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Sökning: onr:"swepub:oai:lup.lub.lu.se:cf81fe78-f8c6-4794-9aa8-4d5129a3d3d3" > Prediction of Ki-67...

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

Ohashi, Akane (författare)
Lund University,Lunds universitet,Diagnostisk radiologi, Malmö,Forskargrupper vid Lunds universitet,LUCC: Lunds universitets cancercentrum,Övriga starka forskningsmiljöer,Radiology Diagnostics, Malmö,Lund University Research Groups,LUCC: Lund University Cancer Centre,Other Strong Research Environments,Kyoto University
Kataoka, Masako (författare)
Kyoto University
Iima, Mami (författare)
Kyoto University
visa fler...
Honda, Maya (författare)
Kansai Electric Power Hospital
Ota, Rie (författare)
Kyoto University
Urushibata, Yuta (författare)
Siemens Healthcare K.K.
Nickel, Marcel Dominik (författare)
Siemens Healthineers
Toi, Masakazu (författare)
Kyoto University
Zackrisson, Sophia (författare)
Lund University,Lunds universitet,Diagnostisk radiologi, Malmö,Forskargrupper vid Lunds universitet,LUCC: Lunds universitets cancercentrum,Övriga starka forskningsmiljöer,LTH profilområde: Avancerade ljuskällor,LTH profilområden,Lunds Tekniska Högskola,Radiology Diagnostics, Malmö,Lund University Research Groups,LUCC: Lund University Cancer Centre,Other Strong Research Environments,LTH Profile Area: Photon Science and Technology,LTH Profile areas,Faculty of Engineering, LTH,Skåne University Hospital
Nakamoto, Yuji (författare)
Kyoto University
Bosmans, Hilde (redaktör/utgivare)
Marshall, Nicholas (redaktör/utgivare)
Van Ongeval, Chantal (redaktör/utgivare)
visa färre...
 (creator_code:org_t)
SPIE, 2022
2022
Engelska.
Ingår i: 16th International Workshop on Breast Imaging, IWBI 2022. - : SPIE. - 0277-786X .- 1996-756X. - 9781510655843 ; 12286
  • Konferensbidrag (refereegranskat)
Abstract Ämnesord
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  • 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.

Ämnesord

MEDICIN OCH HÄLSOVETENSKAP  -- Klinisk medicin -- Radiologi och bildbehandling (hsv//swe)
MEDICAL AND HEALTH SCIENCES  -- Clinical Medicine -- Radiology, Nuclear Medicine and Medical Imaging (hsv//eng)

Nyckelord

ADC
Breast Cancer
Breast MRI
DWI
Image based estimation of prognostic factor
Ki-67
UF DCE-MRI

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