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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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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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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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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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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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