Solitary Nodular Invasive Mucinous Adenocarcinoma of the Lung: Imaging Diagnosis Using the Morphologic-Metabolic Dissociation Sign

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.

Systemic immune-inflammation index predicts survival in patients with resected lung invasive mucinous adenocarcinoma

BACKGROUND

The prognostic-related factors of lung invasive mucinous adenocarcinoma(IMA) are unclear because of its rarity. Various inflammation-based biomarkers were reported to predict the survival of malignant diseases. This study aims to explore the prognostic significance of the systemic immune-inflammation index(SII), which is calculated using absolute platelet, neutrophil, and lymphocyte counts, among patients with invasive mucinous adenocarcinoma.

METHODS

From January 2015 to December 2019, 106 patients were identified as having IMA accepted radical resection and enrolled in the retrospective study. We analyzed the overall survival and disease-free survival using the Kaplan-Meier method and log-rank test. Receiver operating characteristic curve was used to find the optimal SII cut-off values for survival. A Cox regression model was carried out for multivariable analyses.

RESULTS

The study cohort included 53 men and 53 women, with a mean age of 60 years (range 29 to 78 years, median 61 years). The median SII measured before surgery was 378.47 (range: 79.87-1701.97). ROC analyses revealed that the optimal cut-off values of SII was 379.43 for predicting both OS and DFS. An elevated SII (≥379.43) was observed in 52 patients (49.1 %), and was associated with younger age (P = 0.02), advanced T staging (P = 0.042), lymph node metastasis (P = 0.018) and pneumonic-type IMA (P = 0.018). Multivariable analysis showed that SII and pneumonic-type IMA were independent prognostic predictors of OS and DFS in radically resected IMA patients (P < 0.05).

CONCLUSION

High SII is correlated with worse outcome and can be a novel prognostic biomarker for IMA patients accepted radical surgery.

Invasive mucinous adenocarcinoma of the lung: clinicopathological features, 18F-FDG PET/CT findings, and survival outcomes

OBJECTIVE

Invasive mucinous adenocarcinoma (IMA) is a rare subtype of lung adenocarcinoma. This study aimed to retrospectively evaluate the clinicopathological features, ¹⁸F-FDG PET/CT findings, and prognosis of IMA of the lung, as well as to investigate the associations among these variables, to improve the management of such patients.

METHODS

Clinicopathological and ¹⁸F-FDG PET/CT characteristics of 72 patients with pathologically confirmed IMA of the lung were retrospectively collected and investigated, and their predictive efficacy on progression-free survival (PFS) was evaluated.

RESULTS

The median age of the enrolled 72 patients was 61 years (range, 26-79 years), and the male-to-female ratio was 1:1.25. According to the radiological morphology of IMA, solidary nodule/mass type (n = 59, 81.9%) was the most common, followed by GGO type (n = 8, 11.1%) and pneumonia type (n = 5, 6.9%). Lobulated or spiculated margin and pleural traction were the most common radiological signs. The median SUVmax of IMA lesions was 3.0, ranging from 0.5 to 23.1. Higher SUVmax was observed in IMA with non-GGO type, clinical symptom, advanced stage, lobulated margin, pleural traction or spread through air spaces (STAS) (P < 0.05). Moreover, higher SUVmax was related to larger tumor size in non-pneumonia-type IMA (r = 0.708, P < 0.001). The median PFS was 21.3 months, and the 12-, 24- and 36-month PFS rates were 89.8%, 83.3% and 75.5%, respectively. A poorer PFS was significantly associated with SUVmax ≥ 3, advanced stage and STAS.

CONCLUSION

¹⁸F-FDG PET/CT combined with clinicopathological characteristics can aid the diagnosis and prognostic evaluation of lung IMA, which could provide guidance for the appropriate management of such patients.

CT Features of Stage IA Invasive Mucinous Adenocarcinoma of the Lung and Establishment of a Prediction Model

Objective

To investigate computed tomography (CT) features of stage IA invasive mucinous adenocarcinoma (IMA) of the lung and establish a predictive model.

Methods

Fifty-three lesions from 53 cases of stage IA IMA between January 2017 and December 2019 were examined, while 141 lesions from 141 cases of invasive non-mucinous lung adenocarcinoma (INMA) served as control cases. Univariate analysis was performed to compare differences in demographics and CT features between the two groups, and multivariate logistic regression analysis was performed to determine primary influencing factors of solitary nodular IMA. A risk score prediction model was established based on the regression coefficients of these factors, and receiver operating characteristic (ROC) curve analysis was performed to evaluate the predictive performance of the model.

Results

Univariate analysis showed that age, nodule type, maximum nodule diameter, tumor lung interface, lobulation, spiculation, air bronchogram or vacuolar signs, and abnormal vascular changes differed significantly between the two groups (p < 0.05). Compared to INMA, spiculation of IMA was relatively longer and softer. Multivariate logistic regression analysis showed that nodule type, indistinct tumor lung interface, air bronchogram or vacuolar signs, and abnormal vascular changes were the primary influencing factors. A prediction model based on the regression coefficients of these factors was established. ROC curve analysis indicated that the area under the curve was 0.882 (p < 0.05).

Conclusion

Compared to INMA, solitary peripheral stage IA nodular IMA were more common in older patients; they more frequently had indistinct tumor lung interface and air bronchogram or vacuolar signs on CT; spiculation was relatively longer and softer; our risk score prediction model based on nodule type, tumor lung interface, air bronchogram or vacuolar signs, and abnormal vascular changes was established with good predictive efficacy for solitary nodular IMA.

Comparison of Diagnostic Performance of Spread Through Airspaces of Lung Adenocarcinoma Based on Morphological Analysis and Perinodular and Intranodular Radiomic Features on Chest CT Images

Object

STAS is associated with poor differentiation, KRAS mutation and poor recurrence-free survival. The aims of this study are to evaluate the ability of intra- and perinodular radiomic features to distinguish STAS at non-contrast CT.

Patients and Methods

This retrospective study included 216 patients with pathologically confirmed lung adenocarcinoma (STAS+, n = 56; STAS-, n = 160). Texture-based features were extracted from intra- and perinodular regions of 2, 4, 6, 8, 10, and 20 mm distances from the tumor edge using an erosion and expansion algorithm. Traditional radiologic features were also analyzed including size, consolidation tumor ratio (CTR), density, shape, vascular change, cystic airspaces, tumor-lung interface, lobulation, spiculation, and satellite sign. Nine radiomic models were established by using the eight separate models and a total of the eight VOIs (eight-VOI model). Then the prediction efficiencies of the nine radiomic models were compared to predict STAS of lung adenocarcinomas.

Results

Among the traditional radiologic features, CTR, unclear tumor-lung interface, and satellite sign were found to be associated with STAS significantly, and the AUCs were 0.796, 0.677, and 0.606, respectively. Radiomic model of combined tumor bodies and all the distances of perinodular areas (eight-VOI model) had better predictive efficiency for predicting STAS+ lung adenocarcinoma. The AUCs of the eight-VOI model in the training and verification sets were 0.907 (95%CI, 0.862-0.947) in the training set, and 0.897 (95%CI, 0.784-0.985) in the testing set, and 0.909 (95%CI, 0.863-0.949) in the external validation set, and the diagnostic accuracy in the external validation set was 0.849.

Conclusion

Radiomic features from intra- and perinodular regions of nodules can best distinguish STAS of lung adenocarcinoma.

Invasive Mucinous Adenocarcinoma of the Lung Presenting With Multiple Cavities

A 78-year-old woman presented to our hospital with a two-week history of productive cough. Chest computed tomography (CT) showed bilateral multiple pulmonary nodules with cavities. Although the cytology of her sputum revealed adenocarcinoma, she refused any treatment. Following supportive care, 30 months later, she presented to our hospital with dyspnea and fever. Chest CT showed progression of multiple pulmonary nodules and cavities. Despite treatment with antibiotics and palliative care, she died on the 10th day of hospitalization. Pathological autopsy confirmed the diagnosis of pulmonary invasive mucinous adenocarcinoma (IMA). The typical CT findings of IMA include multiple consolidations or ground-glass opacities mimicking pneumonia; rarely, cavitary lesions are also observed. Clinicians should consider IMA as a differential diagnosis for lung cavities.

Fluid-filled Cystic Lesions of the Lungs

A pulmonary cyst usually refers to an air-filled space with a smooth, thin wall. Fluid-filled cystic lesions of the lungs include a range of etiologies such as true cysts, congenital malformations, infections, and benign and malignant neoplasms. With relatively little solid component, these lesions often have similar imaging appearances to one another. This article focuses on key imaging features and clinical characteristics that can be used to narrow the differential diagnosis.

Management of incidental pulmonary nodules: current strategies and future perspectives

Introduction: Detection and characterization of pulmonary nodules is an important issue, because the process is the first step in the management of lung cancers.Areas covered: Literature review was performed on May 15 2019 by using the PubMed, US National Library of Medicine National Institutes of Health, and the National Center for Biotechnology information. CT features helping identify the druggable mutations and predict the prognosis of malignant nodules were presented. Technical advancements in MRI and PET/CT were introduced for providing functional information about malignant nodules. Advances in various tissue biopsy techniques enabling molecular analysis and histologic diagnosis of indeterminate nodules were also presented. New techniques such as radiomics, deep learning (DL) technology, and artificial intelligence showing promise in differentiating between malignant and benign nodules were summarized. Recently, updated management guidelines for solid and subsolid nodules incidentally detected on CT were described. Risk stratification and prediction models for indeterminate nodules under active investigation were briefly summarized.Expert opinion: Advancement in CT knowledge has led to a better correlation between CT features and genomic alterations or tumor histology. Recent advances like PET/CT, MRI, radiomics, and DL-based approach have shown promising results in the characterization and prognostication of pulmonary nodules.