Predictive Performance of Radiomic Features Extracted from Breast MR Imaging in Postoperative Upgrading of Ductal Carcinoma in Situ to Invasive Carcinoma

Hiroko Satake; Fumie Kinoshita; Satoko Ishigaki; Keita Kato; Yusuke Jo; Satoko Shimada; Norikazu Masuda; Shinji Naganawa

doi:10.2463/mrms.mp.2023-0168

Predictive Performance of Radiomic Features Extracted from Breast MR Imaging in Postoperative Upgrading of Ductal Carcinoma in Situ to Invasive Carcinoma

Magn Reson Med Sci. 2024 May 15. doi: 10.2463/mrms.mp.2023-0168. Online ahead of print.

Authors

Hiroko Satake¹, Fumie Kinoshita², Satoko Ishigaki¹, Keita Kato¹, Yusuke Jo¹, Satoko Shimada³, Norikazu Masuda⁴, Shinji Naganawa¹

Affiliations

¹ Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan.
² Division of Data Science, Data Coordinating Center, Department of Advanced Medicine, Nagoya University Hospital, Nagoya, Aichi, Japan.
³ Department of Pathology and Clinical Laboratories, Nagoya University Hospital, Nagoya, Aichi, Japan.
⁴ Department of Breast and Endocrine Surgery, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan.

PMID: 38749758
DOI: 10.2463/mrms.mp.2023-0168

Abstract

Purpose: To investigate the predictive performance of radiomic features extracted from breast MRI for upgrade of ductal carcinoma in situ (DCIS) to invasive carcinoma.

Methods: This retrospective study included 71 women with DCIS lesions diagnosed preoperatively by biopsy. All women underwent breast dynamic contrast-enhanced (DCE) MRI of the breast, which included pre-contrast and five post-contrast phases continuously with a time resolution of 60s. Lesion segmentation was performed manually, and 144 radiomic features of the lesions were extracted from T2-weighted images (T2WI), pre-contrast T1-weighted images (T1WI), and post-contrast 1st, 2nd, and 5th phase subtraction images on DCE-MRI. Qualitative features of mammography, ultrasound, and MRI were also assessed. Clinicopathological features were evaluated using medical records. The least absolute shrinkage and selection operator (LASSO) algorithm was applied for features selection and model building. The predictive performance of postoperative upgrade to invasive carcinoma was assessed using the area under the receiver operating characteristic curve.

Results: Surgical specimens revealed 13 lesions (18.3%) that were upgraded to invasive carcinoma. Among clinicopathological and qualitative features, age was the only significant predictive variable. No significant radiomic features were observed on T2WI and post-contrast 2nd phase subtraction images on DCE-MRI. The area under the curves (AUCs) of the LASSO radiomics model integrated with age were 0.915 for pre-contrast T1WI, 0.862 for post-contrast 1st phase subtraction images, and 0.833 for post-contrast 5th phase subtraction images. The AUCs of the 200-times bootstrap internal validations were 0.885, 0.832, and 0.775.

Conclusion: A radiomics approach using breast MRI may be a promising method for predicting the postoperative upgrade of DCIS. The present study showed that the radiomic features extracted from pre-contrast T1WI and post-contrast subtraction images in the very early phase of DCE-MRI were more predictable.

Keywords: breast; ductal carcinoma in situ; magnetic resonance imaging; radiomics; upgrade.