Literature DB >> 19645517

PET lesion segmentation using automated iso-intensity contouring in head and neck cancer.

Edmund Simon1, Timothy H Fox, Daniel Lee, Anthony F Waller, Paul Pantalone, Ashesh B Jani.   

Abstract

To improve the objectivity of the integration of positron emission tomography (PET), we used the conformality index (CI) to measure the goodness of fit of a given PET iso-SUV (standardized uptake value) level with the GTV defined on PET (GTV(PET)) and CT (GTV(CT)). Twenty-two datasets involving 20 head and neck cancer patients were identified. GTV(PET) and GTV(CT) were delineated manually.An iso-intensity method was developed to automatically segment GTV(PET-ISO) using (a) SUV and (b) maximum intensity thresholding (% Max), over a range of intensities. For each intensity, GTV(PET-ISO) was compared to GTV(PET) using the conformality index CI(PET) (and, similarly, to GTV(CT) using CICT). Comparing GTV(PET) to GTV(PET-ISO) vs comparing GTV(CT) to GTV(PET-ISO), the average peak CI was 0.68 +/- 0.09 vs 0.49 +/- 0.12 (p < 0.001), the optimum iso-SUV was 2.7 +/- 0.7 vs 2.9 +/- 1.0 (p=0. 253), and the % Max SUV was 21.8% +/- 7.6% vs 23.8% +/- 8.6% (p=0. 310), respectively. The radiation oncologist's volumes corresponded to a lower iso-SUV (3.02 +/- 0.58 vs 4.36 +/- 0.77, p< 0.001) and lower % Max SUV (24.1 +/- 9.1% vs 34.3 +/- 11.2%, p<0.001) than those drawn by the nuclear medicine physician. Though manual editing may still be necessary, PET iso-contouring is one method to improve the objectivity of GTV definition in head and neck cancer patients. Iso-SUV's can also be used to study the differences between PET's role as a nuclear medicine diagnostic test versus a radiation oncology treatment planning tool.

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Year:  2009        PMID: 19645517     DOI: 10.1177/153303460900800401

Source DB:  PubMed          Journal:  Technol Cancer Res Treat        ISSN: 1533-0338


  2 in total

1.  Post-Radiotherapy PET Image Outcome Prediction by Deep Learning Under Biological Model Guidance: A Feasibility Study of Oropharyngeal Cancer Application.

Authors:  Hangjie Ji; Kyle Lafata; Yvonne Mowery; David Brizel; Andrea L Bertozzi; Fang-Fang Yin; Chunhao Wang
Journal:  Front Oncol       Date:  2022-05-13       Impact factor: 5.738

2.  Towards More Structure: Comparing TNM Staging Completeness and Processing Time of Text-Based Reports versus Fully Segmented and Annotated PET/CT Data of Non-Small-Cell Lung Cancer.

Authors:  Raphael Sexauer; Thomas Weikert; Kevin Mader; Andreas Wicki; Sabine Schädelin; Bram Stieltjes; Jens Bremerich; Gregor Sommer; Alexander W Sauter
Journal:  Contrast Media Mol Imaging       Date:  2018-11-01       Impact factor: 3.161

  2 in total

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