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

CT-based intratumoral and peritumoral radiomics nomogram to predict spread through air spaces in lung adenocarcinoma with diameter ≤ 3 cm: A multicenter study

Purpose

The aim of this study was to explore and develop a preoperative and noninvasive model for predicting spread through air spaces (STAS) status in lung adenocarcinoma (LUAD) with diameter ≤ 3 cm.

Methods

This multicenter retrospective study included 640 LUAD patients. Center I included 525 patients (368 in the training cohort and 157 in the validation cohort); center II included 115 patients (the test cohort). We extracted radiomics features from the intratumor, extended tumor and peritumor regions. Multivariate logistic regression and boruta algorithm were used to select clinical independent risk factors and radiomics features, respectively. We developed a clinical model and four radiomics models (the intratumor model, extended tumor model, peritumor model and fusion model). A nomogram based on prediction probability value of the optimal radiomics model and clinical independent risk factors was developed to predict STAS status.

Results

Maximum diameter and nodule type were clinical independent risk factors. The extended tumor model achieved satisfactory STAS status discrimination performance with the AUC of 0.74, 0.71 and 0.80 in the three cohorts, respectively, performed better than other radiomics models. The integrated discrimination improvement value revealed that the nomogram outperformed compared to the clinical model with the value of 12 %. Patients with high nomogram score (≥ 77.31) will be identified as STAS-positive.

Conclusions

Peritumoral information is significant to predict STAS status. The nomogram based on the extended tumor model and clinical independent risk factors provided good preoperative prediction of STAS status in LUAD with diameter ≤ 3 cm, aiding surgical decision-making.

CT-based radiomics predictive model for spread through air space of IA stage lung adenocarcinoma

BackgroundSpread through air spaces (STAS) in lung adenocarcinoma means different treatment and worse prognosis.PurposeTo construct a radiomics model based on CT scans to predict the presence of STAS in stage IA lung adenocarcinoma, compared with the traditional clinical model.Material and MethodsThe study included 317 patients (median age = 57.21 years; age range = 45.84-68.61 years) with pathologically confirmed stage IA lung adenocarcinoma. In total, 122 (38.5%) patients were diagnosed with STAS by pathology after the operation. Two experienced radiologists independently segmented the lesions using MITK software and extracted 1791 radiomics features using Python. Single-factor t-test or Mann-Whitney U-test and LASSO were used to screen for radiomics signatures related to STAS. This study constructed a radiomics model, a clinical model, and a combined model, combining radiomics and clinical features. Model performance was evaluated using the area under the curve (AUC).ResultsBy single-factor analysis, four clinical features and 13 radiomics features were significantly associated with STAS. The three models (the clinical, radiomics, and combine models) achieved predictive efficacy, with an AUC of 0.849, 0.867, and 0.939, respectively, in the training set and 0.808, 0.848, and 0.876, respectively, in the testing set.ConclusionThe combined model based on the radiomics and clinical features of preoperative chest CT could be used to preoperatively diagnose the presence of STAS in stage IA lung adenocarcinoma and has an excellent diagnostic performance.

A narrative review of preoperative CT for predicting spread through air spaces of lung cancer

Background and Objective

Spread through air space (STAS) is a recognized mechanism of lung cancer invasion and is associated with patient prognosis. However, current diagnostic accuracy of bronchial cytology and intraoperative frozen section for STAS remains insufficient to meet clinical needs. Therefore, accurate and non-invasive preoperative prediction of STAS is critical for clinical decision-making. In this paper, we review and summarize recent studies on the role of computed tomography (CT) in predicting STAS in lung cancer, and discuss the limitations and future directions of related research in this field.

Methods

Relevant studies were identified through searches on the Web of Science, PubMed, Cochrane Library, and EMBASE. We included English-language articles published between July 2017 and June 2024, focusing on research related to STAS and CT.

Key Content and Findings

This review aimed to assess the viability of preoperative CT imaging for predicting STAS. Current studies suggest that traditional imaging signs enable the assessment of STAS, and with the development of artificial intelligence, the efficacy of STAS prediction models has been greatly enhanced by radiomics and deep learning methods. However, risk stratification studies remain limited and require further refinement through more comprehensive pathological definitions of STAS.

Conclusions

Preoperative CT imaging images demonstrated good predictive efficacy of STAS. However, further progress requires a more comprehensive definition of STAS and validation through large-sample, prospective, and multi-center studies to enhance its clinical applicability.

Diagnostic performance of radiomics analysis for pulmonary cancer airway spread: a systematic review and meta-analysis

PURPOSE

Spread through air spaces (STAS) is a unique metastatic pattern of pulmonary cancer closely associated with patient prognosis. This study evaluates the application of radiomics in the diagnosis of pulmonary cancer STAS through meta-analysis and explores its clinical significance and potential limitations.

METHODS

We systematically searched the PubMed, Embase, and Cochrane Central Register of Controlled Trials databases for relevant studies between inception and April 1, 2024. The main evaluation indicators included sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and area under the curve (AUC). A total of 18 studies, covering 6,642 lung cancer cases, were included in the systematic review.

RESULTS

In the development cohort, the sensitivity of radiomics for diagnosing STAS was 0.80 [95% confidence interval (CI): 0.75-0.84; P < 0.001; I2: 72.8%], and the specificity was 0.79 (95% CI: 0.71-0.85; P < 0.001; I2: 93.4%). In the validation cohort, the sensitivity was 0.81 (95% CI: 0.75-0.86; P < 0.001; I2: 45.8%), and the specificity was 0.74 (95% CI: 0.68-0.80; P < 0.001; I2: 65.0%). The summary AUC for both cohorts was 0.85 (95% CI: 0.82-0.88). Deeks' funnel plot analysis showed no significant publication bias in either cohort (P values: 0.963 and 0.106, respectively).

CONCLUSION

Radiomics analysis demonstrates important clinical significance in the diagnosis of pulmonary cancer STAS, with promising sensitivity and specificity results in both development and validation cohorts.

CLINICAL SIGNIFICANCE

While radiomics analysis offers valuable diagnostic insights for STAS in pulmonary cancer, its limitations must be carefully considered. Future research should focus on addressing these limitations and further exploring the application prospects of radiomics in lung cancer diagnosis and treatment.

Research hotspots and trends in lung cancer STAS: a bibliometric and visualization analysis

Purpose

This study employed the R software bibliometrix and the visualization tools CiteSpace and VOSviewer to conduct a bibliometric analysis of literature on lung cancer spread through air spaces (STAS) published since 2015.

Methods

On September 1, 2024, a computer-based search was performed in the Web of Science (WOS) Core Collection dataset for literature on lung cancer STAS published between January 1, 2015, and August 31, 2024. VOSviewer was used to visually analyze countries, institutions, authors, co-cited authors, and keywords, while CiteSpace was utilized to analyze institutional centrality, references, keyword bursts, and co-citation literature. Descriptive analysis tables were created using Excel 2021.

Results

A total of 243 articles were included from the WOS, with a significant increase in annual publications observed since 2018. China, Kadota K, and Fudan University were leading countries, authors, and institutions by publication volume. The top three authors by co-citation count were Kadota K, Chen C, and Adusumilli PS. The journal with the highest publication volume was Lung Cancer, with the most influential journal among the top 10 being the Journal of Thoracic Oncology. The most frequently cited reference was "Lobectomy Is Associated with Better Outcomes than Sublobar Resection in Spread through Air Spaces (STAS)-Positive T1 Lung Adenocarcinoma: A Propensity Score-Matched Analysis." Keyword clustering categorized the research into four main areas: pathological studies of lung cancer STAS, biological mechanisms, prognostic assessment, and imaging analysis. Current research hotspots include deep learning, lung squamous cell carcinoma, and air spaces STAS.

Conclusion

The current research on lung cancer STAS primarily focuses on pathological studies, biological mechanisms, prognostic assessments, and preoperative imaging model predictions. This study's findings provide new insights and directions for future research in this area.

Systematic review registration

https://www.crd.york.ac.uk/prospero/#myprospero, identifier 589442.

Habitat-based CT radiomics enhances the ability to predict spread through air spaces in stage T1 invasive lung adenocarcinoma

Introduction

Spread through air spaces (STAS) represents a novel invasive pattern in lung adenocarcinoma (LUAD) and is a risk factor for poor prognosis in stage T1 LUAD. This study aims to develop and validate a CT habitat imaging analysis model for predicting STAS in stage T1 invasive LUAD.

Methods

We retrospectively analyzed 217 patients with preoperative stage T1 invasive LUAD (115 STAS-positive and 102 STAS-negative cases, including 151 in the train set and 66 in the test set). Semi-automatic segmentation was performed on the regions of interest (ROIs) in all CT images, with an automatic 3mm expansion around the tumor, considering the intratumoral and peritumoral 3mm area. This area was divided into three sub-regions via K-means clustering, and 1197 radiomic features were extracted from each sub-region and the overall combined region. After dimension reduction through the Mann-Whitney U test, Pearson correlation analysis, and least absolute shrinkage and selection operator(LASSO), the best features for each sub-region and overall were selected. Models were then built using the selected radiomic features through the Adaptive Boosting (AdaBoost) and Multilayer Perceptron (MLP) classifiers. Four different models were established based on different sub-regions and the overall features. The performance of these models was evaluated through receiver operating characteristic curves (AUC) under the DeLong test, calibration curves via the Hosmer-Lemeshow test, and decision curve analysis to assess the performance of these features.

Results

In this study, we evaluated the predictive performance of AdaBoost and MLP classifiers on rad feature models across various subregions and the overall dataset. In the test set, the AdaBoost classifier achieved a maximum AUC of 0.871 in Habitat 3, whereas the MLP classifier demonstrated slightly superior performance with an AUC of 0.879. Both classifiers exhibited high efficiency in habitat 3, with the MLP algorithm showing enhanced model performance.

Conclusions

CT habitat imaging analysis for the preoperative prediction of STAS in stage T1 invasive LUAD shows satisfactory diagnostic performance, with the habitat3 model exhibiting the highest efficacy, reflecting tumor heterogeneity.

Predicting histopathological features of aggressiveness in lung cancer using CT radiomics: a systematic review

PURPOSE

To examine the accuracy of CT radiomics to predict histopathological features of aggressiveness in lung cancer using a systematic review of test accuracy studies.

METHODS

Data sources searched included Medline, Embase, Web of Science, and Cochrane Library from up to 3 November 2023. Included studies reported test accuracy of CT radiomics models to detect the presence of: spread through air spaces (STAS), predominant adenocarcinoma pattern, adenocarcinoma grade, lymphovascular invasion (LVI), tumour infiltrating lymphocytes (TIL) and tumour necrosis, in patients with lung cancer. The primary outcome was test accuracy. Two reviewers independently assessed articles for inclusion and assessed methodological quality using the QUality Assessment of Diagnostic Accuracy Studies-2 tool. A single reviewer extracted data, which was checked by a second reviewer. Narrative data synthesis was performed.

RESULTS

Eleven studies were included in the final analysis. 10/11 studies were in East Asian populations. 4/11 studies investigated STAS, 6/11 investigated adenocarcinoma invasiveness or growth pattern, and 1/11 investigated LVI. No studies investigating TIL or tumour necrosis met inclusion criteria. Studies were of generally mixed to poor methodological quality. Reported accuracies for radiomic models ranged from 0.67 to 0.94.

CONCLUSION

Due to the high risk of bias and concerns regarding applicability, the evidence is inconclusive as to whether radiomic features can accurately predict prognostically important histopathological features of cancer aggressiveness. Many studies were excluded due to lack of external validation. Rigorously conducted prospective studies with sufficient external validity will be required for radiomic models to play a role in improving lung cancer outcomes.

Does dual-layer spectral detector CT provide added value in predicting spread through air spaces in lung adenocarcinoma? A preliminary study

OBJECTIVES

To examine the predictive value of dual-layer spectral detector CT (DLCT) for spread through air spaces (STAS) in clinical lung adenocarcinoma.

METHODS

A total of 225 lung adenocarcinoma cases were retrospectively reviewed for demographic, clinical, pathological, traditional CT, and spectral parameters. Multivariable logistic regression analysis was carried out based on three logistic models, including a model using traditional CT features (traditional model), a model using spectral parameters (spectral model), and an integrated model combining traditional CT and spectral parameters (integrated model). Receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA) were performed to assess these models.

RESULTS

Univariable analysis showed significant differences between the STAS and non-STAS groups in traditional CT features, including nodule density (p < 0.001), pleural indentation types (p = 0.006), air-bronchogram sign (p = 0.031), the presence of spiculation (p < 0.001), long-axis diameter of the entire nodule (LD) (p < 0.001), and consolidation/tumor ratio (CTR) (p < 0.001). Multivariable analysis revealed that LD > 20 mm (odds ratio [OR] = 2.271, p = 0.025) and CTR (OR = 24.208, p < 0.001) were independent predictors in the traditional model, while electronic density (ED) in the venous phase was an independent predictor in the spectral (OR = 1.062, p < 0.001) and integrated (OR = 1.055, p < 0.001) models. The area under the curve (AUC) for the integrated model (0.84) was the highest (spectral model, 0.83; traditional model, 0.80), and the difference between the integrated and traditional models was statistically significant (p = 0.015). DCA showed that the integrated model had superior clinical value versus the traditional model.

CONCLUSIONS

DLCT has added value for STAS prediction in lung adenocarcinoma.

CLINICAL RELEVANCE STATEMENT

Spectral CT has added value for spread through air spaces prediction in lung adenocarcinoma so may impact treatment planning in the future.

KEY POINTS

• Electronic density may be a potential spectral index for predicting spread through air spaces in lung adenocarcinoma. • A combination of spectral and traditional CT features enhances the performance of traditional CT for predicting spread through air spaces.

Predictive value of multiple imaging predictive models for spread through air spaces of lung adenocarcinoma: A systematic review and network meta‑analysis

Spread Through Air Spaces (STAS) is involved in lung adenocarcinoma (LUAD) recurrence, where cancer cells spread into adjacent lung tissue, impacting surgical planning and prognosis assessment. Radiomics-based models show promise in predicting STAS preoperatively, enhancing surgical precision and prognostic evaluations. The present study performed network meta-analysis to assess the predictive efficacy of imaging models for STAS in LUAD. Data were systematically sourced from PubMed, Embase, Scopus, Wiley and Web of Science, according to the Cochrane Handbook for Systematic Reviews of Interventions) and A Measurement Tool to Assess systematic Reviews 2. Using Stata software v17.0 for meta-analysis, surface under the cumulative ranking area (SUCRA) was applied to identify the most effective diagnostic method. Quality assessments were performed using Cochrane Collaboration's risk-of-bias tool and publication bias was assessed using Deeks' funnel plot. The analysis encompassed 14 articles, involving 3,734 patients, and assessed 17 predictive models for STAS in LUAD. According to comprehensive analysis of SUCRA, the machine learning (ML)_Peri_tumour model had the highest accuracy (56.5), the Features_computed tomography (CT) model had the highest sensitivity (51.9) and the positron emission tomography (pet)_CT model had the highest specificity (53.9). ML_Peri_tumour model had the highest predictive performance. The accuracy was as follows: ML_Peri_tumour vs. Features_CT [relative risk (RR)=1.14; 95% confidence interval (CI), 0.99-1.32]; ML_Peri_tumour vs. ML_Tumour (RR=1.04; 95% CI, 0.83-1.30) and ML_Peri_tumour vs. pet_CT (RR=1.04; 95% CI, 0.84-1.29). Comparative analyses revealed heightened predictive accuracy of the ML_Peri_tumour compared with other models. Nonetheless, the field of radiological feature analysis for STAS prediction remains nascent, necessitating improvements in technical reproducibility and comprehensive model evaluation.

Computed Tomography Radiomics for Preoperative Prediction of Spread Through Air Spaces in the Early Stage of Surgically Resected Lung Adenocarcinomas

PURPOSE

To assess the added value of radiomics models from preoperative chest CT in predicting the presence of spread through air spaces (STAS) in the early stage of surgically resected lung adenocarcinomas using multiple validation datasets.

MATERIALS AND METHODS

This retrospective study included 550 early-stage surgically resected lung adenocarcinomas in 521 patients, classified into training, test, internal validation, and temporal validation sets (n=211, 90, 91, and 158, respectively). Radiomics features were extracted from the segmented tumors on preoperative chest CT, and a radiomics score (Rad-score) was calculated to predict the presence of STAS. Diagnostic performance of the conventional model and the combined model, based on a combination of conventional and radiomics features, for the diagnosis of the presence of STAS were compared using the area under the curve (AUC) of the receiver operating characteristic curve.

RESULTS

Rad-score was significantly higher in the STAS-positive group compared to the STAS-negative group in the training, test, internal, and temporal validation sets. The performance of the combined model was significantly higher than that of the conventional model in the training set {AUC: 0.784 [95% confidence interval (CI): 0.722-0.846] vs. AUC: 0.815 (95% CI: 0.759-0.872), p=0.042}. In the temporal validation set, the combined model showed a significantly higher AUC than that of the conventional model (p=0.001). The combined model showed a higher AUC than the conventional model in the test and internal validation sets, albeit with no statistical significance.

CONCLUSION

A quantitative CT radiomics model can assist in the non-invasive prediction of the presence of STAS in the early stage of lung adenocarcinomas.

Improving the prediction of Spreading Through Air Spaces (STAS) in primary lung cancer with a dynamic dual-delta hybrid machine learning model: a multicenter cohort study

BACKGROUND

Reliable pre-surgical prediction of spreading through air spaces (STAS) in primary lung cancer is essential for precision treatment and surgical decision-making. We aimed to develop and validate a dual-delta deep-learning and radiomics model based on pretreatment computed tomography (CT) image series to predict the STAS in patients with lung cancer.

METHOD

Six hundred seventy-four patients with pre-surgery CT follow-up scans (with a minimum interval of two weeks) and primary lung cancer diagnosed by surgery were retrospectively recruited from three Chinese hospitals. The training cohort and internal validation cohort, comprising 509 and 76 patients respectively, were selected from Shanghai Chest Hospital; the external validation cohorts comprised 36 and 53 patients from two other centers, respectively. Four imaging signatures (classic radiomics features and deep learning [DL] features, delta-radiomics and delta-DL features) reflecting the STAS status were constructed from the pretreatment CT images by comprehensive methods including handcrafting, 3D views extraction, image registration and subtraction. A stepwise optimized three-step procedure, including feature extraction (by DL and time-base radiomics slope), feature selection (by reproducibility check and 45 selection algorithms), and classification (32 classifiers considered), was applied for signature building and methodology optimization. The interpretability of the proposed model was further assessed with Grad-CAM for DL-features and feature ranking for radiomics features.

RESULTS

The dual-delta model showed satisfactory discrimination between STAS and non-STAS and yielded the areas under the receiver operating curve (AUCs) of 0.94 (95% CI, 0.92-0.96), 0.84 (95% CI, 0.82-0.86), and 0.84 (95% CI, 0.83-0.85) in the internal and two external validation cohorts, respectively, with interpretable core feature sets and feature maps.

CONCLUSION

The coupling of delta-DL model with delta-radiomics features enriches information such as anisotropy of tumor growth and heterogeneous changes within the tumor during the radiological follow-up, which could provide valuable information for STAS prediction in primary lung cancer.

Computed Tomography Features and Tumor Spread Through Air Spaces in Lung Adenocarcinoma: A Meta-analysis

To compare computed tomography (CT)-based radiologic features in patients, who are diagnosed with lung adenocarcinoma with the pathologically detected spread of tumor cells through air spaces (STAS positive [STAS+]) and those with no STAS. PubMed, Embase, and Scopus databases were systematically searched for observational studies (either retrospective or prospective) of patients with lung adenocarcinoma that had compared CT-based features between STAS+ and STAS-negative cases (STAS-). The pooled effect sizes were reported as odds ratio (OR) and weighted mean difference (WMD). STATA software was used for statistical analysis. The meta-analysis included 10 studies. Compared with STAS-, STAS+ adenocarcinoma was associated with increased odds of solid nodule (OR: 3.30, 95% CI: 2.52, 4.31), spiculation (OR: 2.05, 95% CI: 1.36, 3.08), presence of cavitation (OR: 1.49, 95% CI: 1.00, 2.22), presence of clear boundary (OR: 3.01, 95% CI: 1.70, 5.32), lobulation (OR: 1.65, 95% CI: 1.11, 2.47), and pleural indentation (OR: 1.98, 95% CI: 1.41, 2.77). STAS+ tumors had significant association with the presence of pulmonary vessel convergence (OR: 2.15, 95% CI: 1.61, 2.87), mediastinal lymphadenopathy (OR: 2.06, 95% CI: 1.20, 3.56), and pleural thickening (OR: 2.58, 95% CI: 1.73, 3.84). The mean nodule diameter (mm) (WMD: 6.19, 95% CI: 3.71, 8.66) and the mean solid component (%) (WMD: 24.5, 95% CI: 10.5, 38.6) were higher in STAS+ tumors, compared with STAS- ones. The findings suggest a significant association of certain CT-based features with the presence of STAS in patients with lung adenocarcinoma. These features may be important in influencing the nature of surgical management.

Study on the Relationship between Lung Cancer Stromal Cells and Air Cavity Diffusion Based on an Image Acquisition System

Objective. The study aimed to investigate the role of tumor stromal cells in the pathogenesis of STAS, the relationship between air diffusion (STAS) and tumor stromal cells (TSCs) was studied, and the prognostic significance of TSC and STAS in patients with lung adenocarcinoma was evaluated. Methods. A total of 150 patients with lung cancer diagnosed in the Affiliated Hospital of Jiangsu Province were selected. From the perspective of pathology, medical information technology was used to assist the diagnosis. The data of multiple magnetic resonance images were analyzed by three-dimensional space convolution (CNN), fuzzy neural network (FNN), transfinite learning machine (ELM), and binarization. Result. After data fusion, the specificity and sensitivity of multiple magnetic resonance (MRI) data are significantly higher than those of single MRI data, and the more fusion times, the better the sensitivity and specificity. With the increase in the number of information and data fusion, the proportion of the significant effect and the comprehensive effective rate of patients are on the rise. Multiple MRI data fusion examination and analysis under medical information technology can improve the cure rate of patients, and the 1-year survival rate and the 3-year survival rate of patients have also gradually improved. Conclusion. The MRI data fusion diagnosis method under the application of information technology can improve the sensitivity and specificity of the diagnosis results and comprehensively improve the clinical cure rate and the survival rate at different times of prognosis. In the context of the current big data information age, this multifeature fusion analysis technology is playing a more and more important role in medical treatment. The application of this method and technology not only improves the quality of life of patients but also processes multiple types of data at one time only by using the proposed medical assistant diagnosis model, which can save the diagnosis time to a certain extent. It has effectively realized the medical management and medical service quality and has important promotion significance.