Accuracy and Reproducibility of Intraoperative Assessment on Tumor Spread Through Air Spaces in Stage 1 Lung Adenocarcinomas

STAS: New explorations and challenges for thoracic surgeons

Spread through air spaces (STAS) represents a relatively novel concept in the pathology of lung cancer, and it specifically refers to the dissemination of tumour cells into the parenchymal air spaces adjacent to the primary tumour. In 2015, the World Health Organization (WHO) classified STAS as a new invasive form of lung adenocarcinoma (LUAD). Many studies investigated the role of STAS and revealed its association with the prognosis of LUAD and its influence on the outcomes of other malignant pulmonary neoplasms. Additionally, the underlying mechanisms and predictive models of STAS have received considerable attention in recent years. This paper provides a comprehensive overview of the research advancements and prospects of STAS by examining it from multiple perspectives.

Spread Through Air Spaces (STAS): Inter-responder Agreement and Comparison between Pulmonary and General Pathologists

Spread through air spaces (STAS), an important prognostic indicator included in the 2015 WHO classification, is defined as micropapillary, solid and/or single tumor cell clusters beyond the edge of the main mass and distinct from processing artifacts. This study aimed to assess inter-responder agreement on current STAS criteria vs artifacts, identify discrepancies, and compare responses between pulmonary and general pathologists. A multiple-choice online questionnaire illustrating multiple criteria for STAS vs artifacts was available internationally for 6 days to Pulmonary Pathology Society members, thoracic pathology course attendees and International Association for the Study of Lung Cancer pathology committee members. Additional four questions gathered demographic and practice setting information. 136 unique responses were analyzed. The majority were from North America and Europe (42.6 and 30.2%), practicing pulmonary pathology (70.6%) in academia (64.7%), and with >20 years of experience (31.6%). Excluding trainees, the greatest overall agreement was in defining solid and micropapillary tumor clusters of STAS located > 3 alveolar spaces from the main tumor edge (91.5%) and recognizing strips of ciliated cells as artifacts (97.7%). Lesser agreement on STAS was evident when tumor cell clusters were immediately adjacent to the tumor edge, a single tumor cell cluster was present at the tissue edge, tumor cell clusters were jagged-edged, or tumor cell clusters were admixed with ciliated cell strips (artifacts). There was no significant difference in agreements on STAS for multiple criteria between pulmonary vs. general pathologists. Significant inter-responder agreement on STAS vs. artifacts was achieved only for a few criteria. To improve reproducibility of STAS vs artifacts, areas of lesser agreement require further clarification.

Deep learning-based detection and semi-quantitative model for spread through air spaces (STAS) in lung adenocarcinoma

Tumor spread through air spaces (STAS) is a distinctive metastatic pattern affecting prognosis in lung adenocarcinoma (LUAD) patients. Several challenges are associated with STAS detection, including misdetection, low interobserver agreement, and lack of quantitative analysis. In this research, a total of 489 digital whole slide images (WSIs) were collected. The deep learning-based STAS detection model, named STASNet, was constructed to calculate semi-quantitative parameters associated with STAS density and distance. STASNet demonstrated an accuracy of 0.93 for STAS detection at the tiles level and had an AUC of 0.72-0.78 for determining the STAS status at the WSI level. Among the semi-quantitative parameters, T10S, combined with the spatial location information, significantly stratified stage I LUAD patients on disease-free survival. Additionally, STASNet was deployed into a real-time pathological diagnostic environment, which boosted the STAS detection rate and led to the identification of three easily misidentified types of occult STAS.

Prognostic significance of micronest in cancer stroma in resected lung squamous cell carcinoma

Tumor budding in the cancer stroma has been reported to be a prognostic factor in non-small cell lung cancer. Micronest in cancer stroma (MICS) is often observed as a formation that is larger and more conspicuous than budding, but its clinicopathologic significance is unclear. In this study, we aimed to examine the clinicopathological significance of MICS in lung squamous cell carcinoma (LSqCC). A total of 198 consecutive patients with pathologically diagnosed LSqCC (anyT N0-1M0) were enrolled in this study. MICS were defined as those that met the following criteria: (1) consisting of 5-200 tumor cells or less than 200 μm in diameter and (2) more than 200 μm away from the adjacent main lesion. The prognostic impact of the presence or absence of MICS and the characteristics of MICS-forming cancer cells were evaluated by immunohistochemistry (IHC). MICS was observed in 57 patients (28.8%), and overall survival (OS) and recurrence-free survival (RFS) were significantly shorter in the MICS-positive group (OS: 44.4% vs. 84.4%, p < 0.001; RFS: 30.0% vs. 82.6%, p < 0.001). Univariate and multivariate analyses revealed that the presence of MICS was an independent poor prognostic factor for OS (hazard ratio [HR] 3.54, p < 0.001) and RFS (HR 4.99, p < 0.001). Immunohistochemistry showed that the expression levels of the cell-cell adhesion molecule E-cadherin and hypoxia-induced protein GLUT-1 were significantly decreased in cancer cells forming MICS lesions compared to the tumor component excluding MICS within the same tumor (non-MICS lesions). Our data show that MICS is a distinct morphological feature with important biological and prognostic significance.

Improving intraoperative diagnosis of spread through air spaces: A cryo-embedding-medium inflation method for frozen section analysis

Objective

Accurate intraoperative diagnosis of spread through air spaces (STAS), a known poor prognostic factor in lung cancer, is crucial for guiding surgical decision-making during sublobar resections. This study aimed to evaluate the diagnostic sensitivity of STAS using frozen section (FS) slides prepared with the cryo-embedding medium inflation technique.

Methods

In this prospective study at Shinshu University Hospital, 99 patients undergoing lung resection for tumors <3 cm in size were included, a total of 114 lesions. FS slides were prepared with injecting diluted cryo-embedding medium into the lung parenchyma of resected specimens. The diagnostic performance of these FS slides for STAS detection was evaluated by comparing FS-STAS results with the gold-standard STAS status.

Results

The incidence of STAS, determined by the gold standard, was 43 (38%) of 114 lesions, including 31 (37%) of 84 primary lung cancers and 12 (40%) of 30 metastatic lung tumors. The sensitivity, specificity, positive and negative predictive values, and accuracy of FS slides for STAS detection were 81%, 89%, 81%, 89%, and 86%, respectively. Specifically, in primary lung cancers, these values were 90%, 89%, 82%, 94%, and 89%, respectively. Regarding metastatic lung tumors, the corresponding values were 58%, 89%, 78%, 76%, and 77%, respectively.

Conclusions

Our adapted cryo-embedding medium inflation method has demonstrated enhanced sensitivity in detecting STAS on FS slides, providing results similar to the gold-standard STAS detection. Compared with historical benchmarks, this technique could show excellent performance and be readily incorporated into clinical practice without requiring additional resources beyond those used for standard FS analysis.

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.

CT-Based Intratumoral and Peritumoral Radiomics Nomograms for the Preoperative Prediction of Spread Through Air Spaces in Clinical Stage IA Non-small Cell Lung Cancer

The study aims to investigate the value of intratumoral and peritumoral radiomics and clinical-radiological features for predicting spread through air spaces (STAS) in patients with clinical stage IA non-small cell lung cancer (NSCLC). A total of 336 NSCLC patients from our hospital were randomly divided into the training cohort (n = 236) and the internal validation cohort (n = 100) at a ratio of 7:3, and 69 patients from the other two external hospitals were collected as the external validation cohort. Univariate and multivariate analyses were used to select clinical-radiological features and construct a clinical model. The GTV, PTV5, PTV10, PTV15, PTV20, GPTV5, GPTV10, GPTV15, and GPTV20 models were constructed based on intratumoral and peritumoral (5 mm, 10 mm, 15 mm, 20 mm) radiomics features. Additionally, the radscore of the optimal radiomics model and clinical-radiological predictors were used to construct a combined model and plot a nomogram. Lastly, the ROC curve and AUC value were used to evaluate the diagnostic performance of the model. Tumor density type (OR = 6.738) and distal ribbon sign (OR = 5.141) were independent risk factors for the occurrence of STAS. The GPTV10 model outperformed the other radiomics models, and its AUC values were 0.887, 0.876, and 0.868 in the three cohorts. The AUC values of the combined model constructed based on GPTV10 radscore and clinical-radiological predictors were 0.901, 0.875, and 0.878. DeLong test results revealed that the combined model was superior to the clinical model in the three cohorts. The nomogram based on GPTV10 radscore and clinical-radiological features exhibited high predictive efficiency for STAS status in NSCLC.

Updates on lung adenocarcinoma: invasive size, grading and STAS

Advancements in the classification of lung adenocarcinoma have resulted in significant changes in pathological reporting. The eighth edition of the tumour-node-metastasis (TNM) staging guidelines calls for the use of invasive size in staging in place of total tumour size. This shift improves prognostic stratification and requires a more nuanced approach to tumour measurements in challenging situations. Similarly, the adoption of new grading criteria based on the predominant and highest-grade pattern proposed by the International Association for the Study of Lung Cancer (IASLC) shows improved prognostication, and therefore clinical utility, relative to previous grading systems. Spread through airspaces (STAS) is a form of tumour invasion involving tumour cells spreading through the airspaces, which has been highly researched in recent years. This review discusses updates in pathological T staging, adenocarcinoma grading and STAS and illustrates the utility and limitations of current concepts in lung adenocarcinoma.

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.

Preoperative predictors of spread through air spaces in lung cancer

OBJECTIVE

Spread through air spaces (STAS) is a new histologic feature of invasion of non-small cell lung cancer that lacks sensitivity and specificity on frozen sections and is associated with higher recurrence and worse survival with sublobar resections. Our objective is to identify preoperative characteristics that are predictive of STAS to guide operative decisions.

METHODS

From January 2018 through December 2021, 439 cT1-3N0 M0 patients with non-small cell lung cancer and a median age of 68 years, 255 (58%) women, who underwent primary surgery at our institution were included. Patients who received neoadjuvant therapy and whose STAS status was not documented were excluded. Age, sex, smoking status, tumor size, ground-glass opacities, maximum standardized uptake values, and molecular markers on preoperative biopsy were evaluated as preoperative markers. Comparisons between groups were conducted using standardized mean differences and random forest classification was used for prediction modeling.

RESULTS

Of the 439 patients, 177 had at least 1 STAS-positive tumor, and 262 had no STAS-positive tumors. Overall, 179 STAS tumors and 293 non-STAS tumors were evaluated. Younger age (50 years or younger), solid tumor, size ≥2 cm, and maximum standardized uptake value ≥2.5 were independently predictive of STAS with prediction probabilities of 50%, 40%, 38%, and 40%, respectively. STAS tumors were more likely to harbor KRAS mutations and be PD-L1 negative.

CONCLUSIONS

Young age (50 years or younger), larger (≥2 cm) solid tumors, high maximum standardized uptake values, and presence of KRAS mutation, are risk factors for STAS and can be considered for lobectomy. Smoking status and gender are still controversial risk factors for STAS.

The value of frozen section diagnosis of tumor spread through air spaces in small-sized (≤ 2 cm) non-small cell lung cancer

BACKGROUND

The current accuracy of frozen section diagnosis of tumor spread through air spaces (STAS) in non-small cell lung cancer (NSCLC) is poor. However, the accuracy and prognostic value of STAS assessment on frozen sections in small-sized NSCLC (diameter ≤ 2 cm) is unknown.

METHODS

Three hundred fifty-two patients with clinical stage I NSCLC (≤ 2 cm) were included, of which the paraffin sections and frozen sections were reviewed. The accuracy of STAS diagnosis in frozen sections was assessed using paraffin sections as the gold standard. The relationship between STAS on frozen sections and prognosis was assessed by the Kaplan-Meier method and log-rank tests.

RESULTS

STAS on frozen sections in 58 of 352 patients could not be evaluated. In the other 294 patients, 36.39% (107/294) was STAS-positive on paraffin sections and 29.59% (87/294) on frozen sections. The accuracy of frozen section diagnosis of STAS was 74.14% (218/294), sensitivity was 55.14% (59/107), specificity was 85.02% (159/187) and agreement was moderate (K = 0.418). In subgroup analysis, the Kappa values for frozen section diagnosis of STAS in the consolidation-to-tumor ratio (CTR) ≤ 0.5 group and CTR > 0.5 group were 0.368, 0.415, respectively. In survival analysis, STAS-positive frozen sections were associated with worse recurrence-free survival in the CTR > 0.5 group (P < 0.05).

CONCLUSIONS

The moderate accuracy and prognostic significance of frozen section diagnosis of STAS in clinical stage I NSCLC (≤ 2 cm in diameter; CTR > 0.5) suggests that frozen section assessment of STAS can be applied to the treatment strategy of small-sized NSCLC with CTR > 0.5.

Frozen sections accurately predict the IASLC proposed grading system and prognosis in patients with invasive lung adenocarcinomas

INTRODUCTION

The International Association for the Study of Lung Cancer (IASLC) newly proposed grading system for lung adenocarcinomas (ADC) has been shown to be of prognostic significance. Hence, intraoperative consultation for the grading system was important regarding the surgical decision-making. Here, we evaluated the accuracy and interobserver agreement for IASLC grading system on frozen section (FS), and further investigated the prognostic performance.

METHODS

FS and final pathology (FP) slides were reviewed by three pathologists for tumor grading in 373 stage I lung ADC following surgical resection from January to June 2013 (retrospective cohort). A prospective multicenter cohort (January to June 2021, n = 212) were included to confirm the results.

RESULTS

The overall concordance rates between FS and FP were 79.1% (κ = 0.650) and 89.6% (κ = 0.729) with substantial agreement in retrospective and prospective cohorts, respectively. Presence of complex gland was the only independent predictor of discrepancy between FS and FP (presence versus. absence: odds ratio, 2.193; P = 0.015). The interobserver agreement for IASLC grading system on FS among three pathologists were satisfactory (κ = 0.672 for retrospective cohort; κ = 0.752 for prospective cohort). Moreover, the IASLC grading system by FS diagnosis could well predict recurrence-free survival and overall survival for patients with stage I invasive lung ADC.

CONCLUSIONS

Our results suggest that FS had high diagnostic accuracy and satisfactory interobserver agreement for IASLC grading system. Future prospective studies are merited to validate the feasibility of using FS to match patients into appropriate surgical type.

Updates in pathology and molecular diagnostics to inform the evolving landscape of thoracic surgery and oncology

The pathologic assessment of lung cancers provides essential guidance to the surgeon and oncologist who are considering the best treatment strategies for patients with both early and advanced-stage disease. The management of patients with lung cancer is predicated first and foremost on access to an accurate diagnosis, even when the sample size is limited, as is often the case with use of modern, minimally invasive sampling techniques. Once the diagnosis and disease stage are established, predictive biomarker testing may be essential, particularly for those patients with nonsmall cell lung carcinoma (NSCLC) being considered for immunotherapy or genomic biomarker-driven targeted therapy. This review will discuss the best practices for the diagnosis of NSCLC using morphology and immunohistochemistry, thus providing the surgeon with needed information to understand and critically evaluate pathology reports. Controversial and evolving topics including tumor spread through airspaces, evaluation of multiple tumors, and staging based on invasive tumor size will be addressed. Clinical genomic profiling in NSCLC is driven by published guidelines and reflects evidence based on clinical trials and regulatory approvals. In this fast-moving space, surgeons should be aware of the critical immunohistochemical and genomic biomarkers that drive systemic therapy decisions and anticipate when such testing will be required, both to ensure adequate sampling and to advise the pathologist when tumor material will be required for biomarker analysis. The basic approaches to and sample requirements for molecular biomarker testing will be addressed. As biomarker testing moves exclusively from advanced-stage patients into earlier stage disease, the surgeon should be aware of the relevant markers and work with the pathologist and oncologist to ensure that this information is available to facilitate timely access to therapies not just in the advanced setting, but in consideration of neoadjuvant and adjuvant care.

Preoperative levels of folate receptor-positive circulating tumor cells in different subtypes of early-stage lung adenocarcinoma: Predictive value for determining extent of surgical resection

Background

The objective was to measure the correlations of preoperative levels of folate receptor-positive circulating tumor cells (FR⁺CTCs) with clinical characteristics and histologic subtype in early-stage lung adenocarcinoma, and to determine the predictive value of FR⁺CTC level in preoperative determination of the extent of surgical resection.

Patients and methods

In this retrospective, single-institution, observational study, preoperative FR⁺CTC levels were measured via ligand-targeted enzyme-linked polymerization in patients with early-stage lung adenocarcinoma. Receiver operating characteristic (ROC) analysis was used to identify the optimal cutoff value of FR⁺CTC level for prediction of various clinical characteristics and histologic subtypes.

Results

No significant difference in FR⁺CTC level was observed among patients with adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC) (P = 0.813). Within the non-mucinous adenocarcinoma group, no difference was observed among patients with tumors whose predominant growth patterns were lepidic, acinar, papillary, micropapillary, solid, and complex gland (P = 0.053). However, significant differences in FR⁺CTC level were observed between patients with and without the micropapillary subtype [11.21 (8.22-13.61) vs. 9.85 (7.43-12.63), P = 0.017], between those with and without the solid subtype [12.16 (8.27-14.90) vs. 9.87 (7.50-12.49), P = 0.022], and between those with any of the advanced subtypes (micropapillary, solid, or complex glands) vs. none of these [10.48 (7.83-13.67) vs. 9.76 (7.42-12.42), P = 0.032]. FR⁺CTC level was also correlated with degree of differentiation of lung adenocarcinoma (P = 0.033), presence of visceral pleural invasion (VPI) of lung carcinoma (P = 0.003), and lymph node metastasis of lung carcinoma (P = 0.035).

Conclusion

FR⁺CTC level is of potential predictive value in determining the presence of aggressive histologic patterns (micropapillary, solid, and advanced subtypes), degree of differentiation, and occurrence of VPI and lymph node metastasis in IAC. Measurement of FR⁺CTC level combined with intraoperative frozen sections may represent a more effective method of guiding resection strategy in cases of cT1N0M0 IAC with high-risk factors.

3D convolutional neural network model from contrast-enhanced CT to predict spread through air spaces in non-small cell lung cancer

PURPOSE

The purpose of this study was to compare the efficacy of five non-invasive models, including three-dimensional (3D) convolutional neural network (CNN) model, to predict the spread through air spaces (STAS) status of non-small cell lung cancer (NSCLC), and to obtain the best prediction model to provide a basis for clinical surgery planning.

MATERIALS AND METHODS

A total of 203 patients (112 men, 91 women; mean age, 60 years; age range 22-80 years) with NSCLC were retrospectively included. Of these, 153 were used for training cohort and 50 for validation cohort. According to the image biomarker standardization initiative reference manual, the image processing and feature extraction were standardized using PyRadiomics. The logistic regression classifier was used to build the model. Five models (clinicopathological/CT model, conventional radiomics model, computer vision (CV) model, 3D CNN model and combined model) were constructed to predict STAS by NSCLC. Area under the receiver operating characteristic curves (AUC) were used to validate the capability of the five models to predict STAS.

RESULTS

For predicting STAS, the 3D CNN model was superior to the clinicopathological/CT model, conventional radiomics model, CV model and combined model and achieved satisfactory discrimination performance, with an AUC of 0.93 (95% CI: 0.70-0.82) in the training cohort and 0.80 (95% CI: 0.65-0.86) in the validation cohort. Decision curve analysis indicated that, when the probability of the threshold was over 10%, the 3D CNN model was beneficial for predicting STAS status compared to either treating all or treating none of the patients within certain ranges of risk threshold CONCLUSION: The 3D CNN model can be used for the preoperative prediction of STAS in patients with NSCLC, and was superior to the other four models in predicting patients' risk of developing STAS.

Clinicopathological Impact of the Spread through Air Space in Non-Small Cell Lung Cancer: A Meta-Analysis

This study aimed to elucidate the clinicopathological significance of spread through air space (STAS) in non-small cell lung cancer (NSCLC) through a meta-analysis. Using 47 eligible studies, we obtained the estimated rates of STAS in various histological subtypes of NSCLC and compared the clinicopathological characteristics and prognosis between NSCLC with and without STAS. The estimated STAS rate was 0.368 (95% confidence interval [CI], 0.336–0.0.401) in patients with NSCLC. Furthermore, the STAS rates for squamous cell carcinoma and adenocarcinoma were 0.338 (95% CI, 0.273–0.411) and 0.374 (95% CI, 0.340–0.409), respectively. Among the histological subtypes of adenocarcinoma, micropapillary-predominant tumors had the highest rate of STAS (0.719; 95% CI, 0.652–0.778). The STAS rates of solid- and papillary-predominant adenocarcinoma were 0.567 (95% CI, 0.478–0.652) and 0.446 (95% CI, 0.392–0.501), respectively. NSCLCs with STAS showed a higher visceral pleural, venous, and lymphatic invasion than those without STAS. In addition, anaplastic lymphoma kinase mutations and ROS1 rearrangements were significantly more frequent in NSCLCs with STAS than in those without STAS. The presence of STAS was significantly correlated with worse overall and recurrence-free survival (hazard ratio, 2.119; 95% CI, 1.811–2.480 and 2.372; 95% CI, 2.018–2.788, respectively). Taken together, the presence of STAS is useful in predicting the clinicopathological significance and prognosis of patients with NSCLC.

Assessment of the feasibility of frozen sections for the detection of spread through air spaces (STAS) in pulmonary adenocarcinoma

Spread through air spaces (STAS) is reportedly associated with worse prognosis in sublobar resections of lung adenocarcinoma. Recently, it was proposed that STAS detected on frozen sections can be an indication for lobectomy instead of sublobar resection. We undertook this study to evaluate the reliability of STAS assessment on frozen sections compared to permanent sections, as well as the associations among STAS, tumor grade, and recurrence-free survival (RFS) after sublobar resection. A total of 163 stage I lung adenocarcinoma resections with frozen sections were identified retrospectively. For each case, and for frozen and permanent sections separately, the presence or absence of STAS, as well as the tumor grade, were recorded. Compared to permanent sections, STAS detection on frozen sections had low sensitivity (55%), low positive predictive value (48%), and fair agreement (K = 0.34), whereas there was higher specificity (80%) and negative predictive value (85%). Accuracy was 74%. Tumor grade assessment on frozen sections showed higher sensitivity (77%), positive predictive value (90%), agreement (K = 0.72), specificity (94%), and accuracy (87%), and the same negative predictive value (85%). High-grade histology on frozen sections was associated with shorter RFS (p = 0.02), whereas STAS on frozen sections was not (p = 0.47). Our results suggest that the intraoperative detection of STAS has low sensitivity and positive predictive value. False-positive results may lead to overtreatment of patients with lung cancer. The determination of tumor grade on frozen sections offers better sensitivity and specificity, plus it is associated with RFS, whereas STAS on frozen sections is not. Further study is needed to explore the utility of assessing tumor grade on frozen sections.

Spread Through Air Spaces (STAS) in Non-Small Cell Lung Carcinoma: Evidence Supportive of an In Vivo Phenomenon

Tumor spread through air spaces (STAS) is associated with locoregional recurrence in patients undergoing limited resection (LR) for non-small cell lung carcinoma (NSCLC). We hypothesized that the observation of STAS in both the initial LR specimen and the additional resection specimen from the same patient, processed using different knives, would provide evidence that STAS is an in vivo phenomenon contributing to locoregional recurrence. We retrospectively identified patients with NSCLC (9 adenocarcinoma, 1 squamous cell carcinoma) who underwent LR, had STAS in the LR specimen, and underwent additional resection (lobectomy or LR). The LR and additional resection specimens from each patient were processed at different times using different tissue-processing knives. All specimens were analyzed for STAS. All 10 patients underwent LR with negative margins (R0). All additional resection specimens had STAS: 8 patients had STAS clusters in their completion lobectomy specimens, and 2 had STAS in their additional LR specimens. In 2 patients, STAS was found in the completion lobectomy specimen only after extensive sampling (>10 sections) from the staple line adjacent to the initial LR. The presence of STAS in both the LR and the additional resection specimen processed using different knives supports the concept that STAS is an in vivo phenomenon, rather than an artifact from tissue processing. This observation indicates that occult STAS tumor cells can be present in the lung tissue of the remaining unresected lobe after LR and supports the concept that STAS is a contributing factor for locoregional recurrence following LR.