Prognostic impact of spread through air spaces in lung adenocarcinoma

Significance of Spread Through Air Spaces and Vascular Invasion in Early-stage Adenocarcinoma Survival: A Comprehensive Clinicopathologic Study of 427 Patients for Precision Management

Spread through air spaces (STAS) is a novel invasive pattern of lung cancer associated with poor prognosis in non-small cell cancer (NSCLC). We aimed to investigate the incidence of STAS in a surgical series of adenocarcinomas (ADCs) resected in our thoracic surgery unit and to identify the association of STAS with other clinicopathological characteristics. We retrospectively enrolled patients with stage cT1a-cT2b who underwent resection between 2016 and 2022. For each case, a comprehensive pathologic report was accessible which included histotype, mitoses, pleural invasion, fibrosis, tumor infiltrating lymphocytes, necrosis, inflammation, vascular and perineural invasion, as well as STAS. PD-L1 expression was also investigated. A total of 427 patients with ADCs underwent surgery. Regarding overall survival (OS), no significant difference was observed between the STAS positive (STAS+) and STAS negative (STAS-) groups ( P =0.44). However, vascular invasion (VI) was associated with a poorer survival probability ( P =0.018). STAS+/VI+ patients had tendentially worse survival compared with STAS+/VI- ( P =0.089). ADCs with pathologic evidence of immune system (IS) activation (TILs>10% and PD-L1≥1) demonstrated significantly increased OS compared with ADCs with no IS and VI. In terms of recurrence rate, no statistical differences were found between the STAS+ and STAS- samples ( P =0.2). VI was also linked to a significantly elevated risk of recurrence ( P =0.0048). Our study suggests that in resected early-stage ADCs, STAS+ does not seem to influence recurrence or mortality. VI was instead an adverse pathologic prognostic factor for both survival and recurrence, whereas IS seemed to be protective.

TDASD: Generating medically significant fine-grained lung adenocarcinoma nodule CT images based on stable diffusion models with limited sample size

BACKGROUND AND OBJECTIVES

Spread through air spaces (STAS) is an emerging lung cancer infiltration pattern. Predicting its spread through CT scans is crucial. However, limited STAS data makes this prediction task highly challenging. Stable diffusion is capable of generating more diverse and higher-quality images compared to traditional GAN models, surpassing the dominating GAN family models in image synthesis over the past few years. To alleviate the issue of limited STAS data, we propose a method TDASD based on stable diffusion, which is able to generate high-resolution CT images of pulmonary nodules corresponding to specific nodular signs according to the medical professionals.

METHODS

First, we apply the stable diffusion method for fine-tuning training on publicly available lung datasets. Subsequently, we extract nodules from our hospital's lung adenocarcinoma data and apply slight rotations to the original nodule CT slices within a reasonable range before undergoing another round of fine-tuning through stable diffusion. Finally, employing DDIM and Ksample sampling methods, we generate lung adenocarcinoma nodule CT images with signs based on prompts provided by doctors. The method we propose not only safeguards patient privacy but also enhances the diversity of medical images under limited data conditions. Furthermore, our approach to generating medical images incorporates medical knowledge, resulting in images that exhibit pertinent medical features, thus holding significant value in tumor discrimination diagnostics.

RESULTS

Our TDASD method has the capability to generate medically meaningful images by optimizing input prompts based on medical descriptions provided by experts. The images generated by our method can improve the model's classification accuracy. Furthermore, Utilizing solely the data generated by our method for model training, the test results on the original real dataset reveal an accuracy rate that closely aligns with the testing accuracy achieved through training on real data.

CONCLUSIONS

The method we propose not only safeguards patient privacy but also enhances the diversity of medical images under limited data conditions. Furthermore, our approach to generating medical images incorporates medical knowledge, resulting in images that exhibit pertinent medical features, thus holding significant value in tumor discrimination diagnostics.

Lung Cancer Surgery in Octogenarians: Implications and Advantages of Artificial Intelligence in the Preoperative Assessment

The general world population is aging and patients are often diagnosed with early-stage lung cancer at an advanced age. Several studies have shown that age is not itself a contraindication for lung cancer surgery, and therefore, more and more octogenarians with early-stage lung cancer are undergoing surgery with curative intent. However, octogenarians present some peculiarities that make surgical treatment more challenging, so an accurate preoperative selection is mandatory. In recent years, new artificial intelligence techniques have spread worldwide in the diagnosis, treatment, and therapy of lung cancer, with increasing clinical applications. However, there is still no evidence coming out from trials specifically designed to assess the potential of artificial intelligence in the preoperative evaluation of octogenarian patients. The aim of this narrative review is to investigate, through the analysis of the available international literature, the advantages and implications that these tools may have in the preoperative assessment of this particular category of frail patients. In fact, these tools could represent an important support in the decision-making process, especially in octogenarian patients in whom the diagnostic and therapeutic options are often questionable. However, these technologies are still developing, and a strict human-led process is mandatory.

The effect of spread through air spaces on postoperative recurrence-free survival in patients with multiple primary lung cancers

PURPOSE

The purpose of the study was to investigate the effect of spread through air spaces (STAS) on the postoperative prognosis of patients with multiple primary lung cancers staged from IA to IB based on tumor size.

METHODS

Clinicopathological and follow-up data of 122 patients with multiple primary lung cancers diagnosed at stages IA-IB and surgically treated at the Department of Thoracic Surgery, Shenzhen people's Hospital from January 2019 to December 2021 were retrospectively analyzed. The study involved 42 males and 80 females. STAS status was used to divide them into two groups (87 cases in STAS (-) and 35 cases in STAS (+)). A logistic regression analysis, univariate and multivariate Cox regression analysis, and Kaplan-Meier curves (K-M) were used to determine how STAS affected recurrence-free survival (RFS) in patients.

RESULTS

STAS (+) had a significantly higher recurrence rate than STAS (-). STAS was predicted by smoking history (P = 0.044), main tumor diameter (P = 0.02), and solid nodules on chest CT (P = 0.02). STAS incidence was not significantly different between lobectomy and sublobar resection groups (P = 0.17). Solid nodules on CT, tumor diameter, vascular invasion, pleural invasion, and STAS were significant predictors of recurrence in the univariate Cox regression analysis. Tumor diameter, pleural invasion and STAS were significant prognostic factors for recurrence in the multivariate Cox regression analysis. Furthermore, STAS (+) group was at greater risk of recurrence than STAS (-) group (34% vs. 0%, P < 0.05)。.

CONCLUSION

Stage IA-IB multiple primary lung cancer patients with STAS (+) had a higher recurrence rate and a shorter overall survival rate.

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

Background

Tumor spread through air spaces (STAS) in lung adenocarcinoma is a novel mechanism of invasion. STAS has been proposed as an independent predictor of poor prognosis. The aim of this study was to evaluate the correlations between STAS status and other clinicopathologic variables and to assess the prognostic implications of STAS and the distance from the edge of the tumor to the farthest STAS in patients with resected lung adenocarcinoma.

Material and methods

This is a single-institution retrospective observational study. We included all patients with resected lung adenocarcinoma from January 2017 to December 2018 at La Paz University Hospital. The cut-off for the distance from the edge of the tumor to the farthest STAS was 1.5 mm and was assessed by the area under the receiver operating characteristic curve.

Results

A total of 73 patients were included. STAS was found in 52 patients (71.2%). Histological grade 3 (P = 0.035) and absence of lepidic pattern (P = 0.022) were independently associated with the presence of STAS. The median recurrence-free survival (RFS) was 48.06 months [95% confidence interval (CI) 33.58 months to not reached]. STAS-positive patients had shorter median RFS [39.23 months (95% CI 29.34-49.12 months)] than STAS-negative patients (not reached) (P = 0.04). STAS-positive patients with a distance from the edge of the tumor to the farthest STAS ≥1.5 mm had an even shorter median RFS [37.63 months (95% CI 28.14-47.11 months)]. For every 1 mm increase in distance, the risk of mortality increased by 1.26 times (P = 0.04).

Conclusions

Histological grade 3 and absence of lepidic pattern were independently associated with the presence of STAS. STAS was associated with a higher risk of recurrence. The distance from the edge of the tumor to the farthest STAS also had an impact on overall survival.

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Lung adenocarcinoma patients with STAS had higher risk of recurrence.

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Patients with STAS and a distance from the edge of the tumor to the farthest STAS ≥1.5 mm had an even shorter RFS.

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The distance from the edge of the tumor to the farthest STAS also had an impact on overall survival.