Detection of prostate cancer: Quantitative multiparamotric mr imaging models developed using registered correlative histopathology

Greg Metzger, Chaitanya Kalavagunta, Benjamin D Spilseth, Patrick J Bolan, Xiufeng Li, Diane Hutter, Jung W. Nam, Andrew D Johnson, Jonathan C. Henriksen, Laura Moench, Badrinath R Konety, Christopher A Warlick, Stephen C. Schmechel, Joe Koopmeiners

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27 Citations (Scopus)

Abstract

Purpose: To develop multiparametric magnetic resonance (MR) imaging models to generate a quantitative, user-independent, voxel- wise composite biomarker score (CBS) for detection of pros-Tate cancer by using coregistered correlative histopathologic results, and to compare performance of CBS-based detection with that of single quantitative MR imaging parameters. Materials and Institutional review board approval and informed consent Methods: Were obtained. Patients with a diagnosis of prostate cancer underwent multiparametric MR imaging before surgery for treatment. All MR imaging voxels in the prostate were classified as cancer or noncancer on the basis of coregistered histopathologic data. Predictive models were developed by using more than one quantitative MR imaging parameter to generate CBS maps. Model development and evaluation of quantitative MR imaging parameters and CBS were performed separately for the peripheral zone and the whole gland. Model accuracy was evaluated by using the area tinder the receiver operating characteristic curve (AUC), and confidence intervals were calculated with the bootstrap procedure. The improvement in classification accuracy was evaluated by comparing the AUC for the multiparametric model and the single best-performing quantitative MR imaging parameter at the individual level and in aggregate. Results: Quantitative T2, apparent diffusion coefficient (ADC), volume transfer constant (Ktrans), rellux rate constant (kep), and area under the gadolinium concentration curve at 90 seconds (AUGC90) were significantly different between cancer and non- cancer voxels (P < .001), with ADC showing the best accuracy (peripheral zone AUC, 0.82; whole gland AUC, 0.74). Pour- parameter models demonstrated the best performance in both the peripheral zone (AUC, 0.85; P = .010 vs ADC alone) and whole gland (AUC, 0.77; P = .043 vs ADC alone). Individual- level analysis showed statistically significant improvement in AUC in 82% (23 of 28) and 71% (24 of 34) of patients with peripheral-zone and whole-gland models, respectively, compared with ADC] alone. Model-based CBS maps for cancer detection showed improved visualization of cancer location and extent. Conclusion: Quantitative multiparametric MR imaging models developed by using coregistered correlative histopathologic data yielded a voxel-wise CBS that outperformed single quantitative MR imaging parameters for detection of prostate cancer, especially when the models were assessed at the individual level.

Original languageEnglish (US)
Pages (from-to)805-816
Number of pages12
JournalRadiology
Volume279
Issue number3
DOIs
StatePublished - Jun 1 2016

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Prostatic Neoplasms
Magnetic Resonance Imaging
Area Under Curve
Biomarkers
Neoplasms
Research Ethics Committees
Gadolinium
Informed Consent
ROC Curve
Prostate
Confidence Intervals

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Detection of prostate cancer : Quantitative multiparamotric mr imaging models developed using registered correlative histopathology. / Metzger, Greg; Kalavagunta, Chaitanya; Spilseth, Benjamin D; Bolan, Patrick J; Li, Xiufeng; Hutter, Diane; Nam, Jung W.; Johnson, Andrew D; Henriksen, Jonathan C.; Moench, Laura; Konety, Badrinath R; Warlick, Christopher A; Schmechel, Stephen C.; Koopmeiners, Joe.

In: Radiology, Vol. 279, No. 3, 01.06.2016, p. 805-816.

Research output: Contribution to journalArticle

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title = "Detection of prostate cancer: Quantitative multiparamotric mr imaging models developed using registered correlative histopathology",
abstract = "Purpose: To develop multiparametric magnetic resonance (MR) imaging models to generate a quantitative, user-independent, voxel- wise composite biomarker score (CBS) for detection of pros-Tate cancer by using coregistered correlative histopathologic results, and to compare performance of CBS-based detection with that of single quantitative MR imaging parameters. Materials and Institutional review board approval and informed consent Methods: Were obtained. Patients with a diagnosis of prostate cancer underwent multiparametric MR imaging before surgery for treatment. All MR imaging voxels in the prostate were classified as cancer or noncancer on the basis of coregistered histopathologic data. Predictive models were developed by using more than one quantitative MR imaging parameter to generate CBS maps. Model development and evaluation of quantitative MR imaging parameters and CBS were performed separately for the peripheral zone and the whole gland. Model accuracy was evaluated by using the area tinder the receiver operating characteristic curve (AUC), and confidence intervals were calculated with the bootstrap procedure. The improvement in classification accuracy was evaluated by comparing the AUC for the multiparametric model and the single best-performing quantitative MR imaging parameter at the individual level and in aggregate. Results: Quantitative T2, apparent diffusion coefficient (ADC), volume transfer constant (Ktrans), rellux rate constant (kep), and area under the gadolinium concentration curve at 90 seconds (AUGC90) were significantly different between cancer and non- cancer voxels (P < .001), with ADC showing the best accuracy (peripheral zone AUC, 0.82; whole gland AUC, 0.74). Pour- parameter models demonstrated the best performance in both the peripheral zone (AUC, 0.85; P = .010 vs ADC alone) and whole gland (AUC, 0.77; P = .043 vs ADC alone). Individual- level analysis showed statistically significant improvement in AUC in 82{\%} (23 of 28) and 71{\%} (24 of 34) of patients with peripheral-zone and whole-gland models, respectively, compared with ADC] alone. Model-based CBS maps for cancer detection showed improved visualization of cancer location and extent. Conclusion: Quantitative multiparametric MR imaging models developed by using coregistered correlative histopathologic data yielded a voxel-wise CBS that outperformed single quantitative MR imaging parameters for detection of prostate cancer, especially when the models were assessed at the individual level.",
author = "Greg Metzger and Chaitanya Kalavagunta and Spilseth, {Benjamin D} and Bolan, {Patrick J} and Xiufeng Li and Diane Hutter and Nam, {Jung W.} and Johnson, {Andrew D} and Henriksen, {Jonathan C.} and Laura Moench and Konety, {Badrinath R} and Warlick, {Christopher A} and Schmechel, {Stephen C.} and Joe Koopmeiners",
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T2 - Quantitative multiparamotric mr imaging models developed using registered correlative histopathology

AU - Metzger, Greg

AU - Kalavagunta, Chaitanya

AU - Spilseth, Benjamin D

AU - Bolan, Patrick J

AU - Li, Xiufeng

AU - Hutter, Diane

AU - Nam, Jung W.

AU - Johnson, Andrew D

AU - Henriksen, Jonathan C.

AU - Moench, Laura

AU - Konety, Badrinath R

AU - Warlick, Christopher A

AU - Schmechel, Stephen C.

AU - Koopmeiners, Joe

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N2 - Purpose: To develop multiparametric magnetic resonance (MR) imaging models to generate a quantitative, user-independent, voxel- wise composite biomarker score (CBS) for detection of pros-Tate cancer by using coregistered correlative histopathologic results, and to compare performance of CBS-based detection with that of single quantitative MR imaging parameters. Materials and Institutional review board approval and informed consent Methods: Were obtained. Patients with a diagnosis of prostate cancer underwent multiparametric MR imaging before surgery for treatment. All MR imaging voxels in the prostate were classified as cancer or noncancer on the basis of coregistered histopathologic data. Predictive models were developed by using more than one quantitative MR imaging parameter to generate CBS maps. Model development and evaluation of quantitative MR imaging parameters and CBS were performed separately for the peripheral zone and the whole gland. Model accuracy was evaluated by using the area tinder the receiver operating characteristic curve (AUC), and confidence intervals were calculated with the bootstrap procedure. The improvement in classification accuracy was evaluated by comparing the AUC for the multiparametric model and the single best-performing quantitative MR imaging parameter at the individual level and in aggregate. Results: Quantitative T2, apparent diffusion coefficient (ADC), volume transfer constant (Ktrans), rellux rate constant (kep), and area under the gadolinium concentration curve at 90 seconds (AUGC90) were significantly different between cancer and non- cancer voxels (P < .001), with ADC showing the best accuracy (peripheral zone AUC, 0.82; whole gland AUC, 0.74). Pour- parameter models demonstrated the best performance in both the peripheral zone (AUC, 0.85; P = .010 vs ADC alone) and whole gland (AUC, 0.77; P = .043 vs ADC alone). Individual- level analysis showed statistically significant improvement in AUC in 82% (23 of 28) and 71% (24 of 34) of patients with peripheral-zone and whole-gland models, respectively, compared with ADC] alone. Model-based CBS maps for cancer detection showed improved visualization of cancer location and extent. Conclusion: Quantitative multiparametric MR imaging models developed by using coregistered correlative histopathologic data yielded a voxel-wise CBS that outperformed single quantitative MR imaging parameters for detection of prostate cancer, especially when the models were assessed at the individual level.

AB - Purpose: To develop multiparametric magnetic resonance (MR) imaging models to generate a quantitative, user-independent, voxel- wise composite biomarker score (CBS) for detection of pros-Tate cancer by using coregistered correlative histopathologic results, and to compare performance of CBS-based detection with that of single quantitative MR imaging parameters. Materials and Institutional review board approval and informed consent Methods: Were obtained. Patients with a diagnosis of prostate cancer underwent multiparametric MR imaging before surgery for treatment. All MR imaging voxels in the prostate were classified as cancer or noncancer on the basis of coregistered histopathologic data. Predictive models were developed by using more than one quantitative MR imaging parameter to generate CBS maps. Model development and evaluation of quantitative MR imaging parameters and CBS were performed separately for the peripheral zone and the whole gland. Model accuracy was evaluated by using the area tinder the receiver operating characteristic curve (AUC), and confidence intervals were calculated with the bootstrap procedure. The improvement in classification accuracy was evaluated by comparing the AUC for the multiparametric model and the single best-performing quantitative MR imaging parameter at the individual level and in aggregate. Results: Quantitative T2, apparent diffusion coefficient (ADC), volume transfer constant (Ktrans), rellux rate constant (kep), and area under the gadolinium concentration curve at 90 seconds (AUGC90) were significantly different between cancer and non- cancer voxels (P < .001), with ADC showing the best accuracy (peripheral zone AUC, 0.82; whole gland AUC, 0.74). Pour- parameter models demonstrated the best performance in both the peripheral zone (AUC, 0.85; P = .010 vs ADC alone) and whole gland (AUC, 0.77; P = .043 vs ADC alone). Individual- level analysis showed statistically significant improvement in AUC in 82% (23 of 28) and 71% (24 of 34) of patients with peripheral-zone and whole-gland models, respectively, compared with ADC] alone. Model-based CBS maps for cancer detection showed improved visualization of cancer location and extent. Conclusion: Quantitative multiparametric MR imaging models developed by using coregistered correlative histopathologic data yielded a voxel-wise CBS that outperformed single quantitative MR imaging parameters for detection of prostate cancer, especially when the models were assessed at the individual level.

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