Pretreatment prediction of tumour spread through air spaces in clinical stage I non-small-cell lung cancer

Ding, Yun; Chen, Yiyong; Wen, H. Joseph; Li, Jiuzhen; Chen, Jinzhan; Xu, Min; Geng, Hua; You, Lisheng; Pan, Xiaojie; Sun, Di

doi:10.1093/ejcts/ezac248

Cited by 6 publications

(6 citation statements)

References 25 publications

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“…Furthermore, 95% (152/160) of the STAS patients had a CTR value greater than 0.5, and the results were consistent with the study by Ding and colleagues. 11 Moreover, a definite correlation between the Rad‐score and CTR was observed, with a Wilcoxon test p‐ value < 0.01, which suggested a discernible association between STAS and CTR.…”

Section: Discussionmentioning

confidence: 90%

“…Due to the prevalent utilization of thin‐section computed tomography (CT) and the increased detection rate of early‐stage NSCLC, 8 STAS has gradually become known and understood in academia. Several previous studies have reported that STAS usually presents as solid or subsolid nodules in CT images and is correlated with factors such as maximum tumor diameters (Tdmax), solid component diameters, and consolidation tumor ratio (CTR) 9–11 . Nevertheless, the interpretation of CT images relies highly on the diagnostic experience of doctors.…”

Section: Introductionmentioning

confidence: 99%

“…Several previous studies have reported that STAS usually presents as solid or subsolid nodules in CT images and is correlated with factors such as maximum tumor diameters (Tdmax), solid component diameters, and consolidation tumor ratio (CTR). 9 , 10 , 11 Nevertheless, the interpretation of CT images relies highly on the diagnostic experience of doctors. Radiomics can help analyze medical images and transform them into quantifiable multidimensional data, which presents the possibility of objectively identifying lesions at both the macro and microlevels.…”

Section: Introductionmentioning

confidence: 99%

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CT‐based radiomics model to predict spread through air space in resectable lung cancer

Gong,

Yin,

Sun

et al. 2023

Cancer Medicine

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BackgroundSpread through air space (STAS) has been identified as a pathological pattern associated with lung cancer progression. Patients with STAS were related to a worse prognosis compared with patients without STAS. The objective of this study was to establish a radiomics model capable of forecasting STAS before surgery, which can assist surgeons in selecting the most appropriate operation type for patients with STAS.MethodThere were 537 eligible patients retrospectively included in this study. ROI segmentation was performed manually on all CT images to identify the region of interest. From each segmented lesion, a total of 1688 features were extracted. The tumor size, maximum tumor diameters, and tumor type were also recorded. Using Spearman's correlation coefficient to calculate the correlation and redundancy of elements, and redundant features less than 0.80 were removed. In order to reduce the level of overfitting and avoid statistical biases, a dimension reduction process of the dataset was conducted to decrease the number of features. Finally, a radiomics model included 44 features was established to predict STAS. To evaluate the performance of the model, the receiver operating characteristic (ROC) curve was used, and the area under the curve (AUC) was calculated, and the accuracy of the model was verified by 10‐fold cross‐validation.ResultsThe incidence of STAS was 38.2% (205/537). The tumor type, maximum tumor diameters, and consolidation tumor ratio were significantly different between STAS group and non‐STAS group. The training group included 430 patients, while the test group was consisted with 107. The training group achieved an AUC of 0.825 (sensitivity, 0.875; specificity, 0.621; and accuracy, 0.749) and the test group had an AUC of 0.802 (sensitivity, 0.797; specificity,0.688; and accuracy, 0.748). The 10‐fold cross‐validation had an AUC of 0.834.ConclusionCT‐based radiomic model can predict STAS effectively, which is of great importance to guide the selection of operation types before surgery.

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Section: Discussionmentioning

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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CT‐based radiomics model to predict spread through air space in resectable lung cancer

Gong,

Yin,

Sun

et al. 2023

Cancer Medicine

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“…In studies that aimed to create a prediction model using radiomic values, it was suggested that those values can guide the preoperative prediction of STAS in early stage lung adenocarcinoma and related surgeries [10]. In fact, there was a study that stated there was good calibration in the nomogram prediction model of STAS in clinical Stage I non-small cell lung cancer (NSCLC) [11]. However, these studies were retrospective, which can lead to selection bias, and they were conducted in a limited number of centers and with a limited number of patients.…”

Section: Discussionmentioning

confidence: 99%

The Effect of STAS Positivity in Lung Cancer

INAN,

İNAN,

CELIK

et al. 2023

Preprint

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Aim of study: The spread through air spaces (STAS) phenomenon, which describes the presence of tumor cells in the air spaces of lung cancer, has been associated with an increased risk of local recurrence. We performed retrospective analyses to examine the presence of STAS and to evaluate its clinical results and its relationship with clinicopathological parameters. Materials and Methods: A total of 149 surgically resected lung cancer cases were analyzed retrospectively. Detailed analyses were performed on demographic- radiological-clinical-histological features. Results: The mean age of the patients was 63 (IQR = 11; range, 22–81), among whom 31 were female and 118 were male. The incidence of STAS was not different between the histological groups (p = 0.427). There was no difference between SUVmax value in STAS-positive and negative patients (p = 0.970). The recurrence rate, survival, and median tumor size were not different from each other in the STAS-positive and STAS-negative groups (p = 1,000, p = 0.086, p = 0.292, respectively). Conclusion: STAS is an independent risk factor for poor prognosis. Therefore, it may be possible to provide more personalized information by using clinicopathological markers that will facilitate preoperative prediction of STAS presence.

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“…In the sublobar resection group but not in the lobectomy group, Eguchi et al discovered that the risk for recurrence of STAS-positive NSCLC patients was considerably higher than that of STAS-negative patients [12]. Several strategies are currently being used for the preoperative prediction of STAS, including CT-based radiomics, intraoperative frozen section assessment, and nomogram prediction models based on clinical factors [13][14][15][16]. However, due to the limited predictive e cacy and low widespread clinical application, preoperative STAS prediction remains challenging and should be further developed in a larger context.Peripheral blood indexes have exhibited an increasingly promising role in predicting the prognosis and evaluating the e cacy of antitumour treatment.…”

mentioning

confidence: 99%

Significance of peripheral blood indicators in predicting tumor spread through air spaces and survival in pathological stage I lung adenocarcinoma

Wang

et al. 2022

Preprint

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Objective: Tumor spread through air spaces(STAS) is a poor prognostic factor for early-stage non-small-cell cancers. This investigation sought to determine the correlations of preoperative peripheral blood parameters with STAS and survival outcomes in pathological stage I lung adenocarcinoma (ADC). Methods: We retrospectively reviewed 633 stage I ADC patients who underwent radical surgical resection for the presence of STAS using HE-stained pathological sections. The baseline clinicopathological features, preoperative peripheral blood indexes and follow-up data were analysed. Independent indicators of STAS were identified using multivariate logistic regression. Kaplan‒Meier analyses were used to examine overall survival (OS) and recurrence-free survival (RFS). Multivariate Cox regression analysis wasused to identifyindependent prognostic variables. Results: STAS was discovered in 285 (45.0%) of the 633 patients. STAS positivity was related to gender, smoking status, disease stage, predominant histological pattern, and differentiation. The multivariate logistic regression identified a level of carcinoembryonic antigen (CEA) ≥5 ug/L and absolute monocyte count (AMC) ≥0.38 G/L as an independent predictor of STAS (p=0.005; p=0.013) among the hematological parameters. STAS positivity was an independent poor prognostic factor for RFS and OS in the CEA <5 µg/L subgroup but not in the CEA ≥5 µg/L subgroup (RFS: HR=2.616, 95% CI=1.414-4.839, p=0.002; OS: HR=5.534, 95% CI=1.186-25.816, p=0.029). In STAS-negative patients but not in STAS-positive patients, CEA demonstrated an independent predictive influence for recurrence and death (RFS: HR=6.488, 95% CI=2.475-17.010, p=0.005; OS: HR=19.569, 95% CI=2.487-153.983, p=0.005). Conclusions: Preoperative hematological examination can be prioritised in predicting the presence of STAS, and CEA ≥5 ug/L and AMC ≥ 0.38 G/L were independent risk predictors for STAS in pathological stage I lung adenocarcinoma. Combining preoperative hematological markers with STAS can optimize the prediction of cancer mortality or recurrence following patient subclassification.

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Pretreatment prediction of tumour spread through air spaces in clinical stage I non-small-cell lung cancer

Cited by 6 publications

References 25 publications

CT‐based radiomics model to predict spread through air space in resectable lung cancer

CT‐based radiomics model to predict spread through air space in resectable lung cancer

The Effect of STAS Positivity in Lung Cancer

Significance of peripheral blood indicators in predicting tumor spread through air spaces and survival in pathological stage I lung adenocarcinoma

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