Establishment and visualization of a model based on high-resolution CT qualitative and quantitative features for prediction of micropapillary or solid components in invasive lung adenocarcinoma

Quantifying intratumoral heterogeneity within sub-regions to predict high-grade patterns in clinical stage I solid lung adenocarcinoma

BACKGROUND

This study aims to quantify intratumoral heterogeneity (ITH) using preoperative CT image and evaluate its ability to predict pathological high-grade patterns, specifically micropapillary and/or solid components (MP/S), in patients diagnosed with clinical stage I solid lung adenocarcinoma (LADC).

METHODS

In this retrospective study, we enrolled 457 patients who were postoperatively diagnosed with clinical stage I solid LADC from two medical centers, assigning them to either a training set (n = 304) or a test set (n = 153). Sub-regions within the tumor were identified using the K-means method. Both intratumoral ecological diversity features (hereafter referred to as ITH) and conventional radiomics (hereafter referred to as C-radiomics) were extracted to generate ITH scores and C-radiomics scores. Next, univariate and multivariate logistic regression analyses were employed to identify clinical-radiological (Clin-Rad) features associated with the MP/S (+) group for constructing the Clin-Rad classification. Subsequently, a hybrid model which presented as a nomogram was developed, integrating the Clin-Rad classification and ITH score. The performance of models was assessed using the receiver operating characteristic (ROC) curves, and the area under the curve (AUC), accuracy, sensitivity, and specificity were determined.

RESULTS

The ITH score outperformed both C-radiomics scores and Clin-Rad classification, as evidenced by higher AUC values in the training set (0.820 versus 0.810 and 0.700, p = 0.049 and p = 0.031, respectively) and in the test set (0.805 versus 0.771 and 0.732, p = 0.041 and p = 0.025, respectively). Finally, the hybrid model consistently demonstrated robust predictive capabilities in identifying presence of MP/S components, achieving AUC of 0.830 in the training set and 0.849 in the test set (all p < 0.05).

CONCLUSION

The ITH derived from sub-region within the tumor has been shown to be a reliable predictor for MP/S (+) in clinical stage I solid LADC.

Preoperative CT-based radiomics nomogram to predict the micropapillary pattern in lung adenocarcinoma of size 2 cm or less

Purpose

To develop and validate a radiomics nomogram model for predicting the micropapillary pattern (MPP) in lung adenocarcinoma (LUAD) tumors of ≤2 cm in size.

Methods

In this study, 300 LUAD patients from our institution were randomly divided into the training cohort (n = 210) and an internal validation cohort (n = 90) at a ratio of 7:3, besides, we selected 65 patients from another hospital as the external validation cohort. The region of interest of the tumor was delineated on the computed tomography (CT) images, and radiomics features were extracted. A nomogram model was established using radiomics features, clinical features and conventional radiographic features. The nomogram model was compared with the radiomics model and the clinical model alone to test its diagnostic validity. Receiver operating characteristic (ROC) curves, areas under the ROC curves and decision curve analysis (DCA) results were plotted to evaluate the model performance and clinical application.

Results

The nomogram model exhibited superior performance, with an AUC of 0.905 (95% confidence interval [CI]: 0.857-0.951) in the training cohort, which decreased to 0.817 (95% CI: 0.698-0.936) in the external validation cohort. The clinical model had AUCs of 0.820 (95% CI: 0.753-0.886) and 0.730 (95% CI: 0.572-0.888) in the training and external validation cohorts, respectively. The radiomics model had AUCs of 0.895 (95% CI: 0.840-0.949) and 0.800 (95% CI: 0.675-0.924) for training and external validation, respectively. DCA confirmed that the nomogram model had the better clinical benefit.

Conclusions

The nomogram model achieved promising prediction efficiency for identifying the presence of the MPP in LUAD tumors ≤2 cm, allowing clinicians to develop more rational and efficacious personalized treatment strategies.

Application and progress of non-invasive imaging in predicting lung invasive non-mucinous adenocarcinoma under the new IASLC grading guidelines

Lung cancer is the leading cause of cancer-related deaths worldwide, with invasive non-mucinous adenocarcinoma (INMA) being the most common type and carrying a poor prognosis. In 2020, the International Association for the Study of Lung Cancer (IASLC) pathology committee proposed a new histological grading system, which offers more precise prognostic assessments by combining the proportions of major and high-grade histological patterns. Accurate identification of lung INMA grading is crucial for clinical diagnosis, treatment planning, and prognosis evaluation. Currently, non-invasive imaging methods (such as CT, PET/CT, and MRI) are increasingly being studied to predict the new grading of lung INMA, showing promising application prospects. This review outlines the establishment and prognostic efficiency of the new IASLC grading system, highlights the application and latest progress of non-invasive imaging techniques in predicting lung INMA grading, and discusses their role in personalized treatment of lung INMA and future research directions. CRITICAL RELEVANCE STATEMENT: The new IASLC grading system has important prognostic implications for patients with lung invasive non-mucinous adenocarcinoma (INMA), and non-invasive imaging methods can be used to predict it, thereby improving patient prognoses. KEY POINTS: The new IASLC grading system more accurately prognosticates for patients with lung INMA. Preoperative prediction of the new grading is challenging because of the complexity of INMA subtypes. It is feasible to apply non-invasive imaging methods to predict the new IASLC grading system.

Development and Validation of a Deep Learning Radiomics Model to Predict High-Risk Pathologic Pulmonary Nodules Using Preoperative Computed Tomography

RATIONALE AND OBJECTIVES

To accurately identify the high-risk pathological factors of pulmonary nodules, our study constructed a model combined with clinical features, radiomics features, and deep transfer learning features to predict high-risk pathological pulmonary nodules.

MATERIALS AND METHODS

The study cohort consisted of 469 cases of lung adenocarcinoma patients, divided into a training cohort (n = 400) and an external validation cohort (n = 69). We obtained computed tomography (CT) semantic features and clinical characteristics, as well as extracted radiomics and deep transfer learning (DTL) features from the CT images. Selected features were used for constructing prediction models using the logistic regression (LR) algorithm. The performance of the models was evaluated through metrics including the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, calibration curve, and decision curve analysis.

RESULTS

The clinical model achieved an AUC of 0.774 (95% CI: 0.728-0.821) in the training cohort and 0.762 (95% confidence interval [CI]: 0.650-0.873) in the external validation cohort. The radiomics model demonstrated an AUC of 0.847 (95% CI: 0.810-0.884) in the training cohort and 0.800 (95% CI: 0.693-0.907) in the external validation cohort. The radiomics-DTL (Rad-DTL) model showed an AUC of 0.871 (95% CI: 0.838-0.905) in the training cohort and 0.806 (95% CI: 0.698-0.914) in the external validation cohort. The proposed combined model yielded AUC values of 0.872 and 0.814 in the training and external validation cohorts, respectively. The combined model demonstrated superiority over both the clinical model and the Rad-DTL model. There were no statistically significant differences observed in the comparison between the combined model incorporating clinical features and the Rad-DTL model. Decision curve analysis (DCA) indicated that the models provided a net benefit in predicting high-risk pathologic pulmonary nodules.

CONCLUSION

Rad-DTL signature is a potential biomarker for predicting high-risk pathologic pulmonary nodules using preoperative CT, determining the appropriate surgical strategy, and guiding the extent of resection.

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.

Predicting High-Grade Patterns in Stage I Solid Lung Adenocarcinoma: A Study of 371 Patients Using Refined Radiomics and Deep Learning-Guided CatBoost Classifier

INTRODUCTION

This study aimed to devise a diagnostic algorithm, termed the Refined Radiomics and Deep Learning Features-Guided CatBoost Classifier (RRDLC-Classifier), and evaluate its efficacy in predicting pathological high-grade patterns in patients diagnosed with clinical stage I solid lung adenocarcinoma (LADC).

METHODS

In this retrospective study, a total of 371 patients diagnosed with clinical stage I solid LADC were randomly categorized into training and validation sets in a 7:3 ratio. Uni- and multivariate logistic regression analyses were performed to examine the imaging findings that can be used to predict pathological high-grade patterns meticulously. Employing redundancy and the least absolute shrinkage and selection operator regression, a radiomics model was developed. Subsequently, radiomics refinement and deep learning features were employed using a machine learning algorithm to construct the RRDLC-Classifier, which aims to predict high-grade patterns in clinical stage I solid LADC. Evaluation metrics, such as receiver operating characteristic curves, areas under the curve (AUCs), accuracy, sensitivity, and specificity, were computed for assessment.

RESULTS

The RRDLC-Classifier attained the highest AUC of 0.838 (95% confidence interval [CI]: 0.766-0.911) in predicting high-grade patterns in clinical stage I solid LADC, followed by radiomics with an AUC of 0.779 (95% CI: 0.675-0.883), and imaging findings with an AUC of 0.6 (95% CI: 0.472-0.726).

CONCLUSIONS

This study introduces the RRDLC-Classifier, a novel diagnostic algorithm that amalgamates refined radiomics and deep learning features to predict high-grade patterns in clinical stage I solid LADC. This algorithm may exhibit excellent diagnostic performance, which can facilitate its application in precision medicine.