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NCT06370234
The Prediction Model of NAC Response for Breast Cancer Based on The Parametric Dynamics Features.
NA trial testing Whole body 18F-FDG Positron Emission Tomography in Breast Cancer in 60 participants. Completed in 3 March 2020.
30 June 2019
Quick facts
| Lead sponsor | National Taiwan University Hospital |
|---|---|
| Phase | NA |
| Status | Completed |
| Study type | INTERVENTIONAL |
| Allocation | na |
| Design | single group |
| Masking | none |
| Primary purpose | diagnostic |
| Enrollment | 60 |
| Start date | 21 April 2015 |
| Primary completion | 30 June 2019 |
| Estimated completion | 3 March 2020 |
Drugs / interventions tested
- Whole body 18F-FDG Positron Emission Tomography
Conditions studied
- Breast Cancer — all drugs for Breast Cancer →
- Chemotherapy Effect — all drugs for Chemotherapy Effect →
- Diffusion Weighted MRI — all drugs for Diffusion Weighted MRI →
- PET Imaging — all drugs for PET Imaging →
Sponsor
National Taiwan University Hospital
Who can join
20 and older, female only, with Breast Cancer or Chemotherapy Effect. Patients with the condition only — healthy volunteers not accepted.
Sponsor's own description
The main purpose of this study is to develop a computer-aided prediction model for NAC treatment response. Based on the heterogeneity of internal parametric tumor composition commonly observed, this study will utilize the histologic characteristics and treatment response to investigate the image features as input data for predicting treatment response using Deep Learning technology. Using this technique, preoperative treatment evaluation may be facilitated by tumor heterogeneity analysis from developed dynamic radiomics, and the possibility of personal medicine can be realized not far ahead. In the first two years of this study using images from DCE-MRI, PET/CT and QDS-IR, we plan to develop the image processing algorithms, including segmenting breast and tumor region, extracting image feature which reflects angiogenic properties and permeability of tumor, which are highly correlated with NAC treatment response. During the third year of the project, the morphology and texture features from first two years can be combined for PET/MRI and prediction model can be achieved in accordance with the features extracted from dynamic features extraction using longitudinal images of PET/MRI.
Publications & conference data
No peer-reviewed publications indexed yet for this trial. Completed trials usually publish results within 12-18 months.
Verify or expand the search:
- PubMed search for NCT06370234
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Verify against primary sources
- ClinicalTrials.gov — authoritative US registry record
- WHO ICTRP — international registry index
- EU Clinical Trials Register
- Sponsor press releases (Google)
- Trial protocol + status: ClinicalTrials.gov NCT06370234 (US National Library of Medicine, public domain)
- Drug + disease cross-links: matched in real time against Drug Landscape's normalised drug + company + condition tables
- Sponsor: as reported to ClinicalTrials.gov by National Taiwan University Hospital
- Last refreshed: 17 April 2024
Drug Landscape aggregates and links these public records for informational use only. Always verify against the primary source before clinical or regulatory decisions. Canonical URL: https://druglandscape.com/trial/NCT06370234.
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