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Proceedings Paper

Tumor classification using perfusion volume fractions in breast DCE-MRI
Author(s): Sang Ho Lee; Jong Hyo Kim; Jeong Seon Park; Sang Joon Park; Yun Sub Jung; Jung Joo Song; Woo Kyung Moon
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Paper Abstract

This study was designed to classify contrast enhancement curves using both three-time-points (3TP) method and clustering approach at full-time points, and to introduce a novel evaluation method using perfusion volume fractions for differentiation of malignant and benign lesions. DCE-MRI was applied to 24 lesions (12 malignant, 12 benign). After region growing segmentation for each lesion, hole-filling and 3D morphological erosion and dilation were performed for extracting final lesion volume. 3TP method and k-means clustering at full-time points were applied for classifying kinetic curves into six classes. Intratumoral volume fraction for each class was calculated. ROC and linear discriminant analyses were performed with distributions of the volume fractions for each class, pairwise and whole classes, respectively. The best performance in each class showed accuracy (ACC), 84.7% (sensitivity (SE), 100%; specificity (SP), 66.7% to a single class) to 3TP method, whereas ACC, 73.6% (SE, 41.7%; SP, 100% to a single class) to k-means clustering. The best performance in pairwise classes showed ACC, 75% (SE, 83.3%; SP, 66.7% to four class pairs and SE, 58.3%; SP, 91.7% to a single class pair) to 3TP method and ACC, 75% (SE, 75%; SP, 75% to a single class pair and SE, 66.7%; SP, 83.3% to three class pairs) to k-means clustering. The performance in whole classes showed ACC, 75% (SE, 83.3%; SP, 66.7%) to 3TP method and ACC, 75% (SE, 91.7%; 58.3%) to k-means clustering. The results indicate that tumor classification using perfusion volume fractions is helpful in selecting meaningful kinetic patterns for differentiation of malignant and benign lesions, and that two different classification methods are complementary to each other.

Paper Details

Date Published: 17 March 2008
PDF: 10 pages
Proc. SPIE 6915, Medical Imaging 2008: Computer-Aided Diagnosis, 69152D (17 March 2008); doi: 10.1117/12.774370
Show Author Affiliations
Sang Ho Lee, Seoul National Univ. College of Medicine (South Korea)
Jong Hyo Kim, Seoul National Univ. College of Medicine (South Korea)
Jeong Seon Park, Seoul National Univ. College of Medicine (South Korea)
Sang Joon Park, Seoul National Univ. College of Medicine (South Korea)
Yun Sub Jung, Seoul National Univ. College of Medicine (South Korea)
Jung Joo Song, Seoul National Univ. College of Medicine (South Korea)
Woo Kyung Moon, Seoul National Univ. College of Medicine (South Korea)


Published in SPIE Proceedings Vol. 6915:
Medical Imaging 2008: Computer-Aided Diagnosis
Maryellen L. Giger; Nico Karssemeijer, Editor(s)

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