Does lung adenocarcinoma subtyping offer clinical benefits?—a retrospective population-based cohort study from Sweden

Introduction

Survival in lung cancer (LC) has generally increased due to early diagnosis, improvements in treatment and changing histology towards the relatively less fatal adenocarcinoma (1,2). Increase in adenocarcinoma compared to other histological types of LC has been a universal trend; by 2020 adenocarcinoma had become the most common male LC histology in most countries and female histology in all countries (3), possibly due to decreasing smoking rates as adenocarcinoma is less associated with smoking than the other histologies. Due to the prominence of adenocarcinoma, World Health Organization (WHO) has introduced several modifications to the histological classification of adenocarcinoma since 1967 (4,5).

In 2011 two survival studies on early-stage adenocarcinoma were published from the USA (N=210) and Australia (N=514, stage I), describing survival differences for adenocarcinoma subtypes (4,6). Such results were used in the 2015 WHO classification distinguishing lepidic, acinar, papillary, micropapillary and solid types defined by predominant growth pattern in biopsies and cytology specimens (7,8). The subtypes differ by presentation and prognosis, lepidic presenting in low-grade with a good prognosis, acinar and papillary with intermediate grade and prognosis and lastly micropapillary and solid with high grade and poor prognosis (8,9). In the 2021 WHO classification, the distinctions between adenocarcinoma subtypes were further refined by introducing a 3-tiered grading system based on predominant histological patterns, specifying grade 1 (well differentiated) with lepidic predominance, grade 2 (moderately differentiated) with acinar and papillary predominance and grade 3 (poorly differentiated) with high-grade patterns (10).

Practical adaptation of the adenocarcinoma subclassification in global thorax clinics has been variable. A recent review listed 65 studies reporting on the subtyping but the results were inconsistent, for example, on relative abundance (11). The authors concluded that the problem is in classification and called for more guidance to reduce subjective errors (11). Although refined subtypes were introduced to confer prognostic and therapeutic information, clinical judgements still largely rely on stage, mutational profile and PD-L1 status (5,12). Recently, DNA methylation patterns have been proposed to provide further therapeutic information (13). The above survival studies published in 2011 on adenocarcinoma subtypes were replicated in 2022 in a smaller UK study of 262 patients (14). Among the larger studies, adenocarcinoma patients were pooled from several clinical trials and 575 patients were assigned histological subtypes (15). Overall survival did not differ between the subtypes but lepidic and acinar/papillary showed higher disease-free survival than micropapillary/solid types. A Chinese study on 2,268 adenocarcinomas analyzed lymph node involvement in the subtypes (16). In 47% of patients with solid and micropapillary types, nodal involvement was observed and this decreased to an intermediate level in patients with papillary and acinar subtypes and to 0% in lepidic patients. Survival in individual adenocarcinoma subtypes has been analyzed in several large studies based on the Surveillance, Epidemiology, and End Results (SEER) database covering lepidic, invasive mucinous, acinar, colloid and papillary types (17-22). Among 600 patients with defined adenocarcinoma subtypes, major genetic pathways and possible drug targets were characterized (23). A detailed genomic and proteomic analysis of 103 Chinese adenocarcinoma subtypes was published (24).

The variable adaptation of the WHO subtype classifications in clinical practice reflects doubts about their benefits, which the 2021 WHO classification tries to rectify by focusing on well and moderately differentiated subtypes (lepidic, acinar and papillary). One obvious way to test the benefits would be to show that the classification results in a homogeneous patient population with uniform survival kinetics, which can be assessed, e.g., by using the Weibull regression model (25,26). In fact, none of the above-cited articles show stage-specific survival kinetics. In the present study, we report on stage-specific survival kinetics to fill in the remaining knowledge gap, particularly in relation to the 2021 WHO classification. We collected adenocarcinoma subtypes in patients from the Swedish cancer registry in ages 23–93 years between 2005 and 2021. The available subtypes were lepidic, papillary, invasive mucinous and colloid adenocarcinoma, which we compared to adenocarcinoma not otherwise specified (NOS). We report periodic sex-age-stage histological distributions and overall survival in these patients, with a specific aim to test for homogeneity in survival outcomes, ascertained by survival kinetics fitted with the Weibull distribution (25,26). We present this article in accordance with the STROBE reporting checklist (available at https://atm.amegroups.com/article/view/10.21037/atm-2026-0046/rc).

Methods

All patients diagnosed with LC were identified from the Swedish cancer registry between years 2005 and 2021 using International Classification of Diseases (ICD)-7 code 1621—‘LC specified as primary’. The cancer registry has a national coverage of cancers and deaths, and a very high rate (98%) of morphological verification by pathologists (27). For survival studies, it is important that no patients are lost in follow-up, which is the case with the present data. Pathology and cytology reports were collected and inspected by regional cancer registries before delivery to the central cancer registry (27). Data on tumour topography were available since 1993. Staging data [tumour, node, metastases (TNM), sixth edition was used until 2009 and then seventh edition until 2018 when the eighth edition was adopted] were almost complete after 2005 but mostly missing before 2004 (28). The cancer registry has detailed clinical data on patients but does not collect information on comorbidities, smoking or molecular markers (27). Thus, these could not be considered in analyses. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Regional Ethics Review Board in Lund University, February 6, 2013 (Reference 2012/795 and subsequent amendments). The Regional Ethics Review Board in Lund University waived the need to obtain informed consent.

Data on 30,780 patients with adenocarcinoma were available, and 1418 of these had information on subtype. The individual histological subtypes were defined by ICD-O-3 classification (5-digit code) and the following subtypes were included: papillary adenocarcinoma (82603), colloid adenocarcinoma (84803), bronchioloalveolar cancer NOS (82503) and bronchioloalveolar carcinoma mucinous (82533); the latter two were labelled according to the WHO 2015 terminology to ‘lepidic’ and ‘invasive mucinous’, respectively (7,10). We noted that the cancer registry did not receive adenocarcinoma subtype information geographically uniformly from Sweden. While 22% of adenocarcinoma NOS data originated from Stockholm (the capital), those for the subtypes were between 32% to 52% from Stockholm. In order to secure regional balance in the origins of adenocarcinoma NOS we tested that survival was equal between Stockholm and the rest of the country. In this regard, we also assessed homogeneity of survival estimates by dividing the 95% confidence interval (CI) by survival figures.

Only the first LC for each patient was included in the analysis. The earliest diagnostic year for the invasive mucinous subtype was 2012 (median 2019). The other histologies were available since the beginning of the study period (2005). The analysis excluded patients with multiple LCs at the first diagnosis date, with tumors of T classes Tis and T0, and with tumors discovered at autopsy; also excluded were patients with a diagnosis date prior to immigration to Sweden. The cancer registry does not include cancers from the cause of death notifications (27).

Non-parametric survival estimates were calculated by the Kaplan-Meier method using the survminer package in R. Survival data were also modelled parametrically using the Weibull distribution (29), where the hazard function is parametrized as:

h(t)=kλ(tλ)(k−1)[1]

In the formula, h(t) is hazard in time t, lambda is scale parameter controlling spread of survival time distribution, k the Weibull shape parameter.

The interpretation of the Weibull shape parameter (k):

• k<1 (decreasing mortality): initial high mortality as vulnerable individuals die;
• k=1 (constant mortality): mortality due to random external events, no inherent age-related mortality;
• k>1 (increasing mortality): mortality increases over time for late events and/or aging.

In addition to adenocarcinoma NOS stages 1 and 2, we also modelled stages 3 and 4, as well as the elderly in the overall background population, with the Weibull distribution.

Distribution of T stages between sexes was compared by Fisher’s exact test with Benjamini-Hochberg correction for multiple comparisons.

Statistical analysis

All statistical analyses and data visualization were done using SAS and R (version 4.4.0). Differences were considered significant when their 95% CIs were non-overlapping. In epidemiology, CIs are used instead of P values because they are more informative, showing the magnitude and the direction of the effect, thus allowing assessment of both statistical and clinical significance. CIs reveal the plausible ranges of effect sizes rather than a binary call “significant/not significant” (30,31).

Results

The summary of the studied cohort is shown in Table 1 with dominant adenocarcinoma NOS (13,734 male and 17,046 female patients). All adenocarcinoma subtypes (N=1,418) were more common in women compared to men. The case numbers decreased in the following order: lepidic, colloid, invasive mucinous, and papillary; however, it should be noted that these numbers are significantly influenced by changes in subtype classification and its adoption (e.g., invasive mucinous cases were first listed in 2015) and do not reflect true subtype distribution in adenocarcinoma patients. The subtypes accounted for less than 5% of the adenocarcinoma NOS patients. Median diagnostic ages were around 70 years for all.

The extent of tumor growth (T-stage) at the time of diagnosis was distributed differently across histologies (Figure 1). In adenocarcinoma NOS, about 33% was T4, 28% was T1, 27% was T2 and 12% was T3. In the lepidic subtype, T1 cases dominated (51%). The majority of patients with papillary and colloid subtypes were diagnosed at T1 and T2; T4 was the most frequent stage in invasive mucinous adenocarcinoma (40%). Women were more likely than men to be diagnosed with T1 in adenocarcinoma NOS (29% vs. 25%, P<0.001 for T distribution difference) and colloid (35% vs. 23%, P=0.04); for other subtypes, the T-stage distribution was not significantly different across sexes (data not shown). The distribution of N- and M-stages is shown in Table S1. N0 and M0 were the most common stages for all subtypes but adenocarcinoma NOS.

Figure 1 Distribution of T-stages according to histology. NOS, not otherwise specified; T, stage.

Kaplan-Meier survival curves by sex and histology (Figure 2) revealed different survival patterns between the histological subtypes and showed a female survival advantage. Data allowing, survival curves were plotted up to 200 months (16.7 years), with vertical lines for 1, 5 and 10 years. Survival in lepidic and papillary adenocarcinoma was similar during the first years of follow-up, but later lepidic patients survived better. Patients with invasive mucinous survived better than colloid or adenocarcinoma NOS patients; for the latter two, male 50% survival was about 1 year.

Figure 2 Kaplan-Meier survival analysis by sex and histology. The dashed vertical lines mark 1-, 5- and 10-year time points. NOS, not otherwise specified.

Survival at 1 and 5 years after diagnosis is shown in Table S2. It documents a significant female advantage in survival at year 1 and 5 for adenocarcinoma NOS and lepidic type, and also at year 5 for colloid types. The 5-year survival (female/male) was 0.24/0.17 for adenosquamous NOS, 0.59/0.46 for lepidic, 0.51/0.36 for papillary, 0.46/0.39 for invasive mucinous and 0.36/0.22 for colloid types. Survival for all adenocarcinoma subtypes was better than for adenocarcinoma NOS (non-overlapping 95% CIs, except for 5-year male colloid type). Survival for the lepidic subtype was significantly better than that for the colloid subtype (non-overlapping 95% CIs). Case numbers for papillary and invasive mucinous patients were small and thus 95% CIs were wide. Data in Table S2 enabled us to assess homogeneity of survival estimates, as data were collected over a period of 16 years, with an over-presentation of cases from Stockholm. For male and female lepidic survival, 95% CIs were only 13% (10/75) and 8% (7/84) of the survival percentages. For the other subtypes (male/female), they were 26/19% for papillary, 36/16% for invasive mucinous and 25/18% for colloid subtypes; the variation depended on case numbers.

Survival curves for each subtype were further assessed by T stage (Figure 3). A striking feature for lepidic and papillary T1 survival plots was their apparent linearity. In T1, 5-year survival for lepidic patients was 80%, for invasive mucinous 75%, for papillary 65%, for colloid 55% and lowest for adenocarcinoma NOS 45%. Advanced T stages were associated with worse survival and for T4, 1-year survival was equal for lepidic, papillary and invasive mucinous types (55%) and lower in colloid (35%) and adenocarcinoma NOS (30%). The 5-year survival in T4 was about 20% for lepidic, papillary and invasive mucinous types, 10% for adenocarcinoma NOS, but only 1% in colloid adenocarcinoma. Survival by T stage is shown in Figure S1. Female survival was better than male survival for the lepidic type at all stages and for colloid adenocarcinoma at T1 and T2. For the other subtypes, female advantage was clear for T1.

Figure 3 Survival curves for each adenocarcinoma subtype by T stage. The dashed vertical lines mark 1-, 5- and 10-year time points. NOS, not otherwise specified; T, stage.

We further refined survival analysis for T1 (top) and T2 (bottom) considering only cases with N0 and M0 (Figure S2). For T1, all female survival curves, except for the colloid subtype, were visually linear, as were male plots for lepidic and papillary subtypes. Case numbers for T2 were low, and the interpretation was less clear; however, lepidic survival for men and women was close to linear up to 10 years.

The data were modelled further using the Weibull distribution; the plots for males and females, and for T1 and T2 (N0, M0) are shown in Figure 4 with Weibull shape parameter k. The solid lines show Weibull fits to the data. Data are shown up to 200 months, except for some papillary and colloid sets with few cases. Because of low case numbers, no data for invasive mucinous type were shown. For adenocarcinoma NOS, male and female T1 k-values were slightly over 1.00 (1.09 and 1.26), while k-values for T2 cases were essentially 1.00. For lepidic type, all k-values were above 1.00 (except male T2, 0.91). For papillary type, case numbers were few and k-values were high. For colloid type, all k-values were below 1.00, except for female T2 (1.40).

Figure 4 Fitting of Weibull survival distribution model to adenocarcinoma subtypes (blue line), superimposed on empirical Kaplan-Meier survival estimates (as survival probability). Shadings show 95% CIs for the empirical Kaplan-Meier curves. Weibull shape parameter k is shown for each subgroup. CI, confidence interval; NOS, not otherwise specified.

As comparison, we show Weibull-modelled survival data for T3 and T4 (N0, M0) adenocarcinoma NOS (Figure S3). At longer follow-up times for T3 cases, the model fits were not optimal. For T3 cases, k-values were about 0.9, and for T4 cases, they were 0.85.

As a second comparison, we wanted to test the Weibull distribution for individuals in the unselected background population, 60- and 80-year-olds at the beginning of the follow-up (Figure S4). Model fits were excellent as they superimposed the empirical estimates. For 60-year-olds, k-values were 1.46 for men and women. For 80-year-olds, female k-values were higher than the male one (1.52 vs. 1.30).

Discussion

Diagnostic classification aims to define homogeneous subgroups that could be similarly treated, and that would have identical outcomes. In cancer, TNM classification has been developed and improved for that purpose, and it has proven clinical merits, also in LC. We tested here WHO-defined adenocarcinoma subtypes for uniform survival kinetics with the Kaplan-Meier method and for homogeneity using the Weibull distribution for fitted survival curves. Four subtypes were included: lepidic, papillary, invasive mucinous and colloid, and these were compared to adenocarcinoma NOS. For invasive mucinous and adenocarcinoma NOS, T4 stages were commonest, while for the others, T1 (lepidic) or T1 and T2 were the commonest stages at diagnosis. Accordingly, lepidic type showed the best 5-year survival of 0.59 for women, who generally survived better than men (Table S2). Papillary and invasive mucinous had about equal survival (0.51 and 0.46 among women); women with colloid type (0.36) survived somewhat better than those with adenocarcinoma NOS (0.24).

Survival differences may reflect different growth patterns of adenocarcinoma subtypes. Lepidic subtype is characterized by neoplastic cells growing along the surface of alveolar spaces forming the lepidic pattern but without destroying the underlying architecture (10,32). In the papillary subtype, invasive cells are arranged in layers that grow by filling alveolar spaces and invading surrounding lung tissue (10,32). The mucinous invasive subtype is characterized by columnar cells containing abundant intracytoplasmic mucin and penetrating the alveolar lining or filling the alveolar spaces (10,32). Colloid subtype showed the lowest survival; it is histologically characterized by pools of extracellular mucin and destructive invasion, replacing native tissue with mucin (10,32). The subtypes display distinct mutation patterns, which provide therapeutic targets but, unfortunately, the national cancer registry does not collect such data (10).

Survival curves for adenocarcinoma subtypes were typically curvilinear, as is known for cancers in general (Figure 2). They start with a steep decline when fatally ill patients are dying and then bend for a more linear slope when surviving patients are slowly dying. However, when we analyzed survival data by T stage, survival slopes for T1 of the lepidic and papillary types for both sexes were almost straight lines (Figure 3). We then continued for all subtypes considering T1 and T2 classes with N0 and M0 and found a consistent tendency towards linear survival slopes (up to 200 months, 17 years, Figure 4). The likely explanation is that patients diagnosed with T1 and T2 lacking metastasis are relatively homogeneous groups for whom treatment has been successful and who die in age-related stochastic (random) process unrelated to their adenocarcinoma. We searched global LC literature for previous evidence on linear type of survival experience in LC, but all data, even on localized adenocarcinoma of N0, M0, showed bending slopes with relatively steep initial decline (33,34). However, a large Chinese study on stage I LC showed linear survival up to 10 years until 2018, without further comments (35).

The general interpretation of the Weibull results is either time-related increasing mortality (k>1.0) or decreasing mortality (k<1.0), as completely linear survival experience (k=1.0) with constant mortality would be rare among humans (25,26). Weibull modelling on unselected 60-year-old men and women from the background population showed almost linear survival rates with Weibull k-values of 1.46; for 80-year-old healthy persons, k-values were 1.30 (men) and 1.52, and survival curves modestly bended after more than 10 years of follow-up, most likely due to age-related mortality. Weibull k-values for lepidic and papillary T1 and T2 stages (N0, M0) were largely over 1.00, although case numbers for papillary patients were low and model fits were not optimal (Figure 4). The positive k-values for patients with adenocarcinoma subtypes (stages T1 and T2) suggest low disease-related mortality, maybe indicating slow appearance of metastases and reasonable homogeneity of diagnostics. These k-values were only slightly below those of the unselected background population with inherent age-related mortality. In the same analysis, male and female k-values for T1 adenocarcinoma NOS were marginally positive (1.09 and 1.26), while for T2 they were 0.98 and 1.00. In T3 and T4 adenocarcinoma NOS, k-values were below 1.00, typically suggesting early killing by fatal metastases and better survival of the remaining patients (Figure S3).

Large survival studies have been published from the SEER database but often considering a single subtype. For lepidic patients, 50% survival has been reported to vary between 30 months and over 80 months, which is in the range that we observed (Figure 2) (20,21). For colloid type, the SEER study separated patients undergoing surgery (57%), whose 50% survival was over 80 months, compared to those without surgery of 15 months (22). Our data were over 10 months for men and under 20 months for women (Figure 2). For papillary type, the SEER data gave 50% survival at 25 months, compared to our 40 and 60 months for men and women, respectively (17).

WHO has revised the classification of adenocarcinoma many times; in the 2004 publication, ‘adenocarcinoma mixed subtype’ was the most frequent subtype, representing 80% of all (36). A further connotation was that adenocarcinomas presenting purely a single subtype were uncommon (36). In later WHO classifications the subtypes were further refined, but apparently both thorax clinics and cancer registries have not systematically followed these classifications. For example, the recent European Society of Medical Oncology (ESMO) clinical guidelines on LC make only a statement that ‘for adenocarcinoma morphological features should be clearly presented’ (37).

The limited subtyping of adenocarcinoma in Sweden and the lack of subtypes such as acinar, is also the main limitation of the present study, reducing the study population. However, we could include lepidic and papillary subtypes, which are included (with acinar type) as sentinel histologies in the WHO 2021 grading classification of invasive nonmucinous adenocarcinomas (10). We have no guarantee about diagnostic consistency as the definitions of subtypes may have shifted in the course of the 16 years covered, in response to the WHO classification changes. We found only one Swedish study where three adenocarcinoma subtypes were used and the total number was 317, mainly on stages 1 and 2 (38); histological description of LC samples was published earlier (39). In view of the diagnostic consistency, it may be an advantage that a relatively large number of samples were obtained from the Stockholm area, dominated by Karolinska University Hospital, assuming the classification principles were easier to keep uniform in a few locations than in the whole country. Survival data from Stockholm did not deviate from the overall survival data in this cohort. Other evidence on homogeneity of survival data was the relatively low variability of the survival data: 95% CIs of survival estimates ranging from 13% to 36% for male and 8% to 19% for female 1-year survival (Table S2).

Conclusions

In conclusion, we documented female survival advantage for all adenocarcinoma subtypes, many of which were statistically significant. Survival declined in order: lepidic, papillary, invasive mucous, colloid and adenocarcinoma NOS. Survival in T1 stage was superior and T4 inferior for all subtypes. Survival in T1 and T2 classes with N0 and M0 for lepidic, papillary and adenocarcinoma NOS types was close to linear and the Weibull k-values were 1.00 or slightly higher (except for papillary type with small case numbers), suggesting that patients were homogeneously diagnosed and treated. Survival benefits of lepidic and papillary subtypes over adenocarcinoma NOS give justification for adenocarcinoma subclassification and also provide an independent validation of the WHO 2021 adenocarcinoma grading classification, specifying lepidic as grade 1 and papillary (and acinar) as grade 2 against grade 3 of high-grade histological subtypes (10). Lung adenocarcinoma remains a life-threatening disease and better recognition of adenocarcinoma subtypes, together with molecular and clinical data, could help individualize treatment combinations for each patient.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://atm.amegroups.com/article/view/10.21037/atm-2026-0046/rc

Data Sharing Statement: Available at https://atm.amegroups.com/article/view/10.21037/atm-2026-0046/dss

Peer Review File: Available at https://atm.amegroups.com/article/view/10.21037/atm-2026-0046/prf

Funding: This study was supported by the SALVAGE project (No. CZ.02.01.01/00/22_008/0004644), co-financed by the European Union and the state budget of the Czech Republic; the Cooperatio Program, research area SURG; Jane and Aatos Erkko Foundation; Sigrid Juselius Foundation; Finnish Cancer Organizations; and Helsinki University Central Hospital.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://atm.amegroups.com/article/view/10.21037/atm-2026-0046/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 Regional Ethics Review Board in Lund University, February 6, 2013 (Reference 2012/795 and subsequent amendments). The Regional Ethics Review Board in Lund University waived the need to obtain informed consent.

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. Arnold M, Rutherford MJ, Bardot A, et al. Progress in cancer survival, mortality, and incidence in seven high-income countries 1995-2014 (ICBP SURVMARK-2): a population-based study. Lancet Oncol 2019;20:1493-505. [Crossref] [PubMed]
2. Hemminki K, Zitricky F, Försti A, et al. Age-Specific Survival in Lung Cancer in the Nordic Countries through a Half Century. J Biomed Res Environ Sci 2024;5:996-1005.
3. Zhang Y, Vaccarella S, Morgan E, et al. Global variations in lung cancer incidence by histological subtype in 2020: a population-based study. Lancet Oncol 2023;24:1206-18. [Crossref] [PubMed]
4. 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]
5. Désage AL, Picot T, Forest F. Towards a clinically applicable histomolecular classification of lung adenocarcinomas? Transl Lung Cancer Res 2023;12:953-6. [Crossref] [PubMed]
6. Yoshizawa A, Motoi N, Riely GJ, et al. Impact of proposed IASLC/ATS/ERS classification of lung adenocarcinoma: prognostic subgroups and implications for further revision of staging based on analysis of 514 stage I cases. Mod Pathol 2011;24:653-64. [Crossref] [PubMed]
7. 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]
8. Zha L, Matsu-Ura T, Sluka JP, et al. Morphological basis of the lung adenocarcinoma subtypes. iScience 2024;27:109742. [Crossref] [PubMed]
9. Lengel HB, Connolly JG, Jones GD, et al. The Emerging Importance of Tumor Genomics in Operable Non-Small Cell Lung Cancer. Cancers (Basel) 2021;13:3656. [Crossref] [PubMed]
10. Nicholson AG, Tsao MS, Beasley MB, et al. The 2021 WHO Classification of Lung Tumors: Impact of Advances Since 2015. J Thorac Oncol 2022;17:362-87. [Crossref] [PubMed]
11. Xin S, Wen M, Tian Y, et al. Impact of histopathological subtypes on invasive lung adenocarcinoma: from epidemiology to tumour microenvironment to therapeutic strategies. World J Surg Oncol 2025;23:66. [Crossref] [PubMed]
12. Duma N, Santana-Davila R, Molina JR. Non-Small Cell Lung Cancer: Epidemiology, Screening, Diagnosis, and Treatment. Mayo Clin Proc 2019;94:1623-40. [Crossref] [PubMed]
13. Guidry K, Vasudevaraja V, Labbe K, et al. DNA Methylation Profiling Identifies Subgroups of Lung Adenocarcinoma with Distinct Immune Cell Composition, DNA Methylation Age, and Clinical Outcome. Clin Cancer Res 2022;28:3824-35. [Crossref] [PubMed]
14. Jhala H, Harling L, Rodrigo A, et al. Clinicopathological predictors of survival in resected primary lung adenocarcinoma. J Clin Pathol 2022;75:310-5. [Crossref] [PubMed]
15. Tsao MS, Marguet S, Le Teuff G, et al. Subtype Classification of Lung Adenocarcinoma Predicts Benefit From Adjuvant Chemotherapy in Patients Undergoing Complete Resection. J Clin Oncol 2015;33:3439-46. [Crossref] [PubMed]
16. Yu Y, Jian H, Shen L, et al. Lymph node involvement influenced by lung adenocarcinoma subtypes in tumor size ≤3 cm disease: A study of 2268 cases. Eur J Surg Oncol 2016;42:1714-9. [Crossref] [PubMed]
17. Zhang Y, Xie H, Zhang Z, et al. The Characteristics and Nomogram for Primary Lung Papillary Adenocarcinoma. Open Med (Wars) 2020;15:92-102. [Crossref] [PubMed]
18. Tang H, Qiao C, Wang Y, et al. Characteristics and Prognostic Nomogram for Primary Lung Lepidic Adenocarcinoma. Can Respir J 2022;2022:3676547. [Crossref] [PubMed]
19. Meng FJ, Sun ZN, Wang ZN, et al. Prognostic factors and survival outcome of primary pulmonary acinar cell carcinoma. Thorac Cancer 2021;12:2439-48. [Crossref] [PubMed]
20. Zhang G, Wang X, Jia J, et al. Development and validation of a nomogram for predicting survival in patients with surgically resected lung invasive mucinous adenocarcinoma. Transl Lung Cancer Res 2021;10:4445-58. [Crossref] [PubMed]
21. Chen Z, Li M, Ma K, et al. Analysis of the clinicopathological characteristics, genetic phenotypes, and prognostic of pure mucinous adenocarcinoma. Cancer Med 2020;9:517-29. [Crossref] [PubMed]
22. Zhang J, Liu D. A novel nomogram to predict the overall survival of patients with colloid adenocarcinoma of the lung. Transl Cancer Res 2021;10:759-67. [Crossref] [PubMed]
23. Caso R, Sanchez-Vega F, Tan KS, et al. The Underlying Tumor Genomics of Predominant Histologic Subtypes in Lung Adenocarcinoma. J Thorac Oncol 2020;15:1844-56. [Crossref] [PubMed]
24. Xu JY, Zhang C, Wang X, et al. Integrative Proteomic Characterization of Human Lung Adenocarcinoma. Cell 2020;182:245-261.e17. [Crossref] [PubMed]
25. Baak JPA, Li H, Guo H. Clinical and Biological Interpretation of Survival Curves of Cancer Patients, Exemplified With Stage IV Non-Small Cell Lung Cancers With Long Follow-up. Front Oncol 2022;12:837419. [Crossref] [PubMed]
26. Plana D, Fell G, Alexander BM, et al. Cancer patient survival can be parametrized to improve trial precision and reveal time-dependent therapeutic effects. Nat Commun 2022;13:873. [Crossref] [PubMed]
27. Pukkala E, Engholm G, Højsgaard Schmidt LK, et al. Nordic Cancer Registries - an overview of their procedures and data comparability. Acta Oncol 2018;57:440-55. [Crossref] [PubMed]
28. Zitricky F, Sundquist K, Sundquist J, et al. Increase in second primary lung cancers in a nation-wide cohort study from Sweden. Thorax 2026;thorax-2025-223426.
29. Harrell FE. Regression modeling strategies: with applications to linear models, logistic regression, and survival analysis. New York: Springer; 2001.
30. Rothman KJ, Greenland S. Modern Epidemiology. 2nd ed. Philadelphia: Lippincott-Raven; 1998.
31. Hunter MK, Russell AJ, Maldonado G, et al. Exploring the proper use of p-values and confidence intervals in leading epidemiology journals. Glob Epidemiol 2026;11:100247. [Crossref] [PubMed]
32. Thunnissen E, Beasley MB, Borczuk A, et al. Defining Morphologic Features of Invasion in Pulmonary Nonmucinous Adenocarcinoma With Lepidic Growth: A Proposal by the International Association for the Study of Lung Cancer Pathology Committee. J Thorac Oncol 2023;18:447-62. [Crossref] [PubMed]
33. Liu C, Fang C, Shen D. Comparative survival analysis of stage T1-T2N0M0 lung squamous cell carcinoma and adenocarcinoma using SEER data, and nomogram analysis for early-stage lung squamous cell carcinoma. Transl Cancer Res 2025;14:1691-709. [Crossref] [PubMed]
34. Tissera S, Billah B, Brand M, et al. Stage-Specific Guideline Concordant Treatment Impacts on Survival in Nonsmall Cell Lung Cancer: A Novel Quality Indicator. Clin Lung Cancer 2024;25:e466-78. [Crossref] [PubMed]
35. Wang C, Shao J, Song L, et al. Persistent increase and improved survival of stage I lung cancer based on a large-scale real-world sample of 26,226 cases. Chin Med J (Engl) 2023;136:1937-48. [Crossref] [PubMed]
36. IARC. Tumours of the lung, pleura, thymus and heart Travis W, Brambilla E, Müller-Hermekinh K, Harris, CC, editor: World Health Organisation; 2004.
37. Hendriks LE, Kerr KM, Menis J, et al. Non-oncogene-addicted metastatic non-small-cell lung cancer: ESMO Clinical Practice Guideline for diagnosis, treatment and follow-up. Ann Oncol 2023;34:358-76. [Crossref] [PubMed]
38. Salomonsson A, Micke P, Mattsson JSM, et al. Comprehensive analysis of RNA binding motif protein 3 (RBM3) in non-small cell lung cancer. Cancer Med 2020;9:5609-19. [Crossref] [PubMed]
39. Micke P, Mattsson JS, Djureinovic D, et al. The Impact of the Fourth Edition of the WHO Classification of Lung Tumours on Histological Classification of Resected Pulmonary NSCCs. J Thorac Oncol 2016;11:862-72.