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Purpose: We assessed the predictive potential of positron emission tomography (PET)/CT-based radiomics, lesion volume, and routine blood markers for early differentiation of pseudoprogression from true progression at 3 months. Experimental Design: 112 patients with metastatic melanoma treated with immune checkpoint inhibition were included in our study. Median follow-up duration was 22 months. 716 metastases were segmented individually on CT and 2[18F]fluoro-2-deoxy-D-glucose (FDG)-PET imaging at three timepoints: baseline (TP0), 3 months (TP1), and 6 months (TP2). Response was defined on a lesion-individual level (RECIST 1.1) and retrospectively correlated with FDG-PET/CT radiomic features and the blood markers LDH/S100. Seven multivariate prediction model classes were generated. Results: Two-year (median) overall survival, progression-free survival, and immune progression–free survival were 69% (not reached), 24% (6 months), and 42% (16 months), respectively. At 3 months, 106 (16%) lesions had progressed, of which 30 (5%) were identified as pseudoprogression at 6 months. Patients with pseudoprogressive lesions and without true progressive lesions had a similar outcome to responding patients and a significantly better 2-year overall survival of 100% (30 months), compared with 15% (10 months) in patients with true progressions/without pseudoprogression (P = 0.002). Patients with mixed progressive/pseudoprogressive lesions were in between at 53% (25 months). The blood prediction model (LDH+S100) achieved an AUC = 0.71. Higher LDH/S100 values indicated a low chance of pseudoprogression. Volume-based models: AUC = 0.72 (TP1) and AUC = 0.80 (delta-volume between TP0/TP1). Radiomics models (including/excluding volume-related features): AUC = 0.79/0.78. Combined blood/volume model: AUC = 0.79. Combined blood/radiomics model (including volume-related features): AUC = 0.78. The combined blood/radiomics model (excluding volume-related features) performed best: AUC = 0.82. Conclusions: Noninvasive PET/CT-based radiomics, especially in combination with blood parameters, are promising biomarkers for early differentiation of pseudoprogression, potentially avoiding added toxicity or delayed treatment switch.

Background Tumor immune-evasion and associated failure of immunotherapy can potentially be overcome by radiotherapy, which however also has detrimental effects on tumor-infiltrating and circulating lymphocytes (CL). We therefore established a model to simulate the radiation-dose delivered to CL. Methods A MATLAB-model was established to quantify the CL-dose during SBRT of liver metastases by considering the factors: hepatic blood-flow, −velocity and transition-time of individual hepatic segments, as well as probability-based recirculation. The effects of intra-hepatic tumor-location and size, fractionation and treatment planning parameters (VMAT, 3DCRT, photon-energy, dose-rate and beam-on-time) were analyzed. A threshold dose ≥0.5Gy was considered inactivating CL and CL0.5 (%) is the proportion of inactivated CL. Results Mean liver dose was mostly influenced by treatment-modality, whereas CL0.5 was mostly influenced by beam-on-time. 3DCRT and VMAT (10MV-FFF) resulted in lowest CL0.5 values of 16 and 19%. Metastasis location influenced CL0.5, with a mean of 19% for both apical and basal and 31% for the central location. PTV-volume significantly increased CL0.5 from 27 to 67% (10MV-FFF) and from 31 to 98% (6MV-FFF) for PTV-volumes ranging from 14cm3 to 268cm3. Conclusion A simulation-model was established, quantifying the strong effects of treatment-technique, tumor-location and tumor-volume on dose to CL with potential implications for immune-optimized treatment-planning in the future. Electronic supplementary material The online version of this article (doi: 10.1186/s13014-018-0952-y) contains supplementary material, which is available to authorized users.

Tumor metastasis and immune evasion present major challenges of cancer treatment. Radiotherapy can overcome immunosuppressive tumor microenvironments. Anecdotal reports suggest abscopal anti-tumor immune responses. This study assesses abscopal effects of radiotherapy in combination with mRNA-based cancer vaccination (RNActive®). C57BL/6 mice were injected with ovalbumin-expressing thymoma cells into the right hind leg (primary tumor) and left flank (secondary tumor) with a delay of 4 days. Primary tumors were irradiated with 3 × 2 Gy, while secondary tumors were shielded. RNA and combined treatment groups received mRNA-based RNActive® vaccination. Radiotherapy and combined radioimmunotherapy significantly delayed primary tumor growth with a tumor control in 15 and 53% of mice, respectively. In small secondary tumors, radioimmunotherapy significantly slowed growth rate compared to vaccination (p = 0.002) and control groups (p = 0.01). Cytokine microarray analysis of secondary tumors showed changes in the cytokine microenvironment, even in the non-irradiated contralateral tumors after combination treatment. Combined irradiation and immunotherapy is able to induce abscopal responses, even with low, normofractionated radiation doses. Thus, the combination of mRNA-based vaccination with irradiation might be an effective regimen to induce systemic anti-tumor immunity.

9575 Background: Distinguishing progressive disease (PD) from pseudoprogression (PP) in patients treated with immune-checkpoint inhibition (ICI) is challenging and usually requires confirmation follow-up imaging or invasive diagnostic techniques. This project aimed to identify predictive radiomic signatures for PP from CT imaging. Methods: The response to ICI of 105 metastatic melanoma patients with 645 metastases was retrospectively correlated with radiomic signatures (172 total features). All metastatic lesions were delineated at 3 time points: prior to ICI (t0), at 3 (t1) and 6 months (t2). Response was defined individually for each metastasis using RECIST 1.1, comparing baseline t0 to t2. Three prediction models for PP were built: CT radiomics at t0 and t1, as well as the relative difference between both t0 and t1 (delta-radiomics). Results: Median follow-up was 18 months and 2-year OS and PFS were 72% and 25%, respectively. Median OS: not reached, median PFS: 6 months. Response per lesion at t1: 13% complete remission (CR), 19% partial remission (PR), 52% stable disease (SD) and 16% PD. At t2: 16% CR, 31% PR, 38% SD and 15% PD. 106 progressive lesions were identified at t1, of which, 26 changed to SD, 1 to CR and 3 to PR at t2, resulting in 30 PPs (4.7%). Metastasis location significantly influenced response rates but was not associated with PP (p = 0.4). Lung metastases had significantly higher response rates than soft tissue (p < 0.001), liver (p < 0.001) and bone metastases (p = 0.008). Univariate analysis followed by removal of correlated features revealed no significant radiomic features associated with PP at t0. One independent feature was identified at t1 (AUC 0.74), while delta-radiomics was the best performing approach, identifying four independent features (AUC 0.72 to 0.81). A final multivariate delta radiomics logistic regression model was generated and internally validated, achieving an AUC of 0.81 (± 0.11, 10-fold cross-validation). Conclusions: Metastasis location significantly influenced response rates and CT-based delta-radiomics is a promising biomarker for early differentiation between pseudoprogression and true progression in metastatic melanoma patients treated with ICI.