Image-Based Modeling of Normal Tissue Complication Probability for Radiation Therapy

Abstract

Radiation therapy dose distributions to eradicate tumor cells are typically constrained in extent or intensity to minimize the risk of injury to nearby critical normal tissues. With the widespread use of 3D image-based treatment planning systems, the question naturally arises how patient-specific anatomy and treatment differences affect outcome. It has long been known, that for many organs, variations in the fractional volume irradiated to high doses greatly alters the dose to achieve a given complication level (the "isoeffective dose") [1]. Smaller irradiated fractional volumes often lead to a much lower risk of complication; this is often referred to as the "volume-effect" in the literature, but would be more correctly referred to as the "dose-volume" effect. Normal tissue complication probability (NTCP) modeling is simply the ongoing effort to understand the risk of normal tissue injury as a function of the 3D dose distribution. Recently, there has been a steady accumulation of NTCP studies [2], and this is expected to continue or even accelerate in the future. NTCP models are particularly needed when the "volume-effect" becomes important (i.e., injury depends on the detailed dose distribution), such as for skin, lung, or liver. In this chapter we will review the basic principles of NTCP modeling, as well as publications related to selected endpoints (xerostomia, radiation pneumonitis, late rectal toxicity), and several issues related to the use of NTCP models, especially relating to their safe use. Other recent reviews which further discuss data on endpoints of interest in treatment planning include Deasy and Fowler [3], Moiseenko et al. [4], and the slightly older but still invaluable Seminars in Radiation Oncology issue, edited by Randy Ten Haken, devoted to dose-volume effects in normal tissues [2]. A useful review of models and model principles are the chapters by Jackson and Yorke [5], and Yorke [6]. Many technical issues in modeling dose-volume outcomes were also discussed by Deasy et al. [7].This chapter describes standard NTCP models as well as our own approach, which is more data-driven and image-based [8]. This contrasts with the more common approach of assuming the validity of a specific model and then attempting to fit the model parameters to a given data set. The term "image-based" indicates

Radiation-Induced Reductions in Regional Lung Perfusion: 0.1–12 Year Data From a Prospective Clinical Study

“…Several investigators have noted higher rates of pneumonitis in patients with caudal vs. cranial tumors (32,35) and observed that the rate of pneumonitis is better related to the RT doses delivered to the caudal rather than cranial lung (36,37). Investigators at the NKI group also noted that dose to the posterior lung was more predictive for pneumonitis than was dose to the anterior lung.…”

Bioinformatics of Treatment Response

“…These metrics are extracted from the DVH such as volume receiving certain dose (Vx); minimum dose to x% volume (D x ); mean, maximum, and minimum dose; etc. More details are in our review chapter [20]. Moreover, we have developed a dedicated software tool called "Dose response explorer" (DREES) for deriving these metrics and modeling of radiotherapy response [28].…”

“…Radiotherapy outcome models could be divided according to the underlying principle into (1) analytical models, which employ biophysical understanding of irradiation effects such as the linear quadratic (LQ) model, and (2) data-driven models, which are phenomenological models and depend on parameters available from the collected clinical and dosimetric data [20]. In the context of data-driven and multivariable modeling of outcomes, the observed treatment outcome (e.g., TCP or NTCP) is considered as the result of mathematical mapping of several dosimetric, clinical, or biological input variables [19].…”

“…16.1. Therefore, recent years have witnessed the emergence of data-driven models utilizing informatics techniques, in The successful informatics approach should be able to resolve this interaction "puzzle" in the observed treatment outcome (e.g., local control or toxicity) for each individual patient [21] which dose-volume metrics are combined with other patient-or disease-based prognostic factors [4,5,[14][15][16][17][18][19][20].…”

Nonlinear Kernel-Based Approaches for Predicting Normal Tissue Toxicities

“…Therefore, recent approaches have focused more on datadriven models, in which dosimetric metrics are mixed with other patient or disease-based prognostic factors [4]. This approach is motivated by recent reports of imagespecific outcomes findings [5,6].…”