Prognostic impact of spread through air spaces in patients with ≤2 cm stage IA lung adenocarcinoma.

Background: In 2015, the World Health Organization (WHO) included spread through air space (STAS) as a new invasive mode of lung cancer. As a new mode of lung cancer dissemination, STAS has a significant and negative impact on patient prognosis. The surgical approach as well as lymph node dissection (LND) for STAS-positive patients is currently unclear. The aim of this study was to investigate the impact of different surgical approaches to STAS and LND on the prognosis of patients with ≤2 cm stage IA lung adenocarcinoma (LUAD). This study also investigated the possible relationship between STAS and the micropapillary histological subtype and its impact on patient prognosis. Methods: A total of 212 patients with LUAD were included in this study from January 2016 to December 2017, and the overall survival (OS) of the patients was compared. The chi-square test and t-test were applied to compare the clinicopathological data of the patients, and the Cox model was used for the multivariate survival analysis. Results: Of the 212 patients, 93 (43.9%) were STAS positive. The univariate analysis showed that the surgical approach, LND type, micropapillary pattern (MP), solid pattern, and STAS were risk factors for OS. The multivariate analysis showed that the surgical approach, MP, and STAS were risk factors for OS. The STAS-positive patients who underwent lobectomy had a better prognosis than those who underwent sublobar resection; however, there was no significant difference between the two surgical procedures in the STAS-negative group. Additionally, the STAS-positive patients who underwent systematic lymph node dissection (SLND) had a better prognosis than those who underwent limited lymph node dissection (LLND); however, there was no significant difference between the two LNDs in the STAS-negative group. Conclusions: STAS plays an important role in patient prognosis and is an independent risk factor for OS of patients with ≤2 cm stage IA LUAD. When STAS is positive, the choice of lobectomy with SLND may result in a better long-term prognosis for patients.

A validated model to predict spread through air space in lung adenocarcinoma.

Background: Spread through air space (STAS) is currently considered to be a significant predictor of a poor outcome of pulmonary adenocarcinoma. Preoperative prediction of STAS is of great importance for treatment planning. The aim of the present study was to establish a nomogram based on computed tomography (CT) features for predicting STAS in lung adenocarcinoma and to assess the prognosis of the patients with STAS. Methods: A retrospective cohort study was performed in Wuhan Union Hospital from December 2015 to March 2021. The sample was divided into training and testing cohorts. Clinicopathologic and radiologic variables were recorded. The independent risk factors for STAS were determined by stepwise regression and then incorporated into the nomogram. Receiver operating characteristic (ROC) curves and calibration curves analysed by the Hosmer-Lemeshow test were used to evaluate the performance of the model. Decision curve analysis (DCA) was conducted to determine the clinical value of the nomogram. The Kaplan-Meier method was used for survival analysis and the multivariable Cox proportional hazards regression model was used to identify independent predictors for recurrence-free survival (RFS) and overall survival (OS). Results: The sample included 244 patients who underwent surgical resection for primary lung adenocarcinoma. The training cohort included 199 patients (68 STAS-positive and 131 STAS-negative patients), and the testing cohort included 45 patients (15 STAS-positive and 30 STAS-negative patients). The preoperative CT features associated with STAS were shape, ground-glass opacity (GGO) ratio and spicules. The nomogram including these three factors had good discriminative power, and the areas under the ROC curve were 0.875 and 0.922 for the training and testing data sets, respectively, with well-fitted calibration curves. DCA showed that the nomogram was clinically useful. STAS-positive patients had significantly worse OS and RFS than STAS-negative patients (both P<0.01). OS and RFS at 5-year for STAS-positive patients were 63.1% and 59.5%, respectively. Multivariate analysis showed that age [hazard ratio (HR), 1.1; 95% confidence interval (CI): 1.035-1.169; P=0.002], diameter (HR, 1.06; 95% CI: 1.04-1.11; P=0.03) and surgical margin (HR, 32.8; 95% CI: 6.8-158.3; P<0.001) were independent risk factors for OS. Adjuvant therapy (HR, 7.345; 95% CI: 2.52-21.41; P<0.001), N stage (N2) (HR, 0.239; 95% CI: 0.069-0.828; P=0.02) and surgical margin (HR, 15.6; 95% CI: 5.9-41.1; P<0.001) were found to be independent risk factors for RFS. Conclusions: The outcome of STAS-positive patients was worse. The nomogram incorporating the identified CT features could be applied to facilitate individualized preoperative prediction of STAS and selection of rational therapy.

Evaluation of the novel International Association for the Study of Lung Cancer grading system in adenocarcinoma with spread through air space.

Background: The International Association for the Study of Lung Cancer (IASLC) pathology panel has proposed a new grading system for invasive lung adenocarcinoma (LADC). This study aims to validate this novel grading system for invasive LADC using propensity score matching (PSM), with a specific focus on patients exhibiting spread through air space (STAS). Methods: We retrospectively analyzed the clinicopathologic features of a large cohort of 910 non-mucinous LADCs with STAS from 2017 to 2020 and classified them according to the novel grading system. We applied PSM to adjust for potential confounders between the grading groups. Kaplan-Meier and Cox proportional hazards models were adopted for prognostic evaluation. Results: The results showed that the IASLC grading system (grades 2 and 3) stratified well in terms of recurrence-free survival (RFS) and overall survival (OS) (P=0.02 and P=0.02, respectively) after matching, with Grade 3 being an independent predictor of RFS [hazard ratio (HR), 1.533; P=0.02] and OS (HR, 2.765; P=0.02) in multivariable models. The concordance index (C-index) and area under the curve (AUC) of the IASLC system were 0.719 and 0.754 for recurrence and 0.844 and 0.891 for death, respectively. In addition, anaplastic lymphoma kinase (ALK) fusion and tumor protein p53 (TP53) mutations were detected more frequently in grade 3 tumors, while epidermal growth factor receptor (EGFR) mutations were more prevalent in grade 2 tumors. The IASLC grade did not predict the benefit of adjuvant chemotherapy (ACT). Conclusions: This study suggests that the new IASLC grading system is a valuable prognostic tool for patients with STAS-positive LADC.

Micropapillary Pattern in Invasive Mucinous Adenocarcinoma of the Lung: Comparison With Invasive Non-Mucinous Adenocarcinoma.

Background. The presence of a micropapillary pattern is associated with poor outcomes in lung adenocarcinoma. This study aimed to assess the clinicopathological features of micropapillary pattern in mucinous adenocarcinoma of the lung. Methods. The patients were stratified into three groups: the invasive mucinous adenocarcinoma group (60 patients), the mixed invasive mucinous adenocarcinoma group (33 patients), and the invasive non-mucinous adenocarcinoma group (237 patients). The presence of micropapillary pattern and its clinicopathological features were analyzed and compared between the three groups. Results. Compared to mixed invasive mucinous adenocarcinoma, invasive mucinous adenocarcinoma had lower frequencies of micropapillary pattern (28.3% vs 87.9%, respectively; P < .001) and lymph node metastasis (00.0% vs 15.1%, respectively; P = .005). The frequency of tumor spread through air space (STAS) in invasive mucinous adenocarcinoma (23.3%) was higher than that in invasive non-mucinous adenocarcinoma (6.3%; P < .001), while lower than that in mixed invasive mucinous adenocarcinoma (60.6%; P < .001). When the three groups were all accompanied by micropapillary pattern, there was no obvious difference in STAS between invasive mucinous adenocarcinoma with micropapillary pattern and mixed mucinous adenocarcinoma with micropapillary pattern (P > .05). No filigree pattern occurred in invasive mucinous adenocarcinoma with micropapillary pattern. Conclusions. The micropapillary pattern is frequently observed in invasive mucinous adenocarcinoma and has a better prognosis compared to mixed invasive mucinous adenocarcinoma and invasive non-mucinous adenocarcinoma. However, the malignancy of the micropapillary pattern in mixed mucinous adenocarcinoma was similar to that in invasive non-mucinous adenocarcinoma, even with the presence of mucus. These findings suggest that the development mechanisms of the micropapillary pattern in invasive mucinous adenocarcinoma and mixed mucinous adenocarcinoma may differ.

The differential prognostic impact of spread through air spaces in early-stage lung adenocarcinoma after lobectomy according to the pT descriptor.

OBJECTIVES: We evaluated the differential prognostic impact of spread through air spaces (STAS) in early-stage lung adenocarcinoma after lobectomy according to the pT descriptor. METHODS: The study population included 506 patients who underwent lobectomy with mediastinal lymph node dissection for pT1b, pT1c, and pT2a adenocarcinoma between 2011 and 2016. We divided the study population into 2 groups according to STAS status, ie, STAS (+) versus STAS (-), and stratified them according to the pT descriptor. A Cox proportional hazard model and inverse probability of treatment weight-adjusted Kaplan-Meier curves were used to evaluate the prognostic impact of STAS on recurrence-free survival (RFS) and its independency in each stratum. RESULTS: Multivariable Cox proportional hazard regression analysis demonstrated that in pT1b and pT1c strata, STAS (+) patients had a 7.02-fold and 2.89-fold greater risk of recurrence than STAS (-) patients, respectively. However, in the pT2a stratum, STAS did not affect RFS. And the RFS of the STAS (+) pT1b/c strata was similar to that of the pT2a stratum. In the pT1b/c strata, inverse probability of treatment weighting-adjusted Kaplan-Meier curves also showed that RFS was significantly worse when STAS was present. Furthermore, the risks for locoregional and distant recurrence were both greater when STAS was present. CONCLUSIONS: The presence of STAS increased the risk of recurrence independently from other poor prognostic factors in patients with pT1b/cN0M0 adenocarcinoma who underwent lobectomy, but not in pT2a patients. The presence of STAS in pT1b/cN0M0 adenocarcinoma was associated with a similar risk of recurrence to that of pT2aN0M0 adenocarcinoma.

Role of radiomics in predicting lung cancer spread through air spaces in a heterogeneous dataset.

Background: Spread through air spaces (STAS) has been reported as a negative prognostic factor in patients with lung cancer undergoing sublobar resection. Radiomics has been recently proposed to predict STAS using preoperative computed tomography (CT). However, limitations of previous studies included the strict selection of imaging acquisition protocols, leading to results hardly applicable to daily clinical practice. The aim of this study is to test a radiomics-based prediction model of STAS in a practice-based dataset. Methods: A training cohort of 99 consecutive patients (65 STAS+ and 34 STAS-) with resected lung adenocarcinoma (ADC) was retrospectively collected. Preoperative CT images were collected from different centers regardless model and scanner manufacture, acquisition and reconstruction protocol, contrast phase and pixel size. Radiomics features were selected according to separation power and P value stability within different preprocessing setups and bootstrapping resampling. A prospective cohort of 50 patients (33 STAS+ and 17 STAS-) was enrolled for the external validation. Results: Only the five features with the highest stability were considered for the prediction model building. Radiomics, radiological and mixed radiomics-radiological prediction models were created, showing an accuracy of 0.66±0.02 after internal validation and reaching an accuracy of 0.78 in the external validation. Conclusions: Radiomics-based prediction models of STAS may be useful to properly plan surgical treatment and avoid oncological ineffective sublobar resections. This study supports a possible application of radiomics-based models on data with high variance in acquisition, reconstruction and preprocessing, opening a new chance for the use of radiomics in the prediction of STAS. Trial Registration: ClinicalTrials.gov identifier: NCT04893200.

Predictive value of radiological features on spread through air space in stage cIA lung adenocarcinoma.

BACKGROUND: Spread through air space (STAS) is a risk factor for disease recurrence in patients with stage IA lung adenocarcinoma (LUAD) who undergo limited resection. Preoperative prediction of STAS could help intraoperative surgical decision-making in small LUAD patients. The aim of the study was to evaluate the predictive value of radiological features on STAS in stage cIA LUAD. METHODS: A case-control study was designed through retrospective analysis of the radiological features of patients who underwent curative surgery for LUAD with a clinical tumor size ≤3 cm. Univariable and multivariable analyses were used to identify the independent risk factors for STAS. The predicted probability of STAS was calculated by a specific formula. Receiver operating characteristic (ROC) curves were used to determine a cut-off value with appropriate specificity while maintaining high sensitivity for STAS positivity. RESULTS: STAS was frequently observed in acinar predominant (P<0.001), micropapillary predominant (P<0.001) and solid predominant (P<0.001) tumors and was significantly associated with larger pT size (P<0.001), presence of micropapillary component (P<0.001), lymphovascular invasion (LVI) (P<0.001), visceral pleura invasion (VPI) (P<0.001), both N1 and N2 lymph node metastasis (P<0.001) and ALK rearrangement (P<0.001). STAS-positivity was significantly associated with the presence of cavitation (P=0.047), lobulation (P=0.009), air bronchogram (P<0.001), and vascular convergence (P=0.016) and was also associated with greater maximum tumor diameter (P<0.001), maximum solid component diameter (P<0.001), maximum tumor area (P<0.001), consolidation/tumor ratio (CTR) (P<0.001), tumor disappearance ratio (TDR) (P<0.001) and computed tomography (CT) value (P<0.001). Multivariable analysis showed that STAS was associated with air bronchogram (P=0.042), maximum tumor diameter (P=0.015), maximum solid component diameter (P=0.022) and CTR (P<0.001). The ROC curve showed that the area under the curve (AUC) was 0.726 in the model for predicting STAS, with a sensitivity and specificity of 95.2% and 36.8%, respectively. CONCLUSIONS: STAS-positive LUAD was associated with air bronchogram, maximum tumor diameter, maximum solid component diameter and CTR. These radiological features could predict STAS with excellent sensitivity but inferior specificity.

A CT-based logistic regression model to predict spread through air space in lung adenocarcinoma.

BACKGROUND: Spread through air space (STAS) is a novel invasive pattern of lung adenocarcinoma and is also a risk factor for recurrence and worse prognosis of lung adenocarcinoma. This study aimed to develop and validate a computed tomography (CT)-based logistic regression model to predict STAS in lung adenocarcinoma. METHODS: This retrospective study was approved by the institutional review board of two centers and included 578 patients (462 from center I and 116 from center II) with pathologically confirmed lung adenocarcinoma. STAS was identified from 90 center I patients (19.5%) and 28 center II patients (24.1%) from. The maximum diameter, nodule area, and area of solid components in part-solid nodules were measured. Twenty-one semantic characteristics were assessed. Univariate analysis was used to select CT characteristics, which were associated with STAS in the patient cohort of center I. Multivariable logistic regression was used to develop a CT characteristics-based model on those variables with statistical significance. The model was validated in the validation cohort and then tested in the external test cohort (patients from center II). The diagnostic performance of the model was measured by area under the curve (AUC) of receiver operating characteristic (ROC). RESULTS: At univariate analysis, age and 11 CT characteristics, including the maximum diameter of the tumor, the maximum area of the tumor, the area and ratio of the solid component, nodule type, pleural thickening, pleural retraction, mediastinal lymph node enlargement, vascular cluster sign, and lobulation, specula were found to be significantly associated with STAS. The optimal logistic regression model included age, maximum diameter and ratio of solid component with odds ratio (OR) value of 0.967 (95% CI: 0.944-0.988), 1.027 (95% CI: 1.008-1.046) and 5.14 (95% CI: 2.180-13.321), respectively. This model achieved an AUC of 0.801 (95% CI: 0.709-0.892) and 0.692 (95% CI: 0.518-0.866) in the validation cohort and the external test cohort, respectively. The difference was not statistically significant (P=0.280). CONCLUSIONS: CT-based logistic regression machine learning model could preoperatively predict STAS in lung adenocarcinoma with excellent diagnosis performance, which could be supplementary to routine CT interpretation.