Skip to main content

Lund University Publications

LUND UNIVERSITY LIBRARIES

Prediction of Ki-67 expression of breast cancer with a multiparametric model using MRI parameters from ultrafast DCE-MRI and DWI

Ohashi, Akane LU ; Kataoka, Masako ; Iima, Mami ; Honda, Maya ; Ota, Rie ; Urushibata, Yuta ; Nickel, Marcel Dominik ; Toi, Masakazu ; Zackrisson, Sophia LU and Nakamoto, Yuji (2022) 16th International Workshop on Breast Imaging, IWBI 2022 In Proceedings of SPIE - The International Society for Optical Engineering 12286.
Abstract

The purpose of this study is to investigate the prediction of Ki-67 expression of breast cancers using MRI parameters from ultrafast (UF) DCE-MRI, DWI, T2WI, and the lesion size. Breast MRI was performed with a 3T scanner using dedicated breast coils. UF DCE-MRI was obtained using Compressed Sensing-VIBE (prototype sequence). As a kinetic parameter of UF DCE-MRI, maximum slope (MS) was defined as percentage relative enhancement (%/s), and time to enhance (TTE) was defined as the time interval between the aorta and lesion enhancement. The apparent diffusion coefficient (ADC) was derived from DWI. Two radiologists measured each MR parameter, and inter-rater agreement was evaluated. Univariate and multivariate logistic regression analyses... (More)

The purpose of this study is to investigate the prediction of Ki-67 expression of breast cancers using MRI parameters from ultrafast (UF) DCE-MRI, DWI, T2WI, and the lesion size. Breast MRI was performed with a 3T scanner using dedicated breast coils. UF DCE-MRI was obtained using Compressed Sensing-VIBE (prototype sequence). As a kinetic parameter of UF DCE-MRI, maximum slope (MS) was defined as percentage relative enhancement (%/s), and time to enhance (TTE) was defined as the time interval between the aorta and lesion enhancement. The apparent diffusion coefficient (ADC) was derived from DWI. Two radiologists measured each MR parameter, and inter-rater agreement was evaluated. Univariate and multivariate logistic regression analyses were perfomed to predict low Ki-67 (< 14%) and high Ki-67 (≥ 14%) expression using MS, TTE, ADC, T2-signal intensity (SI), and lesion size. The significant parameters (p-values of < 0.05) were selected for the prediction model, and the diagnostic performance of the model was evaluated using ROC curve analysis. A total of 191 invasive carcinomas defined as mass lesions were included (72 low Ki-67/ 119 high Ki-67 lesions). The inter-rater agreements of all parameters were excellent. After univariate and multivariate logistic regression analysis, ADC and lesion size remained significant parameters. Using these significant parameters, the multi-parametric prediction model yielded an AUC of 0.77 (95%CI of 0.70-0.84) (sensitivity 72.3%, specificity 76.4%, and PPV 83.5%, and NPV 62.5%). DWI parameter (ADC) may be more valuable than UF DCE-MRI parameters (MS, TTE) to predict high Ki-67 in mass-shaped invasive breast carcinoma.

(Less)
Please use this url to cite or link to this publication:
author
; ; ; ; ; ; ; ; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
ADC, Breast Cancer, Breast MRI, DWI, Image based estimation of prognostic factor, Ki-67, UF DCE-MRI
host publication
16th International Workshop on Breast Imaging, IWBI 2022
series title
Proceedings of SPIE - The International Society for Optical Engineering
editor
Bosmans, Hilde ; Marshall, Nicholas and Van Ongeval, Chantal
volume
12286
article number
122860B
publisher
SPIE
conference name
16th International Workshop on Breast Imaging, IWBI 2022
conference location
Leuven, Belgium
conference dates
2022-05-22 - 2022-05-25
external identifiers
  • scopus:85136133937
ISSN
1996-756X
0277-786X
ISBN
9781510655843
DOI
10.1117/12.2625747
language
English
LU publication?
yes
id
cf81fe78-f8c6-4794-9aa8-4d5129a3d3d3
date added to LUP
2022-10-11 13:45:23
date last changed
2024-06-13 14:59:51
@inproceedings{cf81fe78-f8c6-4794-9aa8-4d5129a3d3d3,
  abstract     = {{<p>The purpose of this study is to investigate the prediction of Ki-67 expression of breast cancers using MRI parameters from ultrafast (UF) DCE-MRI, DWI, T2WI, and the lesion size. Breast MRI was performed with a 3T scanner using dedicated breast coils. UF DCE-MRI was obtained using Compressed Sensing-VIBE (prototype sequence). As a kinetic parameter of UF DCE-MRI, maximum slope (MS) was defined as percentage relative enhancement (%/s), and time to enhance (TTE) was defined as the time interval between the aorta and lesion enhancement. The apparent diffusion coefficient (ADC) was derived from DWI. Two radiologists measured each MR parameter, and inter-rater agreement was evaluated. Univariate and multivariate logistic regression analyses were perfomed to predict low Ki-67 (&lt; 14%) and high Ki-67 (≥ 14%) expression using MS, TTE, ADC, T2-signal intensity (SI), and lesion size. The significant parameters (p-values of &lt; 0.05) were selected for the prediction model, and the diagnostic performance of the model was evaluated using ROC curve analysis. A total of 191 invasive carcinomas defined as mass lesions were included (72 low Ki-67/ 119 high Ki-67 lesions). The inter-rater agreements of all parameters were excellent. After univariate and multivariate logistic regression analysis, ADC and lesion size remained significant parameters. Using these significant parameters, the multi-parametric prediction model yielded an AUC of 0.77 (95%CI of 0.70-0.84) (sensitivity 72.3%, specificity 76.4%, and PPV 83.5%, and NPV 62.5%). DWI parameter (ADC) may be more valuable than UF DCE-MRI parameters (MS, TTE) to predict high Ki-67 in mass-shaped invasive breast carcinoma. </p>}},
  author       = {{Ohashi, Akane and Kataoka, Masako and Iima, Mami and Honda, Maya and Ota, Rie and Urushibata, Yuta and Nickel, Marcel Dominik and Toi, Masakazu and Zackrisson, Sophia and Nakamoto, Yuji}},
  booktitle    = {{16th International Workshop on Breast Imaging, IWBI 2022}},
  editor       = {{Bosmans, Hilde and Marshall, Nicholas and Van Ongeval, Chantal}},
  isbn         = {{9781510655843}},
  issn         = {{1996-756X}},
  keywords     = {{ADC; Breast Cancer; Breast MRI; DWI; Image based estimation of prognostic factor; Ki-67; UF DCE-MRI}},
  language     = {{eng}},
  publisher    = {{SPIE}},
  series       = {{Proceedings of SPIE - The International Society for Optical Engineering}},
  title        = {{Prediction of Ki-67 expression of breast cancer with a multiparametric model using MRI parameters from ultrafast DCE-MRI and DWI}},
  url          = {{http://dx.doi.org/10.1117/12.2625747}},
  doi          = {{10.1117/12.2625747}},
  volume       = {{12286}},
  year         = {{2022}},
}