The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Tianzi Jiang - One of the best experts on this subject based on the ideXlab platform.
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corresponding anatomical and coactivation architecture of the human precuneus showing similar connectivity patterns with macaques
NeuroImage, 2019Co-Authors: Jiaojian Wang, Benjamin Becker, Lijie Wang, Hai Li, Xudong Zhao, Tianzi JiangAbstract:Abstract The precuneus (PCun) is one of the most expanded areas of the association cortex and plays an important role in integrating information from different modalities. However, whether the functional architecture of PCun is shared by humans and macaques is an open question. We used both anatomical connectivity and task-dependent coactivation patterns to parcellate the human PCun and consistently identified three Subregions in the human PCun using two independent datasets. Two Subregions were located in the dorsal PCun and one subregion was located in the ventral PCun. This parcellation scheme for the PCun was supported by identifying the subregion-specific networks and by functional characterization. Then, the absolute and relative gray matter volume of precuneus in human and macaque was calculated and significantly smaller absolute and relative gray matter volume in macaque was identified. Next, three macaque PCun Subregions were defined based on our tractographic atlas. Finally, the whole brain anatomical connectivity patterns and connectivity fingerprints with 17 predefined homologous target brain areas were mapped for each PCun subregion and revealed that the PCun shares similar anatomical connectivity patterns in humans and macaques. The similar anatomical connectivity patterns of PCun were validated by an independent in-house dataset. Our findings demonstrated that anatomical connectivity patterns can reflect the functional architecture of the PCun in humans and that the functional architecture of the PCun is similar in humans and macaques.
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correspondent functional topography of the human left inferior parietal lobule at rest and under task revealed using resting state fmri and coactivation based parcellation
Human Brain Mapping, 2017Co-Authors: Jiaojian Wang, Benjamin Becker, Simon B Eickhoff, Tianzi JiangAbstract:The human left inferior parietal lobule (LIPL) plays a pivotal role in many cognitive functions and is an important node in the default mode network (DMN). Although many previous studies have proposed different parcellation schemes for the LIPL, the detailed functional organization of the LIPL and the exact correspondence between the DMN and LIPL Subregions remain unclear. Mounting evidence indicates that spontaneous fluctuations in the brain are strongly associated with cognitive performance at the behavioral level. However, whether a consistent functional topographic organization of the LIPL during rest and under task can be revealed remains unknown. Here, they used resting-state functional connectivity (RSFC) and task-related coactivation patterns separately to parcellate the LIPL and identified seven Subregions. Four Subregions were located in the supramarginal gyrus (SMG) and three Subregions were located in the angular gyrus (AG). The subregion-specific networks and functional characterization revealed that the four anterior Subregions were found to be primarily involved in sensorimotor processing, movement imagination and inhibitory control, audition perception and speech processing, and social cognition, whereas the three posterior Subregions were mainly involved in episodic memory, semantic processing, and spatial cognition. The results revealed a detailed functional organization of the LIPL and suggested that the LIPL is a functionally heterogeneous area. In addition, the present study demonstrated that the functional architecture of the LIPL during rest corresponds with that found in task processing.
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connectivity profiles reveal a transition subarea in the parahippocampal region that integrates the anterior temporal posterior medial systems
The Journal of Neuroscience, 2016Co-Authors: Junjie Zhuo, Chunshui Yu, Yuanchao Zhang, Tianzi JiangAbstract:Traditional anatomical studies of the parahippocampal region (PHR) defined the lateral portion into two Subregions, the perirhinal (PRC) and parahippocampal (PHC) cortices. Based on this organization, several models suggested that the PRC and the PHC play different roles in memory through connections with different memory-related brain networks. To identify the key components of the human PHR, we used a well accepted connection-based parcellation method on two independent datasets. Our parcellation divided the PRC and PHC into three Subregions, specifically, the rostral PRC, caudal PRC (PRCc), and PHC. The connectivity profile for each subregion showed that the rostral PRC was connected to the anterior temporal (AT) system and the PHC was connected to the posterior medial (PM) system. The transition area (PRCc) integrated the AT-PM systems. These results suggest that the lateral PHR not only contains functionally segregated Subregions, but also contains a functionally integrated subregion.
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connectivity based parcellation of the human posteromedial cortex
Cerebral Cortex, 2014Co-Authors: Yaqin Zhang, Jiaojian Wang, Tianzi Jiang, Chunshui Yu, Yuanchao Zhang, Yu ZhangAbstract:Regional structural and functional variations in the posteromedial cortex (PMC) have been found in both animals and humans, strongly suggesting the presence of subdivisions. However, there is no consensus on how to subdivide the human PMC. Here, we investigated the anatomical parcellation scheme and the connectivity pattern of each subdivision of the human PMC using diffusion tensor imaging data from 2 independent groups of volunteers. The parcellation analyses of the 2 datasets consistently demonstrated that the human PMC can be parcellated into 5 Subregions. The dorsal portion of the PMC was subdivided into anterior, central, and posterior Subregions, which participate in sensorimotor, associative, and visual functions. The ventral PMC contained a transitional region in the dorsal portion and a ventral subregion that is the core of the default mode network. The parcellation results for the human PMC and its anatomical connectivity patterns were further supported by evidence from the macaque PMC. Furthermore, functional connectivity analysis revealed that each subregion exhibited a specific pattern similar to that of its anatomical connectivity. The proposed parcellation scheme may facilitate the study of the human PMC at a subtler level and improve our understanding of its functions.
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Subregions of the human superior frontal gyrus and their connections
NeuroImage, 2013Co-Authors: Wei Li, Jiaojian Wang, Tianzi Jiang, Chunshui YuAbstract:The superior frontal gyrus (SFG) is located at the superior part of the prefrontal cortex and is involved in a variety of functions, suggesting the existence of functional Subregions. However, parcellation schemes of the human SFG and the connection patterns of each subregion remain unclear. We firstly parcellated the human SFG into the anteromedial (SFGam), dorsolateral (SFGdl), and posterior (SFGp) Subregions based on diffusion tensor tractography. The SFGam was anatomically connected with the anterior and mid-cingulate cortices, which are critical nodes of the cognitive control network and the default mode network (DMN). The SFGdl was connected with the middle and inferior frontal gyri, which are involved in the cognitive execution network. The SFGp was connected with the precentral gyrus, caudate, thalamus, and frontal operculum, which are nodes of the motor control network. Resting-state functional connectivity analysis further revealed that the SFGam was mainly correlated with the cognitive control network and the DMN; the SFGdl was correlated with the cognitive execution network and the DMN; and the SFGp was correlated with the sensorimotor-related brain regions. The SFGam and SFGdl were further parcellated into three and two subclusters that are well corresponding to Brodmann areas. These findings suggest that the human SFG consists of multiple dissociable Subregions that have distinct connection patterns and that these Subregions are involved in different functional networks and serve different functions. These results may improve our understanding on the functional complexity of the SFG and provide us an approach to investigate the SFG at the subregional level. (C) 2013 Elsevier Inc. All rights reserved.
Jiaojian Wang - One of the best experts on this subject based on the ideXlab platform.
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delineating functional segregations of the human middle temporal gyrus with resting state functional connectivity and coactivation patterns
Human Brain Mapping, 2019Co-Authors: Jinping Xu, Jiaojian Wang, Tian Li, Ziyun Xu, Xianjun Fu, Qingmao HuAbstract:: Although the middle temporal gyrus (MTG) has been parcellated into Subregions with distinguished anatomical connectivity patterns, whether the structural topography of MTG can inform functional segregations of this area remains largely unknown. Accumulating evidence suggests that the brain's underlying organization and function can be directly and effectively delineated with resting-state functional connectivity (RSFC) by identifying putative functional boundaries between cortical areas. Here, RSFC profiles were used to explore functional segregations of the MTG and defined four Subregions from anterior to posterior in two independent datasets, which showed a similar pattern with MTG parcellation scheme obtained using anatomical connectivity. The functional segregations of MTG were further supported by whole brain RSFC, coactivation, and specific RFSC, and coactivation mapping. Furthermore, the fingerprint with predefined 10 networks and functional characterizations of each subregion using meta-analysis also identified functional distinction between Subregions. The specific connectivity analysis and functional characterization indicated that the bilateral most anterior Subregions mainly participated in social cognition and semantic processing; the ventral middle Subregions were involved in social cognition in left hemisphere and auditory processing in right hemisphere; the bilateral ventro-posterior Subregions participated in action observation, whereas the left subregion was also involved in semantic processing; both of the dorsal Subregions in superior temporal sulcus were involved in language, social cognition, and auditory processing. Taken together, our findings demonstrated MTG sharing similar structural and functional topographies and provide more detailed information about the functional organization of the MTG, which may facilitate future clinical and cognitive research on this area.
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corresponding anatomical and coactivation architecture of the human precuneus showing similar connectivity patterns with macaques
NeuroImage, 2019Co-Authors: Jiaojian Wang, Benjamin Becker, Lijie Wang, Hai Li, Xudong Zhao, Tianzi JiangAbstract:Abstract The precuneus (PCun) is one of the most expanded areas of the association cortex and plays an important role in integrating information from different modalities. However, whether the functional architecture of PCun is shared by humans and macaques is an open question. We used both anatomical connectivity and task-dependent coactivation patterns to parcellate the human PCun and consistently identified three Subregions in the human PCun using two independent datasets. Two Subregions were located in the dorsal PCun and one subregion was located in the ventral PCun. This parcellation scheme for the PCun was supported by identifying the subregion-specific networks and by functional characterization. Then, the absolute and relative gray matter volume of precuneus in human and macaque was calculated and significantly smaller absolute and relative gray matter volume in macaque was identified. Next, three macaque PCun Subregions were defined based on our tractographic atlas. Finally, the whole brain anatomical connectivity patterns and connectivity fingerprints with 17 predefined homologous target brain areas were mapped for each PCun subregion and revealed that the PCun shares similar anatomical connectivity patterns in humans and macaques. The similar anatomical connectivity patterns of PCun were validated by an independent in-house dataset. Our findings demonstrated that anatomical connectivity patterns can reflect the functional architecture of the PCun in humans and that the functional architecture of the PCun is similar in humans and macaques.
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correspondent functional topography of the human left inferior parietal lobule at rest and under task revealed using resting state fmri and coactivation based parcellation
Human Brain Mapping, 2017Co-Authors: Jiaojian Wang, Benjamin Becker, Simon B Eickhoff, Tianzi JiangAbstract:The human left inferior parietal lobule (LIPL) plays a pivotal role in many cognitive functions and is an important node in the default mode network (DMN). Although many previous studies have proposed different parcellation schemes for the LIPL, the detailed functional organization of the LIPL and the exact correspondence between the DMN and LIPL Subregions remain unclear. Mounting evidence indicates that spontaneous fluctuations in the brain are strongly associated with cognitive performance at the behavioral level. However, whether a consistent functional topographic organization of the LIPL during rest and under task can be revealed remains unknown. Here, they used resting-state functional connectivity (RSFC) and task-related coactivation patterns separately to parcellate the LIPL and identified seven Subregions. Four Subregions were located in the supramarginal gyrus (SMG) and three Subregions were located in the angular gyrus (AG). The subregion-specific networks and functional characterization revealed that the four anterior Subregions were found to be primarily involved in sensorimotor processing, movement imagination and inhibitory control, audition perception and speech processing, and social cognition, whereas the three posterior Subregions were mainly involved in episodic memory, semantic processing, and spatial cognition. The results revealed a detailed functional organization of the LIPL and suggested that the LIPL is a functionally heterogeneous area. In addition, the present study demonstrated that the functional architecture of the LIPL during rest corresponds with that found in task processing.
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connectivity based parcellation of the human posteromedial cortex
Cerebral Cortex, 2014Co-Authors: Yaqin Zhang, Jiaojian Wang, Tianzi Jiang, Chunshui Yu, Yuanchao Zhang, Yu ZhangAbstract:Regional structural and functional variations in the posteromedial cortex (PMC) have been found in both animals and humans, strongly suggesting the presence of subdivisions. However, there is no consensus on how to subdivide the human PMC. Here, we investigated the anatomical parcellation scheme and the connectivity pattern of each subdivision of the human PMC using diffusion tensor imaging data from 2 independent groups of volunteers. The parcellation analyses of the 2 datasets consistently demonstrated that the human PMC can be parcellated into 5 Subregions. The dorsal portion of the PMC was subdivided into anterior, central, and posterior Subregions, which participate in sensorimotor, associative, and visual functions. The ventral PMC contained a transitional region in the dorsal portion and a ventral subregion that is the core of the default mode network. The parcellation results for the human PMC and its anatomical connectivity patterns were further supported by evidence from the macaque PMC. Furthermore, functional connectivity analysis revealed that each subregion exhibited a specific pattern similar to that of its anatomical connectivity. The proposed parcellation scheme may facilitate the study of the human PMC at a subtler level and improve our understanding of its functions.
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Subregions of the human superior frontal gyrus and their connections
NeuroImage, 2013Co-Authors: Wei Li, Jiaojian Wang, Tianzi Jiang, Chunshui YuAbstract:The superior frontal gyrus (SFG) is located at the superior part of the prefrontal cortex and is involved in a variety of functions, suggesting the existence of functional Subregions. However, parcellation schemes of the human SFG and the connection patterns of each subregion remain unclear. We firstly parcellated the human SFG into the anteromedial (SFGam), dorsolateral (SFGdl), and posterior (SFGp) Subregions based on diffusion tensor tractography. The SFGam was anatomically connected with the anterior and mid-cingulate cortices, which are critical nodes of the cognitive control network and the default mode network (DMN). The SFGdl was connected with the middle and inferior frontal gyri, which are involved in the cognitive execution network. The SFGp was connected with the precentral gyrus, caudate, thalamus, and frontal operculum, which are nodes of the motor control network. Resting-state functional connectivity analysis further revealed that the SFGam was mainly correlated with the cognitive control network and the DMN; the SFGdl was correlated with the cognitive execution network and the DMN; and the SFGp was correlated with the sensorimotor-related brain regions. The SFGam and SFGdl were further parcellated into three and two subclusters that are well corresponding to Brodmann areas. These findings suggest that the human SFG consists of multiple dissociable Subregions that have distinct connection patterns and that these Subregions are involved in different functional networks and serve different functions. These results may improve our understanding on the functional complexity of the SFG and provide us an approach to investigate the SFG at the subregional level. (C) 2013 Elsevier Inc. All rights reserved.
Vincent A Magnotta - One of the best experts on this subject based on the ideXlab platform.
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an mri based parcellation method for the temporal lobe
NeuroImage, 2000Co-Authors: Benedicto Crespofacorro, Nancy C Andreasen, Daniel S Oleary, Gregory Harris, Baiquan Zhang, Vincent A MagnottaAbstract:The temporal lobe has long been a focus of attention with regard to the underlying pathology of several major psychiatric illnesses. Previous postmortem and imaging studies describing regional volume reductions or perfusion defects in temporal Subregions have shown inconsistent findings, which are in part due to differences in the definition of the Subregions and the methodology of measurement. The development of precise reproducible parcellation systems on magnetic resonance images may help improve uniformity of results in volumetric MR studies and unravel the complex activation patterns seen in functional neuroimaging studies. The present study describes detailed guidelines for the parcellation of the temporal neocortex. It parcels the entire temporal neocortex into 16 Subregions: temporal pole, heschl's gyrus, planum temporale, planum polare, superior temporal gyrus (rostral and caudal), middle temporal gyrus (rostral, intermediate, and caudal), inferior temporal gyrus (rostral, intermediate, and caudal), occipitotemporal gyrus (rostral and caudal), and parahippocampal gyrus (rostral and caudal). Based upon topographic landmarks of individual sulci, every subregion was consecutively traced on a set of serial coronal slices. In spite of the huge variability of sulcal topography, the sulcal landmarks could be identified reliably due to the simultaneous display of three orthogonal (transaxial, coronal, and sagittal) planes, triangulated gray matter isosurface, and a 3-D-rendered image. The reliability study showed that the temporal neocortex could be parceled successfully and reliably; intraclass correlation coefficient for each subregion ranged from 0.62 to 0.99. Ultimately, this method will permit us to detect subtle morphometric impairments or to find abnormal patterns of functional activation in the temporal Subregions that might reflect underlying neuropathological processes in psychiatric illnesses such as schizophrenia.
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human frontal cortex an mri based parcellation method
NeuroImage, 1999Co-Authors: Benedicto Crespofacorro, Nancy C Andreasen, Daniel S Oleary, A K Wiser, James Bailey, Gregory Harris, Vincent A MagnottaAbstract:The frontal lobe is not a single anatomical and functional brain region. Several lines of research have demonstrated that particular Subregions within the frontal lobe are associated with specific motor and cognitive functions in the human being. Our main purpose is to develop a magnetic resonance image (MRI)-based parcellation method of the frontal lobe that permits us to explore plausible abnormalities in functionally relevant frontal Subregions in brain illnesses. We describe a procedure using MRI for subdividing the entire frontal cortex into 11 Subregions: supplementary motor area (SMA), rostral anterior cingulate gyrus (r-ACiG), caudal anterior cingulate gyrus (c-ACiG), superior cingulate gyrus (SCiG), medial frontal cortex (MFC), straight gyrus (SG), orbitofrontal cortex (OFC), precentral gyrus (PCG), superior frontal gyrus (SFG), inferior frontal gyrus (IFG), and middle frontal gyrus (MFG). Our method posits to conserve the topographic uniqueness of individual brains and is based on our ability to visualize both the three-dimensional (3D) rendered brain and the three orthogonal planes simultaneously. The reliability study for gray matter volume and surface area of each subregion was performed on a set of 10 MR scans by two raters. The intraclass R coefficients for gray matter volume of each subregion ranged between 0.86 and 0.99. We describe here a reproducible and reliable topography-based parcellation method of the frontal lobe that will allow us to use new approaches to understand the role of particular frontal cortical Subregions in schizophrenia and other brain illnesses.
Guanghua Gong - One of the best experts on this subject based on the ideXlab platform.
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intratumor partitioning and texture analysis of dynamic contrast enhanced dce mri identifies relevant tumor Subregions to predict pathological response of breast cancer to neoadjuvant chemotherapy
Journal of Magnetic Resonance Imaging, 2016Co-Authors: Guanghua Gong, Yi CuiAbstract:Purpose To predict pathological response of breast cancer to neoadjuvant chemotherapy (NAC) based on quantitative, multiregion analysis of dynamic contrast enhancement magnetic resonance imaging (DCE-MRI). Materials and Methods In this Institutional Review Board-approved study, 35 patients diagnosed with stage II/III breast cancer were retrospectively investigated using 3T DCE-MR images acquired before and after the first cycle of NAC. First, principal component analysis (PCA) was used to reduce the dimensionality of the DCE-MRI data with high temporal resolution. We then partitioned the whole tumor into multiple Subregions using k-means clustering based on the PCA-defined eigenmaps. Within each tumor subregion, we extracted four quantitative Haralick texture features based on the gray-level co-occurrence matrix (GLCM). The change in texture features in each tumor subregion between pre- and during-NAC was used to predict pathological complete response after NAC. Results Three tumor Subregions were identified through clustering, each with distinct enhancement characteristics. In univariate analysis, all imaging predictors except one extracted from the tumor subregion associated with fast washout were statistically significant (P < 0.05) after correcting for multiple testing, with area under the receiver operating characteristic (ROC) curve (AUC) or AUCs between 0.75 and 0.80. In multivariate analysis, the proposed imaging predictors achieved an AUC of 0.79 (P = 0.002) in leave-one-out cross-validation. This improved upon conventional imaging predictors such as tumor volume (AUC = 0.53) and texture features based on whole-tumor analysis (AUC = 0.65). Conclusion The heterogeneity of the tumor subregion associated with fast washout on DCE-MRI predicted pathological response to NAC in breast cancer. J. Magn. Reson. Imaging 2016;44:1107–1115.
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tu d 207b 05 intra tumor partitioning and texture analysis of dce mri identifies relevant tumor Subregions to predict early pathological response of breast cancer to neoadjuvant chemotherapy
Medical Physics, 2016Co-Authors: Guanghua Gong, Yi CuiAbstract:Purpose: To predict early pathological response of breast cancer to neoadjuvant chemotherapy (NAC) based on quantitative, multi-region analysis of dynamic contrast enhancement magnetic resonance imaging (DCE-MRI). Methods: In this institution review board-approved study, 35 patients diagnosed with stage II/III breast cancer were retrospectively investigated using DCE-MR images acquired before and after the first cycle of NAC. First, principal component analysis (PCA) was used to reduce the dimensionality of the DCE-MRI data with a high-temporal resolution. We then partitioned the whole tumor into multiple Subregions using k-means clustering based on the PCA-defined eigenmaps. Within each tumor subregion, we extracted four quantitative Haralick texture features based on the gray-level co-occurrence matrix (GLCM). The change in texture features in each tumor subregion between pre- and during-NAC was used to predict pathological complete response after NAC. Results: Three tumor Subregions were identified through clustering, each with distinct enhancement characteristics. In univariate analysis, all imaging predictors except one extracted from the tumor subregion associated with fast wash-out were statistically significant (p< 0.05) after correcting for multiple testing, with area under the ROC curve or AUCs between 0.75 and 0.80. In multivariate analysis, the proposed imaging predictors achieved an AUC of 0.79 (p = 0.002) in leave-one-out cross validation. This improved upon conventional imaging predictors such as tumor volume (AUC=0.53) and texture features based on whole-tumor analysis (AUC=0.65). Conclusion: The heterogeneity of the tumor subregion associated with fast wash-out on DCE-MRI predicted early pathological response to neoadjuvant chemotherapy in breast cancer.
Yi Cui - One of the best experts on this subject based on the ideXlab platform.
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intratumor partitioning and texture analysis of dynamic contrast enhanced dce mri identifies relevant tumor Subregions to predict pathological response of breast cancer to neoadjuvant chemotherapy
Journal of Magnetic Resonance Imaging, 2016Co-Authors: Guanghua Gong, Yi CuiAbstract:Purpose To predict pathological response of breast cancer to neoadjuvant chemotherapy (NAC) based on quantitative, multiregion analysis of dynamic contrast enhancement magnetic resonance imaging (DCE-MRI). Materials and Methods In this Institutional Review Board-approved study, 35 patients diagnosed with stage II/III breast cancer were retrospectively investigated using 3T DCE-MR images acquired before and after the first cycle of NAC. First, principal component analysis (PCA) was used to reduce the dimensionality of the DCE-MRI data with high temporal resolution. We then partitioned the whole tumor into multiple Subregions using k-means clustering based on the PCA-defined eigenmaps. Within each tumor subregion, we extracted four quantitative Haralick texture features based on the gray-level co-occurrence matrix (GLCM). The change in texture features in each tumor subregion between pre- and during-NAC was used to predict pathological complete response after NAC. Results Three tumor Subregions were identified through clustering, each with distinct enhancement characteristics. In univariate analysis, all imaging predictors except one extracted from the tumor subregion associated with fast washout were statistically significant (P < 0.05) after correcting for multiple testing, with area under the receiver operating characteristic (ROC) curve (AUC) or AUCs between 0.75 and 0.80. In multivariate analysis, the proposed imaging predictors achieved an AUC of 0.79 (P = 0.002) in leave-one-out cross-validation. This improved upon conventional imaging predictors such as tumor volume (AUC = 0.53) and texture features based on whole-tumor analysis (AUC = 0.65). Conclusion The heterogeneity of the tumor subregion associated with fast washout on DCE-MRI predicted pathological response to NAC in breast cancer. J. Magn. Reson. Imaging 2016;44:1107–1115.
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tu d 207b 05 intra tumor partitioning and texture analysis of dce mri identifies relevant tumor Subregions to predict early pathological response of breast cancer to neoadjuvant chemotherapy
Medical Physics, 2016Co-Authors: Guanghua Gong, Yi CuiAbstract:Purpose: To predict early pathological response of breast cancer to neoadjuvant chemotherapy (NAC) based on quantitative, multi-region analysis of dynamic contrast enhancement magnetic resonance imaging (DCE-MRI). Methods: In this institution review board-approved study, 35 patients diagnosed with stage II/III breast cancer were retrospectively investigated using DCE-MR images acquired before and after the first cycle of NAC. First, principal component analysis (PCA) was used to reduce the dimensionality of the DCE-MRI data with a high-temporal resolution. We then partitioned the whole tumor into multiple Subregions using k-means clustering based on the PCA-defined eigenmaps. Within each tumor subregion, we extracted four quantitative Haralick texture features based on the gray-level co-occurrence matrix (GLCM). The change in texture features in each tumor subregion between pre- and during-NAC was used to predict pathological complete response after NAC. Results: Three tumor Subregions were identified through clustering, each with distinct enhancement characteristics. In univariate analysis, all imaging predictors except one extracted from the tumor subregion associated with fast wash-out were statistically significant (p< 0.05) after correcting for multiple testing, with area under the ROC curve or AUCs between 0.75 and 0.80. In multivariate analysis, the proposed imaging predictors achieved an AUC of 0.79 (p = 0.002) in leave-one-out cross validation. This improved upon conventional imaging predictors such as tumor volume (AUC=0.53) and texture features based on whole-tumor analysis (AUC=0.65). Conclusion: The heterogeneity of the tumor subregion associated with fast wash-out on DCE-MRI predicted early pathological response to neoadjuvant chemotherapy in breast cancer.
