Tumor classification using perfusion volume fractions in breast DCE-MRI

Sang Ho Lee; Jong Hyo Kim; Jeong Seon Park; Sang Joon Park; Yun Sub Jung; Jung Joo Song; Woo Kyung Moon

doi:10.1117/12.774370

17 March 2008 Tumor classification using perfusion volume fractions in breast DCE-MRI

Sang Ho Lee, Jong Hyo Kim, Jeong Seon Park, Sang Joon Park, Yun Sub Jung, Jung Joo Song, Woo Kyung Moon

Proceedings Volume 6915, Medical Imaging 2008: Computer-Aided Diagnosis; 69152D (2008) https://doi.org/10.1117/12.774370
Event: Medical Imaging, 2008, San Diego, California, United States

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.

Citation Download Citation

Sang Ho Lee, Jong Hyo Kim, Jeong Seon Park, Sang Joon Park, Yun Sub Jung, Jung Joo Song, and Woo Kyung Moon "Tumor classification using perfusion volume fractions in breast DCE-MRI", Proc. SPIE 6915, Medical Imaging 2008: Computer-Aided Diagnosis, 69152D (17 March 2008); https://doi.org/10.1117/12.774370

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