Predictive Role of Tumor-Stroma Ratio for Survival of Patients With Non-Small Cell Lung Cancer: A Meta-Analysis

Combined expert-in-the-loop-random forest multiclass segmentation U-net based artificial intelligence model: evaluation of non-small cell lung cancer in fibrotic and non-fibrotic microenvironments

BACKGROUND

The tumor microenvironment (TME) plays a key role in lung cancer initiation, proliferation, invasion, and metastasis. Artificial intelligence (AI) methods could potentially accelerate TME analysis. The aims of this study were to (1) assess the feasibility of using hematoxylin and eosin (H&E)-stained whole slide images (WSI) to develop an AI model for evaluating the TME and (2) to characterize the TME of adenocarcinoma (ADCA) and squamous cell carcinoma (SCCA) in fibrotic and non-fibrotic lung.

METHODS

The cohort was derived from chest CT scans of patients presenting with lung neoplasms, with and without background fibrosis. WSI images were generated from slides of all 76 available pathology cases with ADCA (n = 53) or SCCA (n = 23) in fibrotic (n = 47) or non-fibrotic (n = 29) lung. Detailed ground-truth annotations, including of stroma (i.e., fibrosis, vessels, inflammation), necrosis and background, were performed on WSI and optimized via an expert-in-the-loop (EITL) iterative procedure using a lightweight [random forest (RF)] classifier. A convolution neural network (CNN)-based model was used to achieve tissue-level multiclass segmentation. The model was trained on 25 annotated WSI from 13 cases of ADCA and SCCA within and without fibrosis and then applied to the 76-case cohort. The TME analysis included tumor stroma ratio (TSR), tumor fibrosis ratio (TFR), tumor inflammation ratio (TIR), tumor vessel ratio (TVR), tumor necrosis ratio (TNR), and tumor background ratio (TBR).

RESULTS

The model's overall classification for precision, sensitivity, and F1-score were 94%, 90%, and 91%, respectively. Statistically significant differences were noted in TSR (p = 0.041) and TFR (p = 0.001) between fibrotic and non-fibrotic ADCA. Within fibrotic lung, statistically significant differences were present in TFR (p = 0.039), TIR (p = 0.003), TVR (p = 0.041), TNR (p = 0.0003), and TBR (p = 0.020) between ADCA and SCCA.

CONCLUSION

The combined EITL-RF CNN model using only H&E WSI can facilitate multiclass evaluation and quantification of the TME. There are significant differences in the TME of ADCA and SCCA present within or without background fibrosis. Future studies are needed to determine the significance of TME on prognosis and treatment.

Preoperative CT-based radiomic prognostic index to predict the benefit of postoperative radiotherapy in patients with non-small cell lung cancer: a multicenter study

BACKGROUND

The value of postoperative radiotherapy (PORT) for patients with non-small cell lung cancer (NSCLC) remains controversial. A subset of patients may benefit from PORT. We aimed to identify patients with NSCLC who could benefit from PORT.

METHODS

Patients from cohorts 1 and 2 with pathological Tany N2 M0 NSCLC were included, as well as patients with non-metastatic NSCLC from cohorts 3 to 6. The radiomic prognostic index (RPI) was developed using radiomic texture features extracted from the primary lung nodule in preoperative chest CT scans in cohort 1 and validated in other cohorts. We employed a least absolute shrinkage and selection operator-Cox regularisation model for data dimension reduction, feature selection, and the construction of the RPI. We created a lymph-radiomic prognostic index (LRPI) by combining RPI and positive lymph node number (PLN). We compared the outcomes of patients who received PORT against those who did not in the subgroups determined by the LRPI.

RESULTS

In total, 228, 1003, 144, 422, 19, and 21 patients were eligible in cohorts 1-6. RPI predicted overall survival (OS) in all six cohorts: cohort 1 (HR = 2.31, 95% CI: 1.18-4.52), cohort 2 (HR = 1.64, 95% CI: 1.26-2.14), cohort 3 (HR = 2.53, 95% CI: 1.45-4.3), cohort 4 (HR = 1.24, 95% CI: 1.01-1.52), cohort 5 (HR = 2.56, 95% CI: 0.73-9.02), cohort 6 (HR = 2.30, 95% CI: 0.53-10.03). LRPI predicted OS (C-index: 0.68, 95% CI: 0.60-0.75) better than the pT stage (C-index: 0.57, 95% CI: 0.50-0.63), pT + PLN (C-index: 0.58, 95% CI: 0.46-0.70), and RPI (C-index: 0.65, 95% CI: 0.54-0.75). The LRPI was used to categorize individuals into three risk groups; patients in the moderate-risk group benefited from PORT (HR = 0.60, 95% CI: 0.40-0.91; p = 0.02), while patients in the low-risk and high-risk groups did not.

CONCLUSIONS

We developed preoperative CT-based radiomic and lymph-radiomic prognostic indexes capable of predicting OS and the benefits of PORT for patients with NSCLC.

Prognostic Significance of Tumor-associated Stroma in Nasopharyngeal Carcinoma: A Multicenter Study

Assessment of tumor-associated stroma has shown a reliable prognostic value in recent research. We evaluated the prognostic value of tumor-stroma ratio (TSR) in a large multicenter cohort of nasopharyngeal carcinoma (NPC). We used the conventional hematoxylin and eosin-stained slides of 115 cases of NPC to assess TSR as described in recent guidelines. The amount of tumor-associated stroma was assessed as a percentage and then tumors were classified as stroma-high (>50%) or stroma-low (≤50%). Kaplan-Meier curves, χ 2 test, and Cox regression univariable and multivariable analyses were carried out. A total of 48 (41.7%) tumors were stroma-high and 67 (58.3%) tumors were stroma-low. In the Cox regression multivariable analysis, the tumors categorized as stroma-high were associated with a worse overall survival with a hazard ratio of 2.30 (95% CI: 1.27-4.15, P =0.006) and with poor disease-specific survival (hazard ratio=1.87, 95% CI: 1.07-3.28, P =0.029). The assessment of TSR in NPC is simple and cost-effective, and it has a significant prognostic value. TSR can aid in risk stratification and clinical decision-making in NPC.

Clinical predictors and outcomes of non-expandable lung following percutaneous catheter drainage in lung cancer patients with malignant pleural effusion

Non-expandable lung (NEL) often occurs during pleural fluid drainage in patients with malignant pleural effusion (MPE). However, data regarding the predictors and prognostic impact of NEL on primary lung cancer patients with MPE receiving pleural fluid drainage, compared to malignant pleural mesothelioma (MPM), are limited. This study was aimed to investigate the clinical characteristics of lung cancer patients with MPE developing NEL following ultrasonography (USG)-guided percutaneous catheter drainage (PCD) and compare the clinical outcomes between those with and without NEL. Clinical, laboratory, pleural fluid, and radiologic data and survival outcomes of lung cancer patients with MPE undergoing USG-guided PCD were retrospectively reviewed and compared between those with and without NEL. Among 121 primary lung cancer patients with MPE undergoing PCD, NEL occurred in 25 (21%). Higher pleural fluid lactate dehydrogenase (LDH) levels and presence of endobronchial lesions were associated with development of NEL. The median time to catheter removal was significantly extended in those with NEL compared to those without (P = .014). NEL was significantly associated with poor survival outcome in lung cancer patients with MPE undergoing PCD, along with poor Eastern Cooperative Oncology Group (ECOG) performance status (PS), the presence of distant metastasis, higher serum C-reactive protein (CRP) levels, and not receiving chemotherapy. NEL developed in one-fifth of lung cancer patients undergoing PCD for MPE and was associated with high pleural fluid LDH levels and the presence of endobronchial lesions. NEL may negatively affect overall survival in lung cancer patients with MPE receiving PCD.