Therapy operating characteristic curves: tools for precision chemotherapy

Longing to act: Bayesian inference as a framework for craving in behavioral addiction

Traditionally, craving is considered a defining feature of drug addiction. Accumulating evidence suggests that craving can also exist in behavioral addictions (e.g., gambling disorder) without drug-induced effects. However, the degree to which mechanisms of craving overlap between classic substance use disorders and behavioral addictions remains unclear. There is, therefore, an urgent need to develop an overarching theory of craving that conceptually integrates findings across behavioral and drug addictions. In this review, we will first synthesize existing theories and empirical findings related to craving in both drug-dependent and -independent addictive disorders. Building on the Bayesian brain hypothesis and previous work on interoceptive inference, we will then propose a computational theory for craving in behavioral addiction, where the target of craving is execution of an action (e.g., gambling) rather than a drug. Specifically, we conceptualize craving in behavioral addiction as a subjective belief about physiological states of the body associated with action completion and is updated based on both a prior belief ("I need to act to feel good") and sensory evidence ("I cannot act"). We conclude by briefly discussing the therapeutic implications of this framework. In summary, this unified Bayesian computational framework for craving generalizes across addictive disorders, provides explanatory power for ostensibly conflicting empirical findings, and generates strong hypotheses for future empirical studies. The disambiguation of the computational components underlying domain-general craving using this framework will lead to a deeper understanding of, and effective treatment targets for, behavioral and drug addictions.

Stochastic models for objects and images in oncology and virology: application to PI3K-Akt-mTOR signaling and COVID-19 disease

Purpose: The goal of this research is to develop innovative methods of acquiring simultaneous multidimensional molecular images of several different physiological random processes (PRPs) that might all be active in a particular disease such as COVID-19. Approach: Our study is part of an ongoing effort at the University of Arizona to derive biologically accurate yet mathematically tractable models of the objects of interest in molecular imaging and of the images they produce. In both cases, the models are fully stochastic, in the sense that they provide ways to estimate any estimable property of the object or image. The mathematical tool we use for images is the characteristic function, which can be calculated if the multivariate probability density function for the image data is known. For objects, which are functions of continuous variables rather than discrete pixels or voxels, the characteristic function becomes infinite dimensional, and we refer to it as the characteristic functional. Results: Several innovative mathematical results are derived, in particular for simultaneous imaging of multiple PRPs. Then the application of these methods to cancers that disrupt the mammalian target of rapamycin signaling pathway and to COVID-19 are discussed qualitatively. One reason for choosing these two problems is that they both involve lipid rafts. Conclusions: We found that it was necessary to employ a new algorithm for energy estimation to do simultaneous single-photon emission computerized tomography imaging of a large number of different tracers. With this caveat, however, we expect to be able to acquire and analyze an unprecedented amount of molecular imaging data for an individual COVID patient.

Objective assessment of the effects of tumor motion in radiation therapy

PURPOSE

Internal organ motion reduces the accuracy and efficacy of radiation therapy. However, there is a lack of tools to objectively (based on a medical or scientific task) assess the dosimetric consequences of motion, especially on an individual basis. We propose to use therapy operating characteristic (TOC) analysis to quantify the effects of motion on treatment efficacy for individual patients. We demonstrate the application of this tool with pancreatic stereotactic body radiation therapy (SBRT) clinical data and explore the origin of motion sensitivity.

METHODS

The technique is described as follows. (a) Use tumor-motion data measured from patients to calculate the motion-convolved dose of the gross tumor volume (GTV) and the organs at risk (OARs). (b) Calculate tumor control probability (TCP) and normal tissue complication probability (NTCP) from the motion-convolved dose-volume histograms. (c) Construct TOC curves from TCP and NTCP models. (d) Calculate the area under the TOC curve (AUTOC) and use it as a figure of merit for treatment efficacy. We used tumor motion data measured from patients to calculate the relation between AUTOC and motion magnitude for 25 pancreatic SBRT treatment plans. Furthermore, to explore the driving factor of motion sensitivity of a given plan, we compared the dose distribution of motion-sensitive plans and motion-robust plans and studied the dependence of motion sensitivity to motion directions.

RESULTS

Our technique is able to recognize treatment plans that are sensitive to motion. Under the presence of motion, the treatment efficacy of some plans changes from providing high tumor control and low risks of complications to providing no tumor control and high risks of side effects. Several treatment plans experience falloffs in AUTOC at a smaller magnitude of motion than other plans. In our dataset, a potential indicator of a motion-sensitive treatment plan is that the duodenum is in proximity to the tumor in the SI direction.

CONCLUSIONS

The TOC framework can serve as a tool to quantify the effects of internal organ motion in radiation therapy. With pancreatic SBRT clinical data, we applied this tool to study the change in treatment efficacy induced by motion for individual treatment plans. This framework could potentially be used clinically to understand the effects of motion in an individual patient and to design a patient-specific motion management plan. This framework could also be used in research to evaluate different components of the treatment process, such as motion-management techniques, treatment-planning algorithms, and treatment margins.

Physiological random processes in precision cancer therapy

Many different physiological processes affect the growth of malignant lesions and their response to therapy. Each of these processes is spatially and genetically heterogeneous; dynamically evolving in time; controlled by many other physiological processes, and intrinsically random and unpredictable. The objective of this paper is to show that all of these properties of cancer physiology can be treated in a unified, mathematically rigorous way via the theory of random processes. We treat each physiological process as a random function of position and time within a tumor, defining the joint statistics of such functions via the infinite-dimensional characteristic functional. The theory is illustrated by analyzing several models of drug delivery and response of a tumor to therapy. To apply the methodology to precision cancer therapy, we use maximum-likelihood estimation with Emission Computed Tomography (ECT) data to estimate unknown patient-specific physiological parameters, ultimately demonstrating how to predict the probability of tumor control for an individual patient undergoing a proposed therapeutic regimen.