Homological radiomics analysis for prognostic prediction in lung cancer patients

Kenta Ninomiya, Hidetaka Arimura

研究成果: ジャーナルへの寄稿学術誌査読

7 被引用数 (Scopus)


Purpose: This study explored a novel homological analysis method for prognostic prediction in lung cancer patients. Materials and methods: The potential of homology-based radiomic features (HFs) was investigated by comparing HFs to conventional wavelet-based radiomic features (WFs) and combined radiomic features consisting of HFs and WFs (HWFs), using training (n = 135) and validation (n = 70) datasets, and Kaplan–Meier analysis. A total of 13,824 HFs were derived through homology-based texture analysis using Betti numbers, which represent the topologically invariant morphological characteristics of lung cancer. The prognostic potential of HFs was evaluated using statistically significant differences (p-values, log-rank test) to compare the survival curves of high- and low-risk patients. Those patients were stratified into high- and low-risk groups using the medians of the radiomic scores of signatures constructed with an elastic-net-regularized Cox proportional hazard model. Furthermore, deep learning (DL) based on AlexNet was utilized to compare HFs by stratifying patients into the two groups using a network that was pre-trained with over one million natural images from an ImageNet database. Results: For the training dataset, the p-values between the two survival curves were 6.7 × 10−6 (HF), 5.9 × 10−3 (WF), 7.4 × 10−6 (HWF), and 1.1 × 10−3 (DL). The p-values for the validation dataset were 3.4 × 10−5 (HF), 6.7 × 10−1 (WF), 1.7 × 10−7 (HWF), and 1.2 × 10−1 (DL). Conclusion: This study demonstrates the excellent potential of HFs for prognostic prediction in lung cancer patients.

ジャーナルPhysica Medica
出版ステータス出版済み - 1月 2020

All Science Journal Classification (ASJC) codes

  • 生物理学
  • 放射線学、核医学およびイメージング
  • 物理学および天文学(全般)


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