Prognostic analysis and development of a predictive model for pulmonary invasive mucinous adenocarcinoma

Introduction

Lung cancer is currently the leading cause of cancer-related mortality worldwide, with its incidence and mortality rates increasing year on year (1,2). In 2015, the World Health Organization updated its cancer classification guidelines, renaming “mucinous bronchoalveolar adenocarcinoma” as “invasive mucinous adenocarcinoma” (IMA), drawing significant attention to this specific subtype (3). IMA is a distinct subtype of lung adenocarcinoma, characterized by invasive columnar or goblet cell patterns, basally located nuclei, and abundant intracytoplasmic mucin (4).

IMA accounts for only 2% to 5% of invasive lung adenocarcinomas, and patients with IMA following surgical treatment have a reported 5-year survival rate of 69.2% (5,6). Compared to other adenocarcinoma subtypes, IMA has notably distinct molecular, clinicopathological, and radiological features (4,5,7). Although primary IMA is relatively rare, its prognostic characteristics remain debatable due to its low frequency and limited availability of survival data (8).

Management of IMA poses significant challenges due to its diagnostic and therapeutic complexities. First, IMA is often misdiagnosed as inflammatory nodules, tuberculosis, pulmonary diffuse lesions, or hamartomas, leading to delayed treatment and poor prognosis. Second, traditional chemotherapy and targeted therapies demonstrate limited efficacy in this disease (9). Therefore, a survival analysis-based comprehensive evaluation of prognostic factors in IMA is critically needed, along with the development of an accurate predictive model for individualized outcome assessment. Additionally, the risk factors influencing the prognosis of IMA remain poorly understood. Nomograms, which are visual tools based on multivariate regression analysis, integrate multiple predictive indicators to quantify risk and provide insights into oncological prognoses (10,11). By clarifying the relationships between variables, nomograms enhance the operability and practicality of predictive models (12). However, no nomograms have been developed specifically to forecast the long-term survival outcomes for patients with IMA.

We conducted a study to assess the survival outcomes of patients with IMA, analyze prognostic factors at the First Affiliated Hospital of Zhengzhou University, and develop a nomogram to predict the 1-, 3-, and 5-year survival rates for these patients. The model was subsequently validated using data from the Surveillance, Epidemiology, and End Results (SEER) database. Ultimately, we aimed to create a highly accurate nomogram with strong clinical utility, offering individualized guidance for treatment in clinical practice. We present this article in accordance with the TRIPOD reporting checklist (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-755/rc).

Methods

Participants

The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the ethics committee of the First Affiliated Hospital of Zhengzhou University (No. 2024-KY-0674-001). The First Affiliated Hospital of Zhengzhou University is one of the largest comprehensive medical centers in China, with over 10,000 inpatient beds and approximately 20,000 outpatient visits daily. It offers extensive specialized care for pulmonary IMA through its departments of respiratory medicine, thoracic surgery, oncology, and radiotherapy. Due to the retrospective nature of the study and the anonymity of the patients, a waiver of informed consent was obtained from the ethics committee of the First Affiliated Hospital of Zhengzhou University.

Between January 2015 and December 2022, we included 310 patients diagnosed with pure IMA at the First Affiliated Hospital of Zhengzhou University. Patients were considered eligible for inclusion if they had a confirmed diagnosis of pulmonary IMA, as verified by senior pathologists, through either percutaneous lung biopsy or surgical resection. Moreover, only patients with a single primary malignant tumor were included, while those with mixed malignancies, incomplete data, or who were pregnant or lactating were excluded. Follow-up was extended to July 2023.

The data collected included demographic characteristics, tumor features, immunohistochemical and genetic profiles, treatment specifics, and patient outcomes. tumor-node-metastasis (TNM) staging was based on pathological staging for patients who underwent surgical therapy as the initial treatment and on clinical staging for those who did not. The primary endpoint was overall survival (OS), defined as the duration from diagnosis to death or last contact. Cumulative survival rates were calculated via Kaplan-Meier curves.

Validation data

For external validation, SEER*Stat software version 8.4.2 (National Cancer Institute) was used to identify 378 patients with IMA diagnosed between January 2006 and December 2015 according to the International Classification of Diseases for Oncology, 3^rd Edition (ICD-O-3) diagnostic codes (8253/3, corresponds to IMA) from the SEER database (http://www.seer.cancer.gov). Follow-up for these cases was continued until December 2020. Similar to the training cohort, the inclusion criteria restricted participation to patients with a single primary malignant tumor, while those diagnosed postmortem or with incomplete data were excluded. Variables collected for this cohort included survival time and status, age, gender, tumor stage, and treatment details.

Development and validation of a prognostic nomogram

Survival analysis was conducted using univariate and multivariate Cox proportional hazards regression to identify prognostic factors in IMA patients.

Variables with statistical significance (P<0.05) in the univariate analysis were incorporated into the multivariate analysis to identify independent prognostic factors. Significant prognostic variables identified in the multivariate Cox regression analysis, along with clinically relevant factors, were used to develop a nomogram to predict 1-, 3-, and 5-year survival rates.

Baseline data between our cohort and the SEER cohort were compared using the Pearson chi-squared test. Additionally, propensity score matching (PSM) with nearest-neighbor matching (1:1 ratio; caliper =0.02) was performed to balance clinical characteristics between the training and validation cohorts.

Statistical analysis

Cumulative survival rates for the two cohorts (ours and the SEER cohorts) were calculated using SPSS 26 (IBM Corp., Armonk, NY, USA). Survival curves for each cohort were generated using the “survminer” package in R version 4.2.2 (The R Foundation for Statistical Computing, Vienna, Austria). Chi-squared tests for cohort comparisons were performed in R, with PSM executed using the “MatchIt” package. Univariate and multivariate Cox proportional hazards regression analyses, nomogram construction, concordance index (C-index) calculation, calibration curves, Kaplan-Meier curves, and log-rank tests were conducted using the “rms”, “foreign”, and “survival” packages in R. Receiver operating characteristic (ROC) curves were generated with the “survivalROC” R package. Decision curve analysis (DCA) was performed using the “dcurves” and “ggplot2” R packages. Hazard ratios (HRs) and 95% confidence intervals (CIs) for prognostic variables were calculated. All statistical analyses were conducted at a significance level of 0.05.

Results

Baseline characteristics

Tables 1,2 summarize the demographic characteristics, tumor features, immunohistochemical markers, genetic profiles, and treatment options for patients with IMA at the First Affiliated Hospital of Zhengzhou University, who were selected as the training group. All enrolled cases were independently reviewed and confirmed by two senior pathologists. The median age of the patients was 61 years, with 53.9% being female and 29.7% identifying as smokers. Notably, 86.5% of patients presented with nodules detected by computed tomography (CT), and 57.7% were classified as stage I or II according to the TNM classification.

Of the 310 patients, 270 underwent testing for the thyroid transcription factor-1 (TTF-1) immunohistochemical index, and 199 underwent testing for the Napsin A immunohistochemical index. Furthermore, genetic testing for anaplastic lymphoma kinase (ALK), epidermal growth factor receptor (EGFR), and Kirsten rat sarcoma viral oncogene homolog (KRAS) mutations was conducted in 277, 250, and 212 patients, respectively. The results indicated positivity for TTF-1 in 53.3% of the cases, Napsin A in 42.2%, ALK gene alterations in 18.8%, EGFR mutations in 4.8%, and KRAS mutations (including mutations of G12A, G12C, G12D, G12S, G12V, G13D, p.Q61H and L56V) in 45.8%.

Surgical treatment was performed in 71.6% (n=222) of all patients, while 57.4% received primary platinum-based chemotherapy. Of all the patients, targeted therapy, mainly targeting ALK gene alterations, was administered to 7.0% of the patients, immunotherapy with programmed cell death protein 1 (PD-1)/programmed death-ligand 1 (PD-L1) inhibitors to 8.7%, and radiation therapy to 3.9%. Of the 222 surgical cases, 118 received surgery only, 4 had neoadjuvant treatment without further therapy, and 100 received adjuvant therapies. In the 88 unresectable cases, 7 received no treatment, 55 had chemotherapy alone, 2 had only targeted therapy, and 24 received combinations of systemic therapies.

Cumulative survival rates

The median follow-up period for patients with IMA was 33 months, with cumulative survival rates of 84.1%, 67.0%, and 51.5% at 1, 3, and 5 years, respectively. In the subgroup analysis based on TNM staging according to the 8^th edition of the American Joint Committee on Cancer (AJCC) staging system, 227 patients were classified as stage IIIA or below encompassing stages I, II, and IIIA, with cumulative survival rates at 1, 3, and 5 years of 94.7%, 82.3%, and 66.2%, respectively. In contrast, 83 patients were classified as stage IIIB or IV; their cumulative survival rates at 1, 3, and 5 years were 55.4%, 24.1%, and 9.0%, respectively, with a median survival time of 16 months (Figure 1).

Figure 1 Kaplan-Meier survival curves for patients with IMA classified and stratified by stages. IMA, invasive mucinous adenocarcinoma.

Prognostic factors of IMA

Univariate analysis identified several significant predictors of survival. Factors associated with an increased risk of death included older age (70–79 years: HR =5.249; P<0.001) and absence of surgery (HR =10.615; P<0.001). In contrast, the female sex (HR =0.555; P=0.003), non-smoking status (HR =0.338; P<0.001), and undergoing scheduled surgery were associated with improved survival. Additionally, pneumonia-type manifestations on CT (HR =3.512; P<0.001), advanced T stage (T4: HR =16.116; P<0.001), higher N stage (N3: HR =7.062; P<0.001), metastatic disease (M1: HR =7.383; P<0.001), and advanced TNM stage (III + IV: HR =9.946; P<0.001) were associated with increased risk (Table 2).

Kaplan-Meier curves and log-rank tests further confirmed the significant differences in survival rates associated with these variables. Specifically, patients aged 70–79 years, males, those with a history of smoking, pneumonia-type CT manifestations, advanced TNM stages, and those who received chemotherapy or immunotherapy demonstrated poorer OS. In contrast, patients with positive TTF-1 expression and ALK gene alterations or those who underwent surgery showed improved OS. However, positivity for Napsin A or EGFR and KRAS gene mutations, as well as radiation therapy and targeted therapy, was not significantly associated with OS (Figure 2).

Figure 2 Survival curves for patients with IMA in the training cohort stratified by key variables. ALK, anaplastic lymphoma kinase; CT, computed tomography; IMA, invasive mucinous adenocarcinoma; M, metastasis; N, node; T, tumor; TNM, tumor-node-metastasis; TTF-1, thyroid transcription factor-1.

Prognostic variables that showed statistical significance in the univariate analysis were further incorporated into the multivariate Cox regression analysis. After adjustments for confounders, multivariate analysis confirmed that older age (70–79 years: HR =3.849; P=0.002), advanced T stage (T4: HR =4.998; P=0.003), metastatic disease (M1: HR =2.073; P=0.02), and absence of surgery (HR =2.731; P=0.005) were independently associated with increased mortality. Conversely, non-smoking status (HR =0.334; P=0.02) remained a protective factor for survival. In contrast, variables such as gender, CT manifestation type, and N stage were excluded from the final model.

PSM between our and the SEER cohorts

The baseline characteristics of patients with IMA between our cohort and the SEER cohort were compared, revealing significant differences across several variables (Table 3). Specifically, our cohort had a lower proportion of patients with IMA aged 70–79 years (P<0.001), those in stages I + II (P=0.01), and those undergoing surgery (P=0.004), while the proportion of patients receiving chemotherapy was significantly higher (P<0.001). Furthermore, there were notable differences in the T and N stage distribution between the two cohorts (P<0.001).

For nomogram construction, our cohort was designated as the training cohort, while the SEER cohort was designated as the validation cohort. We applied a nearest-neighbor PSM method to address these baseline differences, thereby achieving a 1:1 match between patients in the two cohorts. After matching, 212 patients were identified in each group, and no significant differences were observed across any of the variables (P>0.05), confirming the effectiveness of the matching process (Table 3).

Development of a prognostic nomogram

Independent prognostic factors identified through multivariate Cox proportional hazards regression analysis—including age, smoking history, T stage, M stage, and surgical treatment—were incorporated into the nomogram. Chemotherapy was also considered within this model. The nomogram indicated that T stage exerted the most significant influence on OS, followed by age. Each prognostic factor was assigned a specific score within the nomogram, and the cumulative score for each patient was used to predict their 1-, 3-, and 5-year survival (Figure 3).

Figure 3 Nomogram for predicting 1-, 3-, and 5-year survival rates in patients with IMA. IMA, invasive mucinous adenocarcinoma; M, metastasis; T, tumor.

Estimates of the prognostic nomogram

The prognostic model demonstrated robust performance, with a C-index of 0.879 in the training cohort and area under the curve (AUC) values of 0.897, 0.924, and 0.856 for 1-, 3-, and 5-year survival predictions, respectively (Figure 4). Calibration curves (Figure 5) exhibited excellent congruence between predicted and actual outcomes, and the DCA curves (Figure 6) confirmed a clinical net benefit. The model also performed well in the validation cohort, achieving a C-index of 0.776 and AUC values of 0.741, 0.835, and 0.880 for 1-, 3-, and 5-year survival predictions, respectively, revealing the model’s accuracy, strong predictive capacity, and clinical utility across different cohorts.

Figure 4 ROC curves for 1-, 3-, and 5-year OS based on the nomogram. (A-C) Training cohort. (D-F) Validation cohort. AUC, area under the curve; FPR, false positive rate; OS, overall survival; ROC, receiver operating characteristic; TPR, true positive rate.

Figure 5 Calibration curves for 1-, 3-, and 5-year OS based on the nomogram. (A-C) Training cohort. (D-F) Validation cohort. OS, overall survival.

Figure 6 DCA curves for 1-, 3-, and 5-year OS based on the nomogram. (A-C) Training cohort. (D-F) Validation cohort. In all six panels, the blue line (nomogram) lies above both the red and green lines across a range of threshold probabilities. This indicates that using the nomogram provides a higher net benefit compared to treating all or none, both in the training and validation cohorts. The consistency across different time points and datasets (training vs. validation) supports the robustness and generalizability of the nomogram. DCA, decision curve analysis; OS, overall survival.

Discussion

IMA, previously known as “bronchioloalveolar carcinoma”, is a rare variant of pulmonary adenocarcinoma (13). The scarcity of this condition has historically limited sample sizes in a previous study (13), leading to ongoing debates regarding its prognosis for survival and associated clinical characteristics. Thus, more comprehensive studies with larger cohorts are needed to clarify the prognosis and related factors in patients with IMA. This study analyzed data from 310 patients with IMA at the First Affiliated Hospital of Zhengzhou University and 378 patients with IMA from the SEER database. Our findings identified independent risk factors for OS in patients with IMA, including age between 70 and 79 years, a history of smoking, advanced T stage, the presence of distant metastases, and the absence of surgical intervention. Additionally, male patients and those with pneumonia-type IMA exhibited poorer prognoses, while positivity for TTF-1 and ALK gene alterations was associated with improved survival outcomes. We also developed a highly accurate nomogram for predicting individual prognosis, achieving a C-index of 0.879 in the training cohort and 0.776 in the validation cohort. This tool offers substantial value in enhancing clinical management and optimizing therapeutic strategies for patients with IMA by providing precise survival predictions tailored to individual risk profiles.

The prognosis of IMA remains controversial, with some studies reporting poor outcomes (14,15) and others suggesting longer survival compared to non-mucinous adenocarcinoma (16). In this study, the 1-, 3-, and 5-year cumulative survival rates for patients with IMA were 84.1%, 67.0%, and 51.5%, respectively, indicating a favorable prognosis. Differences in survival rates across studies may reflect variations in cohort composition, including stage distribution and age. For instance, the SEER database study reported lower survival rates, which may be attributed to a higher proportion of older and advanced-stage patients (17). Our cohort’s higher survival rates may be attributed to a younger age distribution and a higher proportion of patients undergoing surgical intervention.

Our study found that older age, smoking status, advanced T stage, metastatic disease, and the absence of surgery were significantly associated with poorer OS in patients with IMA. Older age, a well-established prognostic factor, has been consistently linked to poorer OS in lung cancer, including IMA, as supported by previous studies (4,18). Older age is often associated with decreased physiological reserve (19), more comorbidities (20), and reduced tolerance to surgery or systemic therapies (21,22), all of which can contribute to poorer survival outcomes. Similarly, our study found that smoking status was strongly associated with poorer OS, aligning with studies reporting the detrimental effects of smoking on lung cancer progression and survival (23,24). Smoking contributes to tumor aggressiveness and resistance to therapy, further worsening prognosis.

The advanced T stage, a core component of the TNM classification system, also emerged as a significant predictor of poorer OS. As documented in the literature, higher pathological stages and larger tumor sizes, which directly affect the T stage, are strongly associated with increased risks of postoperative recurrence and reduced OS (25-27). A larger tumor burden reflects greater invasiveness and often correlates with more extensive disease spread, which limits the efficacy of curative interventions such as surgery.

Moreover, surgery was associated with a significantly lower HR, highlighting its crucial role in IMA management. For early-stage IMA (IA and IB), subgroup analyses in other studies have indicated no additional survival benefits from adjuvant chemotherapy, whereas platinum-based chemotherapy does not appear to improve OS or progression-free survival (PFS) in advanced-stage IMA (28-30). These findings reveal that IMA responds poorly to conventional chemotherapy, positioning surgery as the most effective treatment option. Although immune checkpoint inhibitors (ICIs) have transformed the landscape of lung cancer therapy, their efficacy in IMA is limited. Factors such as KRAS mutations, lower PD-L1 expression, and reduced abundance of CD8⁺ tumor-infiltrating lymphocytes contribute to poor ICI responsiveness (31-33). However, elevated B7-H3 expression in IMA presents a potential immunotherapeutic target (34). These results emphasize the primary role of surgery in improving survival for patients with IMA and highlight the need for novel therapeutic approaches.

Our study revealed the significance of pneumonia-type IMA, which poses diagnostic challenges and exhibits distinct clinical features as compared to the nodule type (35). Among the 310 patients examined, nodule-type IMA was more prevalent (268 cases) than was pneumonia-type IMA (42 cases). Pneumonia-type IMA often presents as large and patchy high-density consolidations on CT, mimicking lobar pneumonia (18). Patients with this type are more likely to experience chronic cough with white, foamy sputum, larger tumor volumes, and a greater number of lymph node metastases. Assessing tumor size in pneumonia-type IMA is challenging due to the coexistence of invasive and mucinous spreading components, with CT often underestimating invasive tumor size as compared to pathology (36). Fluorodeoxyglucose positron emission tomography-CT provides enhanced prognostic information by integrating tumor metabolism and morphology, with higher standardized uptake values correlating with increased tumor invasiveness and poorer prognosis (37).

Molecular biomarkers, such as TTF-1 positivity and ALK gene alterations, seem to be highly associated with IMA prognosis. TTF-1, expressed in 53.3% of cases in our study, was significantly associated with prolonged OS (P=0.02) and has been linked to improved disease-free survival in other studies (38-40). Reduced TTF-1 expression in IMA may be associated with NKX2-1 and KRAS mutations (41,42). ALK gene alterations, including the EML4-ALK fusion, are associated with longer OS and support the use of ALK tyrosine kinase inhibitors (TKIs) in ALK-positive cases (43). Conversely, patients with IMA show high KRAS mutation rates (predominantly G12D) and low EGFR mutation rates, distinguishing it from non-mucinous lung adenocarcinoma (4). Both KRAS and EGFR mutations have been linked to poorer survival, KRAS mutations have been associated with reduced PFS and OS, and EGFR mutations have been correlated with limited responses to EGFR-TKIs (44,45). These findings highlight the need for larger-sample studies to confirm the prognostic implications of KRAS and EGFR mutations in IMA.

This study developed a nomogram that integrates key prognostic factors to provide a comprehensive prognosis assessment for each patient. The nomogram exhibited strong predictive performance, evidenced by a high C-index in both the training and validation cohorts, indicating its robustness. ROC curve analysis further confirmed its predictive accuracy, revealing high AUC values for survival predictions, while the calibration plots demonstrated excellent agreement between the predicted and actual survival outcomes. Additionally, DCA supported the clinical utility of the nomogram, highlighting its potential to inform individualized treatment decisions and optimize patient management strategies. By providing precise, personalized survival predictions, the model significantly enhances the prognostic assessment and tailoring of treatment strategies for patients with IMA. Future efforts could focus on expanding the model to include emerging molecular markers, such as B7-H3 expression, to enhance its clinical applicability and address the unmet need for targeted therapeutic approaches in IMA management.

Several limitations to this study should be acknowledged. First, although most patients were diagnosed before 2021, the relatively short follow-up for those enrolled later may impact the assessment of long-term survival. Sensitivity analyses excluding recent cases confirmed the robustness of our findings (data not shown). Second, due to incomplete and non-standardized documentation of recurrence events, only OS was assessed to ensure the robustness and reliability of the predictive model. Third, as a retrospective study, there was inherent selection bias, and the confounding factors (such as N stages) could not be fully accounted for. Fourth, the detailed molecular profiling and PD-L1 expression were not systematically analyzed. Fifth, critical clinical variables such as Eastern Cooperative Oncology Group (ECOG) performance status and comorbidities were not included in the analysis. These factors are known to influence treatment eligibility and survival outcomes independently of tumor characteristics. Prospective studies incorporating longitudinal data and contrast-enhanced imaging modalities could improve the model’s accuracy and utility. Future prospective and multicenter studies incorporating comprehensive clinical, molecular, and imaging data are necessary to validate and further refine these findings.

Conclusions

This study provides valuable insights into the prognostic factors influencing OS in patients with IMA and has developed a novel, robust nomogram for individualized survival prediction. Key prognostic factors, such as age, smoking status, T stage, M stage, and surgical treatment, were identified and integrated into the model, demonstrating strong predictive accuracy and clinical utility in both the training and validation cohorts. By offering precise, personalized survival predictions, the nomogram can serve as a practical tool for enhancing the prognostic assessment of patients with IMA. Future studies incorporating molecular biomarkers, advanced imaging metrics, and prospective validation in multicenter settings are essential to further refine and expand the model’s clinical applicability. These efforts will contribute to a deeper understanding of IMA and pave the way for more accurate prognostic assessments and targeted therapeutic approaches.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-755/rc

Data Sharing Statement: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-755/dss

Peer Review File: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-755/prf

Funding: This work was funded by the Key Scientific and Technological Project of Henan Province (No. 242102311069).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-755/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. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. This study was approved by the ethics committee of the First Affiliated Hospital of Zhengzhou University (No. 2024-KY-0674-001). Due to the retrospective nature of the study and the anonymity of the patients, a waiver of informed consent was obtained from the ethics committee of the First Affiliated Hospital of Zhengzhou University.

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/.

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(English Language Editor: J. Gray)