An artificial intelligence method using FDG PET to predict treatment outcome in diffuse large B cell lymphoma patients

Convolutional neural networks (CNNs) may improve response prediction in diffuse large B-cell lymphoma (DLBCL). The aim of this study was to investigate the feasibility of a CNN using maximum intensity projection (MIP) images from 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) baseline scans to predict the probability of time-to-progression (TTP) within 2 years and compare it with the International Prognostic Index (IPI), i.e. a clinically used score. 296 DLBCL 18F-FDG PET/CT baseline scans collected from a prospective clinical trial (HOVON-84) were analysed. Cross-validation was performed using coronal and sagittal MIPs. An external dataset (340 DLBCL patients) was used to validate the model. Association between the probabilities, metabolic tumour volume and Dmaxbulk was assessed. Probabilities for PET scans with synthetically removed tumors were also assessed. The CNN provided a 2-year TTP prediction with an area under the curve (AUC) of 0.74, outperforming the IPI-based model (AUC = 0.68). Furthermore, high probabilities (> 0.6) of the original MIPs were considerably decreased after removing the tumours (< 0.4, generally). These findings suggest that MIP-based CNNs are able to predict treatment outcome in DLBCL

Heterogeneity in response to incentives: Evidence from field data

This dissertation explores whether observable individual characteristics such as gender, level of education and occupation are good predictors of people’s responses to competitive and cooperative incentive schemes. This inquiry is motivated by the belief that such characteristics can serve as proxies for the unobservable personality traits and attitudes that actually influence the reaction to incentives. Results presented in this dissertation complement related laboratory experiments by discussing evidence that is based on field experiments as well as naturally occurring data. The studies discussed in this dissertation suggest that there are systematic differences between individuals’ reactions to incentives, and such heterogeneities have economically important consequences. Consequently, by taking observable dimensions of heterogeneity between individuals into account we can improve our predictions of people’s responses to incentives and thus design more efficient incentive schemes. Moreover, this dissertation highlights the importance of complementing laboratory experiments with scientific projects based on field data

Women do not play their aces: the consequences of shying away

The underrepresentation of women at the top of hierarchies is often explained by gender differences in preferences. We find support for this claim by analyzing a large dataset from an online card game community, a stylized yet natural setting characterized by self-selection into an uncertain, competitive and male-dominated environment. We observe gender differences in playing behavior consistent with women being more averse towards risk and competition. Moreover, we demonstrate how "shying away" makes female players less successful: despite no gender gap in playing skills, women accumulate lower scores than men due to their relative avoidance of risky and competitive situations

Does relative grading help male students? Evidence from a field experiment in the classroom

This paper analyzes grade incentives by performing a direct comparison of the two most commonly used grading practices: the absolute and the relative grading schemes in a large-scale field experiment. We test whether relative grading, by creating a rank-order tournament in the classroom, provides stronger incentives for male students than absolute grading. We find only weak support in the data for this hypothesis: preparation effort and exam performance are largely unaffected by the grading schemes, except among marginal students. We attribute this finding to students in our sample being unmotivated by grade incentives (beyond passing)

Baseline PET radiomics outperforms the IPI risk score for prediction of outcome in diffuse large B-cell lymphoma

The objective of this study is to externally validate the clinical positron emission tomography (PET) model developed in the HOVON-84 trial and to compare the model performance of our clinical PET model using the international prognostic index (IPI). In total, 1195 patients with diffuse large B-cell lymphoma (DLBCL) were included in the study. Data of 887 patients from 6 studies were used as external validation data sets. The primary outcomes were 2-year progression-free survival (PFS) and 2-year time to progression (TTP). The metabolic tumor volume (MTV), maximum distance between the largest lesion and another lesion (Dmaxbulk), and peak standardized uptake value (SUVpeak) were extracted. The predictive values of the IPI and clinical PET model (MTV, Dmaxbulk, SUVpeak, performance status, and age) were tested. Model performance was assessed using the area under the curve (AUC), and diagnostic performance, using the positive predictive value (PPV). The IPI yielded an AUC of 0.62. The clinical PET model yielded a significantly higher AUC of 0.71 (P &lt; .001). Patients with high-risk IPI had a 2-year PFS of 61.4% vs 51.9% for those with high-risk clinical PET, with an increase in PPV from 35.5% to 49.1%, respectively. A total of 66.4% of patients with high-risk IPI were free from progression or relapse vs 55.5% of patients with high-risk clinical PET scores, with an increased PPV from 33.7% to 44.6%, respectively. The clinical PET model remained predictive of outcome in 6 independent first-line DLBCL studies, and had higher model performance than the currently used IPI in all studies.</p

Optimal timing and criteria of interim PET in DLBCL: a comparative study of 1692 patients

Interim 18F-fluorodeoxyglucose positron emission tomography (Interim- 18F-FDG-PET, hereafter I-PET) has the potential to guide treatment of patients with diffuse large B-cell lymphoma (DLBCL) if the prognostic value is known. The aim of this study was to determine the optimal timing and response criteria for evaluating prognosis with I-PET in DLBCL. Individual patient data from 1692 patients with de novo DLBCL were combined and scans were harmonized. I-PET was performed at various time points during treatment with rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) therapy. Scans were interpreted using the Deauville score (DS) and change in maximum standardized uptake value (DSUVmax ). Multilevel Cox proportional hazards models corrected for International Prognostic Index (IPI) score were used to study the effects of timing and response criteria on 2-year progression-free survival (PFS). I-PET after 2 cycles (I-PET2) and I-PET4 significantly discriminated good responders from poor responders, with the highest hazard ratios (HRs) for I-PET4. Multivariable HRs for a PET-positive result at I-PET2 and I-PET4 were 1.71 and 2.95 using DS4-5, 4.91 and 6.20 using DS5, and 2.93 and 4.65 using DSUVmax , respectively. DSUVmax identified a larger proportion of poor responders than DS5 did. For all criteria, the negative predictive value was >80%, and positive predictive values ranged from 30% to 70% at I-PET2 and I-PET4. Unlike I-PET1, I-PET3 discriminated good responders from poor responders using DS4-5 and DS5 thresholds (HRs, 2.94 and 4.67, respectively). I-PET2 and I-PET4 predict good response equally during R-CHOP therapy in DLBCL. Optimal timing and response criteria depend on the clinical context. Good response at I-PET2 is suggested for de-escalation trials, and poor response using DSUVmax at I-PET4 is suggested for randomized trials that are evaluating new therapie