Multiple training interventions significantly improve reproducibility of PET/CT-based lung cancer radiotherapy target volume delineation using an IAEA study protocol

Konert, Tom; Vogel, Wouter V.; Everitt, Sarah; MacManus, Michael; Thorwarth, Daniela; Fidarova, Elena; Páez, Diana; Sonke, Jan‐Jakob; Hanna, G.G.

doi:10.1016/j.radonc.2016.09.002

Cited by 21 publications

(23 citation statements)

References 32 publications

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“…Therefore, different research groups that dichotomize PET tracer avid lesions by fixed thresholds reported contradicting results [212][213][214]. Inter-observer variabilities can be reduced by training programmes [215] or by collecting and pre-selecting many observer's contouring about the same lesion to achieve an average or consensus contour [151].…”

Section: Delineationmentioning

confidence: 99%

Personalizing Medicine Through Hybrid Imaging and Medical Big Data Analysis

et al. 2018

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Medical imaging has evolved from a pure visualization tool to representing a primary source of analytic approaches toward in vivo disease characterization. Hybrid imaging is an integral part of this approach, as it provides complementary visual and quantitative information in the form of morphological and functional insights into the living body. As such, non-invasive imaging modalities no longer provide images only, but data, as stated recently by pioneers in the field. Today, such information, together with other, non-imaging medical data creates highly heterogeneous data sets that underpin the concept of medical big data. While the exponential growth of medical big data challenges their processing, they inherently contain information that benefits a patient-centric personalized healthcare. Novel machine learning approaches combined with high-performance distributed cloud computing technologies help explore medical big data. Such exploration and subsequent generation of knowledge require a profound understanding of the technical challenges. These challenges increase in complexity when employing hybrid, aka dual-or even multi-modality image data as input to big data repositories. This paper provides a general insight into medical big data analysis in light of the use of hybrid imaging information. First, hybrid imaging is introduced (see further contributions to this special Research Topic), also in the context of medical big data, then the technological background of machine learning as well as state-of-the-art distributed cloud computing technologies are presented, followed by the discussion of data preservation and data sharing trends. Joint data exploration endeavors in the context of in vivo radiomics and hybrid imaging will be presented. Standardization challenges of imaging protocol, delineation, feature engineering, and machine learning evaluation will be detailed. Last, the paper will provide an outlook into the future role of hybrid imaging in view of personalized medicine, whereby a focus will be given to the derivation of prediction models as part of clinical decision support systems, to which machine learning approaches and hybrid imaging can be anchored.

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Section: Delineationmentioning

confidence: 99%

Personalizing Medicine Through Hybrid Imaging and Medical Big Data Analysis

et al. 2018

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“…Training is also an important aspect that needs to be integrated as part of the quality assurance in target volume and OAR outlining. According to Konert et al [10], multiple training interventions are often required in order to change clinical practice in target volume outlining and peer review. Apart from reducing the risk of errors, this introduction of peer review could be used to facilitate continuous professional development for oncologists and may help to support the iterative process of improving.…”

Section: Training On Target Volume and Organ At Risk Outlinesmentioning

confidence: 99%

“…Contouring errors can be minor, caused by the unavoidable difficulty of distinguishing tumour boundaries from adjacent structures, leading to an increased dose to an OARs, or major, caused by a geographical tumour miss, leading to a possible tumour recurrence [8]. Training [9,10] and well-defined protocols [11,12] have been found to reduce the interobserver variation, but they do not eliminate the risk of misinterpreting tumour spread in the images, leading to a gross error in target definition. Numerous studies have shown that quality assurance checks by a second expert or multidisciplinary team (MDT; peer review) can identify gross errors in 17e30% of target volume definitions [8,13e15].…”

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confidence: 99%

Implementing the Royal College of Radiologists' Radiotherapy Target Volume Definition and Peer Review Guidelines: More Still To Do?

et al. 2019

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“…Several publications have reported the benefit of contouring workshops on reducing TVD variability. An example is an International Atomic Energy Agency study over a 1-year period involving 11 pairs of clinicians comprising a radiation oncologist and a nuclear medicine physician ( 31 ). Training consisted of lectures, contouring practice, and group and individualized feedback.…”

Section: Minimizing Tvd Variability In Routine Practicementioning

confidence: 99%

Challenges for Quality Assurance of Target Volume Delineation in Clinical Trials

et al. 2017

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In recent years, new radiotherapy techniques have emerged that aim to improve treatment outcome and reduce toxicity. The standard method of evaluating such techniques is to conduct large scale multicenter clinical trials, often across continents. A major challenge for such trials is quality assurance to ensure consistency of treatment across all participating centers. Analyses from previous studies have shown that poor compliance and protocol violation have a significant adverse effect on treatment outcomes. The results of the clinical trials may, therefore, be confounded by poor quality radiotherapy. Target volume delineation (TVD) is one of the most critical steps in the radiotherapy process. Many studies have shown large inter-observer variations in contouring, both within and outside of clinical trials. High precision techniques, such as intensity-modulated radiotherapy, image-guided brachytherapy, and stereotactic radiotherapy have steep dose gradients, and errors in contouring may lead to inadequate dose to the tumor and consequently, reduce the chance of cure. Similarly, variation in organ at risk delineation will make it difficult to evaluate dose response for toxicity. This article reviews the literature on TVD variability and its impact on dosimetry and clinical outcomes. The implications for quality assurance in clinical trials are discussed.

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Multiple training interventions significantly improve reproducibility of PET/CT-based lung cancer radiotherapy target volume delineation using an IAEA study protocol

Abstract: Multiple training interventions improve PET/CT-based TVD delineation accuracy in NSCLC and reduce interobserver variation.

Cited by 21 publications

References 32 publications

Personalizing Medicine Through Hybrid Imaging and Medical Big Data Analysis

Personalizing Medicine Through Hybrid Imaging and Medical Big Data Analysis

Implementing the Royal College of Radiologists' Radiotherapy Target Volume Definition and Peer Review Guidelines: More Still To Do?

Challenges for Quality Assurance of Target Volume Delineation in Clinical Trials

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