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

Akane Ohashi M.D.; Masako Kataoka M.D.; Mami Iima M.D.; Maya Honda M.D.; Rie Ota M.D.; Yuta Urushibata; Marcel Dominik Nickel; Toi Masakazu M.D.; Sophia Zackrisson M.D.; Yuji Nakamoto M.D.

doi:10.1117/12.2625747

13 July 2022 Prediction of Ki-67 expression of breast cancer with a multi-parametric model using MRI parameters from ultrafast DCE-MRI and DWI

Akane Ohashi M.D., Masako Kataoka M.D., Mami Iima M.D., Maya Honda M.D., Rie Ota M.D., Yuta Urushibata, Marcel Dominik Nickel, Toi Masakazu M.D., Sophia Zackrisson M.D., Yuji Nakamoto M.D.

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Proceedings Volume 12286, 16th International Workshop on Breast Imaging (IWBI2022); 122860B (2022) https://doi.org/10.1117/12.2625747
Event: Sixteenth International Workshop on Breast Imaging, 2022, Leuven, Belgium

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 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.

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Akane Ohashi M.D., Masako Kataoka M.D., Mami Iima M.D., Maya Honda M.D., Rie Ota M.D., Yuta Urushibata, Marcel Dominik Nickel, Toi Masakazu M.D., Sophia Zackrisson M.D., and Yuji Nakamoto M.D. "Prediction of Ki-67 expression of breast cancer with a multi-parametric model using MRI parameters from ultrafast DCE-MRI and DWI", Proc. SPIE 12286, 16th International Workshop on Breast Imaging (IWBI2022), 122860B (13 July 2022); https://doi.org/10.1117/12.2625747

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KEYWORDS

Breast cancer

Magnetic resonance imaging

Diffusion weighted imaging

Tumor growth modeling

Breast

Diagnostics

Tumors

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