Journal article
Diffusion kurtosis MRI as a predictive biomarker of response to neoadjuvant chemotherapy in high grade serous ovarian cancer
- Abstract:
- AbstractThis study assessed the feasibility of using diffusion kurtosis imaging (DKI) as a measure of tissue heterogeneity and proliferation to predict the response of high grade serous ovarian cancer (HGSOC) to neoadjuvant chemotherapy (NACT). Seventeen patients with HGSOC were imaged at 3 T and had biopsy samples taken prior to any treatment. The patients were divided into two groups: responders and non-responders based on Response Evaluation Criteria In Solid Tumours (RECIST) criteria. The following imaging metrics were calculated: apparent diffusion coefficient (ADC), apparent diffusion (Dapp) and apparent kurtosis (Kapp). Tumour cellularity and proliferation were quantified using histology and Ki-67 immunohistochemistry. Mean Kapp before therapy was higher in responders compared to non-responders: 0.69 ± 0.13 versus 0.51 ± 0.11 respectively, P = 0.02. Tumour cellularity correlated positively with Kapp (rho = 0.50, P = 0.04) and negatively with both ADC (rho = −0.72, P = 0.001) and Dapp (rho = −0.80, P < 0.001). Ki-67 expression correlated with Kapp (rho = 0.53, P = 0.03) but not with ADC or Dapp. In conclusion, Kapp was found to be a potential predictive biomarker of NACT response in HGSOC, which suggests that DKI is a promising clinical tool for use oncology and radiology that should be evaluated further in future larger studies.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Preview, Version of record, pdf, 1.5MB, Terms of use)
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- Publisher copy:
- 10.1038/s41598-019-47195-4
Authors
- Publisher:
- Nature Research
- Journal:
- Scientific Reports More from this journal
- Volume:
- 9
- Issue:
- 1
- Pages:
- 10742-10742
- Publication date:
- 2019-07-24
- DOI:
- EISSN:
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2045-2322
- ISSN:
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2045-2322
- Language:
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English
- Keywords:
-
- Pubs id:
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1845072
- Local pid:
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pubs:1845072
- Source identifiers:
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W2958071656
- Deposit date:
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2026-01-08
- ARK identifier:
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- Copyright date:
- 2019
- Licence:
- CC Attribution (CC BY)
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