Improving target delineation on 4-dimensional CT scans in stage I NSCLC using a deformable registration tool

Dam, Iris E. van; Koste, John R. van Sörnsen de; Hanna, G.G.; Muirhead, R.; Slotman, Ben J.; Senan, Suresh

doi:10.1016/j.radonc.2010.05.003

Cited by 62 publications

(46 citation statements)

References 27 publications

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“…Various deformable image registration methods [12,[16][17][18][19] and mechanical indices [20] have been surveyed to calculate the most accurate and robust deformation map as well as differentiation metric, respectively. We developed a workflow that processed breath-hold images at extreme tidal volumes and calculated the volume change and the elasticity of MPNs.…”

Section: Discussionmentioning

confidence: 99%

Noninvasive pulmonary nodule elastometry by CT and deformable image registration

Negahdar

Fasola

et al. 2015

Radiotherapy and Oncology

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Section: Discussionmentioning

confidence: 99%

Noninvasive pulmonary nodule elastometry by CT and deformable image registration

Negahdar

Fasola

et al. 2015

Radiotherapy and Oncology

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“…It has been reported that on average it takes about 1.5 to 2.5 hours to contour on all 10 phases. 18,19 It will still take about 0.9 to 1.5 hours to contour on 6 phases. However, contouring on selective phases would be an easier transition from the practice of contouring on all 10 phases and introduce minimal clinical changes of the contouring practice.…”

Section: Discussionmentioning

confidence: 99%

Determination of internal target volume using selective phases of a 4-dimensional computed tomography scan

Cao

Cui

Champ

et al. 2012

Practical Radiation Oncology

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“…Manual annotations remain the gold standard in nodule segmentation, but semiautomated segmentation methods, often with a final check by a radiologist, have also been used as a means of establishing the “gold standard” for nodule boundary. 80,81 With interobserver variability being reported to be present in manually segmented tumors, 81,82 another (potentially more cumbersome) approach is to try to average or aggregate delineations from multiple individual readers. 66 …”

Section: Radiomics As a Novel Tool To Quantitatively Analyze Tumor Immentioning

confidence: 99%

Novel Quantitative Imaging for Predicting Response to Therapy: Techniques and Clinical Applications

Bera

Velcheti

Madabhushi

2018

American Society of Clinical Oncology Educational Book

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OVERVIEW The current standard of Response Evaluation Criteria in Solid Tumors (RECIST)–based tumor response evaluation is limited in its ability to accurately monitor treatment response. Radiomics, an approach involving computerized extraction of several quantitative imaging features, has shown promise in predicting as well as monitoring response to therapy. In this article, we provide a brief overview of radiomic approaches and the various analytical methods and techniques, specifically in the context of predicting and monitoring treatment response for non–small cell lung cancer (NSCLC). We briefly summarize some of the various types of radiomic features, including tumor shape and textural patterns, both within the tumor and within the adjacent tumor microenvironment. Additionally, we also discuss work in delta-radiomics or change in radiomic features (e.g., texture within the nodule) across longitudinally interspersed images in time for monitoring changes in therapy. We discuss the utility of these approaches for NSCLC, specifically the role of radiomics as a prognostic marker for treatment effectiveness and early therapy response, including chemoradiation, immunotherapy, and trimodality therapy.

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Improving target delineation on 4-dimensional CT scans in stage I NSCLC using a deformable registration tool

Cited by 62 publications

References 27 publications

Noninvasive pulmonary nodule elastometry by CT and deformable image registration

Noninvasive pulmonary nodule elastometry by CT and deformable image registration

Determination of internal target volume using selective phases of a 4-dimensional computed tomography scan

Novel Quantitative Imaging for Predicting Response to Therapy: Techniques and Clinical Applications

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