The predictive role of soluble programmed death ligand 1 in digestive system cancers

IntroductionThe prognostic role of soluble programmed death ligand 1 (sPD-L1) in digestive system cancers (DSCs) remains inconclusive. This study aimed to explore the predictive value of sPD-L1 expression in DSCs.MethodsComprehensive searches were run on the electronic databases (PubMed, Web of Science, EMBASE, and the Cochrane Library) to identify studies that assessed the prognostic role of sPD-L1 in DSCs. Review Manager software (version 5.3) was used for all analyses. Pooled data for survival outcomes were measured as hazard ratios (HRs), 95% confidence intervals (CIs), and odds ratios and their 95% CIs.ResultsThe search identified 18 studies involving 2,070 patients with DSCs. The meta-outcome revealed that a high level of sPD-L1 was related to poorer overall survival (HR, 3.06; 95% CI: 2.22–4.22, p<0.001) and disease-free survival (HR, 2.53; 95% CI: 1.67–3.83, p<0.001) in DSCs. Individually, the prognostic significance of high level of sPD-L1 expression was the highest in hepatic cell carcinoma (HR, 4.76; p<0.001) followed by gastric cancer (HR=3.55, p<0.001).ConclusionsPD-L1 may be a prognostic factor in DSCs for overall survival and disease-free survival. Inflammatory cytokines, treatment approaches, and other factors may affect the expression of sPD-L1. Therefore, the prognostic value of sPD-L1 for recurrence and metastasis should be further investigated. sPD-L1 may also predict response to treatment. Well-designed prospective studies with standard assessment methods should be conducted to determine the prognostic value of sPD-L1 in DSCs

Modern Radiation Further Improves Survival in Non-Small Cell Lung Cancer: An Analysis of 288,670 Patients

Background: Radiation therapy plays an increasingly important role in the treatment of patients with non-small-cell lung cancer (NSCLC). The purpose of the present study is to assess the survival outcomes of radiotherapy treatment compared to other treatment modalities and to determine the potential role of advanced technologies in radiotherapy on improving survival. Methods: We used cancer incidence and survival data from the Surveillance, Epidemiology, and End Results database linked to U.S. Census data to compare survival outcomes of 288,670 patients with stage I-IV NSCLC treated between 1999 and 2008. The primary endpoint was overall survival. Results: Among the 288,670 patients diagnosed with stage I-IV NSCLC, 92,374 (32%) patients received radiotherapy-almost double the number receiving surgery (51,961, 18%). Compared to other treatment groups and across all stages of NSCLC, patients treated with radiotherapy showed greater median and overall survival than patients without radiation treatment (p < 0.0001). Radiotherapy had effectively improved overall survival regardless of age, gender, and histological categorization. Radiotherapy treatment received during the recent time period 2004 - 2008 is correlated with enhanced survival compared to the earlier time period 1999 - 2003. Conclusion: Radiation therapy was correlated with increased overall survival for all patients with primary NSCLC across stages. Combined surgery and radiotherapy treatment also correlates with improved survival, signaling the value of bimodal or multimodal treatments. Population-based increases in overall survival were seen in the recent time period, suggesting the potential role of advanced radiotherapeutic technologies in enhancing survival outcomes for lung cancer patients

Machine Learning to Build and Validate a Model for Radiation Pneumonitis Prediction in Patients with Non–Small Cell Lung Cancer

Purpose: Radiation pneumonitis is an important adverse event in patients with non–small cell lung cancer (NSCLC) receiving thoracic radiotherapy. However, the risk of radiation pneumonitis grade ≥ 2 (RP2) has not been well predicted. This study hypothesized that inflammatory cytokines or the dynamic changes during radiotherapy can improve predictive accuracy for RP2. Experimental Design: Levels of 30 inflammatory cytokines and clinical information in patients with stages I–III NSCLC treated with radiotherapy were from our prospective studies. Statistical analysis was used to select predictive cytokine candidates and clinical covariates for adjustment. Machine learning algorithm was used to develop the generalized linear model for predicting risk RP2. Results: A total of 131 patients were eligible and 17 (13.0%) developed RP2. IL8 and CCL2 had significantly (Bonferroni) lower expression levels in patients with RP2 than without RP2. But none of the changes in cytokine levels during radiotherapy was significantly associated with RP2. The final predictive GLM model for RP2 was established, including IL8 and CCL2 at baseline level and two clinical variables. Nomogram was constructed based on the GLM model. The model's predicting ability was validated in the completely independent test set (AUC = 0.863, accuracy = 80.0%, sensitivity = 100%, specificity = 76.5%). Conclusions: By machine learning, this study has developed and validated a comprehensive model integrating inflammatory cytokines with clinical variables to predict RP2 before radiotherapy that provides an opportunity to guide clinicians

Monte Carlo-based lung cancer treatment planning incorporating PET-defined target volumes

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135172/1/acm20065.pd

Doses of radiation to the pericardium, instead of heart, are significant for survival in patients with non-small cell lung cancer

Background and purpose: Higher cardiac dose was associated with worse overall survival in the RTOG0617 study. Pericardial effusion (PCE) is a common cardiac complication of thoracic radiation therapy (RT). We investigated whether doses of radiation to the heart and pericardium are associated with PCE and overall survival in patients treated with thoracic radiation for non-small cell lung cancer (NSCLC). Materials and Methods: A total of 94 patients with medically inoperable/unresectable NSCLC treated with definitive RT in prospective studies were reviewed for this secondary analysis. Heart and pericardium were contoured consistently according to the RTOG1106 Atlas, with the great vessels and thymus of the upper mediastinal structures included in the upper part of pericardium, only heart chambers included in the heart structure. Clinical factors and dose-volume parameters associated with PCE or survival were identified via Cox proportional hazards modeling. The risk of PCE and death were mapped using DVH atlases. Results: Median follow-up for surviving patients was 58 months. The overall rate of PCE was 40.4%. On multivariable analysis, dosimetric factors of heart and pericardium were significantly associated with the risk of PCE. Pericardial V30 and V55 were significantly correlated with overall survival, but presence of PCE and heart dosimetric factors were not. Conclusion: PCE was associated with both heart and pericardial doses. The significance of pericardial dosimetric parameters, but not heart chamber parameters, on survival suggests the potential significance of radiation damage to the cranial region of pericardium

Genetic Variations in the Transforming Growth Factor-β1 Pathway May Improve Predictive Power for Overall Survival in Non-small Cell Lung Cancer

Purpose: Transforming growth factor-β1 (TGF-β1), a known immune suppressor, plays an important role in tumor progression and overall survival (OS) in many types of cancers. We hypothesized that genetic variations of single nucleotide polymorphisms (SNPs) in the TGF-β1 pathway can predict survival in patients with non-small cell lung cancer (NSCLC) after radiation therapy. Materials and Methods: Fourteen functional SNPs in the TGF-β1 pathway were measured in 166 patients with NSCLC enrolled in a multi-center clinical trial. Clinical factors, including age, gender, ethnicity, smoking status, stage group, histology, Karnofsky Performance Status, equivalent dose at 2 Gy fractions (EQD2), and the use of chemotherapy, were first tested under the univariate Cox's proportional hazards model. All significant clinical predictors were combined as a group of predictors named "Clinical." The significant SNPs under the Cox proportional hazards model were combined as a group of predictors named "SNP." The predictive powers of models using Clinical and Clinical + SNP were compared with the cross-validation concordance index (C-index) of random forest models. Results: Age, gender, stage group, smoking, histology, and EQD2 were identified as significant clinical predictors: Clinical. Among 14 SNPs, BMP2:rs235756 (HR = 0.63; 95% CI:0.42-0.93; p = 0.022), SMAD9:rs7333607 (HR = 2.79; 95% CI 1.22-6.41; p = 0.015), SMAD3:rs12102171 (HR = 0.68; 95% CI: 0.46-1.00; p = 0.050), and SMAD4: rs12456284 (HR = 0.63; 95% CI: 0.43-0.92; p = 0.016) were identified as powerful predictors of SNP. After adding SNP, the C-index of the model increased from 84.1 to 87.6% at 24 months and from 79.4 to 84.4% at 36 months. Conclusion: Genetic variations in the TGF-β1 pathway have the potential to improve the prediction accuracy for OS in patients with NSCLC

Simple Factors Associated With Radiation-Induced Lung Toxicity After Stereotactic Body Radiation Therapy of the Thorax: A Pooled Analysis of 88 Studies

To study the risk factors for radiation-induced lung toxicity (RILT) after stereotactic body radiotherapy (SBRT) of the thorax

Modeling Patient-Specific Dose-Function Response for Enhanced Characterization of Personalized Functional Damage

PURPOSE: Functional-guided radiation therapy (RT) plans have the potential to limit damage to normal tissue and reduce toxicity. Although functional imaging modalities have continued to improve, a limited understanding of the functional response to radiation and its application to personalized therapy has hindered clinical implementation. The purpose of this study was to retrospectively model the longitudinal, patient-specific dose-function response in non-small cell lung cancer patients treated with RT to better characterize the expected functional damage in future, unknown patients. METHODS AND MATERIALS: Perfusion single-photon emission computed tomography/computed tomography scans were obtained at baseline (n = 81), midtreatment (n = 74), 3 months post-treatment (n = 51), and 1 year post-treatment (n = 26) and retrospectively analyzed. Patients were treated with conventionally fractionated RT or stereotactic body RT. Normalized perfusion single-photon emission computed tomography voxel intensity was used as a surrogate for local lung function. A patient-specific logistic model was applied to each individual patient's dose-function response to characterize functional reduction at each imaging time point. Patient-specific model parameters were averaged to create a population-level logistic dose-response model. RESULTS: A significant longitudinal decrease in lung function was observed after RT by analyzing the voxelwise change in normalized perfusion intensity. Generated dose-function response models represent the expected voxelwise reduction in function, and the associated uncertainty, for an unknown patient receiving conventionally fractionated RT or stereotactic body RT. Differential treatment responses based on the functional status of the voxel at baseline suggest that initially higher functioning voxels are damaged at a higher rate than lower functioning voxels. CONCLUSIONS: This study modeled the patient-specific dose-function response in patients with non-small cell lung cancer during and after radiation treatment. The generated population-level dose-function response models were derived from individual patient assessment and have the potential to inform functional-guided treatment plans regarding the expected functional lung damage. This type of patient-specific modeling approach can be applied broadly to other functional response analyses to better capture intrapatient dependencies and characterize personalized functional damage