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Purpose: FDG-PET adds to clinical factors, such tumor stage and p16 status, in predicting local (LF), regional (RF), and distant failure (DF) in poor prognosis locally advanced head and neck cancer (HNC) treated with chemoradiation. We hypothesized that MRI-based quantitative imaging (QI) metrics could add to clinical predictors of treatment failure more significantly than FDG-PET metrics.Materials and methods: Fifty four patients with poor prognosis HNCs who were enrolled in an IRB approved prospective adaptive chemoradiotherapy trial were analyzed. MRI-derived gross tumor volume (GTV), blood volume (BV), and apparent diffusion coefficient (ADC) pre-treatment and mid-treatment (fraction 10), as well as pre-treatment FDG PET metrics, were analyzed in primary and individual nodal tumors. Cox proportional hazards models for prediction of LRF and DF free survival were used to test the additional value of QI metrics over dominant clinical predictors.Results: The mean ADC pre-RT and its change rate mid-treatment were significantly higher and lower in p16– than p16+ primary tumors, respectively. A Cox model identified that high mean ADC pre-RT had a high hazard for LF and RF in p16– but not p16+ tumors (p = 0.015). Most interesting, persisting subvolumes of low BV (TVbv) in primary and nodal tumors mid-treatment had high-risk for DF (p < 0.05). Also, total nodal GTV mid-treatment, mean/max SUV of FDG in all nodal tumors, and total nodal TLG were predictive for DF (p < 0.05). When including clinical stage (T4/N3) and total nodal GTV in the model, all nodal PET parameters had a p-value of >0.3, and only TVbv of primary tumors had a p-value of 0.06.Conclusion: MRI-defined biomarkers, especially persisting subvolumes of low BV, add predictive value to clinical variables and compare favorably with FDG-PET imaging markers. MRI could be well-integrated into the radiation therapy workflow for treatment planning, response assessment, and adaptive therapy.

In many settings, including oncology, increasing the dose of treatment results in both increased efficacy and toxicity. With the increasing availability of validated biomarkers and prediction models, there is the potential for individualized dosing based on patient specific factors. We consider the setting where there is an existing dataset of patients treated with heterogenous doses and including binary efficacy and toxicity outcomes and patient factors such as clinical features and biomarkers. The goal is to analyze the data to estimate an optimal dose for each (future) patient based on their clinical features and biomarkers. We propose an optimal individualized dose finding rule by maximizing utility functions for individual patients while limiting the rate of toxicity. The utility is defined as a weighted combination of efficacy and toxicity probabilities. This approach maximizes overall efficacy at a prespecified constraint on overall toxicity. We model the binary efficacy and toxicity outcomes using logistic regression with dose, biomarkers and dose–biomarker interactions. To incorporate the large number of potential parameters, we use the LASSO method. We additionally constrain the dose effect to be non‐negative for both efficacy and toxicity for all patients. Simulation studies show that the utility approach combined with any of the modeling methods can improve efficacy without increasing toxicity relative to fixed dosing. The proposed methods are illustrated using a dataset of patients with lung cancer treated with radiation therapy.

To evaluate whether response assessment of newly diagnosed glioblastoma at 3 months using 11 C-methionine-positron emission tomography (MET-PET) is better associated with patient outcome compared with baseline MET-PET or anatomic magnetic resonance imaging alone. Methods and Materials: Patients included were participants in a phase I/II trial of dose-escalated chemoradiation based on anatomic magnetic resonance imaging. Automated segmentation of metabolic tumor volume (MTV) was performed at a threshold of 1.5 times mean cerebellar uptake. Progression-free (PFS) and overall survival were estimated with the Kaplan-Meier method and compared with log-rank tests. Multivariate analysis for PFS and overall survival was performed using Cox proportional hazards, and spatial overlap between imaging and recurrence volumes were analyzed. Results: Among 37 patients, 15 had gross total resection, of whom 10 (67%) had residual MTV, 16 subtotal resection, and 6 biopsy alone. Median radiation therapy dose was 75 Gy (range, 66-81). Median baseline T1 Gd-enhanced tumor volume (GTV-Gd) was 38.0 cm 3 (range, 8.0-81.5). Median pre-CRT MTV was 4.9 cm 3 (range, 0-43.8). Among 25 patients with 3-month MET-PET, MTV was only 2.4 cm 3 (range, 0.004-18.0) in patients with uptake. Patients with MTV Z 0 cm 3 at 3 months had superior PFS (18.2 vs 10.1 months,

Previous studies have shown that children of alcohol use disorder (AUD) parents are more likely to develop alcohol problems as well as antisocial and other behavior problems. The purpose of this study was to examine gender discordance in the effect of early maternal and paternal influences on antisocial behaviors of boys and girls, as well as the environmental factors that moderate the parental effects. Specifically, we examined the effects of childhood and adulthood antisocial behavior of the parents on offspring antisocial behavior as young adults. We also examined whether mothers’ and fathers’ drinking problems when offspring were young children (6–8 years) affected offspring antisocial behavior as young adults (18–21 years). We evaluated 655 children from 339 families in the Michigan Longitudinal Study (MLS), a prospective study of AUD and non-AUD families. Path models were constructed in order to test for the parental contributions to offspring outcomes. We found that both mothers’ and fathers’ antisocial behavior contributed to the children’s young adult antisocial behavior. Only mothers’ drinking problems while their children were little had a significant effect on their sons’ later drinking, but not on their daughters’. These different parental effects suggest that maternal and paternal influences may be mediated by different mechanisms.

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The goal in personalized medicine is to individualize treatment using patient characteristics and improve health outcomes. Selection of optimal dose must balance the effect of dose on both treatment efficacy and toxicity outcomes. We consider a setting with one binary efficacy and one binary toxicity outcome.The goal is to find the optimal dose for each patient using clinical features and biomarkers from available dataset. We propose to use flexible machine learning methods such as random forest and Gaussian process models to build models for efficacy and toxicity depending on dose and biomarkers. A copula is used to model the joint distribution of the two outcomes and the estimates are constrained to have non-decreasing dose-efficacy and dose-toxicity relationships.Numerical utilities are elicited from clinicians for each potential bivariate outcome. For each patient, the optimal dose is chosen to maximize the posterior mean of the utility function. We also propose alternative approaches to optimal dose selection by adding additional toxicity based constraints and an approach taking into account the uncertainty in the estimation of the utility function. The proposed methods are evaluated in a simulation study to compare expected utility outcomes under various estimated optimal dose rules. Gaussian process models tended to have better performance than random forest. Enforcing monotonicity during modeling provided small benefits. Whether and how, correlation between efficacy and toxicity, was modeled, had little effect on performance.The proposed methods are illustrated with a study of patients with liver cancer treated with stereotactic body radiation therapy. K E Y W O R D SGaussian process, random forest, utility matrix INTRODUCTIONPrecision medicine is an approach for the treatment of a disease that takes into account individual variability. In many situations, the clinical decision is whether to give a particular treatment or a standard treatment to the patient, and the statistical goal is to identify the subgroup of patients likely to derive benefit from the treatment compared to standard orThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.