Investigating the potential of diffusion tensor atlases to generate anisotropic clinical tumor volumes in glioblastoma patients.

Background And Purpose: Diffusion tensor imaging (DTI) has been proposed to guide the anisotropic expansion from gross tumor volume to clinical target volume (CTV), aiming to integrate known tumor spread patterns into the CTV. This study investigate the potential of using a DTI atlas as an alternative to patient-specific DTI for generating anisotropic CTVs.

Materials And Methods: The dataset consisted of twenty-eight newly diagnosed glioblastoma patients from a Danish national DTI protocol with post-operative T1-contrast and DTI imaging.

Diffusion tensor transformation for personalizing target volumes in radiation therapy.

Diffusion tensor imaging (DTI) is used in tumor growth models to provide information on the infiltration pathways of tumor cells into the surrounding brain tissue. When a patient-specific DTI is not available, a template image such as a DTI atlas can be transformed to the patient anatomy using image registration. This study investigates a model, the invariance under coordinate transform (ICT), that transforms diffusion tensors from a template image to the patient image, based on the principle that the tumor growth process can be mapped, at any point in time, between the images using the same transformation function that we use to map the anatomy.

Integrating muscle fiber orientation from visible human data into radiotherapy target volumes.

A major challenge in treatment of tumors near skeletal muscle is defining the target volume for suspected tumor invasion into the muscle. This study develops a framework that generates radiation target volumes with muscle fiber orientation directly integrated into their definition. The framework is applied to nineteen sacral tumor patients with suspected infiltration into surrounding muscles.

A reference-point-method-based online proton treatment plan re-optimization strategy and a novel solution to planning constraint infeasibility problem.

. Propose a highly automated treatment plan re-optimization strategy suitable for online adaptive proton therapy. The strategy includes a rapid re-optimization method that generates quality replans and a novel solution that efficiently addresses the planning constraint infeasibility issue that can significantly prolong the re-optimization process.

Embedding machine learning based toxicity models within radiotherapy treatment plan optimization.

This study addresses radiation-induced toxicity (RIT) challenges in radiotherapy (RT) by developing a personalized treatment planning framework. It leverages patient-specific data and dosimetric information to create an optimization model that limits adverse side effects using constraints learned from historical data.The study uses the optimization with constraint learning (OCL) framework, incorporating patient-specific factors into the optimization process.

Computer Simulation to Assess Emergency Department Length of Stay in Pediatric Traumatic Brain Injury.

Objectives: Our study aimed to identify how emergency department (ED) arrival rate, process of care, and physical layout can impact ED length of stay (LOS) in pediatric traumatic brain injury care.

Methods: Process flows and value stream maps were developed for 3 level I pediatric trauma centers. Computer simulation models were also used to examine "what if" scenarios based on ED arrival rates.

The influence of anisotropy on the clinical target volume of brain tumor patients.

Current radiotherapy guidelines for glioma target volume definition recommend a uniform margin expansion from the gross tumor volume (GTV) to the clinical target volume (CTV), assuming uniform infiltration in the invaded brain tissue. However, glioma cells migrate preferentially along white matter tracts, suggesting that white matter directionality should be considered in an anisotropic CTV expansion. We investigate two models of anisotropic CTV expansion and evaluate their clinical feasibility.

Online adaptive planning methods for intensity-modulated radiotherapy.

Online adaptive radiation therapy aims at adapting a patient's treatment plan to their current anatomy to account for inter-fraction variations before daily treatment delivery. As this process needs to be accomplished while the patient is immobilized on the treatment couch, it requires time-efficient adaptive planning methods to generate a quality daily treatment plan rapidly. The conventional planning methods do not meet the time requirement of online adaptive radiation therapy because they often involve excessive human intervention, significantly prolonging the planning phase.

Exploring trade-offs in treatment planning for brain tumor cases with a probabilistic definition of the clinical target volume.

Purpose: This study demonstrates how a novel probabilistic clinical target volume (CTV) concept-the clinical target distribution (CTD)-can be used to navigate the trade-off between target coverage and organ sparing with a semi-interactive treatment planning approach.

Methods: Two probabilistic treatment planning methods are presented that use tumor probabilities to balance tumor control with organ-at-risk (OAR) sparing. The first method explores OAR dose reduction by systematically discarding of CTD voxels with an unfavorable dose-to-probability ratio from the minimum dose coverage objective.

Personalized mid-course FDG-PET based adaptive treatment planning for non-small cell lung cancer using machine learning and optimization.

. Traditional radiotherapy (RT) treatment planning of non-small cell lung cancer (NSCLC) relies on population-wide estimates of organ tolerance to minimize excess toxicity. The goal of this study is to develop a personalized treatment planning based on patient-specific lung radiosensitivity, by combining machine learning and optimization.

Toward Personalized Radiation Therapy of Liver Metastasis: Importance of Serial Blood Biomarkers.

Purpose: To assess the added value of serial blood biomarkers in liver metastasis stereotactic body radiation therapy (SBRT).

Materials And Methods: Eighty-nine patients were retrospectively included. Pre- and midtreatment blood samples were analyzed for potential biomarkers of the treatment response.

Perspectives on the model-based approach to proton therapy trials: A retrospective study of a lung cancer randomized trial.

Purpose: The goal of this study was to assess whether a model-based approach applied retrospectively to a completed randomized controlled trial (RCT) would have significantly altered the selection of patients of the original trial, using the same selection criteria and endpoint for testing the potential clinical benefit of protons compared to photons.

Methods And Materials: A model-based approach, based on three widely used normal tissue complication probability (NTCP) models for radiation pneumonitis (RP), was applied retrospectively to a completed non-small cell lung cancer RCT (NCT00915005). It was assumed that patients were selected by the model-based approach if their expected ΔNTCP value was above a threshold of 5%.

Towards optimal stopping in radiation therapy.

A typical fractionated radiotherapy (RT) course is a long and arduous process, demanding significant financial, physical, and mental commitments from patients. Each additional session of RT significantly increases the physical and psychological burden on patients and leads to higher radiation exposure in organs-at-risk (OAR), while, in some cases, the therapeutic benefits might not be high enough to justify the risks. Today, through technological advancements in molecular biology, imaging, and genetics more information is gathered about individual patient response before, during, and after the treatment.

Adaptive treatment-length optimization in spatiobiologically integrated radiotherapy.

Recent theoretical research on spatiobiologically integrated radiotherapy has focused on optimization models that adapt fluence-maps to the evolution of tumor state, for example, cell densities, as observed in quantitative functional images acquired over the treatment course. We propose an optimization model that adapts the length of the treatment course as well as the fluence-maps to such imaged tumor state. Specifically, after observing the tumor cell densities at the beginning of a session, the treatment planner solves a group of convex optimization problems to determine an optimal number of remaining treatment sessions, and a corresponding optimal fluence-map for each of these sessions.

Simulation of the Emergency Department Care Process for Pediatric Traumatic Brain Injury.

The treatment of patients in the emergency department (ED) with severe pediatric traumatic brain injury (TBI) is challenging, and treatment process strategies that facilitate good outcomes are not well documented. The overall objective of this study was to identify factors that can affect the care process associated with pediatric TBI. This objective was achieved using a discrete-event simulation model of patients with TBI as they progress through the ED treatment process of a Level I trauma center.

Examining Emergency Department Treatment Processes in Severe Pediatric Traumatic Brain Injury.

Background: In the treatment of pediatric traumatic brain injury (TBI), timely treatment of patients can affect the outcome. Our objectives were to examine the treatment process of acute pediatric TBI and the impact of non-value-added time (NVAT) on patient outcomes.

Methods: Data for 136 pediatric trauma patients (age < 18 years) with severe TBI from 2 trauma centers in the United States were collected.