Magnetic resonance-based imaging biopsy with signatures including topological Betti number features for prediction of primary brain metastatic sites

Physical and Engineering Sciences in Medicine - Tập 46 - Trang 1411-1426 - 2023
Mai Egashira1, Hidetaka Arimura2, Kazuma Kobayashi3, Kazutoshi Moriyama1, Takumi Kodama1, Tomoki Tokuda4, Kenta Ninomiya5, Hiroyuki Okamoto6, Hiroshi Igaki7
1Division of Medical Quantum Science, Department of Health Science, Graduate School of Medical Science, Kyushu University, Fukuoka, Japan
2Division of Medical Quantum Science, Department of Health Sciences, Faculty of Medical Sciences, Kyushu University, Fukuoka, Japan
3Department of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, Japan
4Joint Graduate School of Mathematics for Innovation, Kyushu University, Fukuoka, Japan
5Sanford Burnham Prebys Medical Discovery Institute, San Diego, USA
6Radiation Safety and Quality Assurance Division, National Cancer Center Hospital, Tokyo, Japan
7Department of Radiation Oncology, National Cancer Center Hospital, Tokyo, Japan

Tóm tắt

This study incorporated topology Betti number (BN) features into the prediction of primary sites of brain metastases and the construction of magnetic resonance-based imaging biopsy (MRB) models. The significant features of the MRB model were selected from those obtained from gray-scale and three-dimensional wavelet-filtered images, BN and inverted BN (iBN) maps, and clinical variables (age and gender). The primary sites were predicted as either lung cancer or other cancers using MRB models, which were built using seven machine learning methods with significant features chosen by three feature selection methods followed by a combination strategy. Our study dealt with a dataset with relatively smaller brain metastases, which included effective diameters greater than 2 mm, with metastases ranging from 2 to 9 mm accounting for 17% of the dataset. The MRB models were trained by T1-weighted contrast-enhanced images of 494 metastases chosen from 247 patients and applied to 115 metastases from 62 test patients. The most feasible model attained an area under the receiver operating characteristic curve (AUC) of 0.763 for the test patients when using a signature including features of BN and iBN maps, gray-scale and wavelet-filtered images, and clinical variables. The AUCs of the model were 0.744 for non-small cell lung cancer and 0.861 for small cell lung cancer. The results suggest that the BN signature boosted the performance of MRB for the identification of primary sites of brain metastases including small tumors.

Tài liệu tham khảo

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