Wenjuan Ma1, Yumei Zhao2, Yu Ji2, Xinpeng Guo2, Xiqi Jian3, Peifang Liu4, Shandong Wu5. 1. Department of Breast Imaging, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, 24 Huanhuxi Rd, Hexi District, Tianjin 300060, China; Key Laboratory of Breast Cancer Prevention and Therapy, 24 Huanhuxi Rd, Hexi District, Tianjin 300060, China; Tianjin's Clinical Research Center for Cancer, 24 Huanhuxi Rd, Hexi District, Tianjin 300060, China; Department of Biomedical and Engineering, Tianjin Medical University, Tianjin, China. 2. Department of Breast Imaging, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, 24 Huanhuxi Rd, Hexi District, Tianjin 300060, China; Key Laboratory of Breast Cancer Prevention and Therapy, 24 Huanhuxi Rd, Hexi District, Tianjin 300060, China; Tianjin's Clinical Research Center for Cancer, 24 Huanhuxi Rd, Hexi District, Tianjin 300060, China. 3. Department of Biomedical and Engineering, Tianjin Medical University, Tianjin, China. 4. Department of Breast Imaging, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, 24 Huanhuxi Rd, Hexi District, Tianjin 300060, China; Key Laboratory of Breast Cancer Prevention and Therapy, 24 Huanhuxi Rd, Hexi District, Tianjin 300060, China; Tianjin's Clinical Research Center for Cancer, 24 Huanhuxi Rd, Hexi District, Tianjin 300060, China. Electronic address: liupf2017@126.com. 5. Departments of Radiology, Biomedical Informatics, and Bioengineering, University of Pittsburgh, 3362 Fifth Ave, Pittsburgh, PA 15213. Electronic address: wus3@upmc.edu.
Abstract
RATIONALE AND OBJECTIVES: This study aimed to investigate whether quantitative radiomic features extracted from digital mammogram images are associated with molecular subtypes of breast cancer. MATERIALS AND METHODS: In this institutional review board-approved retrospective study, we collected 331 Chinese women who were diagnosed with invasive breast cancer in 2015. This cohort included 29 triple-negative, 45 human epidermal growth factor receptor 2 (HER2)-enriched, 36 luminal A, and 221 luminal B lesions. A set of 39 quantitative radiomic features, including morphologic, grayscale statistic, and texture features, were extracted from the segmented lesion area. Three binary classifications of the subtypes were performed: triple-negative vs non-triple-negative, HER2-enriched vs non-HER2-enriched, and luminal (A + B) vs nonluminal. The Naive Bayes machine learning scheme was employed for the classification, and the least absolute shrink age and selection operator method was used to select the most predictive features for the classifiers. Classification performance was evaluated by the area under receiver operating characteristic curve and accuracy. RESULTS: The model that used the combination of both the craniocaudal and the mediolateral oblique view images achieved the overall best performance than using either of the two views alone, yielding an area under receiver operating characteristic curve (or accuracy) of 0.865 (0.796) for triple-negative vs non-triple-negative, 0.784 (0.748) for HER2-enriched vs non-HER2-enriched, and 0.752 (0.788) for luminal vs nonluminal subtypes. Twelve most predictive features were selected by the least absolute shrink age and selection operator method and four of them (ie, roundness, concavity, gray mean, and correlation) showed a statistical significance (P< .05) in the subtype classification. CONCLUSIONS: Our study showed that quantitative radiomic imaging features of breast tumor extracted from digital mammograms are associated with breast cancer subtypes. Future larger studies are needed to further evaluate the findings.
RATIONALE AND OBJECTIVES: This study aimed to investigate whether quantitative radiomic features extracted from digital mammogram images are associated with molecular subtypes of breast cancer. MATERIALS AND METHODS: In this institutional review board-approved retrospective study, we collected 331 Chinese women who were diagnosed with invasive breast cancer in 2015. This cohort included 29 triple-negative, 45 humanepidermal growth factor receptor 2 (HER2)-enriched, 36 luminal A, and 221 luminal B lesions. A set of 39 quantitative radiomic features, including morphologic, grayscale statistic, and texture features, were extracted from the segmented lesion area. Three binary classifications of the subtypes were performed: triple-negative vs non-triple-negative, HER2-enriched vs non-HER2-enriched, and luminal (A + B) vs nonluminal. The Naive Bayes machine learning scheme was employed for the classification, and the least absolute shrink age and selection operator method was used to select the most predictive features for the classifiers. Classification performance was evaluated by the area under receiver operating characteristic curve and accuracy. RESULTS: The model that used the combination of both the craniocaudal and the mediolateral oblique view images achieved the overall best performance than using either of the two views alone, yielding an area under receiver operating characteristic curve (or accuracy) of 0.865 (0.796) for triple-negative vs non-triple-negative, 0.784 (0.748) for HER2-enriched vs non-HER2-enriched, and 0.752 (0.788) for luminal vs nonluminal subtypes. Twelve most predictive features were selected by the least absolute shrink age and selection operator method and four of them (ie, roundness, concavity, gray mean, and correlation) showed a statistical significance (P< .05) in the subtype classification. CONCLUSIONS: Our study showed that quantitative radiomic imaging features of breast tumor extracted from digital mammograms are associated with breast cancer subtypes. Future larger studies are needed to further evaluate the findings.
Authors: Virendra Kumar; Yuhua Gu; Satrajit Basu; Anders Berglund; Steven A Eschrich; Matthew B Schabath; Kenneth Forster; Hugo J W L Aerts; Andre Dekker; David Fenstermacher; Dmitry B Goldgof; Lawrence O Hall; Philippe Lambin; Yoganand Balagurunathan; Robert A Gatenby; Robert J Gillies Journal: Magn Reson Imaging Date: 2012-08-13 Impact factor: 2.546
Authors: William Rogers; Sithin Thulasi Seetha; Turkey A G Refaee; Relinde I Y Lieverse; Renée W Y Granzier; Abdalla Ibrahim; Simon A Keek; Sebastian Sanduleanu; Sergey P Primakov; Manon P L Beuque; Damiënne Marcus; Alexander M A van der Wiel; Fadila Zerka; Cary J G Oberije; Janita E van Timmeren; Henry C Woodruff; Philippe Lambin Journal: Br J Radiol Date: 2020-02-26 Impact factor: 3.039