The delineation of target volumes for radiotherapy of lung cancer patients

Vorwerk, Hilke; Beckmann, Gabriele; Bremer, Michael; Degen, Maria; Dietl, B.; Fietkau, Rainer; Gsänger, Tammo; Hermann, Robert Michael; Herrmann, Markus K. A.; Höller, Ulrike; Kampen, M. van; Körber, W; Maier, Burkhard; Martin, Thomas J.; Metz, Michael; Richter, R.; Siekmeyer, Birgit; Steder, Martin; Wagner, Daniela; Hess, Clemens F.; Weiss, Elisabeth; Christiansen, Hans

doi:10.1016/j.radonc.2009.03.014

Cited by 83 publications

(54 citation statements)

References 25 publications

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“…The majority of studies deal with approaches that emphasize overlapping areas [2,22,23,24,25,29,37,38,45,48,51]. However, comparing results of the studies is not straightforward because different parameters have been used.…”

Section: Critical Discussion Of Evaluation Parameters For Inter-obsermentioning

confidence: 99%

“…Due to the variety of used metrics (common to encompassing volume ratio, Jaccard index, or Dice similarity coefficient, etc. ), it represents the domain where harmonization in reporting is lacking [2,5,6,8,10,19,22,24,25,26,29,32,34,37,38,41,42,44,45,46,48,51,52,53]. When moving to 3D representation, metrics such as variation of the center of mass (COM) allow the evaluation of the volume displacement in space [3,10,25,37,46,47,52,53].…”

Section: Critical Discussion Of Evaluation Parameters For Inter-obsermentioning

confidence: 99%

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Critical discussion of evaluation parameters for inter-observer variability in target definition for radiation therapy

et al. 2012

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Section: Critical Discussion Of Evaluation Parameters For Inter-obsermentioning

confidence: 99%

Section: Critical Discussion Of Evaluation Parameters For Inter-obsermentioning

confidence: 99%

Critical discussion of evaluation parameters for inter-observer variability in target definition for radiation therapy

et al. 2012

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“…Lesion segmentation can be performed manually, or semi-/automatically [151,207]. Manual delineation employs slice-byslice contouring tools to delineate objects in medical images, which is subject to inter-observer variability depending on the level of expertise of the operator [158,208].…”

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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“…Even in the hands, or rather eyes, of experts, there is wide disagreement in the delineation of structures in which there is great interest in reducing treatment volumes in order to spare normal tissue or escalate dose. Thus, recent reports have indicated high between-observer variation in the identification/delineation of prostatic tumour volumes (Gao et al, 2007), lung cancer (Vorwerk et al, 2009), and the delineation of the postlumpectomy surgical cavity in breast cancer (Landis et al, 2007). An additional problem is the fact that the target is moving: patients breath, swallow, their hearts beat etc.…”

Section: New Developmentsmentioning

confidence: 99%

Accident prevention in day-to-day clinical radiation therapy practice

Baeza

2012

Ann ICRP

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Nearly 50-60% of cancer patients will undergo radiotherapy at some point in their treatment. Around 85% of the world's population live in developing countries served by approximately 30% of the world's radiotherapy facilities. It has been suggested that 1 megavoltage unit is required for every 500 new treatment courses per year, while others estimate that 1 megavoltage unit is needed for every 300 new treatments. However, these numbers do not necessarily take into account the development of new technologies and treatment modalities, which are more time- and resource-intensive. The International Commission on Radiological Protection has emphasised that 'purchasing new equipment without a concomitant effort on education and training and on a programme of quality assurance is dangerous', and 'the decision to implement a new technology for radiation therapy should be based on a thorough evaluation of the expected benefits, rather than being driven by the technology itself'. It is estimated that the rate of serious mistakes could be as high as 0.2%, which is several orders of magnitude higher than the rate reported for commercial aviation. So, how safe is safe? It can be stated that the development of a culture of safety is critical and requires efforts in education and training, which could prove difficult in overloaded departments.

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The delineation of target volumes for radiotherapy of lung cancer patients

Cited by 83 publications

References 25 publications

Critical discussion of evaluation parameters for inter-observer variability in target definition for radiation therapy

Critical discussion of evaluation parameters for inter-observer variability in target definition for radiation therapy

Personalizing Medicine Through Hybrid Imaging and Medical Big Data Analysis

Accident prevention in day-to-day clinical radiation therapy practice

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