Prognostic effects of preoperative obstructive pneumonitis or atelectasis and comparison with tumor size in non-small cell lung cancer

Exploration and machine learning model development for T2 NSCLC with bronchus infiltration and obstructive pneumonia/atelectasis

In the 8th edition of the American Joint Committee on Cancer (AJCC) staging system for Non-Small Cell Lung Cancer (NSCLC), tumors exhibiting main bronchial infiltration (MBI) near the carina and those presenting with complete lung obstructive pneumonia/atelectasis (P/ATL) have been reclassified from T3 to T2. Our investigation into the Surveillance, Epidemiology, and End Results (SEER) database, spanning from 2007 to 2015 and adjusted via Propensity Score Matching (PSM) for additional variables, disclosed a notably inferior overall survival (OS) for patients afflicted with these conditions. Specifically, individuals with P/ATL experienced a median OS of 12 months compared to 15 months (p < 0.001). In contrast, MBI patients demonstrated a slightly worse prognosis with a median OS of 22 months versus 23 months (p = 0.037), with both conditions significantly correlated with lymph node metastasis (All p < 0.001). Upon evaluating different treatment approaches for these particular T2 NSCLC variants, while adjusting for other factors, surgery emerged as the optimal therapeutic strategy. We counted those who underwent surgery and found that compared to surgery alone, the MBI/(P/ATL) group experienced a much higher proportion of preoperative induction therapy or postoperative adjuvant therapy than the non-MBI/(P/ATL) group (41.3%/54.7% vs. 36.6%). However, for MBI patients, initial surgery followed by adjuvant treatment or induction therapy succeeded in significantly enhancing prognosis, a benefit that was not replicated for P/ATL patients. Leveraging the XGBoost model for a 5-year survival forecast and treatment determination for P/ATL and MBI patients yielded Area Under the Curve (AUC) scores of 0.853 for P/ATL and 0.814 for MBI, affirming the model's efficacy in prognostication and treatment allocation for these distinct T2 NSCLC categories.

Simultaneous pathological findings in biopsy specimens of patients with surgically resected lung carcinoids and their role in survival

Pulmonary carcinoid tumors are rare, low-grade malignant tumors that constitute 1–2% of all lung tumors. The present study aimed to describe the simultaneous pathological findings in biopsy specimens of patients with surgically resected lung carcinoids and determine their association with survival rates. For this purpose, 108 patients with resected carcinoid lung tumors were followed-up for 96 months and analyzed for simultaneous pathological findings in biopsy specimens. Among these, simultaneous pathological findings were found in 82 patients. The association between these findings and patient survival rates was evaluated. Atelectasis was a simultaneous finding in 52.4% of the patients, desquamative interstitial pneumonia (DIP) in 13.4%, emphysema in 24.4% and bronchiectasis in 9.8%. The survival rate was 100% for the patients with atelectasis, 81.8% for the patients with DIP, 90% for the patients with emphysema and 75% for the patients with bronchiectasis (P<0.05). According to the univariate analysis, the type of carcinoid was associated with patient survival with better survival rates for patients with typical carcinoids, while age, sex, stage and simultaneous pathological findings were not associated with patient survival. On the whole, there was a statistically significant difference in the survival rates of patients with resected lung carcinoids with different simultaneous pathological findings. However, further studies are warranted to assess the role of these findings in the survival of these patients.

Self-Expandable Metallic Stent Implantation Combined With Bronchial Artery Infusion Chemoembolization in the Treatment of Lung Cancer With Complete Atelectasis

Background

Atelectasis is a common complication of lung cancer, and there are few reports about the treatment methods. This study retrospectively analyzed the safety and effectiveness of endotracheal metal stent implantation combined with arterial infusion chemoembolization in the treatment of non-small cell lung cancer with complete atelectasis.

Methods

The clinical data of patients with non-small cell lung cancer and complete atelectasis treated by self-expandable metallic stent implantation combined with arterial infusion chemotherapy were retrospectively analyzed. The clinical efficacy was evaluated and postoperative adverse reactions were observed. Progression-free survival and overall survival were analyzed by Kaplan-Meier method.

Results

In all, 42 endotracheal metallic stents were implanted in 42 patients under fluoroscopy. 5–7 days after stent implantation, CT showed that 24 patients (57.1%) had complete lung recruitment, and that 13 (31.0%) had partial lung recruitment. The technical success rate was 100%, and the clinical success rate was 88.1% (37/42). 5–7 days after stent implantation, bronchial artery infusion chemoembolization was performed in all patients. The median progression-free survival and overall survival were 6 months (95% CI: 2.04-9.66) and 10 months (95% CI: 7.22-12.79), respectively.

Conclusion

Self-expandable metallic stent implantation combined with arterial infusion chemoembolization may be an effective and safe strategy in the treatment of lung cancer with atelectasis clinically.

Tumor size improves the accuracy of the prognostic prediction of T4a stage colon cancer

The aim of this study was to evaluate the potential impact of tumor size on the long-term outcome of colon cancer (CC) patients after curative surgery. A total of 782 curatively resected T4a stage CC patients without distant metastasis were enrolled. Patients were categorized into 2 groups according to the best threshold of tumor size: larger group (LG) and smaller group (SG). Propensity score matching was used to adjust for the differences in baseline characteristics. The ideal cutoff point of tumor size was 5 cm. In the multivariate analysis for the whole study series, tumor size was an independent prognostic factor. Patients in the LG had significant lower 5-year overall survival (OS) and relapse-free survival (RFS) rates (OS: 63.5% versus 75.2%, P < 0.001; RFS: 59.5% versus 72.4%, P < 0.001) than those in the SG. After matching, patients in the LG still demonstrated significant lower 5-year OS and RFS rates than those in the SG. The modified tumor-size-node-metastasis (mTSNM) staging system including tumor size was found to be more appropriate for predicting the OS and RFS of T4a stage CC than TNM stage, and the -2log likelihood of the mTSNM staging system was smaller than the value of TNM stage. In conclusion, tumor size was an independent prognostic factor for OS and RFS. We maintain that tumor size should be incorporated into the staging system to enhance the accuracy of the prognostic prediction of T4a stage CC patients.

Prognostic Value of Deep Learning-Mediated Treatment Monitoring in Lung Cancer Patients Receiving Immunotherapy

BACKGROUND

Checkpoint inhibitors provided sustained clinical benefit to metastatic lung cancer patients. Nonetheless, prognostic markers in metastatic settings are still under research. Imaging offers distinctive advantages, providing whole-body information non-invasively, while routinely available in most clinics. We hypothesized that more prognostic information can be extracted by employing artificial intelligence (AI) for treatment monitoring, superior to 2D tumor growth criteria.

METHODS

A cohort of 152 stage-IV non-small-cell lung cancer patients (NSCLC) (73 discovery, 79 test, 903CTs), who received nivolumab were retrospectively collected. We trained a neural network to identify morphological changes on chest CT acquired during patients' follow-ups. A classifier was employed to link imaging features learned by the network with overall survival.

RESULTS

Our results showed significant performance in the independent test set to predict 1-year overall survival from the date of image acquisition, with an average area under the curve (AUC) of 0.69 (p < 0.01), up to AUC 0.75 (p < 0.01) in the first 3 to 5 months of treatment, and 0.67 AUC (p = 0.01) for durable clinical benefit (6 months progression-free survival). We found the AI-derived survival score to be independent of clinical, radiological, PDL1, and histopathological factors. Visual analysis of AI-generated prognostic heatmaps revealed relative prognostic importance of morphological nodal changes in the mediastinum, supraclavicular, and hilar regions, lung and bone metastases, as well as pleural effusions, atelectasis, and consolidations.

CONCLUSIONS

Our results demonstrate that deep learning can quantify tumor- and non-tumor-related morphological changes important for prognostication on serial imaging. Further investigation should focus on the implementation of this technique beyond thoracic imaging.

Segmentation in Non-Small Cell Lung Tumors Using 18F-FDG PET/CT: Tips and Rules

The assessment of tumor parameters derived from F-FDG PET/CT in oncology provides valuable information in non-small cell lung cancer. A proper segmentation should delineate tumor with high accuracy, being the most important step to measure metabolic parameters. However, there is still no consensus about the optimal methodology. Additionally, some clinical conditions inherently tied to tumor and imaging can limit the proper tumor delineation. We present some practical cases that represent different aspects to consider during segmentation of primary non-small cell lung cancer by using F-FDG-PET/CT and some possible solutions to tackle with the most common limitations in clinical practice.

COV is a readily available quantitative indicator of metabolic heterogeneity for predicting survival of patients with early and locally advanced NSCLC manifesting as central lung cancer

OBJECTIVES

The aim of our study was to investigate the value of a simple metabolic heterogeneity parameter, COV (coefficient of variation), by 18 F-fluorodeoxyglucose (FDG) positron emission tomography (PET) in the prognosis prediction of central lung cancer in early and locally advanced non-small-cell lung cancer (NSCLC).

METHODS

Seventy-three patients with NSCLC manifesting as central lung cancer were included retrospectively, and we used the COV to evaluate metabolic heterogeneity. Univariate and multivariate analyses were used to evaluate the predictive value in terms of overall survival (OS) and progression-free survival (PFS).

RESULT

For all 73 patients with pathologically confirmed NSCLC, 69.9 % had SCC, and 30.1 % had ADC or other types of NSCLC. The COV was a statistically significant factor in the univariate analysis for the OS rate. The optimal cut-off value was 23.1366, with sensitivity = 0.737 and specificity = 0.771. The COV values were dichotomized by this value and included with atelectasis in the Cox multivariate analysis. Both COV and atelectasis were independent risk factors for OS as follows: for COV (HR, 3.162, P = 0.0002), the 2-year OS rate was 62.5 % and 26.9 % in the low and high COV groups, respectively. For atelectasis (HR 2.047, P = 0.041), the 2-year OS rate was 30.6 % and 65.2 % in the groups with and without atelectasis, respectively (P = 0.017). For PFS, only COV (HR, 2.636, P = 0.001) was a significant predictor. The 2-year PFS rate was 29.7 % in the low COV group and 8% in the high COV group.

CONCLUSION

The pre-treatment metabolic heterogeneity parameter COV is a simple and easy way to predict the OS and PFS of patients with NSCLC manifesting as central lung cancer. Therefore, COV plays an important role in prognostic risk classification in NSCLC. The presence of atelectasis could also be a risk factor for poor prognosis of OS.