An alternative approach to histopathological validation of PET imaging for radiation therapy image-guidance: A proof of concept

Axente, Marian; He, Jun; Bass, C.; Sundaresan, Gobalakrishnan; Zweit, Jamal; Williamson, Jeffrey F.; Pugachev, Andrei

doi:10.1016/j.radonc.2013.12.017

Cited by 5 publications

(5 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Despite the challenges with respect to the accuracy of the reference contours, these pathology‐validated images provide an important test for PET‐AS methods. This justifies the need for further improvement of the current experimental approaches, as well as development of new techniques to improve the accuracy of histopathological validation …”

Section: Components Of An Evaluation Standardmentioning

confidence: 99%

“…This justifies the need for further improvement of the current experimental approaches, as well as development of new techniques to improve the accuracy of histopathological validation. 176,179,180 It is important to note that because histopathological validation of PET image segmentation is carried out for a particular tissue and tracer pair, it cannot be implied that the results apply to alternative PET tracer/tissue combinations; hence, one should exercise care when using PET-AS algorithms to segment tracer/tissue pair images different from those for which they were validated. It should be noted that evaluating PET segmentation against anatomic or surgical delineation could be potentially misleading since biodistribution of a particular PET radiotracer may not conform to these structures.…”

Section: Histopathologicalmentioning

confidence: 99%

See 1 more Smart Citation

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

et al. 2017

View full text Add to dashboard Cite

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 ...

show abstract

Section: Components Of An Evaluation Standardmentioning

confidence: 99%

Section: Histopathologicalmentioning

confidence: 99%

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

et al. 2017

View full text Add to dashboard Cite

show abstract

“… 51 – 62 Validation of new segmentation algorithms in terms of accuracy and robustness is of crucial importance for the potential clinical application of (semi-)automatic PET-based contouring. 63 , 64 So far only a few clinical trials have been carried out in which dose escalation was prescribed on an 18 F-FDG PET avid area inside the GTV. 65 – 67 Currently, two multicentre trials are testing the potential of redistributing the radiation dose to the metabolically most 18 F-FDG PET avid part of the tumour in non-small-cell lung cancer (NSCLC) 65 and also in HNC.…”

Section: Pet For Rt Target Volume Delineation and Assessment Of Functmentioning

confidence: 99%

Functional imaging for radiotherapy treatment planning: current status and future directions—a review

Thorwarth

2015

BJR

View full text Add to dashboard Cite

In recent years, radiotherapy (RT) has been subject to a number of technological innovations. Today, RT is extremely flexible, allowing irradiation of tumours with high doses, whilst also sparing normal tissues from doses. To make use of these additional degrees of freedom, integration of functional image information may play a key role (i) for better staging and tumour detection, (ii) for more accurate RT target volume delineation, (iii) to assess functional information about biological characteristics and individual radiation resistance and (iv) to apply personalized dose prescriptions. In this article, we discuss the current status and future directions of different clinically available functional imaging modalities; CT, MRI, positron emission tomography (PET) as well as the hybrid imaging techniques PET/CT and PET/MRI and their potential for individualized RT.

show abstract

“…In a recently published study, Axente et al [64] proposed another alternative for generating pathological data sets for PET segmentation validation. They tested their approach in a small animal model.…”

Section: Future Directionsmentioning

confidence: 99%

Pathology-validated PET image data sets and their role in PET segmentation

Kirov

Fanchon

2014

Clin Transl Imaging

View full text Add to dashboard Cite

Positron emission tomography (PET)/computed tomography has recently been finding broader application for the diagnosis, treatment and therapy assessment of malignant disease. Accurate definition of the tumor border is extremely important for the success of localized tumor therapies. PET promises to provide the metabolically active tumor volume and, at present, it is used for target definition in a variety of tumors. This process is, however, subject to uncertainties of different origin. Resolving these uncertainties is challenging, since validating PET images and segmentation contours against tumor pathology is experimentally difficult. In addition to accurate lesion contouring, this challenges validation of PET tracers and investigations of tumor functional heterogeneity. In this paper, we briefly review the present studies providing PET image data sets with pathology validation. We focus on the specimen handling techniques aimed at achieving higher geometrical accuracy of the pathology-derived ''ground truth''. We also summarize the main findings obtained for the PET segmentation techniques which have been tested with the help of these data sets. Finally, we provide a critical summary of the current state of the art in pathological validation of PET images and briefly discuss future possibilities in this direction.

show abstract

An alternative approach to histopathological validation of PET imaging for radiation therapy image-guidance: A proof of concept

Cited by 5 publications

References 30 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

Functional imaging for radiotherapy treatment planning: current status and future directions—a review

Pathology-validated PET image data sets and their role in PET segmentation

Contact Info

Product

Resources

About