The prevalence of bullying worldwide is high (UNESCO, 2018). Over the past decades, many anti-bullying interventions have been developed to remediate this problem. However, we lack insight into for whom these interventions work and what individual intervention components drive the total intervention effects. We conducted a large-scale individual participant data (IPD) meta-analysis using data from 39,793 children and adolescents aged five to 20 years (Mage = 12.58, SD = 2.34) who had participated in quasi-experimental or randomized controlled trials of school-based anti-bullying interventions (i.e., 10 studies testing nine interventions). Multilevel logistic regression analyses showed that anti-bullying interventions significantly reduced self-reported victimization (d =  − 0.14) and bullying perpetration (d =  − 0.07). Anti-bullying interventions more strongly reduced bullying perpetration in younger participants (i.e., under age 12) and victimization for youth who were more heavily victimized before the intervention. We did not find evidence to show that the inclusion of specific intervention components was related to higher overall intervention effects, except for an iatrogenic effect of non-punitive disciplinary methods–which was strongest for girls. Exploratory analyses suggested that school assemblies and playground supervision may have harmful effects for some, increasing bullying perpetration in youth who already bullied frequently at baseline. In conclusion, school-based anti-bullying interventions are generally effective and work especially well for younger children and youth who are most heavily victimized. Further tailoring of interventions may be necessary to more effectively meet the needs and strengths of specific subgroups of children and adolescents.

261 Background: Recent clinical trials of novel systemic therapies showed improved survival of patients with metastatic esophageal cancer (EC) and gastric cancer (GC). Survival improvements observed in clinical trials might be unrepresentative for the total population as the percentage of patients whom participate in clinical trials is limited and more than half of all patients receive best supportive care (BSC). The aim of our study is to assess the best-case, typical and worst-case survival scenarios in patients with metastatic esophagogastric cancer. Methods: We selected patients with metastatic EC (including junction) or GC diagnosed in 2006-2019 from the Netherlands Cancer Registry. Survival scenarios were calculated based on percentiles of the survival curve stratified by tumor location and treatment (tumor-directed therapy or BSC). Survival scenarios were calculated for the 10th (best-case), 25th (upper-typical), 75th (lower-typical) and 90th (worst-case) percentiles. Linear trend analysis was performed to test if changes in survival over the diagnosis years were significant. Results: We identified 12739 patients with EC and 6833 patients with GC. Percentage of patients receiving tumor-directed therapy increased from 34% to 47% and 30% to 45% for patients with EC and GC, respectively. The median survival remained unchanged for patients with EC (5.0 months) and improved slightly for patients with GC (3.1 to 3.7 months; P=0.006). For patients with EC survival of the best-case scenario improved (17.4 to 22.8 months; P=0.001), whereas the other scenarios remained unchanged: upper-typical 11.2 to 11.7 (P=0.11), lower-typical 2.1 to 2.0 (P=0.10) and worst-case 0.9 to 0.8 months (P=0.22). For patients with GC survival improved for the best-case (13.1 to 19.5; P=0.005) and upper-typical scenario (6.7 to 10.6 months; P=0.002), whereas the lower-typical (1.2 to 1.4 months; P=0.87) and worst-case (0.6 to 0.6 months; P=0.60) remained unchanged. For patients with EC receiving tumor-directed therapy survival in all scenarios remained unchanged while for patients receiving BSC survival decreased: best-case 11.8 to 9.8 (P=0.005), upper-typical 6.0 to 5.0 (P=0.002), lower-typical 1.4 to 1.0 (P=0.003) and worst-case 0.7 to 0.5 months (P=0.03). For patients with GC receiving tumor-directed therapy survival improved for all scenarios: best-case 19.8 to 30.4 (P=0.005), upper-typical 6.4 to 10.3 (P=0.002), lower-typical 3.6 to 5.4 (P<0.001) and worst-case 1.4 to 2.6 months (P<0.001), and for patients receiving BSC survival for all scenarios remained unchanged. Conclusions: The proportion of patients with EC and GC receiving tumor-directed therapy increased over time. Despite the fact that survival improvements were not observed across all scenarios, at least an increase in survival was observed in certain subgroups of patients.

PURPOSE: When deliberating palliative cancer treatment, insight into patients' attitudes toward striving for quality of life (QL) and length of life (LL) may facilitate goal-concordant care. We investigated the (1) attitudes of patients with advanced cancer toward striving for QL and/or LL and whether these change over time, and (2) characteristics associated with these attitudes (over time). METHODS: We performed a secondary analysis of a randomized controlled trial on improving shared decision making (SDM), without differentiation between intervention arms. Patients (n = 173) with advanced cancer, a median life expectancy of < 12 months without anticancer treatment, and a median survival benefit of < 6 months from systemic therapy were included in seven Dutch hospitals. We used audio-recorded consultations and surveys at baseline (T0), shortly after the consultation (T2), at 3 and 6 months (T3 and T4). Primary outcomes were patients' attitudes toward striving for QL and LL (Quality Quantity Questionnaire; T2, T3, and T4). RESULTS: Overall, patients' attitudes toward striving for QL became less positive over 6 months ( P < .01); attitudes toward striving for LL did not change on group level. Studying individual patients, 76% showed changes in their attitudes toward striving for QL and/or LL at some point during the study, which occurred in various directions. More helplessness/hopelessness ( P < .001), less fighting spirit ( P < .05), less state anxiety ( P < .001), and more observed SDM ( P < .05) related to more positive attitudes toward striving for QL. Lower education, less helplessness/hopelessness, more fighting spirit, and more state anxiety ( P < .001) related to more positive attitudes toward striving for LL. CONCLUSION: Oncologists may explore patients' attitudes toward striving for QL and LL repeatedly and address patients' coping style and emotions during SDM to facilitate goal-concordant care throughout the last phase of life.

423 Background: Despite the advent of precision medicine, prediction of survival outcome of esophageal cancer patients remains a challenge. Here we aim to investigate the value of prediction models integrating multi-signal data including radiomics and circulating tumor DNA (ctDNA) data in addition to clinical data for the prediction of resectable esophageal adenocarcinoma (rEAC) related outcomes. Methods: In total n=111 rEAC patients treated with neoadjuvant chemoradiotherapy (nCRT; n=71) +/- anti-PD-L1 (n=40) were included. Baseline clinical variables (n=9) were based on the SOURCE survival prediction model (van den Boorn et al. JNCCN. 2021). The baseline ctDNA data from plasma was derived from fragmentomic and copy number aberrations (ichorCNA) from shallow whole genome sequencing (<5-fold coverage) and a custom next-generation sequencing panel (n=23 genes). Baseline radiomic original features were extracted by the pyradiomics package from CT-image delineated tumor volumes. An initial redundancy filtering was performed to remove correlating variables (r>0.6). We evaluated the predictive performance of baseline ctDNA and radiomics features on overall survival (OS), progression free survival (PFS), and time to progression (TTP), through fitting Cox-regression models. Four ctDNA features were included in the models: P20-150, ichorCNA, fragment end score and mutation detection. For the radiomics features we performed an additional back- and forward variable selection based on Akaike’s Information Criterion. Using the likelihood ratio test we tested if the model fit changed after adding ctDNA and radiomics features to a model with clinical variables. Results: The addition of radiomics to clinical variables improved model fit for OS (p=0.017). Baseline prediction of OS resulted in a C-index of 0.65 using clinical variables only, 0.65 with ctDNA, 0.68 with radiomics and 0.68 with ctDNA and radiomics combined. For PFS model fit improved after adding radiomics (p=0.020) and ctDNA and radiomics combined (p=0.017). Baseline prediction of PFS resulted in a C-index of 0.64 using clinical variables, 0.65 with ctDNA, 0.67 with radiomics, and 0.68 with ctDNA and radiomics combined. For TTP model fit improved after adding radiomics (p=0.008) and radiomics and ctDNA combined (p=0.002). Baseline prediction of TTP resulted in a C-index of 0.64 with clinical variables, 0.65 with ctDNA, 0.71 with radiomics, and 0.72 with ctDNA and radiomics combined. Based on the cox-regression models using clinical variables and radiomics, risk stratification by splitting the cohort in a high and low risk group was possible for OS, PFS and TTP (p<0.001). Conclusions: Combining clinical variables from SOURCE with radiomics data improved predictions of OS, PFS, and TTP among patients with rEAC. Multi-signal integration of clinical and radiomics variables could potentially be used to identify risk groups.