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

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.

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.

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.

18 F-FDG PET/CT characteristics of IASLC grade 3 invasive adenocarcinoma and the value of 18 F-FDG PET/CT for preoperative prediction: a new prognostication model

OBJECTIVE

This study is performed to investigate the imaging characteristics of the International Association for the Study of Lung Cancer grade 3 invasive adenocarcinoma (IAC) on PET/CT and the value of PET/CT for preoperative predicting this tumor.

MATERIALS AND METHODS

We retrospectively enrolled patients with IAC from August 2015 to September 2022. The clinical characteristics, serum tumor markers, and PET/CT features were analyzed. T test, Mann-Whitney U test, χ 2 test, Logistic regression analysis, and receiver operating characteristic analysis were used to predict grade 3 tumor and evaluate the prediction effectiveness.

RESULTS

Grade 3 tumors had a significantly higher maximum standardized uptake value (SUV max ) and consolidation-tumor-ratio (CTR) ( P  < 0.001), while Grade 1 - 2 tumors were prone to present with air bronchogram sign or vacuole sign ( P  < 0.001). A stepwise logistic regression analysis revealed that smoking history, CEA, SUV max , air bronchogram sign or vacuole sign and CTR were useful predictors for Grade 3 tumors. The established prediction model based on the above 5 parameters generated a high AUC (0.869) and negative predictive value (0.919), respectively.

CONCLUSION

Our study demonstrates that grade 3 IAC has a unique PET/CT imaging feature. The prognostication model established with smoking history, CEA, SUV max , air bronchogram sign or vacuole sign and CTR can effectively predict grade 3 tumors before the operation.

Comparison of the immunotherapy efficacy between invasive mucinous and non-mucinous adenocarcinoma in advanced lung cancer patients with KRAS mutation: a retrospective study

Invasive mucinous adenocarcinoma (IMA) is a rare variant of adenocarcinoma with unique clinical, radiological, and pathological features, among which KRAS mutation is the most common. However, the differences in the efficacy of immunotherapy between KRAS-positive IMA and invasive non-mucinous adenocarcinoma (INMA) patients remain unclear. Patients with KRAS mutated adenocarcinomas receiving immunotherapy between June 2016 and December 2022 were enrolled. Based on mucin-producing status, the patients were placed into two subgroups: the IMA group and INMA group. Patients with IMA were further classified into two subtypes according to the presence of mucin patterns: pure IMA (≥ 90%) and mixed mucinous/nonmucinous adenocarcinoma (≥ 10% of each histological component). Kaplan-Meier Curves and log-rank tests were used to analyze survival. Cox regression analysis of PFS were used to analyze the independent factors associated with efficacy. Sixty-five advanced adenocarcinoma patients with KRAS mutations received immunotherapy, including 24 patients with IMA and 41 with INMA. The median progression-free survival (PFS) was 7.7 months, whereas the median overall survival (OS) was 24.0 months. Significant difference in PFS could be observed in IMA and INMA (3.5 months vs. 8.9 months; P = 0.047). Patients with pure IMA tended toward prolonger survival in contrast to mixed mucinous/nonmucinous adenocarcinoma in PFS (8.4 months vs. 2.3 months; P = 0.349). The multivariable analysis demonstrated that IMA was an independent risk factor for PFS. In KRAS mutated patients, IMA was associated with poorer PFS after immunotherapy compared with INMA.

Invasive mucinous adenocarcinoma misdiagnosed as pneumonia: A case report

Common imaging findings of invasive mucinous adenocarcinoma (IMA) include consolidation of the lung parenchyma, nodules, and ground-glass changes. However, the IMA imaging findings in the present case included diffuse, patchy and blurry density shadows through both lungs. To the best of the authors' knowledge, this image pattern has rarely been reported. The patient provided his consent and authorized the publication of photographs featuring his likeness. The present study reported a patient was diagnosed with IMA via pathologic and genetic analyses. Following antibiotic treatment, the lesions in both sides became larger. Further examinations were completed and IMA was confirmed by biopsy pathohistological examination. Pathological specimens were negative for almost all driver genes mutations, except KRAS. The patients and family refused further treatment, including chemotherapy, radiotherapy and interventional chemotherapy and the patient was discharged from The First Affiliated Hospital of Chengdu Medical College. The present case report emphasized that IMA should be suspected when imaging studies show diffuse lesions throughout both lungs. When a patient does not respond to treatment, clinicians should consider alternative diagnoses.

Predicting the pathological invasiveness of early lung adenocarcinoma prior to surgery using Deauville criteria: reliability and validity

PURPOSE

This retrospective study aimed to investigate the validity and reliability of FDG-PET/CT visual assessment using Deauville criteria to predict pathological invasiveness of early lung adenocarcinoma prior to surgery.

MATERIALS AND METHODS

Between April 2020 and January 2022, 51 patients who underwent surgery for pathological stage 0/I lung adenocarcinoma were enrolled. The pulmonary lesions were divided into two groups according to pathological invasiveness: less invasive (including adenocarcinoma in situ and minimally invasive adenocarcinoma and invasive adenocarcinoma. We compared CT size (total and solid size), SUVmax, and Deauville score between the two groups. Furthermore, we investigated inter-rater and intra-rater agreements regarding the Deauville score. Receiver operating characteristic (ROC) curve analysis was performed to identify the diagnostic performance of each method.

RESULTS

Based on pathologic diagnoses, 51 lesions in the 51 patients were divided into 6 less invasive and 45 invasive adenocarcinoma lesions. According to quadratic-weighted Kappa statistics, inter-rater (k = 0.93) and intra-rater (k = 0.97) agreements among all five components of the Deauville score indicated high agreement. There was a statistically significant difference in CT solid size, SUVmax, and Deauville score between the two groups. There were no significant differences between CT solid size and FDG-PET/CT assessments (AUC = 0.93 for Deauville score and SUVmax, AUC = 0.84 for CT solid size).

CONCLUSION

FDG-PET/CT visual assessment using the Deauville score could assist in deciding upon minimally invasive surgery for early lung adenocarcinoma.