Retrospective analysis of the prognostic implications of tumor spread through air spaces in lung adenocarcinoma patients treated with surgery

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.

Predictors of disease recurrence after radical resection and adjuvant chemotherapy in patients with stage IIb-IIIa squamous cell lung cancer: A retrospective analysis

BACKGROUND

Lung cancer (LC) is a global medical, social and economic problem and is one of the most common cancers and the leading cause of mortality from malignant neoplasms. LC is characterized by an aggressive course, and in the presence of disease recurrence risk factors, patients, even at an early stage, may be indicated for adjuvant therapy to improve survival. However, combined treatment does not always guarantee a favorable prognosis. In this regard, establishing predictors of LC recurrence is highly important both for determining the optimal treatment plan for the patients and for evaluating its effectiveness.

AIM

To establish predictors of disease recurrence after radical resection and adjuvant chemotherapy in patients with stage IIb-IIIa lung squamous cell carcinoma (LSCC).

METHODS

A retrospective case-control cohort study included 69 patients with LSCC who underwent radical surgery at the Orenburg Regional Clinical Oncology Center from 2009 to 2018. Postoperatively, all patients received adjuvant chemotherapy. Histological samples of the resected lung were stained with Mayer's hematoxylin and eosin and examined under a light microscope. Univariate and multivariate analyses were used to identify predictors associated with the risk of disease recurrence. Receiver operating characteristic curves were constructed to discriminate between patients with a high risk of disease recurrence and those with a low risk of disease recurrence. Survival was analyzed using the Kaplan-Meier method. The log-rank test was used to compare survival curves between patient subgroups. Differences were considered to be significant at P < 0.05.

RESULTS

The following predictors of a high risk of disease recurrence in patients with stage IIb-IIa LSCC were established: a low degree of tumor differentiation [odds ratio (OR) = 7.94, 95%CI = 1.08-135.81, P = 0.049]; metastases in regional lymph nodes (OR = 5.67, 95%CI = 1.09-36.54, P = 0.048); the presence of loose, fine-fiber connective tissue in the tumor stroma (OR = 21.70, 95%CI = 4.27-110.38, P = 0.0002); and fragmentation of the tumor solid component (OR = 2.53, 95%CI = 1.01-12.23, P = 0.049). The area under the curve of the predictive model was 0.846 (95%CI = 0.73-0.96, P < 0.0001). The sensitivity, accuracy and specificity of the method were 91.8%, 86.9% and 75.0%, respectively. In the group of patients with a low risk of LSCC recurrence, the 1-, 2- and 5-year disease-free survival (DFS) rates were 84.2%, 84.2% and 75.8%, respectively, while in the group with a high risk of LSCC recurrence the DFS rates were 71.7%, 40.1% and 8.2%, respectively (P < 0.00001). Accordingly, in the first group of patients, the 1-, 2- and 5-year overall survival (OS) rates were 94.7%, 82.5% and 82.5%, respectively, while in the second group of patients, the OS rates were 89.8%, 80.1% and 10.3%, respectively (P < 0.00001).

CONCLUSION

The developed method allows us to identify a group of patients at high risk of disease recurrence and to adjust to ongoing treatment.

The significance of spread through air spaces in the prognostic assessment model of stage I lung adenocarcinoma and the exploration of its invasion mechanism

PURPOSE

Spread through air spaces (STAS) is a crucial invasive mode of lung cancer and has been shown to be associated with early recurrence and metastasis. We aimed to develop a prognostic risk assessment model for stage I lung adenocarcinoma based on STAS and other pathological features and to explore the potential relationship between CXCL-8, Smad2, Snail, and STAS.

METHODS

312 patients who underwent surgery at Harbin Medical University Cancer Hospital with pathologically diagnosed stage I lung adenocarcinoma were reviewed in the study. STAS and other pathological features were identified by H&E staining, and a prognostic risk assessment model was established. The expression levels of CXCL8, Smad2, and Snail were determined by immunohistochemistry.

RESULTS

The nomogram was established based on age, smoking history, STAS, tumor lymphocyte infiltration, tissue subtype, nuclear grade, and tumor size. The C-index for DFS was (training set 0.84 vs validation set 0.77) and for OS was (training set 0.83 vs validation set 0.78). Decision curve analysis showed that the model constructed has a better net benefit than traditional reporting. The prognostic risk score validated the risk stratification value for stage I lung adenocarcinoma. STAS was an important prognostic factor associated with stronger invasiveness and higher expression of CXCL8, Smad2, and Snail. CXCL8 was associated with poorer DFS and OS.

CONCLUSIONS

We developed and validated a survival risk assessment model and the prognostic risk score formula for stage I lung adenocarcinoma. Additionally, we found that CXCL8 could be used as a potential biomarker for STAS and poor prognosis, and its mechanism may be related to EMT.