Diagnostic and prognostic nomograms for bone metastasis in small cell lung cancer

Development and validation of nomogram prognostic model for predicting OS in patients with diffuse large B-cell lymphoma: a cohort study in China

This study comprehensively incorporates pathological parameters and novel clinical prognostic factors from the international prognostic index (IPI) to develop a nomogram prognostic model for overall survival in patients with diffuse large B-cell lymphoma (DLBCL). The aim is to facilitate personalized treatment and management strategies. This study enrolled a total of 783 cases for analysis. LASSO regression and stepwise multivariate COX regression were employed to identify significant variables and build a nomogram model. The calibration curve, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) curve were utilized to assess the model's performance and effectiveness. Additionally, the time-dependent concordance index (C-index) and time-dependent area under the ROC curve (AUC) were computed to validate the model's stability across different time points. The study utilized 8 selected clinical features as predictors to develop a nomogram model for predicting the overall survival of DLBCL patients. The model exhibited robust generalization ability with an AUC exceeding 0.7 at 1, 3, and 5 years. The calibration curve displayed evenly distributed points on both sides of the diagonal, and the slopes of the three calibration curves were close to 1 and statistically significant, indicating high prediction accuracy of the model. Furthermore, the model demonstrated valuable clinical significance and holds the potential for widespread adoption in clinical practice. The novel prognostic model developed for DLBCL patients incorporates readily accessible clinical parameters, resulting in significantly enhanced prediction accuracy and performance. Moreover, the study's use of a continuous general cohort, as opposed to clinical trials, makes it more representative of the broader lymphoma patient population, thus increasing its applicability in routine clinical care.

Outcomes of first-line anti-PD-L1 blockades combined with brain radiotherapy for extensive-stage small-cell lung cancer with brain metastasis

INTRODUCTION

Anti-programmed cell death-ligand 1 (Anti-PD-L1) blockades have become the first-line treatment of extensive-stage small-cell lung cancer (ES-SCLC) from CASPIAN and IMpower133 trials. SCLC has a high incidence of brain metastasis (BM) and brain radiotherapy (BRT) is the main local treatment method, but there is limited data on the BRT-immunotherapy scheme. The aim of the retrospective study is to investigate the clinical efficacy and safety of the first-line anti-PD-L1 blockades combined with BRT in ES-SCLC with BM.

METHODS

Patients with newly diagnosed ES-SCLC with baseline BMs at Shandong Cancer Hospital and Research Institute between 2017 and 2021 were selected. Patients were divided into the anti-PD-L1+BRT group and BRT group. We also assessed the leukoencephalopathy in both groups.

RESULTS

A total of 46 patients were selected. Fifteen were divided into anti-PD-L1+BRT group and 31 to BRT group. The median overall survival (OS) was not reached (NR) vs 15.9 m (P = 0.172). Progression-free survival (PFS) was numerically prolonged with anti-PD-L1 blockades, but the significance was not reached (median: 9.4 m vs 7.4 m, P = 0.362). The median intracranial PFS was not improved, neither (median: 8.2 m vs 8.9 m, P = 0.620). Objective response rate (ORR) in the two groups was 73.33% vs 77.42% (P = 0.949) and disease control rate (DCR) was both 100%. Intracranial ORR and DCR were 53.33% vs 70.97% (P = 0.239) and 73.33% vs 80.65% (P = 0.855), respectively. There was no significant difference in leukoencephalopathy incidence between the two groups.

CONCLUSION

The combination of first-line anti-PD-L1 blockades with BRT did not confer a significant survival benefit in ES-SCLC with BM, without enhancing cranial neurotoxicity.

Machine Learning for the Prediction of Synchronous Organ-Specific Metastasis in Patients With Lung Cancer

Background

This study aimed to develop an artificial neural network (ANN) model for predicting synchronous organ-specific metastasis in lung cancer (LC) patients.

Methods

A total of 62,151 patients who diagnosed as LC without data missing between 2010 and 2015 were identified from Surveillance, Epidemiology, and End Results (SEER) program. The ANN model was trained and tested on an 75/25 split of the dataset. The receiver operating characteristic (ROC) curves, area under the curve (AUC) and sensitivity were used to evaluate and compare the ANN model with the random forest model.

Results

For distant metastasis in the whole cohort, the ANN model had metrics AUC = 0.759, accuracy = 0.669, sensitivity = 0.906, and specificity = 0.613, which was better than the random forest model. For organ-specific metastasis in the cohort with distant metastasis, the sensitivity in bone metastasis, brain metastasis and liver metastasis were 0.913, 0.906 and 0.925, respectively. The most important variable was separate tumor nodules with 100% importance. The second important variable was visceral pleural invasion for distant metastasis, while histology for organ-specific metastasis.

Conclusions

Our study developed a “two-step” ANN model for predicting synchronous organ-specific metastasis in LC patients. This ANN model may provide clinicians with more personalized clinical decisions, contribute to rationalize metastasis screening, and reduce the burden on patients and the health care system.