PurposeAlthough patients with stage III non-small cell lung cancer (NSCLC) are homogeneous according to the TNM staging system, they form a heterogeneous group, which is reflected in the survival outcome. The increasing amount of information for an individual patient and the growing number of treatment options facilitate personalized treatment, but they also complicate treatment decision making. Decision support systems (DSS), which provide individualized prognostic information, can overcome this but are currently lacking. A DSS for stage III NSCLC requires the development and integration of multiple models. The current study takes the first step in this process by developing and validating a model that can provide physicians with a survival probability for an individual NSCLC patient.Methods and MaterialsData from 548 patients with stage III NSCLC were available to enable the development of a prediction model, using stratified Cox regression. Variables were selected by using a bootstrap procedure. Performance of the model was expressed as the c statistic, assessed internally and on 2 external data sets (n=174 and n=130).ResultsThe final multivariate model, stratified for treatment, consisted of age, gender, World Health Organization performance status, overall treatment time, equivalent radiation dose, number of positive lymph node stations, and gross tumor volume. The bootstrapped c statistic was 0.62. The model could identify risk groups in external data sets. Nomograms were constructed to predict an individual patient’s survival probability (www.predictcancer.org). The data set can be downloaded at https://www.cancerdata.org/10.1016/j.ijrobp.2015.02.048.ConclusionsThe prediction model for overall survival of patients with stage III NSCLC highlights the importance of combining patient, clinical, and treatment variables. Nomograms were developed and validated. This tool could be used as a first building block for a decision support system.

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In this phase II trial, the median OS for the entire group was remarkably high; 31.5 months. Furthermore, 5-year OS was still 37.3%. Hypofractionation might contribute to improved OS in LA-NSCLC patients.

Background: Programmed cell death protein 1 (PD-1) antibody treatment is standard of care for melanoma and nonsmall-cell lung cancer (NSCLC). Accurately predicting which patients will benefit is currently not possible. Tumor uptake and biodistribution of the PD-1 antibody might play a role. Therefore, we carried out a positron emission tomography (PET) imaging study with zirconium-89 ( 89 Zr)-labeled pembrolizumab before PD-1 antibody treatment. Patients and methods: Patients with advanced or metastatic melanoma or NSCLC received 37 MBq (1 mCi) 89 Zrpembrolizumab (w2.5 mg antibody) intravenously plus 2.5 or 7.5 mg unlabeled pembrolizumab. After that, up to three PET scans were carried out on days 2, 4, and 7. Next, PD-1 antibody treatment was initiated. 89 Zrpembrolizumab tumor uptake was calculated as maximum standardized uptake value (SUV max ) and expressed as geometric mean. Normal organ uptake was calculated as SUV mean and expressed as a mean. Tumor response was assessed according to (i)RECIST v1.1. Results: Eighteen patients, 11 with melanoma and 7 with NSCLC, were included. The optimal dose was 5 mg pembrolizumab, and the optimal time point for PET scanning was day 7. The tumor SUV max did not differ between melanoma and NSCLC (4.9 and 6.5, P ¼ 0.49). Tumor 89 Zr-pembrolizumab uptake correlated with tumor response (P trend ¼ 0.014) and progression-free (P ¼ 0.0025) and overall survival (P ¼ 0.026). 89 Zr-pembrolizumab uptake at 5 mg was highest in the spleen with a mean SUV mean of 5.8 (standard deviation AE1.8). There was also 89 Zr-pembrolizumab uptake in Waldeyer's ring, in normal lymph nodes, and at sites of inflammation. Conclusion: 89 Zr-pembrolizumab uptake in tumor lesions correlated with treatment response and patient survival. 89 Zrpembrolizumab also showed uptake in lymphoid tissues and at sites of inflammation.