A retrospective cohort study integrating invasive mucinous adenocarcinoma into the IASLC grading system for lung adenocarcinoma

Introduction

Lung cancer is the leading cause of cancer-related mortality worldwide, with Invasive lung adenocarcinoma accounting for approximately 50% of all non-small cell lung cancer (NSCLC) cases (1,2). Invasive mucinous adenocarcinoma (IMA) is histopathologically characterized by tumors with goblet or columnar cells containing abundant intracytoplasmic mucin (3). As a rare variant of adenocarcinoma, IMA constitutes approximately 5% of all pulmonary adenocarcinomas (ADCs). The 2015 World Health Organization (WHO) classification designates IMA as an invasive ADC variant (4). In June 2020, the International Association for the Study of Lung Cancer (IASLC) pathology committee proposed a new histologic grading system for invasive pulmonary ADC (5). This system designates any tumor with ≥20% high-grade patterns (including solid, micropapillary, and complex glandular patterns) as “IASLC high-grade”. Three subsequent validation studies have confirmed the significant prognostic value of this grading system (6-8). Notably, IMA differs from invasive non-mucinous adenocarcinoma (INMA) in key clinical, radiologic, pathologic, and genetic aspects. However, the inclusion of IMA within this grading system has been a subject of debate due to its distinct histological and molecular characteristics.

While some early studies reported poor outcomes for IMA (9,10), recent evidence suggests that its prognosis may be comparable to that of IASLC grade 2 non-mucinous adenocarcinoma (11). And if both mucinous and non-mucinous components are present in the tumor, and each component constitutes ≥10% of the tumor, it should be classified as mixed invasive mucinous and non-mucinous adenocarcinoma (mixed IMA) (12). To address this gap, we propose an expanded IASLC grading system incorporating IMA and mixed IMA. We hypothesize that the inclusion of IMA will improve the stratification of patients based on their histological patterns, leading to more accurate predictions of overall survival (OS) and better-informed therapeutic strategies. We present this article in accordance with the TRIPOD reporting checklist (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2026-0425/rc).

Methods

Study cohorts

The study was single-institutional and well-formatted cohort registrations were involved. Pathological staging of all case was performed using the 8^th edition of tumor-nodule-metastasis (TNM) system. A local cohort and a population-based cohort were included in this study. The local cohort composed of stage I–III cases (1^st Jan 2016–31^th Dec 2020, Shanghai Chest hospital, China) was built as a training set for finding the prognostic effect of mucinous feature in invasive lung adenocarcinoma. Follow-up data were updated through 31^th Dec 2024. The inclusion criteria were: (I) invasive pulmonary adenocarcinoma was histologically confirmed; (II) aged 18–80 years; (III) available semiquantitative record of histologic patterns; (IV) underwent curative surgery without neoadjuvant treatments. The exclusion criteria were: (I) adenocarcinoma in situ, minimally invasive adenocarcinomas, multifocal adenocarcinomas, and other variants of adenocarcinoma; (II) previous cancer history, concomitant presence of other malignancies in the lung, neoadjuvant treatment, incomplete resection; (III) no available percentage of histologic subtypes, or insufficient pathologic evaluation of the whole tumor; and (IV) missing information (such as clinical/pathological staging, age, sex, and follow-up data). The detailed patient selection process, including the number of patients excluded at each step and reasons for exclusion, is presented in Figure S1.

The proposed expanded IASLC grading system was validated in an external multi-institutional cohort of stage I–III cases exacted from Surveillance, Epidemiology, and End Results (SEER) database, Jan 2009–Dec 2016. We re-staged all patients according to the 8^th edition TNM criteria using the available pathological data. The inclusion and exclusion criteria for the validation set were the same as those for the training set. Although a formal sample size calculation was not performed, the cohort size was determined by eligibility criteria during the study period.

Surgery and postoperative follow-up

All patients in training cohort received lobectomy, segmentectomy or wedge resection according to experienced surgeons’ decision, with an intention-to-treat. And these patients were followed every year for 5 years. Physical examination, chest and brain CT scans, ultrasonography of abdominal and neck regions, and serum tumor marker test were checked routinely. Bone scans and PET-CT were reserved only if recurrence or metastasis was suspected. For the SEER cohort, follow-up data were limited to vital status and OS time extracted from the database. No information on specific follow-up procedures or examination schedules was available.

Ethical consideration and registration

This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethics Committee of Shanghai Chest Hospital (No. IS25171). Individual consent for this retrospective analysis was waived. This study was prospectively registered with the Chinese Clinical Trial Registry (registration number: ChiCTR2500112161).

Histologic evaluation

The histology of local cohort was reviewed by two experienced pathologists in a tertiary-care hospital, both blinded to clinical outcomes and patient identifying information. A third judgment was sought in cases of disagreement. The mucinous pattern was semi-quantitatively estimated as all other histologic patterns of 5% increment suggested by WHO classification of lung adenocarcinoma. Figure 1A-1D depicted mucinous-predominant and mucinous sub-predominant lung adenocarcinoma.

Figure 1 Examples of pulmonary invasive adenocarcinoma. (A) The invasive mucinous adenocarcinoma. (B) The invasive mucinous adenocarcinoma pattern shown at higher magnification in the inset of A (red rectangle). (C) The invasive non-mucinous adenocarcinoma pattern. (D) The invasive non-mucinous adenocarcinoma pattern shown at higher magnification in the inset of (C) (red rectangle). Hematoxylin and eosin staining; 10× objective.

Development of the proposed expanded grading system

Consistent with previous reports, our analysis confirmed that the OS of IMA was not significantly different to that of IASLC grade 2 (11). Consequently, we derived an expanded grading rule: mucinous-predominant tumors with <20% high-grade patterns are classified as Grade 2. The objective of our study is to refine the IASLC grading system by integrating IMA and mixed IMA, thereby enhancing its prognostic accuracy and clinical relevance. In this proposed expanded framework, invasive adenocarcinoma with a mucinous predominant subtype and less than 20% high-grade patterns is categorized as Grade 2, building upon the existing IASLC grading criteria.

To assess model validity, we first evaluated in the same institutional training cohort, and then independently validated in an external cohort from the SEER database. No separate internal validation was performed, as the training cohort was solely for model development and preliminary performance evaluation. The original and proposed expanded IASLC grading systems were included as variables in univariate and multivariate survival analyses. The proposed expanded system was evaluated using calibration and discrimination curves, with discrimination quantified by the concordance index (Harrell C-index) (13). Predictive accuracy was assessed using time-dependent receiver operating characteristic (ROC) curves and area under the curves (AUCs) at 1-, 3-, and 5-year. Decision curve analysis (DCA) was conducted to compare the reliability of the two systems (14). The proposed expanded system was further compared with the original using the C-index, net reclassification improvement (NRI), and integrated discrimination improvement (IDI) (15,16).

Statistical analysis

Clinicopathological characteristics were summarized using counts and percentages. OS was defined as time from surgery to death from any cause, censored at last follow-up. Outcome assessment was performed at 1-, 3-, and 5-year post-surgery. Survival curves were estimated using the Kaplan-Meier method, with differences assessed by the log-rank test. Univariable and multivariable Cox proportional hazards models were used to identify independent prognostic factors. In the training cohort, covariates included age, sex, pathological TNM stage, and surgical extent; in the validation cohort (SEER), covariates included age, sex, pathological TNM stage, and surgery type. Hazard ratios (HRs) with 95% confidence intervals (CIs) were calculated. Calibration curves were generated from the same Cox model (including age, sex, pathological TNM stage, and the proposed expanded grading system as covariates) fitted on the training cohort to predict 5-year survival probabilities for each patient, and then applied to the validation cohort for external calibration.

To assess the validity of the proposed expanded grading system, discrimination was evaluated using time-dependent ROC curves and AUCs at 1-, 3-, and 5-year. DCA was performed to compare the clinical utility of the two grading systems. Statistical analyses were conducted using R software (version 4.2.0), with statistical significance set at ≤0.05.

Results

Clinical and pathological characteristics

The Demographic, clinicopathologic and survival information for both training and validation cohorts is illustrated in Table 1. The patient selection process, including the number of patients excluded due to missing data or other reasons, is shown in Figure S1. A total of 692 patients were included in the training cohort, with a mean age of 61 years, and 58.4% were male. Among them, 469 patients were in stage I, 143 in stage II, and 80 in stage III. According to the original IASLC grading system, 245 cases (35.4%) were classified as Grade 1, 316 (45.7%) as Grade 2, and 131 (18.9%) as Grade 3. In training cohort, there were 62 (62.0%) IMA and 38 (38.0%) mixed IMA; in the validation cohort, there were 877 (64.8%) IMA and 498 (36.2%) mixed IMA. In the validation cohort, 1,476 cases (18.8%) were classified as Grade 1, 3,813 (48.5%) as Grade 2, and 2,573 (32.7%) as Grade 3.

Survival of IMA

For the patient follow-up data in the training cohort, the median follow-up time was 54 [49–65] months, and 569 patients (82.2%) were alive. In the validation cohort, 5,672 patients (72.1%) were alive (Table 1). We compared the long-term survival between mucinous adenocarcinoma and Grade 2 populations. In both cohorts, the 5-year OS of IMA patients were not significantly different from that of IASLC grade 2 patients (training: 73.5% vs. 70.4%, P=0.850; validation: 66.5% vs. 67.3%, P=0.12; Figure 2).

Figure 2 Overall survival curves of patients with IMA and IASLC grade 2 in the training cohort (A) and the validation cohort (B). IASLC, International Association for the Study of Lung Cancer; IMA, invasive mucinous adenocarcinoma.

The proposed expanded IASLC grading system

Based on these findings, we proposed an expanded grading system (Table 2): Grade 1: well-differentiated invasive lung adenocarcinoma: lepidic predominant with no or less than 20% of high-grade patterns. Grade 2: moderately differentiated invasive lung adenocarcinoma: acinar or papillary or mucinous predominant with no or less than 20% of high-grade patterns. Grade 3: poorly differentiated lung adenocarcinoma: any tumor with 20% or more of high-grade patterns. According to the proposed expanded IASLC grading system, 290 cases (41.9%) were classified as Grade 1 in the training group, 236 (34.1%) as Grade 2, and 166 (24.0%) as Grade 3. In the validation cohort, 2,209 cases (28.1%) were classified as Grade 1, 3,002 (38.2%) as Grade 2, and 2,651 (33.7%) as Grade 3.

Prognostic value

In the training cohort, among the 692 patients, IMA and mixed IMA patients were excluded from the IASLC grading analyses but were included in the proposed expanded grading analyses. The survival analysis results revealed that, in accordance with the proposed expanded IASLC grading system, the 5-year OS rate in the training group were 87.5% for Grade 1 patients, 73.1% for Grade 2 patients, and 55.5% for Grade 3 patients (P<0.001, Figure 3A and Table 3). This proposed expanded IASLC grading system further stratified patients based on OS (Figure 3A,3B), and its performance was superiorly measured compared to the IASLC grading system (Tables S1,S2). When applied to the validation cohort, the Kaplan-Meier curves for OS under the proposed expanded grading system showed better discrimination than those under the original IASLC grading system (Figure 3C,3D). Moreover, the proposed expanded IASLC grading system demonstrated superior performance metrics (Tables S3,S4).

Figure 3 Overall survival curves for each grading system. (A) The proposed expanded grading system in the training cohort; (B) the IASLC grading system (grade 1–3) in the training cohort, only non-mucinous adenocarcinoma included; (C) the proposed expanded grading system in the validation cohort; (D) the IASLC grading system (grade 1–3) in the validation cohort, only non-mucinous adenocarcinoma included. IASLC, International Association for the Study of Lung Cancer.

Validation of the proposed expanded IASLC grading system

To assess the efficacy of the proposed expanded IASLC grading system, we evaluated its performance in the training cohort and validated it in the external SEER cohort. The survival prediction accuracy of these grading systems was evaluated using time-dependent ROC curves and the corresponding AUCs at 1, 3, and 5 years (Table 3 and Figure 4). The proposed expanded system yielded a C-index of 0.713 and an AUC of 0.739 in the training cohort, compared to 0.701 and 0.729 for the original system (Figure 4). In the validation cohort, the proposed expanded IASLC system achieved a C-index of 0.707 and an AUC of 0.742 (Figure 4C), compared to the original system’s C-index of 0.692 and AUC of 0.675 (Figure 4D). Notably, the ROC curves and the corresponding AUCs indicated that the proposed expanded IASLC grading system exhibited significantly superior OS prediction capabilities compared to the original IASLC grading system (Table 3).

Figure 4 Time-dependent ROC curves and the AUCs at 1-, 3- and 5-year for the proposed expanded grading system (A) and the IASLC grading system, only non-mucinous adenocarcinoma included (B) in the training cohort; for the proposed expanded grading system (C) and the IASLC grading system, only non-mucinous adenocarcinoma included (D) in the validation cohort were used to estimate the prognostic accuracy of the grading systems. AUC, area under the curve; CI, confidence interval; IASLC, International Association for the Study of Lung Cancer; ROC, receiver operating characteristic.

Furthermore, we generated calibration plots for the predicted 5-year OS. The plots generated for both the training and validation cohorts closely approximated the observed survival estimates, indicating excellent calibration within these datasets (Figure S2A,S2B). These findings underscore the reliability of the proposed expanded IASLC grading system in predicting survival outcomes for patients with invasive pulmonary adenocarcinoma. Finally, DCA showed that the proposed expanded IASLC grading system and the original IASLC grading system provided comparable net benefit across the clinically relevant threshold probability range (Figure S2C,S2D).

Discussion

The IASLC grading system was developed to enhance the ability to predict recurrence and mortality rates among individuals diagnosed with lung adenocarcinoma. Many researchers are looking to further refine this grading system (17-20). Currently, there is no internationally accepted risk stratification system for lung IMA and mixed invasive mucinous and non-mucinous adenocarcinoma (mixed IMA). While we introduced an expanded IASLC grading system for invasive pulmonary adenocarcinoma by incorporating IMA and mixed IMA. Based on the novel grading system proposed in this study, tumors containing ≥20% high-grade patterns remain classified as poorly differentiated due to their aggressive biological behavior. In contrast, we found that IMA has survival outcomes not significantly different from those of IASLC grade 2 non-mucinous adenocarcinoma.

When established, the initial IASLC grading system did not consider lung IMA due to its low incidence (5). However, because lung cancer is the leading cause of cancer-related mortality worldwide (1), even a relatively uncommon subtype such as IMA represents a considerable number of patients (3). Therefore, the absence of a standardized grading system for IMA and mixed IMA constitutes a significant gap in prognostic assessment. IMA has distinct clinical, radiological (e.g., airspace consolidation, GGO, air bronchogram), and pathological characteristics from INMA (11). Several imaging characteristics, such as pneumonic-type IMA, were considered poor prognostic radiological factors (21). Compared to INMA, IMA demonstrates lower rates of nodal metastasis, lymphovascular invasion, and pleural invasion, yet exhibits significantly higher incidence of spread through air spaces (STAS) (66.7% vs. 30.5%) (11). This may stem from different mucin protein expression, potentially altering cell polarity and cancer cell migration (22,23). STAS is a validated poor prognostic factor, potentially explaining IMA’s higher intrapulmonary recurrence (24). Our study showed that the prognosis of IMA is comparable to that of acinar or papillary predominant type INMA in OS, similar to previous studies (11). However, the prognosis of patients with IMA was considered inferior to that of patients with lepidic-predominant INMA (11). Consequently, in our study, the IMA was classified as predominant tumor patterns in the proposed expanded IASLC grading system, and mucinous predominant tumors with <20% of high-grade patterns were classified as Grade 2 (moderately differentiated). These findings formed the basis for the proposed expanded grading system, which was subsequently validated in a large-scale external cohort.

Based on the proposed expanded grading system proposed in this study, tumors containing ≥20% high-grade patterns still require classification as poorly differentiated. This is because such tumors exhibit more aggressive biological behavior, whereas mucinous adenocarcinoma did not demonstrate aggressive behavior comparable to or worse than that of high-grade patterns. A key strength of this study lies in its validation using a large-scale, external multi-center database, thereby mitigating biases stemming from single-institution limitations and pathologists’ subjective interpretations. This new grading system enables effective risk stratification for IMA and mixed IMA, underscoring the prognostic value of the IASLC system. By establishing a common language for prognostic stratification, this system also refines prognostic stratification for IMA and mixed IMA, providing clinicians with more precise risk information to integrate with stage, molecular features, and patient factors when making individualized treatment decisions and for incorporating grade as a stratification factor in clinical trial design. Its foundation in the current classification system makes this approach readily implementable and ensures reproducible outcomes.

Some studies highlight the predominantly indolent biological behavior and marked histological/molecular heterogeneity that define lung IMA (11,25,26). Distinct from the high-grade patterns, invasive IMA often show lepidic-predominant growth, histologically correlating with its classification as a relatively low-grade malignant entity (27), which could explain IMA’s relatively favorable biological behavior. While mixed IMA demonstrates comparatively poorer clinical outcomes than both IMA and non-mucinous adenocarcinoma subtypes (28,29). One potential explanation is the prognostic variability observed in mixed IMA may stem from the differential biological behaviors of its non-mucinous components, particularly their histologic subtype composition and relative tumor proportion. Unlike non-mucinous adenocarcinoma, IMA is characterized by distinct histological features, such as abundant intracellular mucin production, and molecular profiles, including frequent KRAS mutations, P53, TTF-1, low PD-L1 expression and rare EGFR mutations (29-31). The integration of IMA into the proposed expanded grading system now provides a histologic framework for future investigations into whether these molecular alterations correlate with grade and whether such correlations predict differential responses to targeted therapies. The biological mechanisms underlying these correlations represent an important direction for future investigation. Another study reported that higher TTF-1 expression in non-mucinous component in mixed IMA, which indicated that a subset of mucinous adenocarcinoma components within mixed IMA are undergoing histologic transition toward non-mucinous adenocarcinoma (32). Currently, there is still a lack of research on the genetic mutations and mechanisms of occurrence in mixed IMA. Future studies should prioritize comprehensive genomic profiling and mechanistic investigations into the molecular pathogenesis of mixed IMA to delineate its unique evolutionary trajectories and therapeutic vulnerabilities.

Current pathological grading systems lack standardized criteria for the stratification of IMA and mixed IMA, creating significant gaps in prognostic assessment and clinical management of these histologically distinct subtypes. In this study, the proposed expanded system classifies tumors into three grades based on predominant patterns and high-grade components, with 5-year OS rates of 88.6%, 76.1%, and 65.5% in the training cohort, and 85.4%, 69.8%, and 60.1% in the validation cohort, respectively. Our study not only successfully integrated IMA and mixed IMA into the proposed expanded pathological grading framework but also demonstrated the validity of the original IASLC system’s 20% high-grade component threshold as a robust stratification criterion. The incorporation of the SEER database, with its extensive population-based dataset spanning diverse demographic and clinical settings, provides robust external validation of our findings. This large-scale external validation not only confirms the reliability of our proposed expanded grading approach but also ensures its applicability across different healthcare systems and practice patterns, significantly strengthening the case for its adoption in routine clinical practice. The proposed expanded grading system is designed to complement, not replace, the TNM staging system. By providing a common language for prognostic stratification, it offers clinicians more precise risk information that can be integrated with stage, molecular features, and patient factors to guide individualized treatment decisions. It should be emphasized that the prognostic utility of tumor histologic features remains constrained, even when applied to the universally adopted IASLC pathologic grading system for non-mucinous adenocarcinoma. Several studies propose incorporating the percentage of lepidic component or integrating histologic characteristics such as necrosis and lymphovascular invasion into the pathologic grading system (17,33,34). Such models consistently achieved satisfying AUC values. Therefore, numerous histologic and non-histologic characteristics can enhance the predictive capacity for recurrence risk. However, it is impractical to incorporate all factors into predictive models. We can adopt the more concise new pathologic grading system as a simple common language for standardized communication among clinicians and researchers.

This study has several limitations. First, the training cohort is derived from a single institution and comprises a relatively modest sample size, which may introduce selection bias and limit the generalizability of the findings. However, this limitation is partially mitigated by the large, population-based SEER validation cohort. Second, lack of integrated genomic data limits the ability to correlate histologic patterns with molecular drivers. Third, detailed data on adjuvant treatment regimens, compliance, and postoperative complications were not available for analysis. This limitation is inherent to retrospective cohort studies and population-based databases such as SEER. Fourth, the lack of recurrence-free survival (RFS) data in the SEER cohort and the difference in follow-up periods between cohorts limit direct comparison of absolute survival rates. However, the consistent relative stratification across grades supports the robustness of the system. Fifth, in the SEER validation cohort, we re-staged some patients using total pathological tumor size rather than invasive size, as the latter is not always available in SEER. Nevertheless, the consistent prognostic performance across both cohorts suggests that this limitation does not invalidate our main findings. And last, the training and validation cohorts differed in stage distribution and demographic characteristics, reflecting real-world heterogeneity. Such differences are inherent to external validation and do not invalidate the findings. Our future research will incorporate standardized RFS collection, and focus on prospective multicenter study to integrate different racial and ethnic groups and genomic mutation profiles into the risk stratification framework, while concurrently evaluating perioperative treatment modalities. This integrative approach aims to establish evidence-based guidelines for optimizing therapeutic strategies in patients with IMA and mixed IMA.

Conclusions

IMA and mixed IMA are unique but not uncommon subtypes of lung adenocarcinoma. Our study proposed a practical pathology grading system for lung adenocarcinoma. This proposed expanded grading system demonstrates satisfactory performance in diverse populations, and it could assist clinicians in understanding the clinical course of IMA and mixed IMA.

Acknowledgments

This work was previously presented as a poster at the World Conference on Lung Cancer (WCLC) 2024 (Abstract No. 3814), and has been substantially expanded into a full manuscript.

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2026-0425/rc

Data Sharing Statement: Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2026-0425/dss

Peer Review File: Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2026-0425/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2026-0425/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethics Committee of Shanghai Chest Hospital (No. IS25171). Individual consent for this retrospective analysis was waived.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Sung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin 2021;71:209-49. [Crossref] [PubMed]
2. Yoo S, Sinha A, Yang D, et al. Integrative network analysis of early-stage lung adenocarcinoma identifies aurora kinase inhibition as interceptor of invasion and progression. Nat Commun 2022;13:1592. [Crossref] [PubMed]
3. Saito T, Tsuta K, Honda O, et al. Prognostic impact of mucin spread, tumor cell spread, and invasive size in invasive mucinous adenocarcinoma of the lung. Lung Cancer 2020;146:50-7. [Crossref] [PubMed]
4. Travis WD, Brambilla E, Nicholson AG, et al. The 2015 World Health Organization Classification of Lung Tumors: Impact of Genetic, Clinical and Radiologic Advances Since the 2004 Classification. J Thorac Oncol 2015;10:1243-60. [Crossref] [PubMed]
5. Moreira AL, Ocampo PSS, Xia Y, et al. A Grading System for Invasive Pulmonary Adenocarcinoma: A Proposal From the International Association for the Study of Lung Cancer Pathology Committee. J Thorac Oncol 2020;15:1599-610. [Crossref] [PubMed]
6. Rokutan-Kurata M, Yoshizawa A, Ueno K, et al. Validation Study of the International Association for the Study of Lung Cancer Histologic Grading System of Invasive Lung Adenocarcinoma. J Thorac Oncol 2021;16:1753-8. [Crossref] [PubMed]
7. Deng C, Zheng Q, Zhang Y, et al. Validation of the Novel International Association for the Study of Lung Cancer Grading System for Invasive Pulmonary Adenocarcinoma and Association With Common Driver Mutations. J Thorac Oncol 2021;16:1684-93. [Crossref] [PubMed]
8. Hou L, Wang T, Chen D, et al. Prognostic and predictive value of the newly proposed grading system of invasive pulmonary adenocarcinoma in Chinese patients: a retrospective multicohort study. Mod Pathol 2022;35:749-56. [Crossref] [PubMed]
9. Russell PA, Wainer Z, Wright GM, et al. Does lung adenocarcinoma subtype predict patient survival?: A clinicopathologic study based on the new International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society international multidisciplinary lung adenocarcinoma classification. J Thorac Oncol 2011;6:1496-504. [Crossref] [PubMed]
10. Qiu ZB, Li HJ, Ding JY, et al. Comparison of survival outcomes between sublobar resection and lobectomy in pathological stage IA pulmonary invasive mucinous adenocarcinoma. Asian J Surg 2025;49:3251-8.
11. Woo W, Yang YH, Cha YJ, et al. 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;13:3310-21. [Crossref] [PubMed]
12. Travis WD, Brambilla E, Burke AP, et al. Introduction to The 2015 World Health Organization Classification of Tumors of the Lung, Pleura, Thymus, and Heart. J Thorac Oncol 2015;10:1240-2. [Crossref] [PubMed]
13. Harrell FE Jr, Lee KL, Mark DB. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med 1996;15:361-87. [Crossref] [PubMed]
14. Vickers AJ, Elkin EB. Decision curve analysis: a novel method for evaluating prediction models. Med Decis Making 2006;26:565-74. [Crossref] [PubMed]
15. Pencina MJ, D'Agostino RB Sr, D'Agostino RB Jr, et al. Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med 2008;27:157-72; discussion 207-12. [Crossref] [PubMed]
16. Van Calster B, Van Huffel S. Integrated discrimination improvement and probability-sensitive AUC variants. Stat Med 2010;29:318-9. [Crossref] [PubMed]
17. Park BJ, Woo W, Cha YJ, et al. 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. [Crossref] [PubMed]
18. Jia J, Zhang G, Wei L, et al. The Battle for Accuracy: Identifying the Most Effective Grading System for Lung Invasive Mucinous Adenocarcinoma. Ann Surg Oncol 2024;31:5717-28. [Crossref] [PubMed]
19. Pan X. The artificial intelligence-based model ANORAK improves histopathological grading of lung adenocarcinoma. Nat Cancer 2024;5:347-63. [Crossref] [PubMed]
20. Zhang Y, Zhang Y, Hu Y, et al. 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. [Crossref] [PubMed]
21. Beck KS, Sung YE, Lee KY, et al. Invasive mucinous adenocarcinoma of the lung: Serial CT findings, clinical features, and treatment and survival outcomes. Thorac Cancer 2020;11:3463-72. [Crossref] [PubMed]
22. Geles A, Gruber-Moesenbacher U, Quehenberger F, et al. Pulmonary mucinous adenocarcinomas: architectural patterns in correlation with genetic changes, prognosis and survival. Virchows Arch 2015;467:675-86. [Crossref] [PubMed]
23. Lakshmanan I, Ponnusamy MP, Macha MA, et al. Mucins in lung cancer: diagnostic, prognostic, and therapeutic implications. J Thorac Oncol 2015;10:19-27. [Crossref] [PubMed]
24. Matsui T, Sakakura N, Koyama S, et al. Comparison of Surgical Outcomes Between Invasive Mucinous and Non-Mucinous Lung Adenocarcinoma. Ann Thorac Surg 2021;112:1118-26. [Crossref] [PubMed]
25. Xu X, Shen W, Wang D, et al. Clinical features and prognosis of resectable pulmonary primary invasive mucinous adenocarcinoma. Transl Lung Cancer Res 2022;11:420-31. [Crossref] [PubMed]
26. Lee JO, Lee GD, Choi S, et al. Surgical prognosis of lung invasive mucinous and non-mucinous adenocarcinoma: propensity score matched analysis. Eur J Cardiothorac Surg 2024;66:ezae316. [Crossref] [PubMed]
27. Takanashi Y, Tajima S, Tsukui M, et al. Non-Mucinous Lepidic Predominant Adenocarcinoma Presenting with Extensive Aerogenous Spread. Rare Tumors 2016;8:6580. [Crossref] [PubMed]
28. Luo J, Wang R, Han B, et al. Analysis of the clinicopathologic characteristics and prognostic of stage I invasive mucinous adenocarcinoma. J Cancer Res Clin Oncol 2016;142:1837-45. [Crossref] [PubMed]
29. Boland JM, Maleszewski JJ, Wampfler JA, et al. Pulmonary invasive mucinous adenocarcinoma and mixed invasive mucinous/nonmucinous adenocarcinoma-a clinicopathological and molecular genetic study with survival analysis. Hum Pathol 2018;71:8-19. [Crossref] [PubMed]
30. Goto E, Takamochi K, Kishikawa S, et al. Stepwise progression of invasive mucinous adenocarcinoma based on radiological and biological characteristics. Lung Cancer 2023;184:107348. [Crossref] [PubMed]
31. Chang WC, Zhang YZ, Nicholson AG. Pulmonary invasive mucinous adenocarcinoma. Histopathology 2024;84:18-31. [Crossref] [PubMed]
32. Cha YJ, Shim HS. Biology of invasive mucinous adenocarcinoma of the lung. Transl Lung Cancer Res 2017;6:508-12. [Crossref] [PubMed]
33. Liu DH, Ye ZH, Chen S, et al. Novel prognostic model for stratifying survival in stage I lung adenocarcinoma patients. J Cancer Res Clin Oncol 2020;146:801-7. [Crossref] [PubMed]
34. Chang C, Sun X, Zhao W, et al. Minor components of micropapillary and solid subtypes in lung invasive adenocarcinoma (≤ 3 cm): PET/CT findings and correlations with lymph node metastasis. Radiol Med 2020;125:257-64. [Crossref] [PubMed]