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Hong TH, Hwang S, Dasgupta A, Abbosh C, Hung T, Bredno J, Walker J, Shi X, Milenkova T, Horn L, Choi JY, Lee HY, Cho JH, Choi YS, Shim YM, Chai S, Rhodes K, Roychowdhury-Saha M, Hodgson D, Kim HK, Ahn MJ. Clinical Utility of Tumor-Naïve Presurgical Circulating Tumor DNA Detection in Early-Stage NSCLC. J Thorac Oncol 2024:S1556-0864(24)00666-X. [PMID: 38992468 DOI: 10.1016/j.jtho.2024.07.002] [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: 05/02/2024] [Revised: 06/15/2024] [Accepted: 07/06/2024] [Indexed: 07/13/2024]
Abstract
OBJECTIVES The use of tumor-informed circulating tumor DNA (ctDNA) testing in patients with early-stage disease before surgery is limited, mainly owing to restricted tissue access and extended turnaround times. This study aimed to evaluate the clinical value of a tumor-naïve, methylation-based cell-free DNA assay in a large cohort of patients with resected NSCLC. METHOD We analyzed presurgical plasma samples from 895 patients with EGFR and anaplastic lymphoma kinase-wild-type, clinical stage I or II NSCLC. The ctDNA status was evaluated for its prognostic significance in relation to tumor volume, metabolic activity, histologic diagnosis, histologic subtypes, and clinical-to-pathologic TNM upstaging. RESULTS Presurgical ctDNA detection was observed in 55 of 414 patients (13%) with clinical stage I lung adenocarcinoma (LUAD) and was associated with poor recurrence-free survival (2-year recurrence-free survival 69% versus 91%; log-rank p < 0.001), approaching that of clinical stage II LUAD. Presurgical ctDNA detection was not prognostic in patients with clinical stage II LUAD or non-LUAD. Within LUAD, tumor volume and positron emission tomography avidity interacted to predict presurgical ctDNA detection. Moreover, presurgical ctDNA detection was predictive of the postsurgical discovery of International Association for the Study of Lung Cancer grade 3 tumors (p < 0.001) and pathologic TNM upstaging (p < 0.001). Notably, presurgical ctDNA detection strongly correlated with higher programmed death-ligand 1 expression in tumors (positive rates 28% versus 55%, p < 0.001), identifying a subgroup likely to benefit from anti-programmed death-ligand 1 therapies. CONCLUSION These findings support the integration of ctDNA testing into routine diagnostic workflows in early-stage NSCLC without the need for tumor tissue profiling. Furthermore, it is clinically useful in identifying patients at high risk who might benefit from innovative treatments, including neoadjuvant immune checkpoint inhibitors.
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Affiliation(s)
- Tae Hee Hong
- Department of Thoracic Surgery, Samsung Medical Center, Seoul, Republic of Korea; Department of Thoracic and Cardiovascular Surgery, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Soohyun Hwang
- Department of Pathology and Translational Genomics, Samsung Medical Center, Seoul, Republic of Korea
| | - Abhijit Dasgupta
- Early Data Science, Oncology Data Science, Oncology R&D, AstraZeneca, Gaithersburg, Maryland
| | - Chris Abbosh
- Translational Medicine, Oncology R&D, AstraZeneca, Cambridge, United Kingdom; SAGA Diagnostics, Cambridge, United Kingdom
| | | | | | - Jill Walker
- Precision Medicine, Oncology R&D, AstraZeneca, Cambridge, United Kingdom
| | - Xiaojin Shi
- Late Development Oncology, AstraZeneca, Gaithersburg, Maryland
| | - Tsveta Milenkova
- Global Medicine Development, AstraZeneca, Cambridge, United Kingdom
| | - Leora Horn
- Late Development Oncology, AstraZeneca, Gaithersburg, Maryland
| | - Joon Young Choi
- Department of Nuclear Medicine, Samsung Medical Center, Seoul, Republic of Korea
| | - Ho Yun Lee
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Seoul, Republic of Korea
| | - Jong Ho Cho
- Department of Thoracic Surgery, Samsung Medical Center, Seoul, Republic of Korea
| | - Yong Soo Choi
- Department of Thoracic Surgery, Samsung Medical Center, Seoul, Republic of Korea
| | - Young Mog Shim
- Department of Thoracic Surgery, Samsung Medical Center, Seoul, Republic of Korea
| | | | | | | | - Darren Hodgson
- Translational Medicine, Oncology R&D, AstraZeneca, Cambridge, United Kingdom
| | - Hong Kwan Kim
- Department of Thoracic Surgery, Samsung Medical Center, Seoul, Republic of Korea
| | - Myung-Ju Ahn
- Department of Hematology-Oncology, Samsung Medical Center, Seoul, Republic of Korea.
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Kudo Y, Nakamura T, Matsubayashi J, Ichinose A, Goto Y, Amemiya R, Park J, Shimada Y, Kakihana M, Nagao T, Ohira T, Masumoto J, Ikeda N. AI-driven Characterization of Solid Pulmonary Nodules on CT Imaging for Enhanced Malignancy Prediction in Small-sized Lung Adenocarcinoma. Clin Lung Cancer 2024; 25:431-439. [PMID: 38760224 DOI: 10.1016/j.cllc.2024.04.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2024] [Revised: 04/21/2024] [Accepted: 04/24/2024] [Indexed: 05/19/2024]
Abstract
OBJECTIVES Distinguishing solid nodules from nodules with ground-glass lesions in lung cancer is a critical diagnostic challenge, especially for tumors ≤2 cm. Human assessment of these nodules is associated with high inter-observer variability, which is why an objective and reliable diagnostic tool is necessary. This study focuses on artificial intelligence (AI) to automatically analyze such tumors and to develop prospective AI systems that can independently differentiate highly malignant nodules. MATERIALS AND METHODS Our retrospective study analyzed 246 patients who were diagnosed with negative clinical lymph node metastases (cN0) using positron emission tomography-computed tomography (PET/CT) imaging and underwent surgical resection for lung adenocarcinoma. AI detected tumor sizes ≤2 cm in these patients. By utilizing AI to classify these nodules as solid (AI_solid) or non-solid (non-AI_solid) based on confidence scores, we aim to correlate AI determinations with pathological findings, thereby advancing the precision of preoperative assessments. RESULTS Solid nodules identified by AI with a confidence score ≥0.87 showed significantly higher solid component volumes and proportions in patients with AI_solid than in those with non-AI_solid, with no differences in overall diameter or total volume of the tumors. Among patients with AI_solid, 16% demonstrated lymph node metastasis, and a significant 94% harbored invasive adenocarcinoma. Additionally, 44% were upstaging postoperatively. These AI_solid nodules represented high-grade malignancies. CONCLUSION In small-sized lung cancer diagnosed as cN0, AI automatically identifies tumors as solid nodules ≤2 cm and evaluates their malignancy preoperatively. The AI classification can inform lymph node assessment necessity in sublobar resections, reflecting metastatic potential.
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Affiliation(s)
- Yujin Kudo
- Department of Surgery, Tokyo Medical University, Japan.
| | | | - Jun Matsubayashi
- Department of Anatomic Pathology, Tokyo Medical University, Japan
| | | | - Yushi Goto
- Department of Surgery, Tokyo Medical University, Japan
| | | | - Jinho Park
- Department of Radiology, Tokyo Medical University, Japan
| | | | | | - Toshitaka Nagao
- Department of Anatomic Pathology, Tokyo Medical University, Japan
| | - Tatsuo Ohira
- Department of Surgery, Tokyo Medical University, Japan
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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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Xia W, Zhang S, Ye Y, Xiao H, Zhang Y, Ning G, Zhang Y, Wang W, Fei GH. Clinicopathological and molecular characterization of resected lung adenocarcinoma: Correlations with histopathological grading systems in Chinese patients. Pathol Res Pract 2024; 259:155359. [PMID: 38810376 DOI: 10.1016/j.prp.2024.155359] [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: 02/07/2024] [Revised: 05/04/2024] [Accepted: 05/20/2024] [Indexed: 05/31/2024]
Abstract
PURPOSE Driver mutations inform lung adenocarcinoma (LUAD) targeted therapy. Association of histopathological attributes and molecular profiles facilitates clinically viable testing platforms. We assessed correlations between LUAD clinicopathological features, mutational landscapes, and two grading systems among Chinese cases. METHODS 79 Chinese LUAD patients undergoing resection were subjected to targeted sequencing. 68 were invasive nonmucinous adenocarcinoma (INMA), graded via: predominant histologic pattern-based grading system (P-GS) or novel IASLC grading system (I-GS). Driver mutation distributions were appraised and correlated with clinical and pathological data. RESULTS Compared to INMA, non-INMA exhibited smaller, well-differentiated tumors with higher mucin content. INMA grade correlated with size, lymph invasion (P-GS), and driver/EGFR mutations. Mutational spectra varied markedly between grades, with EGFR p.L858R and exon 19 deletion mutations predominating in lower grades; while high-grade P-GS tumors often harbored EGFR copy number variants and complex alterations alongside wild-type cases. I-GS upgrade of P-GS grade 2 to grade 3 was underpinned by ≥20 % high-grade regions bearing p.L858R or ALK fusions. Both systems defined tumors of distinctive phenotypic attributes and molecular genotypes. CONCLUSIONS INMA represent larger, mucin-poor, molecularly heterogeneous LUAD with divergent grade-specific mutation profiles. Stronger predictor of clinicopathological attributes and driver mutations, P-GS stratification offers greater accuracy for molecular testing. A small panel encompassing EGFR and ALK captures the majority of P-GS grade 1/2 mutations whereas expanded panels are optimal for grade 3.
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Affiliation(s)
- Wanli Xia
- Department of Thoracic Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, PR China
| | - Siyuan Zhang
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, PR China
| | - Yuanzi Ye
- Department of Pathology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, PR China.
| | - Han Xiao
- Department of Pathology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, PR China
| | - Ying Zhang
- Department of Pathology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, PR China
| | - Guangyao Ning
- Department of Thoracic Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, PR China
| | - Yanbei Zhang
- Department of Geriatric Respiratory and Critical Care, Anhui Geriatric Institute, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, PR China
| | - Wei Wang
- Department of Pathology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, PR China; Intelligent Pathology Institute, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, PR China.
| | - Guang-He Fei
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, PR China; Key Laboratory of Respiratory Diseases Research and Medical Transformation of Anhui Province, Hefei, PR China.
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Hegedűs F, Zombori-Tóth N, Kiss S, Lantos T, Zombori T. Prognostic impact of the IASLC grading system of lung adenocarcinoma: a systematic review and meta-analysis. Histopathology 2024; 85:51-61. [PMID: 38485464 DOI: 10.1111/his.15172] [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: 09/04/2023] [Revised: 02/12/2024] [Accepted: 02/24/2024] [Indexed: 06/09/2024]
Abstract
AIMS Tumour grading is an essential part of the pathologic assessment that promotes patient management. The International Association for the Study of Lung Cancer (IASLC) proposed a grading system for non-mucinous lung adenocarcinoma in 2020. We aimed to validate the prognostic impact of this novel grading system on overall survival (OS) and recurrence-free survival (RFS) based on literature data. METHODS AND RESULTS The review protocol was registered in PROSPERO (CRD42023396059). We aimed to identify randomized or non-randomized controlled trials published after 2020 comparing different IASLC grade categories in Medline, Embase, and CENTRAL. Hazard ratios (HRs) with 95% confidence intervals (CIs) of OS and RFS were pooled and the Quality In Prognosis Studies (QUIPS) tool was used to assess the risk of bias in the included studies. Ten articles were eligible for this review. Regarding OS estimates, grade 1 lung adenocarcinomas were better than grade 3 both in univariate and multivariate analyses (HROSuni = 0.19, 95% CI: 0.05-0.66, p = 0.009; HROSmulti = 0.21, 95% CI: 0.12-0.38, p < 0.001). Regarding RFS estimates, grade 3 adenocarcinomas had a worse prognosis than grade 1 in multivariate analysis (HRRFSmulti: 0.22, 95% CI: 0.14-0.35, p < 0.001). CONCLUSION The literature data and the result of our meta-analysis demonstrate the prognostic relevance of the IASLC grading system. This supports the inclusion of this prognostic parameter in daily routine worldwide.
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Affiliation(s)
- Fanni Hegedűs
- Department of Pathology, Albert Szent-Györgyi Clinical Centre, University of Szeged, Szeged, Hungary
| | - Noémi Zombori-Tóth
- Department of Pulmonology, Albert Szent-Györgyi Clinical Centre, University of Szeged, Szeged, Hungary
| | - Szabolcs Kiss
- Heim Pál National Pediatric Institute, Budapest, Hungary
| | - Tamás Lantos
- Department of Medical Physics and Informatics, Albert Szent-Györgyi Clinical Centre, University of Szeged, Szeged, Hungary
| | - Tamás Zombori
- Department of Pathology, Albert Szent-Györgyi Clinical Centre, University of Szeged, Szeged, Hungary
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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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7
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Chen Y, Wu J, You J, Gao M, Lu S, Sun C, Shu Y, Wang X. Integrating IASLC grading and radiomics for predicting postoperative outcomes in stage IA invasive lung adenocarcinoma. Med Phys 2024. [PMID: 38781536 DOI: 10.1002/mp.17177] [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: 11/09/2023] [Revised: 05/02/2024] [Accepted: 05/10/2024] [Indexed: 05/25/2024] Open
Abstract
BACKGROUND The International Association for the Study of Lung Cancer (IASLC) Pathology Committee introduced a histologic grading system for invasive lung adenocarcinoma (LUAD) in 2020. The IASLC grading system, hinging on the evaluation of predominant and high-grade histologic patterns, has proven to be practical and prognostic for invasive LUAD. However, there are still limitations in evaluating the prognosis of stage IA LUAD. Radiomics may serve as a valuable complement. PURPOSE To establish a model that integrates IASLC grading and radiomics, aimed at predicting the prognosis of stage IA LUAD. METHODS We conducted a retrospective analysis of 628 patients diagnosed with stage IA LUAD who underwent surgical resection between January 2015 and December 2018 at our institution. The patients were randomly divided into the training set (n = 439) and testing set (n = 189) at a ratio of 7:3. Overall survival (OS) and disease-free survival (DFS) were taken as the end points. Radiomics features were obtained by PyRadiomics. Feature selection was performed using the least absolute shrinkage and selection operator (LASSO). The prediction models for OS and DFS were developed using multivariate Cox regression analysis, and the models were visualized through nomogram plots. The model's performance was evaluated using area under the curves (AUC), concordance index (C-index), calibration curves, and survival decision curve analysis (DCA). RESULTS In total, nine radiomics features were selected for the OS prediction model, and 15 radiomics features were selected for the DFS prediction model. Patients with high radiomics scores were associated with a worse prognosis (p < 0.001). We built separate prediction models using radiomics or IASLC alone, as well as a combined prediction model. In the prediction of OS, we observed that the combined model (C-index: 0.812 ± 0.024, 3 years AUC: 0.692, 5 years AUC: 0.792) achieved superior predictive performance than the radiomics (C-index: 0.743 ± 0.038, 3 years AUC: 0.633, 5 years AUC: 0.768) and IASLC grading (C-index: 0.765 ± 0.042, 3 years AUC: 0.658, 5 years AUC: 0.743) models alone. Similar results were obtained in the models for DFS. CONCLUSION The combination of radiomics and IASLC pathological grading proves to be an effective approach for predicting the prognosis of stage IA LUAD. This has substantial clinical relevance in guiding treatment decisions for early-stage LUAD.
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Affiliation(s)
- Yong Chen
- First College of Clinical Medicine, Dalian Medical University, Dalian, China
| | - Jun Wu
- Medical College, Yangzhou University, Yangzhou, China
| | - Jie You
- First College of Clinical Medicine, Dalian Medical University, Dalian, China
| | - Mingjun Gao
- First College of Clinical Medicine, Dalian Medical University, Dalian, China
| | - Shichun Lu
- Department of Thoracic Surgery, Northern Jiangsu People's Hospital Affiliated to Yangzhou University, Yangzhou, China
| | - Chao Sun
- Department of Thoracic Surgery, Northern Jiangsu People's Hospital Affiliated to Yangzhou University, Yangzhou, China
| | - Yusheng Shu
- Department of Thoracic Surgery, Northern Jiangsu People's Hospital Affiliated to Yangzhou University, Yangzhou, China
| | - Xiaolin Wang
- Department of Thoracic Surgery, Northern Jiangsu People's Hospital Affiliated to Yangzhou University, Yangzhou, China
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Miura E, Emoto K, Abe T, Hashiguchi A, Hishida T, Asakura K, Sakamoto M. Establishment of artificial intelligence model for precise histological subtyping of lung adenocarcinoma and its application to quantitative and spatial analysis. Jpn J Clin Oncol 2024:hyae066. [PMID: 38757929 DOI: 10.1093/jjco/hyae066] [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: 01/31/2024] [Accepted: 05/04/2024] [Indexed: 05/18/2024] Open
Abstract
BACKGROUND The histological subtype of lung adenocarcinoma is a major prognostic factor. We developed a new artificial intelligence model to classify lung adenocarcinoma images into seven histological subtypes and adopted the model for whole-slide images to investigate the relationship between the distribution of histological subtypes and clinicopathological factors. METHODS Using histological subtype images, which are typical for pathologists, we trained and validated an artificial intelligence model. Then, the model was applied to whole-slide images of resected lung adenocarcinoma specimens from 147 cases. RESULT The model achieved an accuracy of 99.7% in training sets and 90.4% in validation sets consisting of typical tiles of histological subtyping for pathologists. When the model was applied to whole-slide images, the predominant subtype according to the artificial intelligence model classification matched that determined by pathologists in 75.5% of cases. The predominant subtype and tumor grade (using the WHO fourth and fifth classifications) determined by the artificial intelligence model resulted in similar recurrence-free survival curves to those determined by pathologists. Furthermore, we stratified the recurrence-free survival curves for patients with different proportions of high-grade components (solid, micropapillary and cribriform) according to the physical distribution of the high-grade component. The results suggested that tumors with centrally located high-grade components had a higher malignant potential (P < 0.001 for 5-20% high-grade component). CONCLUSION The new artificial intelligence model for histological subtyping of lung adenocarcinoma achieved high accuracy, and subtype quantification and subtype distribution analyses could be achieved. Artificial intelligence model therefore has potential for clinical application for both quantification and spatial analysis.
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Affiliation(s)
- Eisuke Miura
- Department of Pathology, Keio University School of Medicine, Tokyo, Japan
| | - Katsura Emoto
- Department of Pathology, Keio University School of Medicine, Tokyo, Japan
- Department of Diagnostic Pathology, National Hospital Organization Saitama Hospital, Saitama, Japan
| | - Tokiya Abe
- Department of Pathology, Keio University School of Medicine, Tokyo, Japan
| | - Akinori Hashiguchi
- Department of Pathology, Keio University School of Medicine, Tokyo, Japan
| | - Tomoyuki Hishida
- Division of Thoracic Surgery, Department of Surgery, Keio University School of Medicine, Tokyo, Japan
| | - Keisuke Asakura
- Division of Thoracic Surgery, Department of Surgery, Keio University School of Medicine, Tokyo, Japan
| | - Michiie Sakamoto
- Department of Pathology, Keio University School of Medicine, Tokyo, Japan
- School of Medicine, International University of Health and Welfare, Chiba, Japan
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Hwang S, Hong TH, Kim HK, Cho J, Lee G, Choi S, Park S, Lee SH, Lee Y, Jeon YJ, Lee J, Park SY, Cho JH, Choi YS, Kim J, Zo JI, Shim YM, Choi YL. PD-L1 expression in resected lung adenocarcinoma: prevalence and prognostic significance in relation to the IASLC grading system. Histopathology 2024; 84:1013-1023. [PMID: 38288635 DOI: 10.1111/his.15146] [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: 09/15/2023] [Revised: 12/13/2023] [Accepted: 01/09/2024] [Indexed: 02/21/2024]
Abstract
AIMS Programmed death-ligand 1 (PD-L1) expression is a predictive biomarker for adjuvant immunotherapy and has been linked to poor differentiation in lung adenocarcinoma. However, its prevalence and prognostic role in the context of the novel histologic grade has not been evaluated. METHODS We analysed a cohort of 1233 patients with resected lung adenocarcinoma where PD-L1 immunohistochemistry (22C3 assay) was reflexively tested. Tumour PD-L1 expression was correlated with the new standardized International Association for the Study of Lung Cancer (IASLC) histologic grading system (G1, G2, and G3). Clinicopathologic features including patient outcome were analysed. RESULTS PD-L1 was positive (≥1%) in 7.0%, 23.5%, and 63.0% of G1, G2, and G3 tumours, respectively. PD-L1 positivity was significantly associated with male sex, smoking, and less sublobar resection among patients with G2 tumours, but this association was less pronounced in those with G3 tumours. PD-L1 was an independent risk factor for recurrence (adjusted hazard ratio [HR] = 3.25, 95% confidence intervals [CI] = 1.93-5.48, P < 0.001) and death (adjusted HR = 2.69, 95% CI = 1.13-6.40, P = 0.026) in the G2 group, but not in the G3 group (adjusted HR for recurrence = 0.94, 95% CI = 0.64-1.40, P = 0.778). CONCLUSION PD-L1 expression differs substantially across IASLC grades and identifies aggressive tumours within the G2 subgroup. This knowledge may be used for both prognostication and designing future studies on adjuvant immunotherapy.
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Affiliation(s)
- Soohyun Hwang
- Department of Pathology and Translational Genomics, Sungkyunkwan University School of Medicine, Samsung Medical Center, Seoul, Korea
| | - Tae Hee Hong
- Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, Korea
- Department of Thoracic and Cardiovascular Surgery, Sungkyunkwan University School of Medicine, Samsung Medical Center, Seoul, Korea
| | - Hong Kwan Kim
- Department of Thoracic and Cardiovascular Surgery, Sungkyunkwan University School of Medicine, Samsung Medical Center, Seoul, Korea
- Samsung Medical Center, Patient-Centered Outcomes Research Institute, Seoul, Korea
- Department of Clinical Research Design and Evaluation, SAIHST, Sungkyunkwan University, Seoul, Korea
| | - Juhee Cho
- Samsung Medical Center, Patient-Centered Outcomes Research Institute, Seoul, Korea
- Department of Clinical Research Design and Evaluation, SAIHST, Sungkyunkwan University, Seoul, Korea
- Center for Clinical Epidemiology, Samsung Medical Center, Future Medicine Institute, Seoul, Korea
| | - Genehee Lee
- Samsung Medical Center, Patient-Centered Outcomes Research Institute, Seoul, Korea
- Department of Clinical Research Design and Evaluation, SAIHST, Sungkyunkwan University, Seoul, Korea
| | - Sangjoon Choi
- Department of Pathology and Translational Genomics, Sungkyunkwan University School of Medicine, Samsung Medical Center, Seoul, Korea
| | - Sehhoon Park
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Se-Hoon Lee
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Yoonseo Lee
- Department of Thoracic and Cardiovascular Surgery, Sungkyunkwan University School of Medicine, Samsung Medical Center, Seoul, Korea
| | - Yeong Jeong Jeon
- Department of Thoracic and Cardiovascular Surgery, Sungkyunkwan University School of Medicine, Samsung Medical Center, Seoul, Korea
| | - Junghee Lee
- Department of Thoracic and Cardiovascular Surgery, Sungkyunkwan University School of Medicine, Samsung Medical Center, Seoul, Korea
| | - Seong Yong Park
- Department of Thoracic and Cardiovascular Surgery, Sungkyunkwan University School of Medicine, Samsung Medical Center, Seoul, Korea
| | - Jong Ho Cho
- Department of Thoracic and Cardiovascular Surgery, Sungkyunkwan University School of Medicine, Samsung Medical Center, Seoul, Korea
| | - Yong Soo Choi
- Department of Thoracic and Cardiovascular Surgery, Sungkyunkwan University School of Medicine, Samsung Medical Center, Seoul, Korea
| | - Jhingook Kim
- Department of Thoracic and Cardiovascular Surgery, Sungkyunkwan University School of Medicine, Samsung Medical Center, Seoul, Korea
| | - Jae Il Zo
- Department of Thoracic and Cardiovascular Surgery, Sungkyunkwan University School of Medicine, Samsung Medical Center, Seoul, Korea
| | - Young Mog Shim
- Department of Thoracic and Cardiovascular Surgery, Sungkyunkwan University School of Medicine, Samsung Medical Center, Seoul, Korea
| | - Yoon-La Choi
- Department of Pathology and Translational Genomics, Sungkyunkwan University School of Medicine, Samsung Medical Center, Seoul, Korea
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Jiang MQ, Qian LQ, Shen YJ, Fu YY, Feng W, Ding ZP, Han YC, Fu XL. Who benefit from adjuvant chemotherapy in stage I lung adenocarcinoma? A multi-dimensional model for candidate selection. Neoplasia 2024; 50:100979. [PMID: 38387107 PMCID: PMC10899011 DOI: 10.1016/j.neo.2024.100979] [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: 11/09/2023] [Accepted: 02/14/2024] [Indexed: 02/24/2024]
Abstract
BACKGROUND Despite promising overall survival of stage I lung adenocarcinoma (LUAD) patients, 10-25 % of them still went through recurrence after surgery. [1] While it is still disputable whether adjuvant chemotherapy is necessary for stage I patients. [2] IASLC grading system for non-mucinous LUAD shows that minor high-grade patterns are significant indicator of poor prognosis. [3] Other risk factors, such as, pleura invasion, lympho-vascular invasion, STAS, etc. are also related to poor prognosis. [4-6] There still lack evidence whether IASLC grade itself or together with other risk factors can guide the use of adjuvant therapy in stage I patients. In this article, we tried to establish a multi-variable recurrence prediction model for stage I LUAD patients that is able to identify candidates of adjuvant chemotherapy. METHODS We retrospectively collected patients who underwent lung surgery from 2018.8.1 to 2018.12.31 at our institution and diagnosed with lung adenocarcinoma pT1-2aN0M0 (stage I). Clinical data, manifestation on CT scan, pathologic features, driver gene mutations and follow-up information were collected. Cox proportional hazards regression analyses were performed utilizing the non-adjuvant cohort to predict disease free survival (DFS) and a nomogram was constructed and applied to the total cohort. Kaplan-Meier method was used to compare DFS between groups. Statistical analysis was conducted by R version 3.6.3. FINDINGS A total of 913 stage I LUAD patients were included in this study. Median follow-up time is 48.1 months.4-year and 5-year DFS are 92.9 % and 89.6 % for the total cohort. 65 patient experienced recurrence or death. 4-year DFS are 97.0 %,94.6 % and 76.2 %, and 5-year DFS are 95.5 %, 90.0 % and 74.1 % in IASLC Grade1, 2 and 3, respectively(p < 0.0001). High-risk patients defined by single risk factors, such as, IASLC grade 3, pleura invasion, STAS, less LN resected could not benefit from adjuvant therapy. A LASSO-COX regression model was built and patients are divided into high-risk and low-risk groups. In the high-risk group, patients underwent adjuvant chemotherapy have longer DFS than those who did not (p = 0.024), while in the low-risk group, patients underwent adjuvant chemotherapy have inferior DFS than those who did not (p < 0.001). INTERPRETATION IASLC grading is a significant indicator of DFS, however it could not guide adjuvant therapy in our stage I LUAD cohort. Growth patterns and T indicators together with other risk factors could identify high-risk patients that are potential candidate of adjuvant therapy, including some stage IA LUAD patients.
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Affiliation(s)
- Meng-Qi Jiang
- Department of Radiation Oncology, Shanghai Chest Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Li-Qiang Qian
- Department of Thoracic Surgery, Shanghai Lung Cancer Center, Shanghai Chest Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Yu-Jia Shen
- Department of Radiation Oncology, Shanghai Chest Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Yuan-Yuan Fu
- Department of Radiation Oncology, Shanghai Chest Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Wen Feng
- Department of Radiation Oncology, Shanghai Chest Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Zheng-Ping Ding
- Department of Thoracic Surgery, Shanghai Lung Cancer Center, Shanghai Chest Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Yu-Chen Han
- Department of Pathology, Shanghai Chest Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
| | - Xiao-Long Fu
- Department of Radiation Oncology, Shanghai Chest Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
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11
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Yang H, Liu X, Wang L, Zhou W, Tian Y, Dong Y, Zhou K, Chen L, Wang M, Wu H. 18 F-FDG PET/CT characteristics of IASLC grade 3 invasive adenocarcinoma and the value of 18 F-FDG PET/CT for preoperative prediction: a new prognostication model. Nucl Med Commun 2024; 45:338-346. [PMID: 38312089 DOI: 10.1097/mnm.0000000000001819] [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: 02/06/2024]
Abstract
OBJECTIVE This study is performed to investigate the imaging characteristics of the International Association for the Study of Lung Cancer grade 3 invasive adenocarcinoma (IAC) on PET/CT and the value of PET/CT for preoperative predicting this tumor. MATERIALS AND METHODS We retrospectively enrolled patients with IAC from August 2015 to September 2022. The clinical characteristics, serum tumor markers, and PET/CT features were analyzed. T test, Mann-Whitney U test, χ 2 test, Logistic regression analysis, and receiver operating characteristic analysis were used to predict grade 3 tumor and evaluate the prediction effectiveness. RESULTS Grade 3 tumors had a significantly higher maximum standardized uptake value (SUV max ) and consolidation-tumor-ratio (CTR) ( P < 0.001), while Grade 1 - 2 tumors were prone to present with air bronchogram sign or vacuole sign ( P < 0.001). A stepwise logistic regression analysis revealed that smoking history, CEA, SUV max , air bronchogram sign or vacuole sign and CTR were useful predictors for Grade 3 tumors. The established prediction model based on the above 5 parameters generated a high AUC (0.869) and negative predictive value (0.919), respectively. CONCLUSION Our study demonstrates that grade 3 IAC has a unique PET/CT imaging feature. The prognostication model established with smoking history, CEA, SUV max , air bronchogram sign or vacuole sign and CTR can effectively predict grade 3 tumors before the operation.
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Affiliation(s)
- Hanyun Yang
- GDMPA Key Laboratory for Quality Control and Evaluation of Radiopharmaceuticals, Department of Nuclear Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, China
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12
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Wang S, Li Y, Sun X, Dong J, Liu L, Liu J, Chen R, Li F, Chen T, Li X, Xie G, Ying J, Guo Q, Mao Y, Yang L. Proposed novel grading system for stage I invasive lung adenocarcinoma and a comparison with the 2020 IASLC grading system. Thorac Cancer 2024; 15:519-528. [PMID: 38273667 PMCID: PMC10912529 DOI: 10.1111/1759-7714.15204] [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: 10/08/2023] [Revised: 12/07/2023] [Accepted: 12/08/2023] [Indexed: 01/27/2024] Open
Abstract
BACKGROUND Several studies have proposed grading systems for risk stratification of early-stage lung adenocarcinoma based on histological patterns. However, the reproducibility of these systems is poor in clinical practice, indicating the need to develop a new grading system which is easy to apply and has high accuracy in prognostic stratification of patients. METHODS Patients with stage I invasive nonmucinous lung adenocarcinoma were retrospectively collected from pathology archives between 2009 and 2016. The patients were divided into a training and validation set at a 6:4 ratio. Histological features associated with patient outcomes (overall survival [OS] and progression-free survival [PFS]) identified in the training set were used to construct a new grading system. The newly proposed system was validated using the validation set. Survival differences between subgroups were assessed using the log-rank test. The prognostic performance of the novel grading system was compared with two previously proposed systems using the concordance index. RESULTS A total of 539 patients were included in this study. Using a multioutcome decision tree model, four pathological factors, including the presence of tumor spread through air space (STAS) and the percentage of lepidic, micropapillary and solid subtype components, were selected for the proposed grading system. Patients were accordingly classified into three groups: low, medium, and high risk. The high-risk group showed a 5-year OS of 52.4% compared to 89.9% and 97.5% in the medium and low-risk groups, respectively. The 5-year PFS of patients in the high-risk group was 38.1% compared to 61.7% and 90.9% in the medium and low-risk groups, respectively. Similar results were observed in the subgroup analysis. Additionally, our proposed grading system provided superior prognostic stratification compared to the other two systems with a higher concordance index. CONCLUSION The newly proposed grading system based on four pathological factors (presence of STAS, and percentage of lepidic, micropapillary, and solid subtypes) exhibits high accuracy and good reproducibility in the prognostic stratification of stage I lung adenocarcinoma patients.
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Affiliation(s)
- Shuaibo Wang
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Ye Li
- Ping An Healthcare TechnologyBeijingChina
| | - Xujie Sun
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Jiyan Dong
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Li Liu
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Jingbo Liu
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
- Department of Pathologythe 5th Affiliated Hospital of Qiqihar Medical College/Longnan HospitalDaqingChina
| | - Ruanqi Chen
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Feng Li
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | | | - Xiang Li
- Ping An Healthcare TechnologyBeijingChina
| | - Guotong Xie
- Ping An Healthcare TechnologyBeijingChina
- Ping An Health Cloud Company LimitedBeijingChina
- Ping An International Smart City Technology CoBeijingChina
| | - Jianming Ying
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Qiang Guo
- Big data office, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Yousheng Mao
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Lin Yang
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
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Yang Z, Dong H, Fu C, Zhang Z, Hong Y, Shan K, Ma C, Chen X, Xu J, Pang Z, Hou M, Zhang X, Zhu W, Liu L, Li W, Sun J, Zhao F. A nomogram based on CT intratumoral and peritumoral radiomics features preoperatively predicts poorly differentiated invasive pulmonary adenocarcinoma manifesting as subsolid or solid lesions: a double-center study. Front Oncol 2024; 14:1289555. [PMID: 38313797 PMCID: PMC10834705 DOI: 10.3389/fonc.2024.1289555] [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: 09/13/2023] [Accepted: 01/02/2024] [Indexed: 02/06/2024] Open
Abstract
Background The novel International Association for the Study of Lung Cancer (IASLC) grading system suggests that poorly differentiated invasive pulmonary adenocarcinoma (IPA) has a worse prognosis. Therefore, prediction of poorly differentiated IPA before treatment can provide an essential reference for therapeutic modality and personalized follow-up strategy. This study intended to train a nomogram based on CT intratumoral and peritumoral radiomics features combined with clinical semantic features, which predicted poorly differentiated IPA and was tested in independent data cohorts regarding models' generalization ability. Methods We retrospectively recruited 480 patients with IPA appearing as subsolid or solid lesions, confirmed by surgical pathology from two medical centers and collected their CT images and clinical information. Patients from the first center (n =363) were randomly assigned to the development cohort (n = 254) and internal testing cohort (n = 109) in a 7:3 ratio; patients (n = 117) from the second center served as the external testing cohort. Feature selection was performed by univariate analysis, multivariate analysis, Spearman correlation analysis, minimum redundancy maximum relevance, and least absolute shrinkage and selection operator. The area under the receiver operating characteristic curve (AUC) was calculated to evaluate the model performance. Results The AUCs of the combined model based on intratumoral and peritumoral radiomics signatures in internal testing cohort and external testing cohort were 0.906 and 0.886, respectively. The AUCs of the nomogram that integrated clinical semantic features and combined radiomics signatures in internal testing cohort and external testing cohort were 0.921 and 0.887, respectively. The Delong test showed that the AUCs of the nomogram were significantly higher than that of the clinical semantic model in both the internal testing cohort(0.921 vs 0.789, p< 0.05) and external testing cohort(0.887 vs 0.829, p< 0.05). Conclusion The nomogram based on CT intratumoral and peritumoral radiomics signatures with clinical semantic features has the potential to predict poorly differentiated IPA manifesting as subsolid or solid lesions preoperatively.
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Affiliation(s)
- Zebin Yang
- Department of Radiology, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
| | - Hao Dong
- Department of Radiology, Affiliated Xiaoshan Hospital of Wenzhou Medical University, Hangzhou, China
| | - Chunlong Fu
- Department of Radiology, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
| | - Zening Zhang
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yao Hong
- Department of Radiology, Fourth Affiliated Hospital, College of Medicine, Zhejiang University, Yiwu, China
| | - Kangfei Shan
- Department of Radiology, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
| | - Chijun Ma
- Department of Radiology, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
| | - Xiaolu Chen
- Department of Radiology, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
| | - Jieping Xu
- Department of Radiology, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
| | - Zhenzhu Pang
- Department of Radiology, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
| | - Min Hou
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaowei Zhang
- Department of Pathology, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
| | - Weihua Zhu
- Department of Radiology, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
| | - Linjiang Liu
- Medical Imaging Department, Shenzhen Second People's Hospital/the First Affiliated Hospital of Shenzhen University Health Science Center, Shenzhen, China
| | - Weihua Li
- Medical Imaging Department, Shenzhen Second People's Hospital/the First Affiliated Hospital of Shenzhen University Health Science Center, Shenzhen, China
| | - Jihong Sun
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Department of Radiology, Fourth Affiliated Hospital, College of Medicine, Zhejiang University, Yiwu, China
- Cancer Center, Zhejiang University, Hangzhou, China
| | - Fenhua Zhao
- Department of Radiology, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
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14
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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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15
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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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Mikubo M, Tamagawa S, Kondo Y, Hayashi S, Sonoda D, Naito M, Shiomi K, Ichinoe M, Satoh Y. Micropapillary and solid components as high-grade patterns in IASLC grading system of lung adenocarcinoma: Clinical implications and management. Lung Cancer 2024; 187:107445. [PMID: 38157805 DOI: 10.1016/j.lungcan.2023.107445] [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: 10/07/2023] [Revised: 11/18/2023] [Accepted: 12/13/2023] [Indexed: 01/03/2024]
Abstract
OBJECTIVES The grading system proposed by the International Association for the Study of Lung Cancer is based on a combination of predominant histologic subtypes and the proportion of high-grade components with a cutoff of 20%. We aimed to examine the clinical implications of the grading system beyond the discrimination of patient prognosis, while assessing the biological differences among high-grade subtypes. METHODS We retrospectively reviewed 648 consecutive patients with resected lung adenocarcinomas and examined their clinicopathologic, genotypic, and immunophenotypic features and treatment outcomes. Besides the differences among grades, the clinical impact of different high-grade components: micropapillary (MIP) and solid (SOL) patterns, was individually evaluated. RESULTS Survival outcomes were well-stratified according to the grading system. Grade 3 tumors exhibited aggressive clinicopathologic features, while being an independent prognostic factor in multivariable analysis. A small proportion (<20 %) of high-grade components in grade 2 had a negative prognostic impact. The prognostic difference bordering on the 20 % cutoff of the MIP proportion was validated; however, the proportion of SOL component did not affect prognosis. A survival benefit from adjuvant chemotherapy was observed in grade 3 tumors regardless of histologic subtype, but not in grade 1-2 tumors. The molecular and immunophenotypic features were different among grades, but still heterogeneous in grade 3, with MIP harboring frequent EGFR mutation and SOL exhibiting high PD-L1 expression. The treatment outcome after recurrence was worse in grade 3, but tumors with MIP pattern had an equivalent prognosis to that of grade 1-2 tumors, reflecting the high frequency of molecular targeted therapy. CONCLUSIONS In addition to stratifying patient prognosis, the current grading system could discriminate clinical course, therapeutic effects of adjuvant chemotherapy, and molecular and immunophenotypic features. Further stratification based on biological heterogeneity in grade 3 remains necessary to enhance the role of the grading system in guiding patient management.
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Affiliation(s)
- Masashi Mikubo
- Department of Thoracic Surgery, Kitasato University, School of Medicine, 1-15-1 Kitasato Minami-ku, Sagamihara, Kanagawa 252-0374, Japan.
| | - Satoru Tamagawa
- Department of Thoracic Surgery, Kitasato University, School of Medicine, 1-15-1 Kitasato Minami-ku, Sagamihara, Kanagawa 252-0374, Japan
| | - Yasuto Kondo
- Department of Thoracic Surgery, Kitasato University, School of Medicine, 1-15-1 Kitasato Minami-ku, Sagamihara, Kanagawa 252-0374, Japan
| | - Shoko Hayashi
- Department of Thoracic Surgery, Kitasato University, School of Medicine, 1-15-1 Kitasato Minami-ku, Sagamihara, Kanagawa 252-0374, Japan
| | - Dai Sonoda
- Department of Thoracic Surgery, Kitasato University, School of Medicine, 1-15-1 Kitasato Minami-ku, Sagamihara, Kanagawa 252-0374, Japan
| | - Masahito Naito
- Department of Thoracic Surgery, Kitasato University, School of Medicine, 1-15-1 Kitasato Minami-ku, Sagamihara, Kanagawa 252-0374, Japan
| | - Kazu Shiomi
- Department of Thoracic Surgery, Kitasato University, School of Medicine, 1-15-1 Kitasato Minami-ku, Sagamihara, Kanagawa 252-0374, Japan
| | - Masaaki Ichinoe
- Department of Pathology, Kitasato University, School of Medicine, 1-15-1 Kitasato Minami-ku, Sagamihara, Kanagawa 252-0374, Japan
| | - Yukitoshi Satoh
- Department of Thoracic Surgery, Kitasato University, School of Medicine, 1-15-1 Kitasato Minami-ku, Sagamihara, Kanagawa 252-0374, Japan
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17
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Lami K, Ota N, Yamaoka S, Bychkov A, Matsumoto K, Uegami W, Munkhdelger J, Seki K, Sukhbaatar O, Attanoos R, Berezowska S, Brcic L, Cavazza A, English JC, Fabro AT, Ishida K, Kashima Y, Kitamura Y, Larsen BT, Marchevsky AM, Miyazaki T, Morimoto S, Ozasa M, Roden AC, Schneider F, Smith ML, Tabata K, Takano AM, Tanaka T, Tsuchiya T, Nagayasu T, Sakanashi H, Fukuoka J. Standardized Classification of Lung Adenocarcinoma Subtypes and Improvement of Grading Assessment Through Deep Learning. THE AMERICAN JOURNAL OF PATHOLOGY 2023; 193:2066-2079. [PMID: 37544502 DOI: 10.1016/j.ajpath.2023.07.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/18/2023] [Revised: 06/04/2023] [Accepted: 07/12/2023] [Indexed: 08/08/2023]
Abstract
The histopathologic distinction of lung adenocarcinoma (LADC) subtypes is subject to high interobserver variability, which can compromise the optimal assessment of patient prognosis. Therefore, this study developed convolutional neural networks capable of distinguishing LADC subtypes and predicting disease-specific survival, according to the recently established LADC tumor grades. Consensus LADC histopathologic images were obtained from 17 expert pulmonary pathologists and one pathologist in training. Two deep learning models (AI-1 and AI-2) were trained to predict eight different LADC classes. Furthermore, the trained models were tested on an independent cohort of 133 patients. The models achieved high precision, recall, and F1 scores exceeding 0.90 for most of the LADC classes. Clear stratification of the three LADC grades was reached in predicting the disease-specific survival by the two models, with both Kaplan-Meier curves showing significance (P = 0.0017 and 0.0003). Moreover, both trained models showed high stability in the segmentation of each pair of predicted grades with low variation in the hazard ratio across 200 bootstrapped samples. These findings indicate that the trained convolutional neural networks improve the diagnostic accuracy of the pathologist and refine LADC grade assessment. Thus, the trained models are promising tools that may assist in the routine evaluation of LADC subtypes and grades in clinical practice.
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Affiliation(s)
- Kris Lami
- Department of Pathology Informatics, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan
| | - Noriaki Ota
- Systems Research & Development Center, Technology Bureau, NS Solutions Corp., Yokohama, Japan
| | - Shinsuke Yamaoka
- Systems Research & Development Center, Technology Bureau, NS Solutions Corp., Yokohama, Japan
| | - Andrey Bychkov
- Department of Pathology, Kameda Medical Center, Kamogawa, Japan
| | - Keitaro Matsumoto
- Department of Surgical Oncology, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan
| | - Wataru Uegami
- Department of Pathology, Kameda Medical Center, Kamogawa, Japan
| | | | - Kurumi Seki
- Department of Pathology, Kameda Medical Center, Kamogawa, Japan
| | | | - Richard Attanoos
- Department of Cellular Pathology, Cardiff University, Cardiff, United Kingdom
| | - Sabina Berezowska
- Department of Laboratory Medicine and Pathology, Institute of Pathology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Luka Brcic
- Diagnostic and Research Institute of Pathology, Medical University of Graz, Graz, Austria
| | - Alberto Cavazza
- Unit of Pathologic Anatomy, Azienda USL/IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - John C English
- Department of Pathology, Vancouver General Hospital, Vancouver, British Columbia, Canada
| | - Alexandre Todorovic Fabro
- Department of Pathology and Legal Medicine, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil
| | - Kaori Ishida
- Department of Pathology, Kansai Medical University, Hirakata City, Japan
| | - Yukio Kashima
- Department of Pathology, Hyogo Prefectural Awaji Medical Center, Sumoto City, Japan
| | - Yuka Kitamura
- Department of Pathology Informatics, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan; N Lab Co. Ltd., Nagasaki, Japan
| | - Brandon T Larsen
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Scottsdale, Arizona
| | | | - Takuro Miyazaki
- Department of Surgical Oncology, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan
| | - Shimpei Morimoto
- Innovation Platform & Office for Precision Medicine, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan
| | - Mutsumi Ozasa
- Department of Pathology Informatics, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan
| | - Anja C Roden
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota
| | - Frank Schneider
- Department of Pathology and Laboratory Medicine, Emory University, Atlanta, Georgia
| | - Maxwell L Smith
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Scottsdale, Arizona
| | - Kazuhiro Tabata
- Department of Pathology, Kagoshima University Graduate School of Medical and Dental Sciences, Kagoshima, Japan
| | - Angela M Takano
- Department of Anatomical Pathology, Singapore General Hospital, Singapore
| | - Tomonori Tanaka
- Department of Diagnostic Pathology, Kobe University Hospital, Kobe, Japan
| | - Tomoshi Tsuchiya
- Department of Surgical Oncology, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan
| | - Takeshi Nagayasu
- Department of Surgical Oncology, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan
| | - Hidenori Sakanashi
- Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology, Tsukuba, Japan
| | - Junya Fukuoka
- Department of Pathology Informatics, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan; Department of Pathology, Kameda Medical Center, Kamogawa, Japan.
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18
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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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19
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Kawaguchi M, Kato H, Hanamatsu Y, Suto T, Noda Y, Kaneko Y, Iwata H, Hyodo F, Miyazaki T, Matsuo M. Computed Tomography and 18F-Fluorodeoxyglucose-Positron Emission Tomography/Computed Tomography Imaging Biomarkers of Lung Invasive Non-mucinous Adenocarcinoma: Prediction of Grade 3 Tumour Based on World Health Organization Grading System. Clin Oncol (R Coll Radiol) 2023; 35:e601-e610. [PMID: 37587000 DOI: 10.1016/j.clon.2023.08.002] [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: 08/24/2022] [Revised: 06/02/2023] [Accepted: 08/03/2023] [Indexed: 08/18/2023]
Abstract
AIMS To evaluate computed tomography (CT) and 18F-fluorodeoxyglucose-positron emission tomography/computed tomography (18F-FDG-PET/CT) findings of invasive non-mucinous adenocarcinoma (INMA) of the lung as a predictor of histological tumour grade according to 2021 World Health Organization (WHO) classification. MATERIALS AND METHODS This retrospective study included consecutive patients with surgically resected INMA who underwent both preoperative CT and 18F-FDG-PET/CT. A three-tiered tumour grade was performed based on the fifth edition of the WHO classification of lung tumours. CT imaging features and the maximum standardised uptake value (SUVmax) were compared among the three tumour grades. RESULTS In total, 214 patients with INMA (median age 70 years; interquartile range 65-76 years; 123 men) were histologically categorised: 36 (17%) as grade 1, 102 (48%) as grade 2 and 76 (35%) as grade 3. Pure solid appearance was more frequent in grade 3 (83%) than in grades 1 (0%) and 2 (26%) (P < 0.001). The SUVmax of the entire tumour was higher in grade 3 than in grades 1 and 2 (P < 0.001). Multivariable analysis revealed that pure solid appearance (odds ratio = 94.0; P < 0.001), round/oval shape (odds ratio = 4.01; P = 0.001), spiculation (odds ratio = 2.13; P = 0.04), air bronchogram (odds ratio = 0.40; P = 0.03) and SUVmax (odds ratio = 1.45; P < 0.001) were significant predictors for grade 3 INMAs. CONCLUSION Pure solid appearance, round/oval shape, spiculation, absence of air bronchogram and high SUVmax were associated with grade 3 INMAs. CT and 18F-FDG-PET/CT were potentially useful non-invasive imaging methods to predict the histological grade of INMAs.
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Affiliation(s)
- M Kawaguchi
- Department of Radiology, Gifu University, Gifu, Japan.
| | - H Kato
- Department of Radiology, Gifu University, Gifu, Japan
| | - Y Hanamatsu
- Department of Pathology and Translational Research, Gifu University, Gifu, Japan
| | - T Suto
- Department of Radiology, Gifu University, Gifu, Japan
| | - Y Noda
- Department of Radiology, Gifu University, Gifu, Japan
| | - Y Kaneko
- Department of Radiology, Gifu University, Gifu, Japan
| | - H Iwata
- Department of General and Cardiothoracic Surgery, Gifu University, Gifu, Japan
| | - F Hyodo
- Department of Radiology, Frontier Science for Imaging, Gifu University, Gifu, Japan
| | - T Miyazaki
- Department of Pathology, Gifu University, Gifu, Japan
| | - M Matsuo
- Department of Radiology, Gifu University, Gifu, Japan
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20
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Cho IS, Shim HS, Lee HJ, Suh YJ. Clinical implication of the 2020 International Association for the Study of Lung Cancer histologic grading in surgically resected pathologic stage 1 lung adenocarcinomas: Prognostic value and association with computed tomography characteristics. Lung Cancer 2023; 184:107345. [PMID: 37611496 DOI: 10.1016/j.lungcan.2023.107345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 06/21/2023] [Accepted: 08/10/2023] [Indexed: 08/25/2023]
Abstract
OBJECTIVES To investigate the incremental prognostic value of the 2020 International Association for the Study of Lung Cancer (IASLC) histologic grading system over traditional prognosticators in surgically resected pathologic stage 1 lung adenocarcinomas and to identify the clinical and radiologic characteristics of lung adenocarcinomas reclassified by the 2020 histologic grading system. MATERIALS AND METHODS We retrospectively enrolled 356 patients who underwent surgery for pathologic stage 1 adenocarcinoma between January 2016 and December 2017. The histologic grading was classified according to the predominant histologic subtype (conventional system) and the updated 2020 IASLC grading system. The clinical and computed tomography (CT) characteristics were compared according to the reclassification of the updated system. The performance of prognostic models for recurrence-free survival based on the combination of pathologic tumor size, histologic grade, and CT-based information was compared using the c-index. RESULTS Postoperative recurrence occurred in 6.7% of patients during the follow-up period (mean, 1589.2 ± 406.7 days). Fifty-nine of 244 (24.2%) tumors with intermediate grades in the conventional system were reclassified as grade 3 with the updated grading system. They showed significantly larger solid proportions and higher percentages of pure solid nodules on CT compared to tumors without reclassification (n = 185) (P < 0.05). Prognostic prediction models based on pathology tumor size and histologic grades had significantly higher c-indices (0.754-0.803) compared to the model based on pathologic tumor size only (c-index:0.723, P < 0.05). CONCLUSION The 2020 IASLC histologic grading system has significant incremental prognostic value over the pathologic stage in surgically resected pathologic stage 1 lung adenocarcinoma. Reclassified lung adenocarcinomas using the updated grading system have a larger solid proportion and a higher percentage of pure solid nodules on CT.
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Affiliation(s)
- In Sung Cho
- Department of Radiology, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Hyo Sup Shim
- Department of Pathology, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Hye-Jeong Lee
- Department of Radiology, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Young Joo Suh
- Department of Radiology, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea.
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21
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Ohtani-Kim SJY, Taki T, Tane K, Miyoshi T, Samejima J, Aokage K, Nagasaki Y, Kojima M, Sakashita S, Watanabe R, Sakamoto N, Goto K, Tsuboi M, Ishii G. Efficacy of Preoperative Biopsy in Predicting the Newly Proposed Histologic Grade of Resected Lung Adenocarcinoma. Mod Pathol 2023; 36:100209. [PMID: 37149221 DOI: 10.1016/j.modpat.2023.100209] [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: 12/27/2022] [Revised: 04/05/2023] [Accepted: 04/25/2023] [Indexed: 05/08/2023]
Abstract
A novel histologic grading system for invasive lung adenocarcinomas (LUAD) has been newly proposed and adopted by the World Health Organization (WHO) classification. We aimed to evaluate the concordance of newly established grades between preoperative biopsy and surgically resected LUAD samples. Additionally, factors affecting the concordance rate and its prognostic impact were also analyzed. In this study, surgically resected specimens of 222 patients with invasive LUAD and their preoperative biopsies collected between January 2013 and December 2020 were used. We determined the histologic subtypes of preoperative biopsy and surgically resected specimens and classified them separately according to the novel WHO grading system. The overall concordance rate of the novel WHO grades between preoperative biopsy and surgically resected samples was 81.5%, which was higher than that of the predominant subtype. When stratified by grades, the concordance rate of grades 1 (well-differentiated, 84.2%) and 3 (poorly differentiated, 89.1%) was found to be superior compared to grade 2 (moderately differentiated, 66.2%). Overall, the concordance rate was not significantly different from biopsy characteristics, including the number of biopsy samples, biopsy sample size, and tumor area size. On the other hand, the concordance rate of grades 1 and 2 was significantly higher in tumors with smaller invasive diameters, and that of grade 3 was significantly higher in tumors with larger invasive diameters. Preoperative biopsy specimens can predict the novel WHO grades, especially grades 1 and 3 of surgically resected specimens, more accurately than the former grading system, regardless of preoperative biopsy or clinicopathologic characteristics.
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Affiliation(s)
- Seiyu Jeong-Yoo Ohtani-Kim
- Department of Pathology and Clinical Laboratories, National Cancer Center Hospital East, Kashiwa, Chiba, Japan; Department of Thoracic Surgery, National Cancer Center Hospital East, Kashiwa, Chiba, Japan; Course of Advanced Clinical Research of Cancer, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Tetsuro Taki
- Department of Pathology and Clinical Laboratories, National Cancer Center Hospital East, Kashiwa, Chiba, Japan.
| | - Kenta Tane
- Department of Thoracic Surgery, National Cancer Center Hospital East, Kashiwa, Chiba, Japan
| | - Tomohiro Miyoshi
- Department of Thoracic Surgery, National Cancer Center Hospital East, Kashiwa, Chiba, Japan
| | - Joji Samejima
- Department of Thoracic Surgery, National Cancer Center Hospital East, Kashiwa, Chiba, Japan
| | - Keiju Aokage
- Department of Thoracic Surgery, National Cancer Center Hospital East, Kashiwa, Chiba, Japan
| | - Yusuke Nagasaki
- Department of Pathology and Clinical Laboratories, National Cancer Center Hospital East, Kashiwa, Chiba, Japan; Department of Thoracic Surgery, National Cancer Center Hospital East, Kashiwa, Chiba, Japan; Course of Advanced Clinical Research of Cancer, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Motohiro Kojima
- Department of Pathology and Clinical Laboratories, National Cancer Center Hospital East, Kashiwa, Chiba, Japan
| | - Shingo Sakashita
- Department of Pathology and Clinical Laboratories, National Cancer Center Hospital East, Kashiwa, Chiba, Japan
| | - Reiko Watanabe
- Department of Pathology and Clinical Laboratories, National Cancer Center Hospital East, Kashiwa, Chiba, Japan
| | - Naoya Sakamoto
- Department of Pathology and Clinical Laboratories, National Cancer Center Hospital East, Kashiwa, Chiba, Japan
| | - Koichi Goto
- Department of Thoracic Oncology, National Cancer Center Hospital East, Kashiwa, Japan
| | - Masahiro Tsuboi
- Department of Thoracic Surgery, National Cancer Center Hospital East, Kashiwa, Chiba, Japan
| | - Genichiro Ishii
- Department of Pathology and Clinical Laboratories, National Cancer Center Hospital East, Kashiwa, Chiba, Japan; Course of Advanced Clinical Research of Cancer, Juntendo University Graduate School of Medicine, Tokyo, Japan
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22
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Yang Z, Cai Y, Chen Y, Ai Z, Chen F, Wang H, Han Q, Feng Q, Xiang Z. A CT-Based Radiomics Nomogram Combined with Clinic-Radiological Characteristics for Preoperative Prediction of the Novel IASLC Grading of Invasive Pulmonary Adenocarcinoma. Acad Radiol 2023; 30:1946-1961. [PMID: 36567145 DOI: 10.1016/j.acra.2022.12.006] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Revised: 11/24/2022] [Accepted: 12/03/2022] [Indexed: 12/25/2022]
Abstract
RATIONALE AND OBJECTIVES The novel International Association for the Study of Lung Cancer (IASLC) grading system of invasive lung adenocarcinoma (ADC) demonstrated a remarkable prognostic effect and enabled numerous patients to benefit from adjuvant chemotherapy. We sought to build a CT-based nomogram for preoperative prediction of the IASLC grading. MATERIALS AND METHODS This work retrospectively analyzed the CT images and clinical data of 303 patients with pathologically confirmed invasive ADC. The histological subtypes and radiological characteristics of the patients were re-evaluated. Radiomics features were extracted, and the optimal subset of features was established by ANOVA, spearman correlation analysis, and the least absolute shrinkage and selection operator (LASSO). Univariate and multivariate analyses identified the independent clinical and radiological variables. Finally, multivariate logistic regression analysis incorporated clinical, radiological, and optimal radiomics features into the nomogram. Receiver operating characteristic (ROC) curve, and accuracy were applied to assess the model's performance. Decision curve analysis (DCA), and calibration curve were applied to assess the clinical usefulness. RESULTS Nine selected CT image features were used to develop the radiomics model. The accuracy, precision, sensitivity, and specificity of the radiomics model outperformed the clinic-radiological model in the training and testing sets. Integrating Radscore with independent radiological characteristics showed higher prediction performance than clinic-radiological characteristics alone in the training (AUC, 0.915 vs. 0.882; DeLong, p < 0.05) and testing (AUC, 0.838 vs. 0.782; DeLong, p < 0.05) sets. Good calibration and decision curve analysis demonstrated the clinical usefulness of the nomogram. CONCLUSION Radiomics features effectively predict high-grade ADC. The combined nomogram may facilitate selecting patients who benefit from adjuvant treatment.
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Affiliation(s)
- Zhihe Yang
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, GD, P.R. China,(Z.Y.,Y.C.,Y.C.,Z.A.,Q.H.,Z.X.); School of Life Sciences, South China Normal University, Guangzhou, GD, P.R.China,(Z.Y.,Q.F.)
| | - Yuqin Cai
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, GD, P.R. China,(Z.Y.,Y.C.,Y.C.,Z.A.,Q.H.,Z.X.)
| | - Yirong Chen
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, GD, P.R. China,(Z.Y.,Y.C.,Y.C.,Z.A.,Q.H.,Z.X.)
| | - Zhu Ai
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, GD, P.R. China,(Z.Y.,Y.C.,Y.C.,Z.A.,Q.H.,Z.X.)
| | - Fang Chen
- Department of Pathology, Guangzhou Panyu Central Hospital, Guangzhou, GD, P.R.China,(F.C.,H.W.)
| | - Hao Wang
- Department of Pathology, Guangzhou Panyu Central Hospital, Guangzhou, GD, P.R.China,(F.C.,H.W.)
| | - Qijia Han
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, GD, P.R. China,(Z.Y.,Y.C.,Y.C.,Z.A.,Q.H.,Z.X.)
| | - Qili Feng
- School of Life Sciences, South China Normal University, Guangzhou, GD, P.R.China,(Z.Y.,Q.F.)
| | - Zhiming Xiang
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, GD, P.R. China,(Z.Y.,Y.C.,Y.C.,Z.A.,Q.H.,Z.X.).
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23
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Zhang Y, Hu Y, Zhang S, Zhu M, Lu J, Hu B, Guo X, Zhang Y. Effects of pre-operative biopsy on recurrence and survival in stage I lung adenocarcinoma patients in China. ERJ Open Res 2023; 9:00675-2022. [PMID: 37583968 PMCID: PMC10423981 DOI: 10.1183/23120541.00675-2022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 05/04/2023] [Indexed: 08/17/2023] Open
Abstract
Background Whether pre-operative biopsy affects post-operative recurrence and metastasis of lung cancer patients is still controversial. Methods In order to clarify these disputes, we collected relevant literature to conduct a meta-analysis. To validate the results of the meta-analysis, we retrospectively analysed 575 patients with stage I lung adenocarcinoma who underwent surgical resection at our centre from 2010 to 2018 using propensity score matching and competing risk models. Results 5509 lung cancer patients from 11 articles were included in the meta-analysis. Summary analysis showed that the total recurrence rate of the biopsy group was higher than that of the nonbiopsy group (risk ratio 1.690, 95% CI 1.220-2.330; p=0.001). After propensity score matching, we found that there was no significant correlation between biopsy and total recurrence (risk ratio 1.070, 95% CI 0.540-2.120; p=0.850). In our cohort, of 575 stage I lung adenocarcinomas, 113 (19.7%) patients underwent pre-operative biopsy. During a median (interquartile range) follow-up of 71 (57-93) months, multivariable analyses showed pre-operative biopsy in the overall observation cohort (subdistribution hazard ratio (SHR) 1.522, 95% CI 0.997-2.320; p=0.051) and in the propensity score-matched cohort (SHR 1.134, 95% CI 0.709-1.810; p=0.600) was not significantly correlated with the risk of recurrence and metastasis. Moreover, the pre-operative biopsy did not affect disease-free survival (SHR 0.853, 95% CI 0.572-1.273; p=0.438) or overall survival (SHR 0.647, 95% CI 0.352-1.189; p=0.161). Conclusion Pre-operative biopsy might not increase the risk of recurrence and metastasis, suggesting that these procedures might be safe for patients with stage I lung adenocarcinoma whose diagnosis is difficult to determine before surgery.
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Affiliation(s)
- Yuan Zhang
- Department of Respiratory and Critical Care Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing Institute of Respiratory Medicine, Beijing, China
- These authors contributed equally
| | - Yi Hu
- Department of Respiratory and Critical Care Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing Institute of Respiratory Medicine, Beijing, China
- These authors contributed equally
| | - Shu Zhang
- Department of Respiratory and Critical Care Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing Institute of Respiratory Medicine, Beijing, China
| | - Min Zhu
- Department of Respiratory and Critical Care Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing Institute of Respiratory Medicine, Beijing, China
| | - Jun Lu
- Department of Pathology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Bin Hu
- Department of Thoracic Surgery, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Xiaojuan Guo
- Department of Radiology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Yuhui Zhang
- Department of Respiratory and Critical Care Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing Institute of Respiratory Medicine, Beijing, China
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24
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Bossé Y, Gagné A, Althakfi WA, Orain M, Couture C, Trahan S, Pagé S, Joubert D, Fiset PO, Desmeules P, Joubert P. A Simplified Version of the IASLC Grading System for Invasive Pulmonary Adenocarcinomas With Improved Prognosis Discrimination. Am J Surg Pathol 2023; 47:686-693. [PMID: 37032554 PMCID: PMC10174103 DOI: 10.1097/pas.0000000000002040] [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: 04/11/2023]
Abstract
Tumor grading enables better management of patients and treatment options. The International Association for the Study of Lung Cancer (IASLC) Pathology Committee has recently released a 3-tier grading system for invasive pulmonary adenocarcinoma consisting of predominant histologic patterns plus a cutoff of 20% of high-grade components including solid, micropapillary, and complex glandular patterns. The goal of this study was to validate the prognostic value of the new IASLC grading system and to compare its discriminatory performance to the predominant pattern-based grading system and a simplified version of the IASLC grading system without complex glandular patterns. This was a single-site retrospective study based on a 20-year data collection of patients that underwent lung cancer surgery. All invasive pulmonary adenocarcinomas confirmed by the histologic review were evaluated in a discovery cohort (n=676) and a validation cohort (n=717). The median duration of follow-up in the combined dataset (n=1393) was 7.5 years. The primary outcome was overall survival after surgery. The 3 grading systems had strong and relatively similar predictive performance, but the best parsimonious model was the simplified IASLC grading system (log-rank P =1.39E-13). The latter was strongly associated with survival in the validation set ( P =1.1E-18) and the combined set ( P =5.01E-35). We observed a large proportion of patients upgraded to the poor prognosis group using the IASLC grading system, which was attenuated when using the simplified IASLC grading system. In conclusion, we identified a histologic simpler classification for invasive pulmonary adenocarcinomas that outperformed the recently proposed IASLC grading system. A simplified grading system is clinically convenient and will facilitate widespread implementation.
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Affiliation(s)
- Yohan Bossé
- Institut universitaire de cardiologie et de pneumologie de Québec—Université Laval
- Department of Molecular Medicine, Laval University, Quebec City
| | - Andréanne Gagné
- Institut universitaire de cardiologie et de pneumologie de Québec—Université Laval
| | - Wajd A. Althakfi
- Department of Pathology, King Saud University, Riyadh, Saudi Arabia
| | - Michèle Orain
- Institut universitaire de cardiologie et de pneumologie de Québec—Université Laval
| | - Christian Couture
- Institut universitaire de cardiologie et de pneumologie de Québec—Université Laval
| | - Sylvain Trahan
- Institut universitaire de cardiologie et de pneumologie de Québec—Université Laval
| | - Sylvain Pagé
- Institut universitaire de cardiologie et de pneumologie de Québec—Université Laval
| | - David Joubert
- Faculty of Social Sciences, University of Ottawa, Ottawa, Canada
| | - Pierre O. Fiset
- Department of Pathology, McGill University Health Center, Montreal, QC
| | - Patrice Desmeules
- Institut universitaire de cardiologie et de pneumologie de Québec—Université Laval
| | - Philippe Joubert
- Institut universitaire de cardiologie et de pneumologie de Québec—Université Laval
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25
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Woo W, Cha YJ, Park CH, Moon DH, Lee S. Predictive scoring of high-grade histology among early-stage lung cancer patients: The MOSS score. Thorac Cancer 2023. [PMID: 37201906 DOI: 10.1111/1759-7714.14932] [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] [Received: 03/12/2023] [Revised: 05/01/2023] [Accepted: 05/04/2023] [Indexed: 05/20/2023] Open
Abstract
BACKGROUND Poor prognosis associated with adenocarcinoma of International Association for the Study of Lung Cancer (IASLC) grade 3 has been recognized. In this study we aimed to develop a scoring system for predicting IASLC grade 3 based before surgery. METHODS Two retrospective datasets with significant heterogeneity were used to develop and evaluate a scoring system. The development set was comprised of patients with pathological stage I nonmucinous adenocarcinoma and they were randomly divided into training (n = 375) and validation (n = 125) datasets. Using multivariate logistic regression, a scoring system was developed and internally validated. Later, this new score was further tested in the testing set which was comprised of patients with clinical stage 0-I non-small cell lung cancer (NSCLC) (n = 281). RESULTS Four factors that were related to IASLC grade 3 were used to develop the new scoring system the MOSS score; male (M, point 1), overweight (O, point 1), size>10 mm (S, point 1), and solid lesions (S, point 3). Predictability of IASLC grade 3 increased from 0.4% to 75.2% with scores from 0 to 6. The area under the curve (AUC) of the MOSS was 0.889 and 0.765 for the training and validation datasets, respectively. The MOSS score exhibited similar predictability in the testing set (AUC: 0.820). CONCLUSION The MOSS score, which combines preoperative variables, can be used to identify high-risk early-stage NSCLC patients with aggressive histological features. It can support clinicians in determining a treatment plan and surgical extent. Further refinement of this scoring system with prospective validation is needed.
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Affiliation(s)
- Wongi Woo
- Department of Thoracic and Cardiovascular Surgery, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Yoon Jin Cha
- Department of Pathology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Chul Hwan Park
- Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Duk Hwan Moon
- Department of Thoracic and Cardiovascular Surgery, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Sungsoo Lee
- Department of Thoracic and Cardiovascular Surgery, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
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26
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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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She Y, Zhong Y, Hou L, Zhao S, Zhang L, Xie D, Zhu Y, Wu C, Chen C. Application of the Novel Grading System of Invasive Pulmonary Adenocarcinoma in a Real Diagnostic Scenario: A Brief Report of 9353 Cases. JTO Clin Res Rep 2023; 4:100465. [PMID: 36895916 PMCID: PMC9988662 DOI: 10.1016/j.jtocrr.2023.100465] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 01/15/2023] [Accepted: 01/17/2023] [Indexed: 01/26/2023] Open
Abstract
Introduction The International Association for the Study of Lung Cancer proposed a novel grading system of invasive pulmonary adenocarcinoma (IPA), but the application of this grading system and its genotypic characterization in the real diagnostic scenario has never been reported. Methods We prospectively collected and analyzed the clinicopathological and genotypic features of a cohort of 9353 consecutive patients with resected IPA, including 7134 patients with detection of common driver mutation. Results In the entire cohort, 3 (0.3%) of lepidic, 1207 (19.0%) of acinar, and 126 (23.6%) of papillary predominant IPAs were diagnosed as grade 3. In chronological order, an evident downtrend of the proportion of grade 2 was observed in chronological order. Conversely, the diagnostic ratio of grade 1 (8.0%-14.5%) and grade 3 (27.9%-32.3%) experienced a gradual rise. EGFR mutation was more frequently detected in grade 2 (77.5%) and grade 1 (69.7%) IPA than grade 3 (53.7%, p < 0.001), whereas the mutation rates of KRAS, BRAF, ALK, and ROS1 were higher in grade 3 IPA. More importantly, the rate of EGFR mutation gradually fell as the proportion of high-grade components increased, to 24.3% in IPA with more than 90% high-grade components. Conclusions The grading system for IPA could be applied to stratify patients with different clinicopathological and genotypic features in a real diagnostic scenario.
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Affiliation(s)
- Yunlang She
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China
| | - Yifan Zhong
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China
| | - Likun Hou
- Department of Pathology, 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
| | - Liping Zhang
- Department of Pathology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China
| | - Dong Xie
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China
| | - Yuming Zhu
- 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
| | - Chang Chen
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China
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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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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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E H, Wu J, Ren Y, Xia L, Xu L, Li S, Zhao Y, Li C, She Y, Su C, Wu C, Hou L, Zhao D, Chen C. The IASLC grading system for invasive pulmonary adenocarcinoma: a potential prognosticator for patients receiving neoadjuvant therapy. Ther Adv Med Oncol 2023; 15:17588359221148028. [PMID: 36643658 PMCID: PMC9837269 DOI: 10.1177/17588359221148028] [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: 05/28/2022] [Accepted: 12/12/2022] [Indexed: 01/13/2023] Open
Abstract
Background Grading system for resected invasive pulmonary adenocarcinoma proposed by the International Association for the Study of Lung Cancer (IASLC) was validated as a strong prognostic indicator. Nonetheless, the efficacy of utilizing such grading system in prognostic assessment of patients receiving neoadjuvant therapy still needs elucidating. Methods A retrospective study was conducted including patients with resected adenocarcinoma following neoadjuvant chemotherapy or targeted therapy from August 2012 to December 2020 in Shanghai Pulmonary Hospital. All the surgical specimens were re-evaluated and graded. The prognostic value of the grading system was further validated. Results Ultimately, a total of 198 patients were enrolled in this study, and subdivided into three cohorts according to the grading system. There were 13 (6.6%), 37 (18.7%), and 148 (74.7%) patients belonging to Grades 1, 2, and 3, respectively. IASLC grading system demonstrated significant power in prognosis differentiation of the entire cohort [recurrence-free survival (RFS), p < 0.001; overall survival (OS), p < 0.001] and the neoadjuvant chemotherapy and targeted therapy cohorts separately, and was further verified as a significant prognostic indicator for RFS and OS in multivariable Cox analysis. Since the majority of the patients (84.8%) did not achieve major pathologic response (MPR), representing a wide spectrum of survival, the prognostic value of grading system in non-MPR cohort was further evaluated. Similar results were also obtained that IASLC grading system was assessed significant in univariable analysis of RFS (p < 0.001) and univariable analysis of OS (p = 0.001). Conclusions The prognostic efficacy of pathological evaluation of the residual proportion of pulmonary adenocarcinoma post-neoadjuvant therapy using IASLC grading system was preliminarily verified. Such grading system might assist prognostic evaluation of neoadjuvant cohort other than traditional pathological parameters.
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Affiliation(s)
| | | | | | - Lang Xia
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Long Xu
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Shaoling Li
- Department of Pathology, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Yue Zhao
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Chongwu Li
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Yunlang She
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Chunxia Su
- Department of Medical Oncology, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Chunyan Wu
- Department of Pathology, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China
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Proposal of a revised International Association for the Study of Lung Cancer grading system in pulmonary non-mucinous adenocarcinoma: The importance of the lepidic proportion. Lung Cancer 2023; 175:1-8. [PMID: 36436241 DOI: 10.1016/j.lungcan.2022.11.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2022] [Revised: 09/01/2022] [Accepted: 11/02/2022] [Indexed: 11/09/2022]
Abstract
OBJECTIVES We aimed to measure the validity of the International Association for the Study of Lung Cancer (IASLC) grading system in Korean patients and propose a modification for an increase of its predictability, especially in grade 2 patients. MATERIALS AND METHODS From 2012 to 2017, histopathologic characteristics of 1358 patients with invasive pulmonary adenocarcinoma (stage I-III) from two institutions were retrospectively reviewed and re-classified according to the IASLC grading system. Considering the amount of the lepidic proportion, the validity of the revised model (Lepidic-10), derived from the training cohort (hospital A), was measured using the validation cohort (hospital B). Its predictability was compared to that of the IASLC system. RESULTS Of the 1358 patients, 259 had a recurrence, and 189 died during follow-up. The Harrell's concordance index and area under the curve of the IASLC system were 0.685 and 0.699 for recurrence-free survival (RFS) and 0.669 and 0.679 for death, respectively. From the training cohort, the IASLC grade 2 patients were divided into grades 2a and 2b (Lepidic-10 model) with a 10 % lepidic pattern. This new model further distinguished patients in both institutions that had better performance than the IASLC grading (Hospital A, p < 0.001 for RFS and death; Hospital B, p = 0.0215 for RFS, p = 0.0429 for death). CONCLUSION The IASLC grading system was easily applicable; its clinical use in predicting the prognosis of Korean patients with pulmonary adenocarcinoma was validated. Furthermore, the introduction of the lepidic proportion as an additional criterion to differentiate grade 2 patients improved its predictability.
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Zhang Y, Zhang Y, Hu Y, Zhang S, Zhu M, Hu B, Guo X, Lu J, Zhang Y. Validation of the novel International Association for the Study of Lung Cancer grading system and prognostic value of filigree micropapillary and discohesive growth pattern in invasive pulmonary adenocarcinoma. Lung Cancer 2023; 175:79-87. [PMID: 36481678 DOI: 10.1016/j.lungcan.2022.11.022] [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: 08/05/2022] [Revised: 11/18/2022] [Accepted: 11/30/2022] [Indexed: 12/12/2022]
Abstract
INTRODUCTION The Pathology Committee of the International Association for the Study of Lung Cancer (IASLC) proposed a new histological grading system based on the combination of predominant and high-grade patterns in 2020. MATERIALS AND METHODS Pathological sections from 631 patients with stage I-III invasive lung adenocarcinoma were reviewed. We then determined the histological grade according to the new grading system and confirmed the pathological features that included the filigree micropapillary and discohesive growth pattern. Applying of the novel IASLC grading system in prognosis stratification was verified and the clinical significance of the pathological characteristics was explored. RESULTS Cox multivariable analysis revealed that in the stage I-III invasive lung adenocarcinoma, the IASLC grading system was significantly associated with disease-free survival (DFS) [hazard ratio (HR) = 1.419; 95 % confidence interval (CI): 1.040-1.937; P = 0.027] and overall survival (OS) (HR = 1.899; 95 % CI: 1.168-3.087; P = 0.010). In patients with IASLC Grades 1 and 2, the simultaneous presence of filigree micropapillary and discohesive growth pattern was significantly correlated with DFS (HR = 1.899; 95 % CI:1.168-3.087; P = 0.010). However, the filigree micropapillary and discohesive growth pattern did not affect the OS (HR = 2.786; P = 0.317). The competitive risk model revealed that in the stage I cohort, the simultaneous presence of filigree micropapillary and discohesive growth pattern was a risk factor for recurrence and metastasis [sub- distribution HR (SHR) = 1.987; 95 %CI: 1.122-3.518; P = 0.019]. CONCLUSION Our study verified that the new prognostic stratification system was an effective stratification tool. Filigree micropapillary and discohesive growth pattern may also be risk factors for DFS, postoperative recurrence and metastasis.
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Affiliation(s)
- Yuan Zhang
- Department of Respiratory and Critical Care Medicine, Beijing Institute of Respiratory Medicine and Beijing Chao Yang Hospital, Capital Medical University, Beijing, China
| | - Yanjun Zhang
- Department of Pathology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Yi Hu
- Department of Respiratory and Critical Care Medicine, Beijing Institute of Respiratory Medicine and Beijing Chao Yang Hospital, Capital Medical University, Beijing, China
| | - Shu Zhang
- Department of Respiratory and Critical Care Medicine, Beijing Institute of Respiratory Medicine and Beijing Chao Yang Hospital, Capital Medical University, Beijing, China
| | - Min Zhu
- Department of Respiratory and Critical Care Medicine, Beijing Institute of Respiratory Medicine and Beijing Chao Yang Hospital, Capital Medical University, Beijing, China
| | - Bin Hu
- Department of Thoracic Surgery, Beijing Institute of Respiratory Medicine and Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Xiaojuan Guo
- Department of Radiology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Jun Lu
- Department of Pathology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China.
| | - Yuhui Zhang
- Department of Respiratory and Critical Care Medicine, Beijing Institute of Respiratory Medicine and Beijing Chao Yang Hospital, Capital Medical University, Beijing, China.
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Woo W, Yang YH, Cha YJ, Moon DH, Shim HS, Cho A, Kim BJ, Kim HE, Park BJ, Lee JG, Kim DJ, Paik HC, Lee S, Lee CY. Prognosis of resected invasive mucinous adenocarcinoma compared with the IASLC histologic grading system for invasive nonmucinous adenocarcinoma: Surgical database study in the TKIs era in Korea. Thorac Cancer 2022; 13:3310-3321. [PMID: 36345148 PMCID: PMC9715870 DOI: 10.1111/1759-7714.14687] [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: 08/30/2022] [Revised: 09/26/2022] [Accepted: 09/28/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The prognosis of invasive mucinous adenocarcinoma (IMA) remains controversial and should be clarified by comparison with the International Association for the Study of Lung Cancer (IASLC) histologic grading system for invasive nonmucinous adenocarcinoma (INMA). METHODS This study included patients with IMA who underwent curative resection. Their clinicopathological outcomes were compared with those of patients with INMA. Propensity score matching was performed to compare the prognosis of IMA with IASLC grade 2 or 3. Kaplan-Meier survival curves and log-rank tests were used to analyze recurrence-free survival (RFS) and overall survival (OS). RESULTS The prognoses of IMA and IASLC grade 2 were similar in terms of RFS and OS. Although patients with IMA had better RFS than patients with IASLC grade 3, the OS was not significantly different. After propensity score matching, IMA demonstrated similar RFS to IASLC grade 2 but superior to IASLC grade 3; there was no difference in the OS compared with grades 2/3. Multivariate analysis revealed that tumor size (hazard ratio [HR] = 1.20, p = 0.028), lymphovascular invasion (HR = 127.5, p = 0.003), and maximum standardized uptake value (HR = 1.24, p = 0.005) were poor prognostic predictors for RFS. Patients with IMA demonstrated RFS similar to and significantly better than that of patients with IASLC grades 2 and 3, respectively. For OS, IMA prognosis was between that of IASLC grades 2 and 3. CONCLUSIONS Since the prognosis of IMA among lung adenocarcinomas appears to be relatively worse, further clinical studies investigating IMA-specific treatment and follow-up plans are necessary to draw more inferences.
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Affiliation(s)
- Wongi Woo
- Department of Thoracic and Cardiovascular Surgery, Gangnam Severance HospitalYonsei University College of MedicineSeoulRepublic of Korea
| | - Young Ho Yang
- Department of Thoracic and Cardiovascular Surgery, Severance HospitalYonsei University College of MedicineSeoulRepublic of Korea
| | - Yoon Jin Cha
- Department of Pathology, Gangnam Severance HospitalYonsei University College of MedicineSeoulRepublic of Korea
| | - Duk Hwan Moon
- Department of Thoracic and Cardiovascular Surgery, Gangnam Severance HospitalYonsei University College of MedicineSeoulRepublic of Korea
| | - Hyo Sup Shim
- Department of Pathology, Severance HospitalYonsei University College of MedicineSeoulRepublic of Korea
| | - Arthur Cho
- Department of Nuclear Medicine, Severance HospitalYonsei University College of MedicineSeoulRepublic of Korea
| | - Bong Jun Kim
- Department of Thoracic SurgeryNational Health Insurance Service Ilsan HospitalGoyangRepublic of Korea
| | - Ha Eun Kim
- Department of Thoracic and Cardiovascular Surgery, Severance HospitalYonsei University College of MedicineSeoulRepublic of Korea
| | - Byung Jo Park
- Department of Thoracic and Cardiovascular Surgery, Severance HospitalYonsei University College of MedicineSeoulRepublic of Korea
| | - Jin Gu Lee
- Department of Thoracic and Cardiovascular Surgery, Severance HospitalYonsei University College of MedicineSeoulRepublic of Korea
| | - Dae Joon Kim
- Department of Thoracic and Cardiovascular Surgery, Severance HospitalYonsei University College of MedicineSeoulRepublic of Korea
| | - Hyo Chae Paik
- Department of Thoracic and Cardiovascular Surgery, Severance HospitalYonsei University College of MedicineSeoulRepublic of Korea
| | - Sungsoo Lee
- Department of Thoracic and Cardiovascular Surgery, Gangnam Severance HospitalYonsei University College of MedicineSeoulRepublic of Korea
| | - Chang Young Lee
- Department of Thoracic and Cardiovascular Surgery, Severance HospitalYonsei University College of MedicineSeoulRepublic of Korea
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Pan X, Lin H, Han C, Feng Z, Wang Y, Lin J, Qiu B, Yan L, Li B, Xu Z, Wang Z, Zhao K, Liu Z, Liang C, Chen X, Li Z, Cui Y, Lu C, Liu Z. Computerized tumor-infiltrating lymphocytes density score predicts survival of patients with resectable lung adenocarcinoma. iScience 2022; 25:105605. [PMID: 36505920 PMCID: PMC9730047 DOI: 10.1016/j.isci.2022.105605] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 10/23/2022] [Accepted: 11/14/2022] [Indexed: 11/17/2022] Open
Abstract
A high abundance of tumor-infiltrating lymphocytes (TILs) has a positive impact on the prognosis of patients with lung adenocarcinoma (LUAD). We aimed to develop and validate an artificial intelligence-driven pathological scoring system for assessing TILs on H&E-stained whole-slide images of LUAD. Deep learning-based methods were applied to calculate the densities of lymphocytes in cancer epithelium (DLCE) and cancer stroma (DLCS), and a risk score (WELL score) was built through linear weighting of DLCE and DLCS. Association between WELL score and patient outcome was explored in 793 patients with stage I-III LUAD in four cohorts. WELL score was an independent prognostic factor for overall survival and disease-free survival in the discovery cohort and validation cohorts. The prognostic prediction model-integrated WELL score demonstrated better discrimination performance than the clinicopathologic model in the four cohorts. This artificial intelligence-based workflow and scoring system could promote risk stratification for patients with resectable LUAD.
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Affiliation(s)
- Xipeng Pan
- Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou 510080, China,Guangdong Cardiovascular Institute, 106 Zhongshan Er Road, Guangzhou 510080, China,Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China,School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China
| | - Huan Lin
- Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou 510080, China,Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China,School of Medicine, South China University of Technology, Guangzhou 510006, China
| | - Chu Han
- Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou 510080, China,Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China
| | - Zhengyun Feng
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China
| | - Yumeng Wang
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China
| | - Jiatai Lin
- Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou 510080, China,Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China
| | - Bingjiang Qiu
- Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou 510080, China,Guangdong Cardiovascular Institute, 106 Zhongshan Er Road, Guangzhou 510080, China,Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China
| | - Lixu Yan
- Department of Pathology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China
| | - Bingbing Li
- Department of Pathology, Guangdong Provincial People’s Hospital Ganzhou Hospital (Ganzhou Municipal Hospital), 49 Dagong Road, Ganzhou 341000, China
| | - Zeyan Xu
- Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou 510080, China,Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China,School of Medicine, South China University of Technology, Guangzhou 510006, China
| | - Zhizhen Wang
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China
| | - Ke Zhao
- Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou 510080, China,Guangdong Cardiovascular Institute, 106 Zhongshan Er Road, Guangzhou 510080, China,Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China
| | - Zhenbing Liu
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China
| | - Changhong Liang
- Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou 510080, China,Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China
| | - Xin Chen
- Department of Radiology, Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, Guangzhou 510180, China,Corresponding author
| | - Zhenhui Li
- Guangdong Cardiovascular Institute, 106 Zhongshan Er Road, Guangzhou 510080, China,Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China,Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming 650118, China,Corresponding author
| | - Yanfen Cui
- Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou 510080, China,Guangdong Cardiovascular Institute, 106 Zhongshan Er Road, Guangzhou 510080, China,Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China,Department of Radiology, Shanxi Province Cancer Hospital, Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan 030013, China,Corresponding author
| | - Cheng Lu
- Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou 510080, China,Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China,Corresponding author
| | - Zaiyi Liu
- Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou 510080, China,Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China,Corresponding author
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Woo W, Cha YJ, Kim BJ, Moon DH, Lee S. Validation Study of New IASLC Histology Grading System in Stage I Non-Mucinous Adenocarcinoma Comparing With Minimally Invasive Adenocarcinoma. Clin Lung Cancer 2022; 23:e435-e442. [PMID: 35945128 DOI: 10.1016/j.cllc.2022.06.004] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Revised: 05/08/2022] [Accepted: 06/06/2022] [Indexed: 01/27/2023]
Abstract
BACKGROUND A new histologic grading system for pulmonary non-mucinous invasive adenocarcinoma was proposed by the International Association for the Study of Lung Cancer (IASLC). We evaluated its clinical impact on prognosis in stage I patients, including minimally invasive adenocarcinoma (MIA). PATIENTS AND METHODS 919 patients underwent surgery for lung adenocarcinoma between 2012 and 2019. Stage I patients (n = 500) were retrospectively reviewed. They were divided into 4 categories: MIA and 3 new IASLC grades (grades 1-3). Cox proportional hazards analysis was performed to identify risk factors associated with recurrence and mortality. Furthermore, we compared the predictability of the IASLC grading system with different models that are based on the clinicopathologic characteristics (baseline model), TNM staging, and predominant histologic pattern. The area under the receiver operating characteristic curve (AUC) was calculated for comparison. RESULTS Recurrence-free survival (RFS) and overall survival (OS) were significantly stratified by the IASLC grading system in patients with stage I adenocarcinoma (P < .001 and P = .003, respectively). In multivariate analyses, IASLC grade 3 was a significant factor for RFS (hazard ratio [HR] 3.18, P < .001) and OS (HR 2.31, P = .013). The AUCs of the new IASLC model were 0.781 for recurrence and 0.770 for mortality, compared with those of the predominant pattern (0.769 for recurrence, 0.747 for death) and TNM staging (0.762 for recurrence, 0.747 for death). CONCLUSION The IASLC grading system effectively predicted the prognosis of early-stage adenocarcinoma compared with previous models. The IASLC classification appears to improve the current system; therefore, precise pathologic examination for early-stage adenocarcinoma is warranted.
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Affiliation(s)
- Wongi Woo
- Department of Thoracic and Cardiovascular Surgery, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Yoon-Jin Cha
- Department of Pathology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Bong Jun Kim
- Department of Thoracic and Cardiovascular Surgery, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Duk Hwan Moon
- Department of Thoracic and Cardiovascular Surgery, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea.
| | - Sungsoo Lee
- Department of Thoracic and Cardiovascular Surgery, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
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Yanagawa N, Sugai M, Shikanai S, Sugimoto R, Osakabe M, Uesugi N, Saito H, Maemondo M, Sugai T. The new IASLC grading system for invasive non-mucinous lung adenocarcinoma is a more useful indicator of patient survival compared with previous grading systems. J Surg Oncol 2022; 127:174-182. [PMID: 36098331 DOI: 10.1002/jso.27091] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Revised: 06/17/2022] [Accepted: 09/01/2022] [Indexed: 11/07/2022]
Abstract
BACKGROUND The International Association for the Study of Lung Cancer (IASLC) Pathology Committee recently proposed a new histological grading system for invasive lung adenocarcinoma (ADC). This study evaluated the usefulness of this grading system. METHODS A total of 395 patients with ADC were examined. ADCs were reclassified based on comprehensive histological subtyping according to the IASLC grading system. We evaluated the following histological grading systems for invasive ADC: the architectural (Arch), Sica's grading, and IASLC grading systems. Multivariate analyses of overall and recurrence-free survival (RFS) based on these three grading systems were performed using Cox proportional hazards models. RESULTS Multivariate analysis showed that all three grading systems were useful for predicting the outcomes of patients at all stages. However, the IASLC grading system was superior to the Arch and Sica's grading systems in differentiating grade 3 from grade 1 ADCs in terms of both overall survivals (IASLC vs. Arch vs. Sica's grading systems: hazard ratio [HR] = 3.77 vs. 3.03 vs. 2.63) and RFS (HR = 4.25 vs. 2.69 vs. 2.4). CONCLUSION The newly proposed IASLC grading system was useful for predicting patient outcomes and was superior to the other grading systems in detecting high-grade malignancy.
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Affiliation(s)
- Naoki Yanagawa
- Department of Molecular Diagnostic Pathology, Iwate Medical University, Shiwa-gun, Japan
| | - Mayu Sugai
- Department of Molecular Diagnostic Pathology, Iwate Medical University, Shiwa-gun, Japan
| | - Shunsuke Shikanai
- Department of Molecular Diagnostic Pathology, Iwate Medical University, Shiwa-gun, Japan
| | - Ryo Sugimoto
- Department of Molecular Diagnostic Pathology, Iwate Medical University, Shiwa-gun, Japan
| | - Mitsumasa Osakabe
- Department of Molecular Diagnostic Pathology, Iwate Medical University, Shiwa-gun, Japan
| | - Noriyuki Uesugi
- Department of Molecular Diagnostic Pathology, Iwate Medical University, Shiwa-gun, Japan
| | - Hajime Saito
- Department of Thoracic Surgery, Iwate Medical University, Shiwa-gun, Japan
| | - Makoto Maemondo
- Division of Pulmonary Medicine, Department of Internal Medicine, Iwate Medical University, Shiwa-gun, Japan
| | - Tamotsu Sugai
- Department of Molecular Diagnostic Pathology, Iwate Medical University, Shiwa-gun, Japan
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Ahn B, Yoon S, Kim D, Chun SM, Lee G, Kim HR, Jin Jang S, Sang Hwang H. Clinicopathologic and genomic features of high-grade pattern and their subclasses in lung adenocarcinoma. Lung Cancer 2022; 170:176-184. [PMID: 35820357 DOI: 10.1016/j.lungcan.2022.07.003] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Revised: 06/28/2022] [Accepted: 07/04/2022] [Indexed: 10/17/2022]
Abstract
INTRODUCTION Recent lung adenocarcinoma (LUAD) grading system proposed by the International Association for the Study of Lung Cancer (IASLC) has emphasized the proportion of high-grade patterns (HGPs). We aimed to evaluate the clinicopathologic and genomic characteristics associated with HGP which has not yet been fully investigated. METHODS Tissue samples from 174 patients who underwent surgical resection of LUAD from January to December 2015 were histologically evaluated. Proportions of HGPs, including solid, micropapillary, cribriform, and complex glandular patterns, were individually quantified. Prognostic implications of HGP proportion, both as a continuous variable and as subclasses divided by cutoffs of 20%, 50%, and 90% (low-intermediate grade [LIG], HGP <20%; high grade 1 [HG1], 20-<50%, HG2, 50-<90%; HG3, ≥90%) were evaluated. Different clinicopathologic factors and genomic alterations according to the HGP subclasses were assessed. RESULTS Relative hazards of the HGP gradually elevated as its proportion increased over 20%, the cut-off value established by the IASLC grading system, and the cancer-specific overall survival (OS) of HG1 subclass was not significantly decreased compared to the LIG subclass on univariate analysis. However, further subgrouping showed significantly increased frequencies of male, advanced stage tumors, lymphovascular invasion, and spread through alveolar space in higher HGP subclasses. Also, common LUAD driver mutations, particularly EGFR mutations, were less frequent, whereas alterations in TP53 and cell cycle pathway-related genes were more frequent. Higher HGP subclasses and TP53 gene alteration were associated with shorter cancer-specific OS and RFS in multivariate survival analysis. CONCLUSIONS HGP subclasses of LUAD displayed distinct clinicopathological characteristics and genomic alterations, including TP53 and cell cycle pathway, emphasizing the clinical value of these subclasses in LUAD. Higher HGP subclass and alteration in TP53 may be markers of poor post-operative survival.
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Affiliation(s)
- Bokyung Ahn
- Department of Pathology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Shinkyo Yoon
- Department of Oncology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Deokhoon Kim
- Department of Pathology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Sung-Min Chun
- Department of Pathology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Goeun Lee
- Department of Pathology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Hyeong-Ryul Kim
- Department of Thoracic and Cardiovascular Surgery, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Se Jin Jang
- Department of Pathology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Hee Sang Hwang
- Department of Pathology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.
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Qiu ZB, Wang MM, Yan JH, Zhang C, Wu YL, Zhang S, Zhong WZ. A Novel Radiopathological Grading System to Tailor Recurrence Risk for Pathologic Stage IA Lung Adenocarcinoma. Semin Thorac Cardiovasc Surg 2022; S1043-0679:00135-00136. [PMID: 35709883 DOI: 10.1053/j.semtcvs.2022.06.003] [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: 06/04/2022] [Accepted: 06/06/2022] [Indexed: 02/05/2023]
Abstract
To validate the efficiency of pathologic grading system in pathologic stage IA lung adenocarcinoma (LUAD), and explore whether integrating preoperative radiological features would enhance the performance of recurrence discrimination. We retrospectively collected 510 patients with resected stage IA LUAD between January 2012 and December 2019 from Guangdong Provincial People's Hospital (GDPH). Pathologic grade classification of each case was based on the International Association for the Study of Lung Cancer (IASLC) pathologic staging system. Kaplan-Meier curves was used to assess the power of recurrence stratification. Concordance index (C-Index) and receiver operating characteristic curves (ROC) were used for evaluating the clinical utility of different grading systems for recurrence discrimination. Patients of lower IASLC grade showed improved recurrence-free survival (RFS) (P < 0.0001) where numerically difference was found between grade II and grade III (P = 0.119). By integrating the IASLC grading system and radiological feature, we found the RFS rate decreased as the novel radiopathological (RP) grading system increased (P < 0.0001). The difference of RFS curves between any 2 groups as per the RP grading system was statisticallysignificant (RP grade I vs RP grade II, p = 0.007; RP grade I vs RP grade III, P < 0.0001; RP grade II vs RP grade III, P = 0.0003). Compared with the IASLC grading system, the RP grading system remarkably improved recurrence survival discrimination (C-index: 0.822; area under the curve, 0.845). Integrating imaging features into pathologic grading system enhanced the efficiency of recurrence discrimination for resected stage IA LUAD and might help conduct subsequent management.
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Affiliation(s)
- Zhen-Bin Qiu
- Guangdong Lung Cancer Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China; Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cancer, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China; Shantou University Medical College, Shantou, China
| | - Meng-Min Wang
- Guangdong Lung Cancer Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China; Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cancer, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China; The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China
| | - Jin-Hai Yan
- Department of Pathology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Chao Zhang
- Guangdong Lung Cancer Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China; Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cancer, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Yi-Long Wu
- Guangdong Lung Cancer Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China; Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cancer, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Sheng Zhang
- Guangdong Lung Cancer Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cancer, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Wen-Zhao Zhong
- Guangdong Lung Cancer Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cancer, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
- Shantou University Medical College, Shantou, China
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China
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Lin H, Pan X, Feng Z, Yan L, Hua J, Liang Y, Han C, Xu Z, Wang Y, Wu L, Cui Y, Huang X, Shi Z, Chen X, Chen X, Zhang Q, Liang C, Zhao K, Li Z, Liu Z. Automated whole-slide images assessment of immune infiltration in resected non-small-cell lung cancer: towards better risk-stratification. J Transl Med 2022; 20:261. [PMID: 35672787 PMCID: PMC9172185 DOI: 10.1186/s12967-022-03458-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 05/29/2022] [Indexed: 02/08/2023] Open
Abstract
Background High immune infiltration is associated with favourable prognosis in patients with non-small-cell lung cancer (NSCLC), but an automated workflow for characterizing immune infiltration, with high validity and reliability, remains to be developed. Methods We performed a multicentre retrospective study of patients with completely resected NSCLC. We developed an image analysis workflow for automatically evaluating the density of CD3+ and CD8+ T-cells in the tumour regions on immunohistochemistry (IHC)-stained whole-slide images (WSIs), and proposed an immune scoring system “I-score” based on the automated assessed cell density. Results A discovery cohort (n = 145) and a validation cohort (n = 180) were used to assess the prognostic value of the I-score for disease-free survival (DFS). The I-score (two-category) was an independent prognostic factor after adjusting for other clinicopathologic factors. Compared with a low I-score (two-category), a high I-score was associated with significantly superior DFS in the discovery cohort (adjusted hazard ratio [HR], 0.54; 95% confidence interval [CI] 0.33–0.86; P = 0.010) and validation cohort (adjusted HR, 0.57; 95% CI 0.36–0.92; P = 0.022). The I-score improved the prognostic stratification when integrating it into the Cox proportional hazard regression models with other risk factors (discovery cohort, C-index 0.742 vs. 0.728; validation cohort, C-index 0.695 vs. 0.685). Conclusion This automated workflow and immune scoring system would advance the clinical application of immune microenvironment evaluation and support the clinical decision making for patients with resected NSCLC. Supplementary Information The online version contains supplementary material available at 10.1186/s12967-022-03458-9.
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Prognostic and predictive value of the newly proposed grading system of invasive pulmonary adenocarcinoma in Chinese patients: a retrospective multicohort study. Mod Pathol 2022; 35:749-756. [PMID: 35013526 DOI: 10.1038/s41379-021-00994-5] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 12/05/2021] [Accepted: 12/07/2021] [Indexed: 11/08/2022]
Abstract
Our aim was to validate and analyze the prognostic impact of the novel International Association for the Study of Lung Cancer (IASLC) Pathology Committee grading system for invasive pulmonary adenocarcinomas (IPAs) in Chinese patients and to evaluate its utility in predicting a survival benefit from adjuvant chemotherapy (ACT). In this multicenter, retrospective, cohort study, we included 926 Chinese patients with completely resected stage I IPAs and classified them into three groups (Grade 1, n = 119; Grade 2, n = 431; Grade 3, n = 376) according to the new grading system proposed by the IASLC. Recurrence-free survival (RFS) and overall survival (OS) were estimated by the Kaplan-Meier method, and prognostic factors were assessed using univariable and multivariable Cox proportional hazards models. All included cohorts were well stratified in terms of RFS and OS by the novel grading system. Furthermore, the proposed grading system was found to be independently associated with recurrence and death in the multivariable analysis. Among patients with stage IB IPA (N = 490), the proposed grading system identified patients who could benefit from ACT but who were undergraded by the adenocarcinoma (ADC) classification. The novel grading system not only demonstrated prognostic significance in stage I IPA in a multicenter Chinese cohort but also offered clinical value for directing therapeutic decisions regarding adjuvant chemotherapy.
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Li S, Deng J, She Y, Hou L, Chen C. Clinical Thoughts on the Predictive Value of the Newly Proposed Grading System of Invasive Pulmonary Adenocarcinoma. J Thorac Oncol 2022; 17:e28-e29. [PMID: 35216734 DOI: 10.1016/j.jtho.2021.11.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 11/09/2021] [Indexed: 11/26/2022]
Affiliation(s)
- Shenghui Li
- Department of Pathology, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, People's Republic of China
| | - Jiajun Deng
- Department of Pathology, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, People's Republic of China
| | - Yunlang She
- Department of Pathology, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, People's Republic of China
| | - Likun Hou
- Department of Pathology, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, People's Republic of China
| | - Chang Chen
- Department of Pathology, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, People's Republic of China.
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Fujikawa R, Muraoka Y, Kashima J, Yoshida Y, Ito K, Watanabe H, Kusumoto M, Watanabe SI, Yatabe Y. Clinicopathologic and Genotypic Features of Lung Adenocarcinoma Characterized by the IASLC Grading System. J Thorac Oncol 2022; 17:700-707. [DOI: 10.1016/j.jtho.2022.02.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 01/20/2022] [Accepted: 02/10/2022] [Indexed: 10/19/2022]
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Grading in Lung Adenocarcinoma: Another New Normal. J Thorac Oncol 2021; 16:1601-1604. [PMID: 34561031 DOI: 10.1016/j.jtho.2021.06.031] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 06/15/2021] [Indexed: 11/23/2022]
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