4.6 Article

Intratumoral analysis of digital breast tomosynthesis for predicting the Ki-67 level in breast cancer: A multi-center radiomics study

Journal

MEDICAL PHYSICS
Volume 49, Issue 1, Pages 219-230

Publisher

WILEY
DOI: 10.1002/mp.15392

Keywords

breast; DBT; deep learning; Ki-67; radiomics

Funding

  1. Climbing Fund of National Cancer Center [NCC201806B011]
  2. Special Foundation for the Central Government Guides the Development of Local Science and Technology of Liaoning Province [2018416029]
  3. Wu Jieping Medical Foundation [320.6750.2020-08-22]
  4. Medical-Engineering Joint Fund for Cancer Hospital of China Medical University and Dalian University of technology [LD202029]
  5. Natural Science Foundation of Liaoning Province of China [2020-MS-166]
  6. Natural Science Foundation of Liaoning Province [2021-MS-205]
  7. Foundation of the Education Department of Liaoning Province in China [QN2019035]

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This study aimed to evaluate the Ki-67 level in digital breast tomosynthesis (DBT) images of breast cancer patients using a subregional radiomics approach. Results showed that radiomics features extracted from the inner subregion had higher accuracy compared to the whole tumor region or marginal subregion.
Purpose To non-invasively evaluate the Ki-67 level in digital breast tomosynthesis (DBT) images of breast cancer (BC) patients based on subregional radiomics. Methods A total of 266 patients who underwent DBT scans were consecutively enrolled at two centers, between September 2017 and September 2021. The whole tumor region was partitioned into various intratumoral subregions, based on individual- and population-level clustering. Handcrafted radiomics and deep learning-based features were extracted from the subregions and from the whole tumor region, and were selected by least absolute shrinkage and selection operator (LASSO) regression, yielding radiomics signatures (RSs). The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were calculated to assess the developed RSs. Results Each breast tumor region was partitioned into an inner subregion (S1) and a marginal subregion (S2). The RSs derived from S1 always generated higher AUCs compared with those from S2 or from the whole tumor region (W), for the external validation cohort (AUCs, S1 vs. W, handcrafted RSs: 0.583 [95% CI, 0.429-0.727] vs. 0.559 [95% CI, 0.405-0.705], p-value: 0.920; deep RSs: 0.670 [95% CI, 0.516-0.802] vs. 0.551 [95% CI, 0.397-0.698], p-value: 0.776). The fusion RSs, combining handcrafted and deep learning-based features derived from S1, yielded the highest AUCs of 0.820 (95% CI, 0.714-0.900) and 0.792 (95% CI, 0.647-0.897) for the internal and external validation cohorts, respectively. Conclusions The subregional radiomics approach can accurately predict the Ki-67 level based on DBT data; thus, it may be used as a potential non-invasive tool for preoperative treatment planning in BC.

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