Clinical trial · Observational
PET/MR Radiomics for Breast Cancer Diagnosis
Use of PET/MR Radiomics to Evaluate the Clinical Phenotypes, Response Status of Neoadjuvant Chemotherapy and Long-term Prognosis of Breast Cancer: a Preliminary Study
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
Breast cancer is the most common malignancy in women in our country (2013 cancer registry report, Health Promotion Administration). MRI is a more accurate imaging modality for breast lesion diagnosis, monitoring of treatment response, and local staging than compared with mammography and ultrasound. ¹⁸ F-FDG PET was reported to be used for breast cancer diagnosis, staging, and prediction of treatment response as well. We usually interpret the aforementioned imaging modalities by qualitative methods for decision-making. Radiomics is a process involving the conversion of images to quantitative data for subsequent data mining to improve decisional making for patient care, to adjust the patient management, that is so-called precision medicine. Our study is to use semantic and agnostic features of radiomics by hybrid PET/MR for 1. The pre-operative breast cancer patients (without neoadjuvant chemotherapy before operation). 2. The patients will receive neoadjuvant chemotherapy (NAC). The study intends to investigate the association of PET/MR radiomics data with the probability of metastasis or risk of recurrences and survival. We will also investigate if the BD and BPE (measured on MRI) are associated with molecular subtypes, histologic grade and clinical outcome, risk of metastases, and long-term survival of breast cancer patients for the study participants.
Conditions
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Breast Cancer | Malignant Breast Neoplasm | CURATED_EXACT | 0.92 |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| PET/MR | Radiation | — | UNRESOLVED |
Design
Arms and outcomes
Arms (2)
- label
- Patient recently diagnosed breast cancer who will undergo surgery
- description
- surgery treatment only
- label
- Patients with recently diagnosed breast cancer who will undergo NAC.
- description
- Patients with recently diagnosed breast cancer who will undergo NAC before surgery.
- interventionNames
- Radiation: PET/MR
Primary outcomes (1)
- measure
- Diagnostic performance of PET/MR imaging metrics in prediction of treatment response to chemotherapy
- timeFrame
- 40 weeks
- description
- Determination of the sensitivity, specificity of PET/MR imaging metrics to predict treatment response to neoadjuvant chemotherapy. The treatment response will be determined by RCB (residual cancer burden) index at surgical pathology after completion of neoadjuvant chemotherapy and further categorized as group 1: RCB 0 or I; group 2: RCB II or III. The logistic regression will be performed with the groups (1 or 2) as dependent variable and the different PET/MR imaging metrics as independent variables, the ROC analysis and sensitivity, specificity of the PET/MR imaging metrics will be inferred from the regression models.
Eligibility
Eligibility (as posted)
- Sex
- Female
- Minimum age
- 25 Years
- Maximum age
- 75 Years
Show eligibility criteria text
Inclusion Criteria: * 1\. Women aged 25-75 years old. * 2\. Women with recently diagnosed breast cancer. Exclusion Criteria: * 1\. Estimated GFR (eGFR) \< 60 mL/min/1.73 m2 and blood glucose \> 135 mg/dl; Past/ present history of acute renal failure, renal dialysis, DM. * 2\. Women with metallic fixation, coronary artery stent in recent 3 months; or women with mechanical valve replacement not compatible with MR magnet; or women with aneurysmal clips, pacemakers. * 3\. Past history of claustrophobia. * 4\. Women who are pregnant or who are planning to be pregnant, or who are lactating * 5\. Past history of breast cancer within recent 5 years * 6\. Women undergoing chemotherapy for other disease entity in recent 1 year. * 7\. Women who cannot cooperate with the examinations.
References
Publications (15)
- BACKGROUNDLu W, Chen W. Positron emission tomography/computerized tomography for tumor response assessment-a review of clinical practices and radiomics studies. Transl Cancer Res. 2016 Aug;5(4):364-370. doi: 10.21037/tcr.2016.07.12. PMID 27904837
- BACKGROUNDTateishi U, Miyake M, Nagaoka T, Terauchi T, Kubota K, Kinoshita T, Daisaki H, Macapinlac HA. Neoadjuvant chemotherapy in breast cancer: prediction of pathologic response with PET/CT and dynamic contrast-enhanced MR imaging--prospective assessment. Radiology. 2012 Apr;263(1):53-63. doi: 10.1148/radiol.12111177. PMID 22438441
- BACKGROUNDGillies RJ, Kinahan PE, Hricak H. Radiomics: Images Are More than Pictures, They Are Data. Radiology. 2016 Feb;278(2):563-77. doi: 10.1148/radiol.2015151169. Epub 2015 Nov 18. PMID 26579733
- BACKGROUNDGrimm LJ. Breast MRI radiogenomics: Current status and research implications. J Magn Reson Imaging. 2016 Jun;43(6):1269-78. doi: 10.1002/jmri.25116. Epub 2015 Dec 10. PMID 26663695
- BACKGROUNDGrimm LJ, Zhang J, Mazurowski MA. Computational approach to radiogenomics of breast cancer: Luminal A and luminal B molecular subtypes are associated with imaging features on routine breast MRI extracted using computer vision algorithms. J Magn Reson Imaging. 2015 Oct;42(4):902-7. doi: 10.1002/jmri.24879. Epub 2015 Mar 17. PMID 25777181
- BACKGROUNDGuo W, Li H, Zhu Y, Lan L, Yang S, Drukker K, Morris E, Burnside E, Whitman G, Giger ML, Ji Y; Tcga Breast Phenotype Research Group. Prediction of clinical phenotypes in invasive breast carcinomas from the integration of radiomics and genomics data. J Med Imaging (Bellingham). 2015 Oct;2(4):041007. doi: 10.1117/1.JMI.2.4.041007. Epub 2015 Sep 23. PMID 26835491
- BACKGROUNDLi H, Zhu Y, Burnside ES, Drukker K, Hoadley KA, Fan C, Conzen SD, Whitman GJ, Sutton EJ, Net JM, Ganott M, Huang E, Morris EA, Perou CM, Ji Y, Giger ML. MR Imaging Radiomics Signatures for Predicting the Risk of Breast Cancer Recurrence as Given by Research Versions of MammaPrint, Oncotype DX, and PAM50 Gene Assays. Radiology. 2016 Nov;281(2):382-391. doi: 10.1148/radiol.2016152110. Epub 2016 May 5.