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

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Abstract

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.

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Acknowledgements

The authors would like to express sincere thanks to all members of the Arimura Laboratory (http://web.shs.kyushu-u.ac.jp/~arimura/) for their valuable comments and helpful discussion.

Funding

This study was partially supported by a grant from JSPS KAKENHI (grant number JP20K08084).

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Authors and Affiliations

  1. Division of Medical Quantum Science, Department of Health Science, Graduate School of Medical Science, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan

    Mai Egashira, Kazutoshi Moriyama & Takumi Kodama

  2. Division of Medical Quantum Science, Department of Health Sciences, Faculty of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan

    Hidetaka Arimura

  3. Department of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan

    Kazuma Kobayashi

  4. Joint Graduate School of Mathematics for Innovation, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka, 819-0395, Japan

    Tomoki Tokuda

  5. Sanford Burnham Prebys Medical Discovery Institute, San Diego, CA, USA

    Kenta Ninomiya

  6. Radiation Safety and Quality Assurance Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan

    Hiroyuki Okamoto

  7. Department of Radiation Oncology, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan

    Hiroshi Igaki

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  1. Mai Egashira
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Contributions

Conceptualization: HA and ME. Data curation: KK, HO, HI, KM and ME. Formal analysis: ME, HA, KM, TK and TT. Funding acquisition: HA. Investigation: ME, HA, KM and TK. Methodology: ME, HA, KM, TK and TT. Project administration: HA and KK. Resources: KK, HO and HI. Software: ME, HA, KM and TK. Supervision: HA and KK. Validation: ME and HA. Visualization: ME and HA. Writing—original draft: ME and HA.

Corresponding authors

Correspondence to Hidetaka Arimura or Kazuma Kobayashi.

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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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The protocol for this retrospective study was approved by the institutional review boards of the National Cancer Center Research Institute and Kyushu University Hospital.

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Egashira, M., Arimura, H., Kobayashi, K. et al. Magnetic resonance-based imaging biopsy with signatures including topological Betti number features for prediction of primary brain metastatic sites. Phys Eng Sci Med 46, 1411–1426 (2023). https://doi.org/10.1007/s13246-023-01308-6

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