Quantification of tumor tissue populations by multispectral analysis

Tumor heterogeneity complicates the quantification of a therapeutic response by MRI. To address this issue, a novel approach has been developed that combines MR diffusion imaging with multispectral (MS) analysis to quantify tumor tissue populations. K‐means (KM) clustering of the apparent diffusion...

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Published in	Magnetic resonance in medicine Vol. 51; no. 3; pp. 542 - 551
Main Authors	Carano, Richard A.D., Ross, Adrienne L., Ross, Jed, Williams, Simon P., Koeppen, Hartmut, Schwall, Ralph H., Van Bruggen, Nicholas
Format	Journal Article
Language	English
Published	Hoboken Wiley Subscription Services, Inc., A Wiley Company 01.03.2004
Subjects	Adipose Tissue - pathology Animals Antineoplastic Agents, Phytogenic - therapeutic use Apo2L/TRAIL Apoptosis Regulatory Proteins Camptothecin - analogs & derivatives Camptothecin - therapeutic use Cell Line, Tumor Colonic Neoplasms - drug therapy Colonic Neoplasms - pathology diffusion Disease Models, Animal fas Receptor - therapeutic use Female Humans K-means clustering Ligands Magnetic Resonance Imaging - methods Magnetic Resonance Spectroscopy - methods Membrane Glycoproteins - therapeutic use Mice Mice, Nude multispectral analysis Necrosis Neoplasm Transplantation Remission Induction Skin Neoplasms - drug therapy Skin Neoplasms - pathology Time Factors TNF-Related Apoptosis-Inducing Ligand Topoisomerase I Inhibitors Transplantation, Heterologous tumor Tumor Necrosis Factor-alpha - therapeutic use
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Abstract	Tumor heterogeneity complicates the quantification of a therapeutic response by MRI. To address this issue, a novel approach has been developed that combines MR diffusion imaging with multispectral (MS) analysis to quantify tumor tissue populations. K‐means (KM) clustering of the apparent diffusion coefficient (ADC), T2, and proton density (M0) was employed to estimate the volumes of viable tumor tissue, necrosis, and neighboring subcutaneous adipose tissue in a human colorectal tumor xenograft mouse model. In a second set of experiments, the temporal evolution of the MS tissue classes in response to therapeutic intervention Apo2L/TRAIL and CPT‐11 was observed. The multiple parameters played complementary roles in identifying the various tissues. The ADC was the dominant parameter for identifying regions of necrosis, whereas T2 identified two necrotic subpopulations, and M0 contributed to the differentiation of viable tumor from subcutaneous adipose tissue. MS viable tumor estimates (mean volume = 275 ± 147 mm3) were highly correlated (r = 0.81, P < 0.01) with histological estimates (117 ± 51 mm3). In the treatment study, MS viable tumor volume (at day 10) was 77 ± 67 mm3 for the Apo2L/TRAIL+CPT‐11 group, and was significantly reduced relative to the control group (292 ± 127 mm3, P < 0.01). This method shows promise as a means of detecting an early therapeutic response in vivo. Magn Reson Med 51:542–551, 2004. © 2004 Wiley‐Liss, Inc.
AbstractList	Tumor heterogeneity complicates the quantification of a therapeutic response by MRI. To address this issue, a novel approach has been developed that combines MR diffusion imaging with multispectral (MS) analysis to quantify tumor tissue populations. K‐means (KM) clustering of the apparent diffusion coefficient (ADC), T2, and proton density (M0) was employed to estimate the volumes of viable tumor tissue, necrosis, and neighboring subcutaneous adipose tissue in a human colorectal tumor xenograft mouse model. In a second set of experiments, the temporal evolution of the MS tissue classes in response to therapeutic intervention Apo2L/TRAIL and CPT‐11 was observed. The multiple parameters played complementary roles in identifying the various tissues. The ADC was the dominant parameter for identifying regions of necrosis, whereas T2 identified two necrotic subpopulations, and M0 contributed to the differentiation of viable tumor from subcutaneous adipose tissue. MS viable tumor estimates (mean volume = 275 ± 147 mm3) were highly correlated (r = 0.81, P < 0.01) with histological estimates (117 ± 51 mm3). In the treatment study, MS viable tumor volume (at day 10) was 77 ± 67 mm3 for the Apo2L/TRAIL+CPT‐11 group, and was significantly reduced relative to the control group (292 ± 127 mm3, P < 0.01). This method shows promise as a means of detecting an early therapeutic response in vivo. Magn Reson Med 51:542–551, 2004. © 2004 Wiley‐Liss, Inc. Abstract Tumor heterogeneity complicates the quantification of a therapeutic response by MRI. To address this issue, a novel approach has been developed that combines MR diffusion imaging with multispectral (MS) analysis to quantify tumor tissue populations. K‐means (KM) clustering of the apparent diffusion coefficient (ADC), T 2 , and proton density ( M 0 ) was employed to estimate the volumes of viable tumor tissue, necrosis, and neighboring subcutaneous adipose tissue in a human colorectal tumor xenograft mouse model. In a second set of experiments, the temporal evolution of the MS tissue classes in response to therapeutic intervention Apo2L/TRAIL and CPT‐11 was observed. The multiple parameters played complementary roles in identifying the various tissues. The ADC was the dominant parameter for identifying regions of necrosis, whereas T 2 identified two necrotic subpopulations, and M 0 contributed to the differentiation of viable tumor from subcutaneous adipose tissue. MS viable tumor estimates (mean volume = 275 ± 147 mm 3 ) were highly correlated (r = 0.81, P < 0.01) with histological estimates (117 ± 51 mm 3 ). In the treatment study, MS viable tumor volume (at day 10) was 77 ± 67 mm 3 for the Apo2L/TRAIL+CPT‐11 group, and was significantly reduced relative to the control group (292 ± 127 mm 3 , P < 0.01). This method shows promise as a means of detecting an early therapeutic response in vivo. Magn Reson Med 51:542–551, 2004. © 2004 Wiley‐Liss, Inc. Tumor heterogeneity complicates the quantification of a therapeutic response by MRI. To address this issue, a novel approach has been developed that combines MR diffusion imaging with multispectral (MS) analysis to quantify tumor tissue populations. K-means (KM) clustering of the apparent diffusion coefficient (ADC), T2, and proton density (M0) was employed to estimate the volumes of viable tumor tissue, necrosis, and neighboring subcutaneous adipose tissue in a human colorectal tumor xenograft mouse model. In a second set of experiments, the temporal evolution of the MS tissue classes in response to therapeutic intervention Apo2L/TRAIL and CPT-11 was observed. The multiple parameters played complementary roles in identifying the various tissues. The ADC was the dominant parameter for identifying regions of necrosis, whereas T2 identified two necrotic subpopulations, and M0 contributed to the differentiation of viable tumor from subcutaneous adipose tissue. MS viable tumor estimates (mean volume = 275 +/- 147 mm(3)) were highly correlated (r = 0.81, P < 0.01) with histological estimates (117 +/- 51 mm(3)). In the treatment study, MS viable tumor volume (at day 10) was 77 +/- 67 mm(3) for the Apo2L/TRAIL+CPT-11 group, and was significantly reduced relative to the control group (292 +/- 127 mm(3), P < 0.01). This method shows promise as a means of detecting an early therapeutic response in vivo. Tumor heterogeneity complicates the quantification of a therapeutic response by MRI. To address this issue, a novel approach has been developed that combines MR diffusion imaging with multispectral (MS) analysis to quantify tumor tissue populations. K-means (KM) clustering of the apparent diffusion coefficient (ADC), T2, and proton density (M0) was employed to estimate the volumes of viable tumor tissue, necrosis, and neighboring subcutaneous adipose tissue in a human colorectal tumor xenograft mouse model. In a second set of experiments, the temporal evolution of the MS tissue classes in response to therapeutic intervention Apo2L/TRAIL and CPT-11 was observed. The multiple parameters played complementary roles in identifying the various tissues. The ADC was the dominant parameter for identifying regions of necrosis, whereas T2 identified two necrotic subpopulations, and M0 contributed to the differentiation of viable tumor from subcutaneous adipose tissue. MS viable tumor estimates (mean volume = 275 +/- 147 mm(3)) were highly correlated (r = 0.81, P < 0.01) with histological estimates (117 +/- 51 mm(3)). In the treatment study, MS viable tumor volume (at day 10) was 77 +/- 67 mm(3) for the Apo2L/TRAIL+CPT-11 group, and was significantly reduced relative to the control group (292 +/- 127 mm(3), P < 0.01). This method shows promise as a means of detecting an early therapeutic response in vivo. Tumor heterogeneity complicates the quantification of a therapeutic response by MRI. To address this issue, a novel approach has been developed that combines MR diffusion imaging with multispectral (MS) analysis to quantify tumor tissue populations. K-means (KM) clustering of the apparent diffusion coefficient (ADC), T sub(2), and proton density (M sub(0)) was employed to estimate the volumes of viable tumor tissue, necrosis, and neighboring subcutaneous adipose tissue in a human colorectal tumor xenograft mouse model. In a second set of experiments, the temporal evolution of the MS tissue classes in response to therapeutic intervention Apo2L/TRAIL and CPT-11 was observed. The multiple parameters played complementary roles in identifying the various tissues. The ADC was the dominant parameter for identifying regions of necrosis, whereas T sub(2) identified two necrotic subpopulations, and M sub(0) contributed to the differentiation of viable tumor from subcutaneous adipose tissue. MS viable tumor estimates (mean volume = 275 plus or minus 147 mm super(3)) were highly correlated (r = 0.81, P < 0.01) with histological estimates (117 plus or minus 51 mm super(3)). In the treatment study, MS viable tumor volume (at day 10) was 77 plus or minus 67 mm super(3) for the Apo2L/TRAIL+CPT-11 group, and was significantly reduced relative to the control group (292 plus or minus 127 mm super(3), P < 0.01). This method shows promise as a means of detecting an early therapeutic response in vivo.
Author	Koeppen, Hartmut Carano, Richard A.D. Ross, Adrienne L. Van Bruggen, Nicholas Ross, Jed Schwall, Ralph H. Williams, Simon P.
Author_xml	– sequence: 1 givenname: Richard A.D. surname: Carano fullname: Carano, Richard A.D. email: rcarano@gene.com organization: Department of Physiology, Genentech, Inc., South San Francisco, California – sequence: 2 givenname: Adrienne L. surname: Ross fullname: Ross, Adrienne L. organization: Department of Physiology, Genentech, Inc., South San Francisco, California – sequence: 3 givenname: Jed surname: Ross fullname: Ross, Jed organization: Department of Physiology, Genentech, Inc., South San Francisco, California – sequence: 4 givenname: Simon P. surname: Williams fullname: Williams, Simon P. organization: Department of Physiology, Genentech, Inc., South San Francisco, California – sequence: 5 givenname: Hartmut surname: Koeppen fullname: Koeppen, Hartmut organization: Department of Pathology, Genentech, Inc., South San Francisco, California – sequence: 6 givenname: Ralph H. surname: Schwall fullname: Schwall, Ralph H. organization: Department of Molecular Oncology, Genentech, Inc., South San Francisco, California – sequence: 7 givenname: Nicholas surname: Van Bruggen fullname: Van Bruggen, Nicholas organization: Department of Physiology, Genentech, Inc., South San Francisco, California
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Snippet	Tumor heterogeneity complicates the quantification of a therapeutic response by MRI. To address this issue, a novel approach has been developed that combines... Abstract Tumor heterogeneity complicates the quantification of a therapeutic response by MRI. To address this issue, a novel approach has been developed that...
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SubjectTerms	Adipose Tissue - pathology Animals Antineoplastic Agents, Phytogenic - therapeutic use Apo2L/TRAIL Apoptosis Regulatory Proteins Camptothecin - analogs & derivatives Camptothecin - therapeutic use Cell Line, Tumor Colonic Neoplasms - drug therapy Colonic Neoplasms - pathology diffusion Disease Models, Animal fas Receptor - therapeutic use Female Humans K-means clustering Ligands Magnetic Resonance Imaging - methods Magnetic Resonance Spectroscopy - methods Membrane Glycoproteins - therapeutic use Mice Mice, Nude multispectral analysis Necrosis Neoplasm Transplantation Remission Induction Skin Neoplasms - drug therapy Skin Neoplasms - pathology Time Factors TNF-Related Apoptosis-Inducing Ligand Topoisomerase I Inhibitors Transplantation, Heterologous tumor Tumor Necrosis Factor-alpha - therapeutic use
Title	Quantification of tumor tissue populations by multispectral analysis
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