357 research outputs found
Planning and Evaluation of Radio-Therapeutic Treatment of Head-and-Neck Cancer Using PET/CT scanning
Recommended from our members
Radiomics for Response and Outcome Assessment for Non-Small Cell Lung Cancer.
Routine follow-up visits and radiographic imaging are required for outcome evaluation and tumor recurrence monitoring. Yet more personalized surveillance is required in order to sufficiently address the nature of heterogeneity in nonsmall cell lung cancer and possible recurrences upon completion of treatment. Radiomics, an emerging noninvasive technology using medical imaging analysis and data mining methodology, has been adopted to the area of cancer diagnostics in recent years. Its potential application in response assessment for cancer treatment has also drawn considerable attention. Radiomics seeks to extract a large amount of valuable information from patients' medical images (both pretreatment and follow-up images) and quantitatively correlate image features with diagnostic and therapeutic outcomes. Radiomics relies on computers to identify and analyze vast amounts of quantitative image features that were previously overlooked, unmanageable, or failed to be identified (and recorded) by human eyes. The research area has been focusing on the predictive accuracy of pretreatment features for outcome and response and the early discovery of signs of tumor response, recurrence, distant metastasis, radiation-induced lung injury, death, and other outcomes, respectively. This review summarized the application of radiomics in response assessments in radiotherapy and chemotherapy for non-small cell lung cancer, including image acquisition/reconstruction, region of interest definition/segmentation, feature extraction, and feature selection and classification. The literature search for references of this article includes PubMed peer-reviewed publications over the last 10 years on the topics of radiomics, textural features, radiotherapy, chemotherapy, lung cancer, and response assessment. Summary tables of radiomics in response assessment and treatment outcome prediction in radiation oncology have been developed based on the comprehensive review of the literature
Comparative Study With New Accuracy Metrics for Target Volume Contouring in PET Image Guided Radiation Therapy
[EN] The impact of positron emission tomography (PET)
on radiation therapy is held back by poor methods of defining functional
volumes of interest. Many new software tools are being proposed
for contouring target volumes but the different approaches
are not adequately compared and their accuracy is poorly evaluated
due to the ill-definition of ground truth. This paper compares
the largest cohort to date of established, emerging and proposed
PET contouring methods, in terms of accuracy and variability.
We emphasize spatial accuracy and present a new metric
that addresses the lack of unique ground truth. Thirty methods
are used at 13 different institutions to contour functional volumes
of interest in clinical PET/CT and a custom-built PET phantom representing typical problems in image guided radiotherapy. Contouring
methods are grouped according to algorithmic type, level
of interactivity and how they exploit structural information in hybrid
images. Experiments reveal benefits of high levels of user interaction,
as well as simultaneous visualization of CT images and
PET gradients to guide interactive procedures. Method-wise evaluation
identifies the danger of over-automation and the value of
prior knowledge built into an algorithm.For retrospective patient data and manual ground truth delineation, the authors wish to thank S. Suilamo, K. Lehtio, M. Mokka, and H. Minn at the Department of Oncology and Radiotherapy, Turku University Hospital, Finland. This study was funded by the Finnish Cancer Organisations.Shepherd, T.; Teräs, M.; Beichel, RR.; Boellaard, R.; Bruynooghe, M.; Dicken, V.; Gooding, MJ.... (2012). Comparative Study With New Accuracy Metrics for Target Volume Contouring in PET Image Guided Radiation Therapy. IEEE Transactions on Medical Imaging. 31(12):2006-2024. doi:10.1109/TMI.2012.2202322S20062024311
- …