Dataset Information

CT images and radiotherapy treatment planning of patients with breast cancer: A dataset.

ABSTRACT: The data presented here were originally collected for the research project "CT-Scan processing and analysis in patient with breast cancer after radiotherapy". Also, it reported in our study "Prediction of Lung Tissue Damage by Evaluating Clinical and Dosimetric Parameters in Breast Cancer Patients" (Hasanabdali et al., 2016) [1]. This article describes and directly links to 52 subjects referred to Mahdieh Oncology and Radiotherapy Center from February to August 2015. Treatment planning was done for delivering 50 Gy dose to PTV in 25 fractions. the lungs and heart objects were extracted from CT images along with compliance Dose plan. Dose-volume histogram (DVH) and Dose-mass histogram (DMH) extracted using CT images and dose plan matrix. Moreover, the complete clinical and dosimetric specifications of subjects is attached.

SUBMITTER: Rezaei M

PROVIDER: S-EPMC5480817 | biostudies-other | 2017 Aug

REPOSITORIES: biostudies-other

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CT images and radiotherapy treatment planning of patients with breast cancer: A dataset.

Rezaei Mohammad M Mohammadbeigi Ahmad A Khoshgard Karim K Haghparast Abbas A

Data in brief 20170610

The data presented here were originally collected for the research project "CT-Scan processing and analysis in patient with breast cancer after radiotherapy". Also, it reported in our study "Prediction of Lung Tissue Damage by Evaluating Clinical and Dosimetric Parameters in Breast Cancer Patients" (Hasanabdali et al., 2016) [1]. This article describes and directly links to 52 subjects referred to Mahdieh Oncology and Radiotherapy Center from February to August 2015. Treatment planning was done ...[more]

PMID: 28664175

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Project description:Accurate delineation of organs at risk (OARs) is a precondition for intensity modulated radiation therapy. However, manual delineation of OARs is time consuming and prone to high interobserver variability. Because of image artifacts and low image contrast between different structures, however, the number of available approaches for autosegmentation of structures in the head-neck area is still rather low. In this project, a new approach for automated segmentation of head-neck CT images that combine the robustness of multiatlas-based segmentation with the flexibility of geodesic active contours and the prior knowledge provided by statistical appearance models is presented.The presented approach is using an atlas-based segmentation approach in combination with label fusion in order to initialize a segmentation pipeline that is based on using statistical appearance models and geodesic active contours. An anatomically correct approximation of the segmentation result provided by atlas-based segmentation acts as a starting point for an iterative refinement of this approximation. The final segmentation result is based on using model to image registration and geodesic active contours, which are mutually influencing each other.18 CT images in combination with manually segmented labels of parotid glands and brainstem were used in a leave-one-out cross validation scheme in order to evaluate the presented approach. For this purpose, 50 different statistical appearance models have been created and used for segmentation. Dice coefficient (DC), mean absolute distance and max. Hausdorff distance between the autosegmentation results and expert segmentations were calculated. An average Dice coefficient of DC = 0.81 (right parotid gland), DC = 0.84 (left parotid gland), and DC = 0.86 (brainstem) could be achieved.The presented framework provides accurate segmentation results for three important structures in the head neck area. Compared to a segmentation approach based on using multiple atlases in combination with label fusion, the proposed hybrid approach provided more accurate results within a clinically acceptable amount of time.

Dataset Information

CT images and radiotherapy treatment planning of patients with breast cancer: A dataset.

Publications

CT images and radiotherapy treatment planning of patients with breast cancer: A dataset.

Similar Datasets

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