Pulmonary invasive mucinous adenocarcinoma

Efficacy of Systemic Treatments in Patients With Metastatic Lung Invasive Mucinous Adenocarcinoma

BACKGROUND

Invasive mucinous adenocarcinoma (IMA) is a rare histological subtype of lung invasive adenocarcinoma with unique clinical, radiological, histopathological, and genomic characteristics. There have been limited studies on the effectiveness of systemic therapy for lung IMA, with conflicting results reported.

METHODS

We retrospectively investigated the medical records of patients diagnosed with lung IMA. Patients who were ≥ 18 years of age and received at least 1 course of treatment for metastatic or locally advanced inoperable disease were included in the study. Archive records of 113 patients diagnosed with IMA were screened for the study.

RESULTS

A total of 41 patients with lung IMA were included. The targetable mutation rate was 20.6% (in 6 of 29 patients). Most patients (83.1%) had received platinum-based chemotherapy as a first-line treatment. The objective response rate (ORR) was 25.7%, and median progression-free survival (PFS) and overall survival (OS) were 8.1 months (95% CI, 5.02-11.2) and 17.5 months (95% CI, 11.7-23.3 months), respectively, in the patients who received chemotherapy. The median PFS and ORR were 20.6 (95% CI, 18.9-66.5) and 66.6%, respectively, in epidermal growth factor receptor (EGFR) mutation-positive patients (n = 3) with relevant targeted therapy. Only 1 patient used oxaliplatin and capecitabine combination (XELOX) as chemotherapy in the second-line treatment and achieved a partial response (PR) at 7.2 months.

CONCLUSION

Platinum-based chemotherapies moderately enhance IMA patients' survival rates. Anti-EGFR-targeted drugs are seen as potentially effective in patients with EGFR driver mutation positive. Large, prospective studies are needed to confirm our findings.

Confronting synchronous multiple primary lung cancers: Navigating the intersection of challenges and opportunities

The increased detection of synchronous multiple primary lung cancers (sMPLC) through advanced computed tomography underscores the necessity for innovative therapeutic approaches. sMPLC typically manifests as ground-glass opacities, mixed ground-glass opacities, and/or solid nodules, predominantly in early-stage, non-smoking female patients, with a majority being adenocarcinomas. The high prevalence of EGFR mutations and considerable heterogeneity among lesions pose distinct diagnostic and therapeutic challenges for sMPLC. This study provides a comprehensive review and analysis of recent clinical and radiological studies, genomic profiling, and the efficacy of the "Surgery + X" treatment model for sMPLC. Additionally, the article discusses several intricate and complex sMPLC cases, shedding light on the disease's complexities and identifying existing gaps and potential breakthroughs in clinical diagnosis, treatment, and research. It underscores the critical role of a multidisciplinary approach and advocates for targeted research on sMPLC, highlighting its potential to impact lung cancer research significantly.

Case of lung fine needle aspiration showing mucinous cells and extracellular mucin

Mucinous neoplasm with extracellular mucin can be challenging to interpret on fine needle aspiration and core biopsies. Determining the biologic origin of the mucin/mucinous cells, that is, benign/incidental versus neoplasm, invasive versus in situ, and primary versus metastatic tumors, requires a thorough multidisciplinary evaluation. The work up of these lesions includes morphologic analysis with ancillary immunohistochemical and/or molecular studies and correlation with clinical and imaging studies. This review outlines a practical approach to the diagnosis of mucinous lesions in the lung with comprehensive review of literature.

Combined CT-Based Radiomics and Clinic-Radiological Characteristics for Preoperative Differentiation of Solitary-Type Invasive Mucinous and Non-Mucinous Lung Adenocarcinoma

Purpose

The clinical, pathological, gene expression, and prognosis of invasive mucinous adenocarcinoma (IMA) differ from those of invasive non-mucinous adenocarcinoma (INMA), but it is not easy to distinguish these two. This study aims to explore the value of combining CT-based radiomics features with clinic-radiological characteristics for preoperative diagnosis of solitary-type IMA and to establish an optimal diagnostic model.

Methods

In this retrospective study, a total of 220 patients were enrolled and randomly assigned to a training cohort (n = 154; 73 IMA and 81 INMA) and a testing cohort (n = 66; 31 IMA and 35 INMA). Radiomics features and clinic-radiological characteristics were extracted from plain CT images. The radiomics models for predicting solitary-type IMA were developed by three classifiers: linear discriminant analysis (LDA), logistic regression-least absolute shrinkage and selection operator (LR-LASSO), and support vector machine (SVM). The combined model was constructed by integrating radiomics and clinic-radiological features with the best performing classifier. Receiver operating characteristic (ROC) curves were used to evaluate models' performance, and the area under the curve (AUC) were compared by the DeLong test. Decision curve analysis (DCA) was conducted to assess the clinical utility.

Results

Regarding CT characteristics, tumor lung interface, and pleural retraction were the independent risk factors of solitary-type IMA. The radiomics model using the SVM classifier outperformed the other two classifiers in the testing cohort, with an AUC of 0.776 (95% CI: 0.664-0.888). The combined model incorporating radiomics features and clinic-radiological factors was the optimal model, with AUCs of 0.843 (95% CI: 0.781-0.906) and 0.836 (95% CI: 0.732-0.940) in the training and testing cohorts, respectively.

Conclusion

The combined model showed good ability in predicting solitary-type IMA and can provide a non-invasive and efficient approach to clinical decision-making.

Surgical prognosis of lung invasive mucinous and non-mucinous adenocarcinoma: propensity score matched analysis

OBJECTIVES

Invasive mucinous adenocarcinoma exhibits distinct prognostic outcomes compared to non-mucinous adenocarcinoma (ADC). This study investigated and compared the clinical outcomes and prognostic factors of invasive mucinous and non-mucinous ADC patients.

METHODS

This retrospective study included patients who underwent curative surgery for ADC between 2011 and 2021. Patient characteristics were balanced using propensity score matching. Cumulative incidence was analysed to evaluate cancer recurrence incidence, and the Kaplan-Meier method was used to calculate overall survival (OS) for each group.

RESULTS

A total of 6101 patients were included. After matching, the non-mucinous group and mucinous groups comprised 798 and 408 patients, respectively. The patients in the mucinous group had a lower recurrence incidence than those in the non-mucinous group (P = 0.014). The recurrence incidence in the mucinous group was between those of grades 1 (P = 0.011) and 2 (P = 0.012) and the OS rates were comparable to those of grades 2 (P = 0.6) and 3 (P = 0.2). Multivariable analysis revealed that the maximal standardized uptake value [hazard ratio (HR): 1.13, P = 0.11] and progressed pathological stages (pStage II, HR: 3.9, P = 0.028; pStage III, HR: 8.33, P = 0.038) served as adverse prognostic factors for the mucinous group.

CONCLUSIONS

Patients with mucinous ADC demonstrated lower recurrence incidence and similar OS rates compared to those with non-mucinous ADC. The recurrence incidence of mucinous ADC was between those of International Association for the Study of Lung Cancer grades 1 and 2, with the OS rates comparable to those of grades 2 and 3.

CLINICAL REGISTRATION NUMBER

None.

Nomogram model for the diagnosis of solitary nodular pulmonary mucinous adenocarcinoma

The objective of this study was to develop a nomogram model based on the natural progression of tumor and other radiological features to discriminate between solitary nodular pulmonary mucinous adenocarcinoma and non-mucinous adenocarcinomas. A retrospective analysis was conducted on 15,655 cases of lung adenocarcinoma diagnosed at our institution between January 2010 and June 2023. Primary nodular invasive mucinous adenocarcinomas and non-mucinous adenocarcinomas with at least two preoperative CT scans were included. These patients were randomly assigned to training and validation sets. Univariate and multivariate analyses were employed to compare tumor growth rates and clinical radiological characteristics between the two groups in the training set. A nomogram model was constructed based on the results of multivariate analysis. The diagnostic value of the model was evaluated in both the training and validation sets using calibration curves and receiver operating characteristic curves (ROC). The study included 174 patients, with 58 cases of mucinous adenocarcinoma and 116 cases of non-mucinous adenocarcinoma. The nomogram model incorporated the maximum tumor diameter, the consolidation/tumor ratio (CTR), and the specific growth rate (SGR) to generate individual scores for each patient, which were then accumulated to obtain a total score indicative of the likelihood of developing mucinous or non-mucinous adenocarcinoma. The model demonstrated excellent discriminative ability with an area under the receiver operating characteristic curve of 0.784 for the training set and 0.833 for the testing set. The nomogram model developed in this study, integrating SGR with other radiological and clinical parameters, provides a valuable and accurate tool for differentiating between solitary nodular pulmonary mucinous adenocarcinoma and non-mucinous adenocarcinomas. This prognostic model offers a robust and objective basis for personalized management of patients with pulmonary adenocarcinomas.

Invasive Mucinous Adenocarcinoma in a Newborn With Antenatally Diagnosed Congenital Pulmonary Airway Malformation: A Case Report

Congenital pulmonary airway malformations (CPAMs) are rare multicystic lung lesions typically diagnosed antenatally. We present a case of a term female neonate with antenatally diagnosed CPAM who required pleuro-amniotic shunting at 22 weeks of gestation. The patient was born with a right-sided pneumothorax and severe cardiorespiratory distress, necessitating extracorporeal membrane oxygenation (ECMO). Chest CT confirmed CPAM, revealing multiple cystic lesions in the right middle lobe and a significant contralateral mediastinal shift. On the second day of life, while on ECMO, the patient underwent a right middle lobectomy and an upper lobe anterior segmentectomy via a posterolateral thoracotomy. Post-surgery cardiac CT showed narrowing of the left pulmonary artery, although a perfusion study indicated normal left lung perfusion. Histopathological examination identified CPAM type 1 with invasive mucinous adenocarcinoma (IMA; stage 1: pT1b), featuring low-to-intermediate cellularity and KRAS G12D mutations. The invasive mucinous component measured at least 15 mm but did not invade the visceral pleura. After a gradual weaning process, the patient was successfully extubated and discharged home after 70 days. To our knowledge, this is the first reported case of CPAM type 1 with IMA that underwent pleuro-amniotic shunting in the second trimester.

Establishment and verification of novel TNM staging system for lung mucinous adenocarcinoma

BACKGROUND

Lung adenocarcinoma is a high-mortality rate cancer. Within this category, Lung mucinous adenocarcinoma (LMAC) is a rare and distinct subtype of lung adenocarcinoma necessitating further investigation. The study was launched to compare the difference of survival features between LMAC and lung non-mucinous adenocarcinoma (LNMAC) and to investigate the significance and demand for developing a new staging system tailored to LMAC.

METHODS

This retrospective study assessed the suitableness of the current staging system for LMAC. It compared the overall survival (OS) between LMAC and LNMAC from 2004 to 2020 (LNMAC: 160,387; LMAC: 6,341) and instituted a novel classification framework for LMAC based on US population. Verification group consisting of patients from two Chinese medical centers from 2010 to 2018 (n = 392) was set to ascertain the applicability of this novel system. The primary endpoint was OS. To minimize the bias, propensity score match (PSM) was employed. Survival analysis and Log-rank test were executed to explore the survival features of LMAC.

RESULTS

The results indicated that the existed staging system was not suitable for LMAC. Patients diagnosed with LMAC exhibited a superior OS compared to those with LNMAC in stage IA2 (P < 0.0001), IA3 (P < 0.0001), IB (P = 0.0062), IIA (P = 0.0090), IIB (P = 0.0005). In contrast, a worse OS in stage IVA (P = 0.0103) was found in LMAC patients. The novel classification system proposed for LMAC proved to be highly applicable and demonstrated substantial efficacy, as confirmed by the verification group.

CONCLUSION

The newly established classification system was more effective for LMAC, but it necessitates large-scale verification to confirm its applicability and reliability.

Use of CT-derived radiomic features to preoperatively identify invasive mucinous adenocarcinoma in solitary pulmonary nodules ≤3 cm

Objective

In this study, we aimed to utilize computed tomography (CT)-derived radiomics and various machine learning approaches to differentiate between invasive mucinous adenocarcinoma (IMA) and invasive non-mucinous adenocarcinoma (INMA) preoperatively in solitary pulmonary nodules (SPN) ≤3 cm.

Methods

A total of 538 patients with SPNs measuring ≤3 cm were enrolled, categorized into either the IMA group (n = 50) or INMA group (n = 488) based on postoperative pathology. Radiomic features were extracted from non-contrast-enhanced CT scans and identified using the least absolute shrinkage and selection operator (LASSO) algorithm. In constructing radiomics-based models, logistic regression, support vector machines, classification and regression trees, and k-nearest neighbors were employed. Additionally, a clinical model was developed, focusing on CT radiological features. Subsequently, this clinical model was integrated with the most effective radiomic model to create a combined model. Performance assessments of these models were conducted, utilizing metrics such as the area under the receiver operating characteristic curve (AUC), DeLong's test, net reclassification index (NRI), and integrated discrimination improvement (IDI).

Results

The support vector machine approach showed superior predictive efficiency, with AUCs of 0.829 and 0.846 in the training and test cohorts, respectively. The clinical model had AUCs of 0.760 and 0.777 in the corresponding cohorts. The combined model had AUCs of 0.847 and 0.857 in the corresponding cohorts. Furthermore, compared to the radiomic model, the combined model significantly improved performance in both the training (DeLong test P = 0.045, NRI 0.206, IDI 0.024) and test cohorts (P = 0.029, NRI 0.125, IDI 0.032), as well as compared to the clinical model in both the training (P = 0.01, NRI 0.310, IDI 0.09) and test cohorts (P = 0.047, NRI 0.382, IDI 0.085).

Conclusion

the combined model exhibited excellent performance in distinguishing between IMA and INMA in SPNs ≤3 cm.

Pulmonary mucinous adenocarcinoma: An overview of pathophysiology and advancements in treatment

Pulmonary mucinous adenocarcinoma (PMA), a distinct subtype of non-small cell lung cancer (NSCLC), is characterized by an abundance of mucin-producing cells. Although this subtype comprises a relatively small fraction of lung adenocarcinomas, PMA stands apart due to its unique clinical, pathological, and molecular features. This review comprehensively discusses the pathophysiology and etiology, clinical features, diagnostic methods, treatment strategies, prognosis, and future directions for PMA, drawing from relevant literature and existing studies. Advances in PMA treatment includes surgical intervention, targeted therapy, immunotherapy, and adjuvant therapy. Particularly, we discussed factors influencing the prognosis of PMAs, such as molecular markers, pathological features, and the impact of the latest treatment advances on prognosis. Moreover, we intended this review to be a comprehensive reference for diagnosing, treating, and assessing the prognosis of PMA, providing valuable guidance for clinical practice.

CT Radiomic Nomogram Using Optimal Volume of Interest for Preoperatively Predicting Invasive Mucinous Adenocarcinomas in Patients with Incidental Pulmonary Nodules: A Multicenter, Large-Scale Study

INTRODUCTION

This study evaluated the efficacy of radiomic analysis with optimal volumes of interest (VOIs) on computed tomography images to preoperatively differentiate invasive mucinous adenocarcinoma (IMA) from non-mucinous adenocarcinoma (non-IMA) in patients with incidental pulmonary nodules (IPNs).

METHODS

This multicenter, large-scale retrospective study included 1383 patients with IPNs, 110 (8%) of whom were pathologically diagnosed with IMA postoperatively. Radiomic features were extracted from multi-scale VOI subgroups (VOI-2 mm, VOIentire, VOI + 2 mm, and VOI + 4 mm). Resampling methods, specifically, the synthetic minority oversampling technique, addressed the imbalance between the majority (IMA) and minority (non-IMA) groups. Radiomic features were identified using the least absolute shrinkage and selection operator algorithm. Radscores were calculated by linearly combining the selected features with their weights. A combined nomogram integrating the optimal VOI-based radiomic model with the image-finding classifier was constructed.

RESULTS

Bubble lucency and lower lobe predominance were significant in establishing an image-finding classifier to differentiate between IMA and non-IMA in IPNs, achieving an area under the curve (AUC) value of 0.684 (0.568-0.801). Across all radiomic models, IMA had a higher Radscore than did non-IMA. Specifically, the VOI + 2 mm-based radiomic model exhibited the highest performance, with an AUC of 0.832 (0.753-0.911). The combined nomogram outperformed the recognized image-finding classifier and radiomic models, achieving an AUC of 0.850 (0.776-0.925).

CONCLUSION

A nomogram that combines a recognized image-finding classifier with an optimal VOI-based radiomic model effectively predicts IMA in IPNs, aiding physicians in developing comprehensive treatment strategies.