoracic SurgeryKeiyukai Sapporo HospitalSapporoJapan
| | - Masahiro Abe
- Department of Thoracic SurgeryKeiyukai Sapporo HospitalSapporoJapan
| | - Yasuhiro Suzuki
- Department of Thoracic SurgeryKeiyukai Sapporo HospitalSapporoJapan
| | | | - Tatsuya Kato
- Department of Thoracic SurgeryHokkaido UniversitySapporoJapan
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Fu Y, Zha J, Wu Q, Tang Y, Wang W, Zhou Q, Jiang L. Stromal micropapillary pattern and CD44s expression predict worse outcome in lung adenocarcinomas with micropapillary pattern. Pathol Res Pract 2023; 248:154595. [PMID: 37343380 DOI: 10.1016/j.prp.2023.154595] [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: 03/14/2023] [Revised: 05/09/2023] [Accepted: 06/05/2023] [Indexed: 06/23/2023]
Abstract
OBJECTIVES This study aims to investigate the clinicopathologic characteristics of lung adenocarcinoma with micropapillary pattern (MPP) and the expression of CD44s and CD44v6 in MPP. METHODS A total of 202 patients diagnosed with primary lung adenocarcinoma with MPP were included. We estimated the proportion of MPP in each tumor tissue and divided MPP into aerogenous micropapillary pattern (AMP) and stromal micropapillary pattern (SMP). The expression of CD44s and CD44v6 was estimated by immunohistochemical staining. Clinicopathologic data were collected from the patients' medical records. We also collected patients' follow-up data and used PFS (progression-free survival) as a survival indicator. RESULTS Lung adenocarcinoma with MPP had a high risk of pleural invasion, lymph node metastasis, in advanced TNM stage, and a high rate of EGFR mutation. The presence of SMP indicated a higher rate of pleural invasion, lymphovascular invasion, lymph node metastasis, and a worse PFS compared with pure AMP. We found high expression of CD44s in micropapillary, especially in AMP, while the absence of CD44s expression indicated shorter survival, which was an independent unfavorable factor for PFS. CONCLUSIONS Lung adenocarcinoma with micropapillary pattern indicated an unfavorable prognosis, which had two different pattens, AMP and SMP. SMP indicated a worse survival than AMP, and was an independent unfavorable factor for PFS. So, AMP/SMP subclassification is necessary to evaluate patient's prognosis. Furthermore, the absent expression of CD44s in micropapillary indicated shorter survival, especially in patients with EGFR mutation. Herein, CD44s may be a biological marker for micropapillary lung adenocarcinoma.
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Affiliation(s)
- Yiyun Fu
- Department of Pathology, West China Hospital, Sichuan University, Chengdu, China
| | - Junmei Zha
- Department of Pathology, West China Hospital, Sichuan University, Chengdu, China
| | - Qian Wu
- Department of Pathology, West China Second University Hospital, Sichuan University, Chengdu, China
| | - Yuan Tang
- Department of Pathology, West China Hospital, Sichuan University, Chengdu, China
| | - Weiya Wang
- Department of Pathology, West China Hospital, Sichuan University, Chengdu, China
| | - Qiao Zhou
- Department of Pathology, West China Hospital, Sichuan University, Chengdu, China
| | - Lili Jiang
- Department of Pathology, West China Hospital, Sichuan University, Chengdu, China.
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Xu L, Su H, Zhao S, Si H, Xie H, Ren Y, Gao J, Wang F, Xie X, Dai C, Wu C, Zhao D, Chen C. Development of the semi-dry dot-blot method for intraoperative detecting micropapillary component in lung adenocarcinoma based on proteomics analysis. Br J Cancer 2023; 128:2116-2125. [PMID: 37016102 PMCID: PMC10206083 DOI: 10.1038/s41416-023-02241-x] [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: 08/16/2022] [Revised: 03/06/2023] [Accepted: 03/16/2023] [Indexed: 04/06/2023] Open
Abstract
BACKGROUND Micropapillary (MIP) component was a major concern in determining surgical strategy in lung adenocarcinoma (LUAD). We sought to develop a novel method for detecting MIP component during surgery. METHODS Differentially expressed proteins between MIP-positive and MIP-negative LUAD were identified through proteomics analysis. The semi-dry dot-blot (SDB) method which visualises the targeted protein was developed to detect MIP component. RESULTS Cellular retinoic acid-binding protein 2 (CRABP2) was significantly upregulated in MIP-positive LUAD (P < 0.001), and the high CRABP2 expression zone showed spatial consistency with MIP component. CRABP2 expression was also associated with decreased recurrence-free survival (P < 0.001). In the prospective cohort, the accuracy and sensitivity of detecting MIP component using SDB method by visualising CRABP2 were 82.2% and 72.7%, which were comparable to these of pathologist. Pathologist with the aid of SDB method would improve greatly in diagnostic accuracy (86.4%) and sensitivity (78.2%). In patients with minor MIP component (≤5%), the sensitivity of SDB method (63.6%) was significantly higher than pathologist (45.4%). CONCLUSIONS Intraoperative examination of CRABP2 using SDB method to detect MIP component reached comparable performance to pathologist, and SDB method had notable superiority than pathologist in detecting minor MIP component.
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Affiliation(s)
- Long Xu
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China
| | - Hang Su
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China
| | - Shengnan Zhao
- Department of Pathology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China
| | - Haojie Si
- Department of Thoracic Surgery, 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
| | - Yijiu Ren
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China
| | - Jiani Gao
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China
| | - Fang Wang
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China
| | - Xiaofeng Xie
- Department of Pathology, 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
| | - Chunyan Wu
- Department of Pathology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China
| | - Deping Zhao
- 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, Clinical Center for Thoracic Surgery Research, Tongji University, Shanghai, People's Republic of China.
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Li Y, Byun AJ, Choe JK, Lu S, Restle D, Eguchi T, Tan KS, Saini J, Huang J, Rocco G, Jones DR, Travis WD, Adusumilli PS. Micropapillary and Solid Histologic Patterns in N1 and N2 Lymph Node Metastases Are Independent Factors of Poor Prognosis in Patients With Stages II to III Lung Adenocarcinoma. J Thorac Oncol 2023; 18:608-619. [PMID: 36681298 PMCID: PMC10122702 DOI: 10.1016/j.jtho.2023.01.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 12/15/2022] [Accepted: 01/03/2023] [Indexed: 01/19/2023]
Abstract
INTRODUCTION High-grade histologic patterns are associated with poor prognosis in patients with primary nonmucinous lung adenocarcinoma (ADC). We investigated whether the presence of micropapillary (MIP), solid (SOL), or both patterns in lymph node (LN) metastases has prognostic value. METHODS Patients who underwent lobectomy for pathologic stages II to III lung ADC with N1 or N2 LN metastases (N = 360; 2000-2012) were analyzed. We assessed overall survival (OS), lung cancer-specific cumulative incidence of death (LC-CID), and cumulative incidence of recurrence (CIR) between patients with and without MIP/SOL patterns in LN metastases. Multivariable Cox regression analysis was used to quantify the association between MIP/SOL patterns and outcomes. RESULTS MIP and SOL in LN metastases were associated with a higher incidence of smoking history (p = 0.004), tumor necrosis (p = 0.013), and spread of tumor through air spaces (p < 0.0001), a higher prevalence of MIP or SOL in the primary tumor (p < 0.0001), shorter OS (5-y OS, 40% [95% confidence interval or CI: 29%-56%] versus 63% [48%-83%] for no MIP/SOL in LNs, p = 0.03), higher LC-CID (5-y, 43% [29%-56%] versus 14% [4%-29%], p = 0.013), and higher CIR (5-y, 65% [50%-77%] versus 43% [25%-60%], p = 0.057). MIP and SOL in LN metastases were independently associated with poor outcomes: OS (hazard ratio [HR] = 1.81 [95% CI: 1.00-3.29], p = 0.05), LC-CID (HR = 3.10 [1.30-7.37], p = 0.01), and CIR (HR = 2.06 [1.09-3.90], p = 0.026). CONCLUSIONS MIP/SOL histologic patterns in N1 or N2 LN metastases are associated with worse outcomes in patients with stages II to III lung ADC. MIP/SOL histologic patterns in LN metastases can stratify patients with high-risk stages II to III lung ADC.
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Affiliation(s)
- Yan Li
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York; Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York; Department of Pathology, Union Hospital, Tongji Medical College, Huazhong University of Science & Technology, Hubei, People's Republic of China
| | - Alexander J Byun
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Jennie K Choe
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Shaohua Lu
- Department of Pathology, Zhongshan Hospital, Fudan University, Shanghai, People's Republic of China
| | - David Restle
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Takashi Eguchi
- Division of Thoracic Surgery, Department of Surgery, Shinshu University, Matsumoto, Japan
| | - Kay See Tan
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Jasmeen Saini
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - James Huang
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Gaetano Rocco
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - David R Jones
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - William D Travis
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Prasad S Adusumilli
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York; Center for Cell Engineering, Memorial Sloan Kettering Cancer Center, New York, New York.
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Ito Y, Usui G, Seki M, Fukuyo M, Matsusaka K, Hoshii T, Sata Y, Morimoto J, Hata A, Nakajima T, Rahmutulla B, Kaiho T, Inage T, Tanaka K, Sakairi Y, Suzuki H, Yoshino I, Kaneda A. Association of frequent hypermethylation with high grade histological subtype in lung adenocarcinoma. Cancer Sci 2023. [PMID: 37082886 DOI: 10.1111/cas.15817] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Revised: 03/30/2023] [Accepted: 04/04/2023] [Indexed: 04/22/2023] Open
Abstract
Lung adenocarcinoma is classified morphologically into five histological subtypes according to the WHO classification. While each histological subtype correlates with a distinct prognosis, the molecular basis has not been fully elucidated. Here we conducted DNA methylation analysis of 30 lung adenocarcinoma cases annotated with the predominant histological subtypes and three normal lung cases using the Infinium BeadChip. Unsupervised hierarchical clustering analysis revealed three subgroups with different methylation levels: high-, intermediate-, and low-methylation epigenotypes (HME, IME, and LME). Micropapillary pattern (MPP)-predominant cases and those with MPP components were significantly enriched in HME (p = 0.02 and p = 0.03, respectively). HME cases showed a significantly poor prognosis for recurrence-free survival (p < 0.001) and overall survival (p = 0.006). We identified 365 HME marker genes specifically hypermethylated in HME cases with enrichment of "cell morphogenesis" related genes; 305 IME marker genes hypermethylated in HME and IME, but not in LME, with enrichment "embryonic organ morphogenesis"-related genes; 257 Common marker genes hypermethylated commonly in all cancer cases, with enrichment of "regionalization"-related genes. We extracted surrogate markers for each epigenotype and designed pyrosequencing primers for five HME markers (TCERG1L, CXCL12, FAM181B, HOXA11, GAD2), three IME markers (TBX18, ZNF154, NWD2) and three Common markers (SCT, GJD2, BARHL2). DNA methylation profiling using Infinium data was validated by pyrosequencing, and HME cases defined by pyrosequencing results also showed the worse recurrence-free survival. In conclusion, lung adenocarcinomas are stratified into subtypes with distinct DNA methylation levels, and the high-methylation subtype correlated with MPP-predominant cases and those with MPP components and showed a poor prognosis.
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Affiliation(s)
- Yuki Ito
- Department of General Thoracic Surgery, Graduate School of Medicine, Chiba University, Chiba, Japan
- Department of Molecular Oncology, Graduate School of Medicine, Chiba University, Chiba, Japan
| | - Genki Usui
- Department of Molecular Oncology, Graduate School of Medicine, Chiba University, Chiba, Japan
| | - Motoaki Seki
- Department of Molecular Oncology, Graduate School of Medicine, Chiba University, Chiba, Japan
| | - Masaki Fukuyo
- Department of Molecular Oncology, Graduate School of Medicine, Chiba University, Chiba, Japan
| | - Keisuke Matsusaka
- Department of Molecular Oncology, Graduate School of Medicine, Chiba University, Chiba, Japan
- Department of Pathology, Chiba University Hospital, Chiba, Japan
| | - Takayuki Hoshii
- Department of Molecular Oncology, Graduate School of Medicine, Chiba University, Chiba, Japan
| | - Yuki Sata
- Department of General Thoracic Surgery, Graduate School of Medicine, Chiba University, Chiba, Japan
- Department of Molecular Oncology, Graduate School of Medicine, Chiba University, Chiba, Japan
| | - Junichi Morimoto
- Department of General Thoracic Surgery, Graduate School of Medicine, Chiba University, Chiba, Japan
- Department of Molecular Oncology, Graduate School of Medicine, Chiba University, Chiba, Japan
| | - Atsushi Hata
- Department of General Thoracic Surgery, Graduate School of Medicine, Chiba University, Chiba, Japan
- Department of Molecular Oncology, Graduate School of Medicine, Chiba University, Chiba, Japan
| | - Takahiro Nakajima
- Department of General Thoracic Surgery, Graduate School of Medicine, Chiba University, Chiba, Japan
| | - Bahityar Rahmutulla
- Department of Molecular Oncology, Graduate School of Medicine, Chiba University, Chiba, Japan
| | - Taisuke Kaiho
- Department of General Thoracic Surgery, Graduate School of Medicine, Chiba University, Chiba, Japan
| | - Terunaga Inage
- Department of General Thoracic Surgery, Graduate School of Medicine, Chiba University, Chiba, Japan
| | - Kazuhisa Tanaka
- Department of General Thoracic Surgery, Graduate School of Medicine, Chiba University, Chiba, Japan
| | - Yuichi Sakairi
- Department of General Thoracic Surgery, Graduate School of Medicine, Chiba University, Chiba, Japan
| | - Hidemi Suzuki
- Department of General Thoracic Surgery, Graduate School of Medicine, Chiba University, Chiba, Japan
| | - Ichiro Yoshino
- Department of General Thoracic Surgery, Graduate School of Medicine, Chiba University, Chiba, Japan
| | - Atsushi Kaneda
- Department of Molecular Oncology, Graduate School of Medicine, Chiba University, Chiba, Japan
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Karasaki T, Moore DA, Veeriah S, Naceur-Lombardelli C, Toncheva A, Magno N, Ward S, Bakir MA, Watkins TBK, Grigoriadis K, Huebner A, Hill MS, Frankell AM, Abbosh C, Puttick C, Zhai H, Gimeno-Valiente F, Saghafinia S, Kanu N, Dietzen M, Pich O, Lim EL, Martínez-Ruiz C, Black JRM, Biswas D, Campbell BB, Lee C, Colliver E, Enfield KSS, Hessey S, Hiley CT, Zaccaria S, Litchfield K, Birkbak NJ, Cadieux EL, Demeulemeester J, Van Loo P, Adusumilli PS, Tan KS, Cheema W, Sanchez-Vega F, Jones DR, Rekhtman N, Travis WD, Hackshaw A, Marafioti T, Salgado R, Le Quesne J, Nicholson AG, McGranahan N, Swanton C, Jamal-Hanjani M. Evolutionary characterization of lung adenocarcinoma morphology in TRACERx. Nat Med 2023; 29:833-845. [PMID: 37045996 PMCID: PMC7614478 DOI: 10.1038/s41591-023-02230-w] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 01/24/2023] [Indexed: 04/14/2023]
Abstract
Lung adenocarcinomas (LUADs) display a broad histological spectrum from low-grade lepidic tumors through to mid-grade acinar and papillary and high-grade solid, cribriform and micropapillary tumors. How morphology reflects tumor evolution and disease progression is poorly understood. Whole-exome sequencing data generated from 805 primary tumor regions and 121 paired metastatic samples across 248 LUADs from the TRACERx 421 cohort, together with RNA-sequencing data from 463 primary tumor regions, were integrated with detailed whole-tumor and regional histopathological analysis. Tumors with predominantly high-grade patterns showed increased chromosomal complexity, with higher burden of loss of heterozygosity and subclonal somatic copy number alterations. Individual regions in predominantly high-grade pattern tumors exhibited higher proliferation and lower clonal diversity, potentially reflecting large recent subclonal expansions. Co-occurrence of truncal loss of chromosomes 3p and 3q was enriched in predominantly low-/mid-grade tumors, while purely undifferentiated solid-pattern tumors had a higher frequency of truncal arm or focal 3q gains and SMARCA4 gene alterations compared with mixed-pattern tumors with a solid component, suggesting distinct evolutionary trajectories. Clonal evolution analysis revealed that tumors tend to evolve toward higher-grade patterns. The presence of micropapillary pattern and 'tumor spread through air spaces' were associated with intrathoracic recurrence, in contrast to the presence of solid/cribriform patterns, necrosis and preoperative circulating tumor DNA detection, which were associated with extra-thoracic recurrence. These data provide insights into the relationship between LUAD morphology, the underlying evolutionary genomic landscape, and clinical and anatomical relapse risk.
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Affiliation(s)
- Takahiro Karasaki
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Cancer Metastasis Laboratory, University College London Cancer Institute, London, UK
| | - David A Moore
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Department of Cellular Pathology, University College London Hospitals, London, UK
| | - Selvaraju Veeriah
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | | | - Antonia Toncheva
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Neil Magno
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Sophia Ward
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Advanced Sequencing Facility, The Francis Crick Institute, London, UK
| | - Maise Al Bakir
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Thomas B K Watkins
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Kristiana Grigoriadis
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Cancer Genome Evolution Research Group, Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Ariana Huebner
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Cancer Genome Evolution Research Group, Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Mark S Hill
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Alexander M Frankell
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Christopher Abbosh
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Clare Puttick
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Cancer Genome Evolution Research Group, Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Haoran Zhai
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Francisco Gimeno-Valiente
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Sadegh Saghafinia
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Nnennaya Kanu
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Michelle Dietzen
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Cancer Genome Evolution Research Group, Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Oriol Pich
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Emilia L Lim
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Carlos Martínez-Ruiz
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Genome Evolution Research Group, Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - James R M Black
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Genome Evolution Research Group, Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Dhruva Biswas
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Bill Lyons Informatics Centre, University College London Cancer Institute, London, UK
| | - Brittany B Campbell
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Claudia Lee
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Emma Colliver
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Katey S S Enfield
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Sonya Hessey
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Metastasis Laboratory, University College London Cancer Institute, London, UK
- Computational Cancer Genomics Research Group, University College London Cancer Institute, London, UK
| | - Crispin T Hiley
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
| | - Simone Zaccaria
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Computational Cancer Genomics Research Group, University College London Cancer Institute, London, UK
| | - Kevin Litchfield
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Tumour Immunogenomics and Immunosurveillance Laboratory, University College London Cancer Institute, London, UK
| | - Nicolai J Birkbak
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Department of Molecular Medicine, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark
| | - Elizabeth Larose Cadieux
- Cancer Genomics Laboratory, The Francis Crick Institute, London, UK
- Medical Genomics, University College London Cancer Institute, London, UK
| | - Jonas Demeulemeester
- Cancer Genomics Laboratory, The Francis Crick Institute, London, UK
- Integrative Cancer Genomics Laboratory, Department of Oncology, KU Leuven, Leuven, Belgium
- VIB - KU Leuven Center for Cancer Biology, Leuven, Belgium
| | - Peter Van Loo
- Cancer Genomics Laboratory, The Francis Crick Institute, London, UK
- Department of Genetics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Prasad S Adusumilli
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Center for Cell Engineering, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Kay See Tan
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Waseem Cheema
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Francisco Sanchez-Vega
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - David R Jones
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Natasha Rekhtman
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - William D Travis
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Allan Hackshaw
- Cancer Research UK & UCL Cancer Trials Centre, London, UK
| | - Teresa Marafioti
- Department of Cellular Pathology, University College London Hospitals, London, UK
| | - Roberto Salgado
- Department of Pathology, ZAS Hospitals, Antwerp, Belgium
- Division of Research, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - John Le Quesne
- Cancer Research UK Beatson Institute, Glasgow, UK
- School of Cancer Sciences, University of Glasgow, Glasgow, UK
- Pathology Department, Queen Elizabeth University Hospital, NHS Greater Glasgow and Clyde, Glasgow, UK
| | - Andrew G Nicholson
- Department of Histopathology, Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust, London, UK
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Nicholas McGranahan
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK.
- Cancer Genome Evolution Research Group, Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK.
| | - Charles Swanton
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK.
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK.
- Department of Oncology, University College London Hospitals, London, UK.
| | - Mariam Jamal-Hanjani
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK.
- Cancer Metastasis Laboratory, University College London Cancer Institute, London, UK.
- Department of Oncology, University College London Hospitals, London, UK.
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Histologic Subtypes of Non-Small Cell Lung Cancer: Can We Further Personalize Radiation Therapy? Int J Radiat Oncol Biol Phys 2023; 115:906-908. [PMID: 36822787 DOI: 10.1016/j.ijrobp.2022.11.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 10/23/2022] [Accepted: 11/01/2022] [Indexed: 02/23/2023]
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Volmonen K, Sederholm A, Rönty M, Paajanen J, Knuuttila A, Jartti A. Association of CT findings with invasive subtypes and the new grading system of lung adenocarcinoma. Clin Radiol 2023; 78:e251-e259. [PMID: 36658036 DOI: 10.1016/j.crad.2022.11.011] [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: 05/10/2022] [Revised: 09/23/2022] [Accepted: 11/30/2022] [Indexed: 12/30/2022]
Abstract
AIM To predict the differentiation between invasive growth patterns and new grades of lung adenocarcinoma (LAC) using computed tomography (CT). MATERIALS AND METHODS The CT features of 180 surgically treated LAC patients were compared retrospectively to pathological invasive subtypes and tumour grades as defined by the new grading system published in 2021 by the World Health Organization. Two radiologists reviewed the images semi-quantitatively and independently. Univariable and multivariable regression models were built from the statistical means of their assessments to predict invasive subtypes and grades. The area under the curve (AUC) calculation was used to select the best models. The Youden index was applied to determine the cut-off values for radiological parameters. RESULTS The acinar/papillary patterns were associated with ill-defined margins, lower consolidation/tumour ratio and air bronchogram. The solid growth pattern was associated with a well-defined margin and hypodensity, and the micropapillary (MP) subtype with spiculation. From Grades 1 to 3, the amount of air bronchogram decreased and the consolidation/tumour ratio increased. In the sub-analyses, the best model for differentiating Grade 2 from Grade 1 had the following CT features: solid/subsolid type, consolidation/tumour ratio, well-defined margin, and air bronchogram (AUC = 0.783) and Grade 3 from Grade 2: size of the consolidation part/whole tumour ratio, size of the consolidation part, and well-defined margin (AUC = 0.759). The interobserver agreements between the two radiologists varied between 0.67 and 0.98. CONCLUSIONS Air bronchogram, consolidation/tumour ratio, and well-defined margin are among the best imaging findings to discriminate between both invasive subtypes and the new grades in LAC.
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Affiliation(s)
- K Volmonen
- Radiology, HUS Diagnostic Center, University of Helsinki and Helsinki University Hospital, Haartmaninkatu 4, 00029 HUS Helsinki, Finland.
| | - A Sederholm
- Radiology, HUS Diagnostic Center, University of Helsinki and Helsinki University Hospital, Haartmaninkatu 4, 00029 HUS Helsinki, Finland
| | - M Rönty
- Pathology Department, University of Helsinki and Helsinki University Hospital, Haartmaninkatu 3, 00029 HUS, Helsinki, Finland
| | - J Paajanen
- Cancer Center and Heart and Lung Center, University of Helsinki and Helsinki University Hospital, Haartmaninkatu 4,00029 HUS Helsinki, Finland
| | - A Knuuttila
- Cancer Center and Heart and Lung Center, University of Helsinki and Helsinki University Hospital, Haartmaninkatu 4,00029 HUS Helsinki, Finland
| | - A Jartti
- Department of Diagnostic Radiology, Oulu University Hospital, Kajaanintie 50, 90220 Oulu, Finland
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Lucà S, Zannini G, Morgillo F, Della Corte CM, Fiorelli A, Zito Marino F, Campione S, Vicidomini G, Guggino G, Ronchi A, Accardo M, Franco R. The prognostic value of histopathology in invasive lung adenocarcinoma: a comparative review of the main proposed grading systems. Expert Rev Anticancer Ther 2023; 23:265-277. [PMID: 36772823 DOI: 10.1080/14737140.2023.2179990] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/12/2023]
Abstract
INTRODUCTION An accurate histological evaluation of invasive lung adenocarcinoma is essential for a correct clinical and pathological definition of the tumour. Different grading systems have been proposed to predict the prognosis of invasive lung adenocarcinoma. AREAS COVERED Invasive non mucinous lung adenocarcinoma is often morphologically heterogeneous, consisting of complex combinations of architectural patterns with different proportions. Several grading systems for non-mucinous lung adenocarcinoma have been proposed, being the main based on architectural differentiation and the predominant growth pattern. Herein we perform a thorough review of the literature using PubMed, Scopus and Web of Science and we highlight the peculiarities and the differences between the main grading systems and compare the data about their prognostic value. In addition, we carried out an evaluation of the proposed grading systems for less common histological variants of lung adenocarcinoma, such as fetal adenocarcinoma and invasive mucinous adenocarcinoma. EXPERT OPINION The current IASLC grading system, based on the combined score of predominant growth pattern plus high-grade histological pattern, shows the stronger prognostic significance than the previous grading systems in invasive non mucinous lung adenocarcinoma.
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Affiliation(s)
- Stefano Lucà
- Pathology Unit, Department of Mental and Physical Health and Preventive Medicine, Università degli Studi della Campania "L. Vanvitelli", Naples, Italy
| | - Giuseppa Zannini
- Pathology Unit, Department of Mental and Physical Health and Preventive Medicine, Università degli Studi della Campania "L. Vanvitelli", Naples, Italy
| | - Floriana Morgillo
- Department of Precision Medicine, Medical Oncology, Università degli Studi della Campania Luigi Vanvitelli, Naples, Italy
| | - Carminia Maria Della Corte
- Department of Precision Medicine, Medical Oncology, Università degli Studi della Campania Luigi Vanvitelli, Naples, Italy
| | - Alfonso Fiorelli
- Division of Thoracic Surgery, University of Campania "Luigi Vanvitelli", Piazza Miraglia, 2, 80138, Naples, Italy
| | - Federica Zito Marino
- Pathology Unit, Department of Mental and Physical Health and Preventive Medicine, Università degli Studi della Campania "L. Vanvitelli", Naples, Italy
| | - Severo Campione
- A. Cardarelli Hospital, Department of Advanced Diagnostic-Therapeutic Technologies and Health Services Section of Anatomic Pathology, Naples, Italy
| | - Giovanni Vicidomini
- Division of Thoracic Surgery, University of Campania "Luigi Vanvitelli", Piazza Miraglia, 2, 80138, Naples, Italy
| | - Gianluca Guggino
- Thoracic Surgery Department, AORN A. Cardarelli Hospital, Naples, Italy
| | - Andrea Ronchi
- Pathology Unit, Department of Mental and Physical Health and Preventive Medicine, Università degli Studi della Campania "L. Vanvitelli", Naples, Italy
| | - Marina Accardo
- Pathology Unit, Department of Mental and Physical Health and Preventive Medicine, Università degli Studi della Campania "L. Vanvitelli", Naples, Italy
| | - Renato Franco
- Pathology Unit, Department of Mental and Physical Health and Preventive Medicine, Università degli Studi della Campania "L. Vanvitelli", Naples, Italy
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Fan J, Yao J, Si H, Xie H, Ge T, Ye W, Chen J, Yin Z, Zhuang F, Xu L, Su H, Zhao S, Xie X, Zhao D, Wu C, Zhu Y, Ren Y, Xu N, Chen C. Frozen sections accurately predict the IASLC proposed grading system and prognosis in patients with invasive lung adenocarcinomas. Lung Cancer 2023; 178:123-130. [PMID: 36822017 DOI: 10.1016/j.lungcan.2023.02.010] [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: 11/18/2022] [Revised: 02/09/2023] [Accepted: 02/14/2023] [Indexed: 02/19/2023]
Abstract
INTRODUCTION The International Association for the Study of Lung Cancer (IASLC) newly proposed grading system for lung adenocarcinomas (ADC) has been shown to be of prognostic significance. Hence, intraoperative consultation for the grading system was important regarding the surgical decision-making. Here, we evaluated the accuracy and interobserver agreement for IASLC grading system on frozen section (FS), and further investigated the prognostic performance. METHODS FS and final pathology (FP) slides were reviewed by three pathologists for tumor grading in 373 stage I lung ADC following surgical resection from January to June 2013 (retrospective cohort). A prospective multicenter cohort (January to June 2021, n = 212) were included to confirm the results. RESULTS The overall concordance rates between FS and FP were 79.1% (κ = 0.650) and 89.6% (κ = 0.729) with substantial agreement in retrospective and prospective cohorts, respectively. Presence of complex gland was the only independent predictor of discrepancy between FS and FP (presence versus. absence: odds ratio, 2.193; P = 0.015). The interobserver agreement for IASLC grading system on FS among three pathologists were satisfactory (κ = 0.672 for retrospective cohort; κ = 0.752 for prospective cohort). Moreover, the IASLC grading system by FS diagnosis could well predict recurrence-free survival and overall survival for patients with stage I invasive lung ADC. CONCLUSIONS Our results suggest that FS had high diagnostic accuracy and satisfactory interobserver agreement for IASLC grading system. Future prospective studies are merited to validate the feasibility of using FS to match patients into appropriate surgical type.
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Affiliation(s)
- Junqiang Fan
- Department of Thoracic Surgery, the Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, People's Republic of China
| | - Jie Yao
- Department of Thoracic Surgery, the Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, People's Republic of China
| | - Haojie Si
- Department of Thoracic Surgery, 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
| | - Tengfei Ge
- Department of Thoracic Surgery, Anhui Chest Hospital, Hefei, People's Republic of China
| | - Wei Ye
- Department of Pathology, Anhui Chest Hospital, Hefei, People's Republic of China
| | - Jianle Chen
- Department of Cardiothoracic Surgery, Affiliated Hospital of Nantong University, Nantong, People's Republic of China
| | - Zhongbo Yin
- Department of Pathology, the Sixth People's Hospital of Nantong, Nantong, People's Republic of China
| | - Fenghui Zhuang
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China
| | - Long Xu
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China
| | - Hang Su
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China
| | - Shengnan Zhao
- Department of Pathology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China
| | - Xiaofeng Xie
- Department of Pathology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China
| | - Deping Zhao
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China
| | - Chunyan Wu
- Department of Pathology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China
| | - Yuming Zhu
- 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.
| | - Ning Xu
- Department of Thoracic Surgery, Anhui Chest Hospital, Hefei, People's Republic of China.
| | - Chang Chen
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Clinical Center for Thoracic Surgery Research, Tongji University, Shanghai, People's Republic of China; The First Hospital of Lanzhou University, Lanzhou, Gansu Province, People's Republic of China.
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An ordinal radiomic model to predict the differentiation grade of invasive non-mucinous pulmonary adenocarcinoma based on low-dose computed tomography in lung cancer screening. Eur Radiol 2023; 33:3072-3082. [PMID: 36790469 DOI: 10.1007/s00330-023-09453-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 11/16/2022] [Accepted: 01/18/2023] [Indexed: 02/16/2023]
Abstract
OBJECTIVES To construct a radiomic model of low-dose CT (LDCT) to predict the differentiation grade of invasive non-mucinous pulmonary adenocarcinoma (IPA) and compare its diagnostic performance with quantitative-semantic model and radiologists. METHODS A total of 682 pulmonary nodules were divided into the primary cohort (181 grade 1; 254 grade 2; 64 grade 3) and validation cohort (69 grade 1; 99 grade 2; 15 grade 3) according to scanners. The radiomic and quantitative-semantic models were built using ordinal logistic regression. The diagnostic performance of the models and radiologists was assessed by the area under the curve (AUC) of the receiver operating characteristic curve and accuracy. RESULTS The radiomic model demonstrated excellent diagnostic performance in the validation cohort (AUC, 0.900 (95%CI: 0.847-0.939) for Grade 1 vs. Grade 2/Grade 3; AUC, 0.929 (95%CI: 0.882-0.962) for Grade 1/Grade 2 vs. Grade 3; accuracy, 0.803 (95%CI: 0.737-0.857)). No significant difference in diagnostic performance was found between the radiomic model and radiological expert (AUC, 0.840 (95%CI: 0.779-0.890) for Grade 1 vs. Grade 2/Grade 3, p = 0.130; AUC, 0.852 (95%CI: 0.793-0.900) for Grade 1/Grade 2 vs. Grade 3, p = 0.170; accuracy, 0.743 (95%CI: 0.673-0.804), p = 0.079), but the radiomic model outperformed the quantitative-semantic model and inexperienced radiologists (all p < 0.05). CONCLUSIONS The radiomic model of LDCT can be used to predict the differentiation grade of IPA in lung cancer screening, and its diagnostic performance is comparable to that of radiological expert. KEY POINTS • Early identifying the novel differentiation grade of invasive non-mucinous pulmonary adenocarcinoma may provide guidance for further surveillance, surgical strategy, or more adjuvant treatment. • The diagnostic performance of the radiomic model is comparable to that of a radiological expert and superior to that of the quantitative-semantic model and inexperienced radiologists. • The radiomic model of low-dose CT can be used to predict the differentiation grade of invasive non-mucinous pulmonary adenocarcinoma in lung cancer screening.
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Rusch VW. Initiating the Era of "Precision" Lung Cancer Surgery. N Engl J Med 2023; 388:557-558. [PMID: 36780681 DOI: 10.1056/nejme2215647] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
Affiliation(s)
- Valerie W Rusch
- From the Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York
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Li Y, Liu J, Yang X, Xu F, Wang L, He C, Lin L, Qing H, Ren J, Zhou P. Radiomic and quantitative-semantic models of low-dose computed tomography for predicting the poorly differentiated invasive non-mucinous pulmonary adenocarcinoma. LA RADIOLOGIA MEDICA 2023; 128:191-202. [PMID: 36637740 DOI: 10.1007/s11547-023-01591-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Accepted: 01/04/2023] [Indexed: 01/14/2023]
Abstract
PURPOSE Poorly differentiated invasive non-mucinous pulmonary adenocarcinoma (IPA), based on the novel grading system, was related to poor prognosis, with a high risk of lymph node metastasis and local recurrence. This study aimed to build the radiomic and quantitative-semantic models of low-dose computed tomography (LDCT) to preoperatively predict the poorly differentiated IPA in nodules with solid component, and compare their diagnostic performance with radiologists. MATERIALS AND METHODS A total of 396 nodules from 388 eligible patients, who underwent LDCT scan within 2 weeks before surgery and were pathologically diagnosed with IPA, were retrospectively enrolled between July 2018 and December 2021. Nodules were divided into two independent cohorts according to scanners: primary cohort (195 well/moderate differentiated and 64 poorly differentiated) and validation cohort (104 well/moderate differentiated and 33 poorly differentiated). The radiomic and quantitative-semantic models were built using multivariable logistic regression. The diagnostic performance of the models and radiologists was assessed by area under curve (AUC) of receiver operating characteristic (ROC) curve, accuracy, sensitivity, and specificity. RESULTS No significant differences of AUCs were found between the radiomic and quantitative-semantic model in primary and validation cohorts (0.921 vs. 0.923, P = 0.846 and 0.938 vs. 0.911, P = 0.161). Both the models outperformed three radiologists in the validation cohort (all P < 0.05). CONCLUSIONS The radiomic and quantitative-semantic models of LDCT, which could identify the poorly differentiated IPA with excellent diagnostic performance, might provide guidance for therapeutic decision making, such as choosing appropriate surgical method or adjuvant chemotherapy.
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Affiliation(s)
- Yong Li
- Department of Radiology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, No.55, Section 4, South Renmin Road, Chengdu, 610041, Sichuan, China
| | - Jieke Liu
- Department of Radiology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, No.55, Section 4, South Renmin Road, Chengdu, 610041, Sichuan, China
| | - Xi Yang
- Department of Radiology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, No.55, Section 4, South Renmin Road, Chengdu, 610041, Sichuan, China
| | - Fuyang Xu
- Department of Radiology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, No.55, Section 4, South Renmin Road, Chengdu, 610041, Sichuan, China
| | - Lu Wang
- Department of Radiology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, No.55, Section 4, South Renmin Road, Chengdu, 610041, Sichuan, China
| | - Changjiu He
- Department of Radiology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, No.55, Section 4, South Renmin Road, Chengdu, 610041, Sichuan, China
| | - Libo Lin
- Department of Radiology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, No.55, Section 4, South Renmin Road, Chengdu, 610041, Sichuan, China
| | - Haomiao Qing
- Department of Radiology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, No.55, Section 4, South Renmin Road, Chengdu, 610041, Sichuan, China
| | - Jing Ren
- Department of Radiology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, No.55, Section 4, South Renmin Road, Chengdu, 610041, Sichuan, China
| | - Peng Zhou
- Department of Radiology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, No.55, Section 4, South Renmin Road, Chengdu, 610041, Sichuan, China.
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Tang R, Bi L, Xiang B, Ye L, Chen Y, Li G, Zhao G, Huang Y. [Advances in the Study of Invasive Non-mucinous Adenocarcinoma
with Different Pathological Subtypes]. ZHONGGUO FEI AI ZA ZHI = CHINESE JOURNAL OF LUNG CANCER 2023; 26:22-30. [PMID: 36792077 PMCID: PMC9987059 DOI: 10.3779/j.issn.1009-3419.2022.102.51] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 02/17/2023]
Abstract
Lung cancer is the leading cause of cancer death in the world today, and adenocarcinoma is the most common histopathological type of lung cancer. In May 2021, World Health Organization (WHO) released the 5th edition of the WHO classification of thoracic tumors, which classifies invasive non-mucinous adenocarcinoma (INMA) into lepidic adenocarcinoma, acinar adenocarcinoma, papillary adenocarcinoma, solid adenocarcinoma, and micropapillary adenocarcinoma based on its histological characteristics. These five pathological subtypes differ in clinical features, treatment and prognosis. A complete understanding of the characteristics of these subtypes is essential for the clinical diagnosis, treatment options, and prognosis predictions of patients with lung adenocarcinoma, including recurrence and progression. This article will review the grading system, morphology, imaging prediction, lymph node metastasis, surgery, chemotherapy, targeted therapy and immunotherapy of different pathological subtypes of INMA.
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Affiliation(s)
- Ruke Tang
- Department of Thoracic Surgery I, Third Affiliated Hospital of Kunming Medical University, Kunming 650118, China
| | - Lina Bi
- Department of Nephrology, First Affiliated Hospital of Kunming Medical University, Kunming 650032, China
| | - Bingquan Xiang
- Department of Intensive Care Unit, Third Affiliated Hospital of Kunming Medical University, Kunming 650118, China
| | - Lianhua Ye
- Department of Thoracic Surgery I, Third Affiliated Hospital of Kunming Medical University, Kunming 650118, China
| | - Ying Chen
- Department of Thoracic Surgery I, Third Affiliated Hospital of Kunming Medical University, Kunming 650118, China
| | - Guangjian Li
- Department of Thoracic Surgery I, Third Affiliated Hospital of Kunming Medical University, Kunming 650118, China
| | - Guangqiang Zhao
- Department of Thoracic Surgery I, Third Affiliated Hospital of Kunming Medical University, Kunming 650118, China
| | - Yunchao Huang
- Department of Thoracic Surgery I, Third Affiliated Hospital of Kunming Medical University, Kunming 650118, China
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Dong H, Yin LK, Qiu YG, Wang XB, Yang JJ, Lou CC, Ye XD. Prediction of high-grade patterns of stage IA lung invasive adenocarcinoma based on high-resolution CT features: a bicentric study. Eur Radiol 2023; 33:3931-3940. [PMID: 36600124 DOI: 10.1007/s00330-022-09379-x] [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: 04/20/2022] [Revised: 12/07/2022] [Accepted: 12/14/2022] [Indexed: 01/06/2023]
Abstract
OBJECTIVES This study aims to predict the high-grade pattern (HGP) of stage IA lung invasive adenocarcinoma (IAC) based on the high-resolution CT (HRCT) features. METHODS The clinical, pathological, and HRCT imaging data of 457 patients (from bicentric) with pathologically confirmed stage IA IAC (459 lesions in total) were retrospectively analyzed. The 459 lesions were classified into high-grade pattern (HGP) (n = 101) and non-high-grade pattern (n-HGP) (n = 358) groups depending on the presence of HGP (micropapillary and solid) in pathological results. The clinical and pathological data contained age, gender, smoking history, tumor stage, pathological type, and presence or absence of tumor spread through air spaces (STAS). CT features consisted of lesion location, size, density, shape, spiculation, lobulation, vacuole, air bronchogram, and pleural indentation. The independent predictors for HGP were screened by univariable and multivariable logistic regression analyses. The clinical, CT, and clinical-CT models were constructed according to the multivariable analysis results. RESULTS The multivariate analysis suggested the independent predictors of HGP, encompassing tumor size (p = 0.001; OR = 1.090, 95% CI 1.035-1.148), density (p < 0.001; OR = 9.454, 95% CI 4.911-18.199), and lobulation (p = 0.002; OR = 2.722, 95% CI 1.438-5.154). The AUC values of clinical, CT, and clinical-CT models for predicting HGP were 0.641 (95% CI 0.583-0.699) (sensitivity = 69.3%, specificity = 79.2%), 0.851 (95% CI 0.806-0.896) (sensitivity = 79.2%, specificity = 79.6%), and 0.852 (95% CI 0.808-0.896) (sensitivity = 74.3%, specificity = 85.8%). CONCLUSION The logistic regression model based on HRCT features has a good diagnostic performance for the high-grade pattern of stage IA IAC. KEY POINTS • The AUC values of clinical, CT, and clinical-CT models for predicting high-grade patterns were 0.641 (95% CI 0.583-0.699), 0.851 (95% CI 0.806-0.896), and 0.852 (95% CI 0.808-0.896). • Tumor size, density, and lobulation were independent predictive markers for high-grade patterns. • The logistic regression model based on HRCT features has a good diagnostic performance for the high-grade patterns of invasive adenocarcinoma.
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Affiliation(s)
- Hao Dong
- Department of Radiology, First People's Hospital of Xiaoshan District, Zhejiang, Hangzhou, China
| | - Le-Kang Yin
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.,Shanghai Institute of Medical Imaging, Shanghai, China.,Department of Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Yong-Gang Qiu
- Department of Radiology, First People's Hospital of Xiaoshan District, Zhejiang, Hangzhou, China
| | - Xin-Bin Wang
- Department of Radiology, First People's Hospital of Xiaoshan District, Zhejiang, Hangzhou, China
| | - Jun-Jie Yang
- Department of Pathology, First People's Hospital of Xiaoshan District, Zhejiang, Hangzhou, China
| | - Cun-Cheng Lou
- Department of Radiology, First People's Hospital of Xiaoshan District, Zhejiang, Hangzhou, China
| | - Xiao-Dan Ye
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China. .,Shanghai Institute of Medical Imaging, Shanghai, China. .,Department of Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, China.
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Zhou C, Zhao R, Zhao R, Wang A, Li W. Preoperative levels of folate receptor-positive circulating tumor cells in different subtypes of early-stage lung adenocarcinoma: Predictive value for determining extent of surgical resection. Front Oncol 2023; 13:1119807. [PMID: 37139152 PMCID: PMC10150082 DOI: 10.3389/fonc.2023.1119807] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 03/23/2023] [Indexed: 05/05/2023] Open
Abstract
Background The objective was to measure the correlations of preoperative levels of folate receptor-positive circulating tumor cells (FR+CTCs) with clinical characteristics and histologic subtype in early-stage lung adenocarcinoma, and to determine the predictive value of FR+CTC level in preoperative determination of the extent of surgical resection. Patients and methods In this retrospective, single-institution, observational study, preoperative FR+CTC levels were measured via ligand-targeted enzyme-linked polymerization in patients with early-stage lung adenocarcinoma. Receiver operating characteristic (ROC) analysis was used to identify the optimal cutoff value of FR+CTC level for prediction of various clinical characteristics and histologic subtypes. Results No significant difference in FR+CTC level was observed among patients with adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC) (P = 0.813). Within the non-mucinous adenocarcinoma group, no difference was observed among patients with tumors whose predominant growth patterns were lepidic, acinar, papillary, micropapillary, solid, and complex gland (P = 0.053). However, significant differences in FR+CTC level were observed between patients with and without the micropapillary subtype [11.21 (8.22-13.61) vs. 9.85 (7.43-12.63), P = 0.017], between those with and without the solid subtype [12.16 (8.27-14.90) vs. 9.87 (7.50-12.49), P = 0.022], and between those with any of the advanced subtypes (micropapillary, solid, or complex glands) vs. none of these [10.48 (7.83-13.67) vs. 9.76 (7.42-12.42), P = 0.032]. FR+CTC level was also correlated with degree of differentiation of lung adenocarcinoma (P = 0.033), presence of visceral pleural invasion (VPI) of lung carcinoma (P = 0.003), and lymph node metastasis of lung carcinoma (P = 0.035). Conclusion FR+CTC level is of potential predictive value in determining the presence of aggressive histologic patterns (micropapillary, solid, and advanced subtypes), degree of differentiation, and occurrence of VPI and lymph node metastasis in IAC. Measurement of FR+CTC level combined with intraoperative frozen sections may represent a more effective method of guiding resection strategy in cases of cT1N0M0 IAC with high-risk factors.
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Affiliation(s)
- Chao Zhou
- Department of Thoracic Surgery, Shanghai Chest Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Ran Zhao
- Department of Thoracic Surgery, ShuYang Hospital of Traditional Chinese Medicine, Suqian, China
| | - Ruiying Zhao
- Department of Pathology, Shanghai Chest Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Ansheng Wang
- Department of Thoracic Surgery, The First Affiliated Hospital of Bengbu Medical College, Bengbu, China
- *Correspondence: Wentao Li, ; Ansheng Wang,
| | - Wentao Li
- Department of Thoracic Surgery, Shanghai Chest Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- *Correspondence: Wentao Li, ; Ansheng Wang,
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Ikeda T, Kadota K, Go T, Misaki N, Haba R, Yokomise H. Segmentectomy Provides Comparable Outcomes to Lobectomy for Stage IA Non-small Cell Lung Cancer with Spread through Air Spaces. Semin Thorac Cardiovasc Surg 2023; 35:156-163. [PMID: 35149218 DOI: 10.1053/j.semtcvs.2022.02.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 02/02/2022] [Indexed: 12/20/2022]
Abstract
This study aimed to compare the recurrence-free survival (RFS) and overall survival (OS) among wedge resection (non-anatomical resection), segmentectomy and lobectomy for pathological stage IA non-small cell lung cancer (NSCLC) with spread through air spaces (STAS). Patients underwent surgical treatment for pathological stage IA NSCLC between January 1, 2005, and March 31, 2016, at our hospital. Surgical procedures were classified as lobectomy, segmentectomy, and wedge resection. Among the 555 analyzed cases, STAS was observed in 148 patients (26.7%). STAS was correlated with worse RFS (P < 0.001) and OS (P < 0.001) and was an independent poor prognostic factor for RFS (hazard ratio: 2.37, P < 0.001) and OS (hazard ratio: 2.02, P < 0.001) in the multivariate analysis. In patients with STAS, the RFS and OS in the segmentectomy group were comparable to those in the lobectomy group. However, the RFS and OS in the wedge resection group were significantly lower than those in the lobectomy group (RFS, P < 0.001; OS, P = 0.001). Wedge resection was an independent prognostic factor for poor RFS (hazard ratio [HR] = 3.87; 95% confidence interval [CI] = 1.84 - 8.12, P < 0.001), and poor OS (hazard ratio [HR] = 3.39; 95% confidence interval [CI] = 1.33 - 8.76, P = 0.011) in the multivariate analysis. Segmentectomy is an adequate operation for patients with stage IA NSCLC with or without STAS. However, wedge resection is associated with a higher risk of recurrence in this patient population.
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Affiliation(s)
- Toshihiro Ikeda
- Department of General Thoracic Surgery, Faculty of Medicine, Kagawa University, Kagawa, Japan
| | - Kyuichi Kadota
- Department of Pathology, Faculty of Medicine, Shimane University, Izumo, Shimane, Japan..
| | - Tetsuhiko Go
- Department of General Thoracic Surgery, Faculty of Medicine, Kagawa University, Kagawa, Japan
| | - Noriyuki Misaki
- Department of General Thoracic Surgery, Faculty of Medicine, Kagawa University, Kagawa, Japan
| | - Reiji Haba
- Department of Diagnostic Pathology, Faculty of Medicine, Kagawa University, Kagawa, Japan
| | - Hiroyasu Yokomise
- Department of General Thoracic Surgery, Faculty of Medicine, Kagawa University, Kagawa, Japan
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Tan KS, Hsu M, Adusumilli PS. Pathologic node-negative lung cancer: Adequacy of lymph node yield and a tool to assess the risk of occult nodal disease. Lung Cancer 2022; 174:60-66. [PMID: 36334358 PMCID: PMC10103231 DOI: 10.1016/j.lungcan.2022.10.004] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 07/29/2022] [Accepted: 10/16/2022] [Indexed: 11/06/2022]
Abstract
INTRODUCTION Accurate lymph node (LN) staging is crucial for prognostication in NSCLC. Diagnosis of pN0 disease is based on the absence of positive LNs, irrespective of the number of LNs excised, and is thus susceptible to sampling error. Tumors that are assumed to be pN0 may in fact be understaged. We developed a tool to quantify the risk of occult nodal disease (OND) among patients with pN0 NSCLC in terms of the number of LNs examined. METHODS Patients treated surgically for stage I-III primary NSCLC between 2004 and 2014 (n = 49,356) were extracted from the Surveillance, Epidemiology, and End Results database. The probability of missing a positive node in terms of the number of LNs examined was modeled using a beta-binomial model. A mathematical tool was then used to calculate the negative predictive value (NPV) corresponding to the number of LNs examined. Ranging from 0 to 100%, higher NPV reflects greater confidence in the pN0 diagnosis and a lower probability of OND. RESULTS The median number of LNs examined was 7 for N0, 10 for N1/N2, and 8 for N3 disease. The probability of missing a positive node decreased as LNs examined increased. Additionally, higher T stage required more LNs to confirm an N0 diagnosis. After accounting for false-negative diagnoses, the prevalence of node-positive disease was readjusted from 16% to 22% among patients with T1 disease. According to our tool, with 10 LNs examined, the NPV was 85% (15% probability of OND) for a patient with T3 disease, compared with 95% (5% probability of OND) for a patient with T1 disease. CONCLUSIONS Accurate pN0 diagnosis depends on the number of LNs examined. The proposed tool offers the ability to quantify, in a patient-specific manner, the confidence in a diagnosis of node-negative disease on the basis of the number of LNs examined.
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Affiliation(s)
- Kay See Tan
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, 485 Lexington Avenue, New York, NY 10017, United States.
| | - Meier Hsu
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, 485 Lexington Avenue, New York, NY 10017, United States
| | - Prasad S Adusumilli
- Department of Surgery, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, United States
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