Objective assessment of image quality VI: imaging in radiation therapy

Barrett, Harrison H.; Kupinski, Matthew A.; Müeller, Stefan; Halpern, Howard J.; Morris, John C.; Dwyer, Róisín M.

doi:10.1088/0031-9155/58/22/8197

Cited by 13 publications

(17 citation statements)

References 56 publications

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“…The quality of an image is defined by several parameters, which may have different importance for different tasks . Segmentation differs from typical diagnostic tasks in that it seeks to identify the boundary locations and therefore uses a much larger parameter space.…”

Section: Discussion Of Segmentation Limitations Dependencies and Immentioning

confidence: 99%

“…The quality of an image is defined by several parameters, which may have different importance for different tasks. [193][194][195][196] Segmentation differs from typical diagnostic tasks in that it seeks to identify the boundary locations and therefore uses a much larger parameter space. Based on this, there is an expectation that this problem is more ill-posed and requires less noisy data to reduce errors.…”

Section: C Dependence On Scanner Image Acquisition and Reconstrumentioning

confidence: 99%

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Classification and evaluation strategies of auto-segmentation approaches for PET: Report of AAPM task group No. 211

et al. 2017

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Purpose The purpose of this educational report is to provide an overview of the present state-of-the-art PET auto-segmentation (PET-AS) algorithms and their respective validation, with an emphasis on providing the user with help in understanding the challenges and pitfalls associated with selecting and implementing a PET-AS algorithm for a particular application. Approach A brief description of the different types of PET-AS algorithms is provided using a classification based on method complexity and type. The advantages and the limitations of the current PET-AS algorithms are highlighted based on current publications and existing comparison studies. A review of the available image datasets and contour evaluation metrics in terms of their applicability for establishing a standardized evaluation of PET-AS algorithms is provided. The performance requirements for the algorithms and their dependence on the application, the radiotracer used and the evaluation criteria are described and discussed. Finally, a procedure for algorithm acceptance and implementation, as well as the complementary role of manual and auto-segmentation are addressed. Findings A large number of PET-AS algorithms have been developed within the last 20 years. Many of the proposed algorithms are based on either fixed or adaptively selected thresholds. More recently, numerous papers have proposed the use of more advanced image analysis paradigms to perform semi-automated delineation of the PET images. However, the level of algorithm validation is variable and for most published algorithms is either insufficient or inconsistent which prevents recommending a single algorithm. This is compounded by the fact that realistic image configurations with low signal-to-noise ratios (SNR) and heterogeneous tracer distributions have rarely been used. Large variations in the evaluation methods used in the literature point to the need for a standardized evaluation protocol. Conclusions Available comparison studies suggest that PET-AS algorithms relying on advanced image analysis paradigms provide generally more accurate segmentation than approaches based on PET activity thresholds, particularly for realistic configurations. However, this may not be the case for simple shape lesions in situations with a narrower range of parameters, where simpler methods may also perform well. Recent algorithms which employ some type of consensus or automatic selection between several PET-AS methods have potential to overcome the limitations of the individual methods when appropriately trained. In either case, accuracy evaluation is required for each different PET scanner and scanning and image reconstruction protocol. For the simpler, less robust approaches, adaptation to scanning conditions, tumor type, and tumor location by optimization of parameters is necessary. The results from the method evaluation stage can be used to estimate the contouring uncertainty. All PET-AS contours should be critically verified by a physician. A standard test, i.e., a benchmark dedicated to ...

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Section: Discussion Of Segmentation Limitations Dependencies and Immentioning

confidence: 99%

Section: C Dependence On Scanner Image Acquisition and Reconstrumentioning

confidence: 99%

Classification and evaluation strategies of auto-segmentation approaches for PET: Report of AAPM task group No. 211

et al. 2017

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“…The mathematical and computational aspects of TOC curves are treated in detail in Barrett et al (2013), 6 the sixth in a series of theoretical papers on Objective Assessment of Image Quality, hence referred to as OAIQ VI. Two schematic TOC curves are depicted in Fig.…”

Section: Metrics Of Therapeutic Success: Toc Curvesmentioning

confidence: 99%

“…3, we discuss Therapy Operating Characteristic (TOC) curves. 5,6 In radiation therapy, a TOC curve is a plot of the probability of tumor control vs. the probability of specified normal-tissue complications as the overall radiation dose level is varied, e.g. by varying the beam current in external-beam radiotherapy or the total injected activity in radionuclide therapy.…”

Section: Introductionmentioning

confidence: 99%

“…The area under a TOC curve, denoted AUTOC, can be used as a figure of merit for therapeutic efficacy, analogous to the area under an ROC curve (AUROC), which is a figure of merit for diagnostic efficacy. 2 In radiation therapy we have shown 5,6 that AUTOC can be computed for a single patient by using image data along with radiobiological models for tumor response and adverse side effects. These radiobiological models and other mathematical models relevant to radiation therapy and chemotherapy are briefly reviewed in Sec.…”

Section: Introductionmentioning

confidence: 99%

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Quantifying and reducing uncertainties in cancer therapy

et al. 2015

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There are two basic sources of uncertainty in cancer chemotherapy: how much of the therapeutic agent reaches the cancer cells, and how effective it is in reducing or controlling the tumor when it gets there. There is also a concern about adverse effects of the therapy drug. Similarly in external-beam radiation therapy or radionuclide therapy, there are two sources of uncertainty: delivery and efficacy of the radiation absorbed dose, and again there is a concern about radiation damage to normal tissues. The therapy operating characteristic (TOC) curve, developed in the context of radiation therapy, is a plot of the probability of tumor control vs. the probability of normal-tissue complications as the overall radiation dose level is varied, e.g. by varying the beam current in external-beam radiotherapy or the total injected activity in radionuclide therapy. The TOC can be applied to chemotherapy with the administered drug dosage as the variable. The area under a TOC curve (AUTOC) can be used as a figure of merit for therapeutic efficacy, analogous to the area under an ROC curve (AUROC), which is a figure of merit for diagnostic efficacy. In radiation therapy AUTOC can be computed for a single patient by using image data along with radiobiological models for tumor response and adverse side effects. In this paper we discuss the potential of using mathematical models of drug delivery and tumor response with imaging data to estimate AUTOC for chemotherapy, again for a single patient. This approach provides a basis for truly personalized therapy and for rigorously assessing and optimizing the therapy regimen for the particular patient. A key role is played by Emission Computed Tomography (PET or SPECT) of radiolabeled chemotherapy drugs.

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Objective assessment of the effects of tumor motion in radiation therapy

et al. 2019

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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.

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Objective assessment of image quality VI: imaging in radiation therapy

Cited by 13 publications

References 56 publications

Classification and evaluation strategies of auto-segmentation approaches for PET: Report of AAPM task group No. 211

Classification and evaluation strategies of auto-segmentation approaches for PET: Report of AAPM task group No. 211

Quantifying and reducing uncertainties in cancer therapy

Objective assessment of the effects of tumor motion in radiation therapy

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