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
1 other identifier
observational
120
1 country
1
Brief Summary
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.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P50-P75 for all trials
Started Jul 2018
Longer than P75 for all trials
1 active site
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
Click on a node to explore related trials.
Study Timeline
Key milestones and dates
Study Start
First participant enrolled
July 6, 2018
CompletedFirst Submitted
Initial submission to the registry
June 13, 2022
CompletedFirst Posted
Study publicly available on registry
July 20, 2022
CompletedPrimary Completion
Last participant's last visit for primary outcome
December 31, 2022
CompletedStudy Completion
Last participant's last visit for all outcomes
December 31, 2022
CompletedJuly 20, 2022
July 1, 2022
4.5 years
June 13, 2022
July 18, 2022
Conditions
Keywords
Outcome Measures
Primary Outcomes (1)
Diagnostic performance of PET/MR imaging metrics in prediction of treatment response to chemotherapy
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.
40 weeks
Secondary Outcomes (3)
Comparison of PET/MR imaging metrics among patients with different histologic grades
2 weeks
Comparison of PET/MR imaging metrics among patients with different molecular subtypes.
2 weeks
Performance of PET/MR imaging metrics to predict the recurrence status.
5 years
Study Arms (2)
Patient recently diagnosed breast cancer who will undergo surgery
surgery treatment only
Patients with recently diagnosed breast cancer who will undergo NAC.
Patients with recently diagnosed breast cancer who will undergo NAC before surgery.
Interventions
Study 1- pre-NAC PET/MR; Study 2- first follow-up PET/MR is performed after first dose of NAC; Study 3, second follow-up PET/MR is performed after third or fourth dose of the NAC. The NAC protocol is mainly antracycline-based followed by taxane-based regimen for a total of 6-8 cycles.
Eligibility Criteria
We will recruit two types of study participants in our study: 1. Patients with recently diagnosed breast cancer who will undergo surgery (without NAC, but may undergo postoperative adjuvant chemotherapy and other regimens after surgery) 2. Patients with recently diagnosed breast cancer who will undergo NAC.
You may qualify if:
- \. Women aged 25-75 years old.
- \. Women with recently diagnosed breast cancer.
You may not qualify if:
- \. 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.
- \. 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.
- \. Past history of claustrophobia.
- \. Women who are pregnant or who are planning to be pregnant, or who are lactating
- \. Past history of breast cancer within recent 5 years
- \. Women undergoing chemotherapy for other disease entity in recent 1 year.
- \. Women who cannot cooperate with the examinations.
Contact the study team to confirm eligibility.
Sponsors & Collaborators
Study Sites (1)
Department of Radiology,Taipei Veterans General Hospital
Taipei, 112304, Taiwan
Related Publications (15)
Lu 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: 27904837BACKGROUNDTateishi 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: 22438441BACKGROUNDGillies 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: 26579733BACKGROUNDGrimm 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: 26663695BACKGROUNDGrimm 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: 25777181BACKGROUNDGuo 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: 26835491BACKGROUNDLi 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.
PMID: 27144536BACKGROUNDLi H, Zhu Y, Burnside ES, Huang E, Drukker K, Hoadley KA, Fan C, Conzen SD, Zuley M, Net JM, Sutton E, Whitman GJ, Morris E, Perou CM, Ji Y, Giger ML. Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set. NPJ Breast Cancer. 2016;2:16012-. doi: 10.1038/npjbcancer.2016.12. Epub 2016 May 11.
PMID: 27853751BACKGROUNDZhu Y, Li H, Guo W, Drukker K, Lan L, Giger ML, Ji Y. Deciphering Genomic Underpinnings of Quantitative MRI-based Radiomic Phenotypes of Invasive Breast Carcinoma. Sci Rep. 2015 Dec 7;5:17787. doi: 10.1038/srep17787.
PMID: 26639025BACKGROUNDWu LA, Chang RF, Huang CS, Lu YS, Chen HH, Chen JY, Chang YC. Evaluation of the treatment response to neoadjuvant chemotherapy in locally advanced breast cancer using combined magnetic resonance vascular maps and apparent diffusion coefficient. J Magn Reson Imaging. 2015 Nov;42(5):1407-20. doi: 10.1002/jmri.24915. Epub 2015 Apr 15.
PMID: 25875904BACKGROUNDAsselin MC, O'Connor JP, Boellaard R, Thacker NA, Jackson A. Quantifying heterogeneity in human tumours using MRI and PET. Eur J Cancer. 2012 Mar;48(4):447-55. doi: 10.1016/j.ejca.2011.12.025. Epub 2012 Jan 20.
PMID: 22265426BACKGROUNDChoi JS, Ko ES, Ko EY, Han BK, Nam SJ. Background Parenchymal Enhancement on Preoperative Magnetic Resonance Imaging: Association With Recurrence-Free Survival in Breast Cancer Patients Treated With Neoadjuvant Chemotherapy. Medicine (Baltimore). 2016 Mar;95(9):e3000. doi: 10.1097/MD.0000000000003000.
PMID: 26945421BACKGROUNDTixier F, Le Rest CC, Hatt M, Albarghach N, Pradier O, Metges JP, Corcos L, Visvikis D. Intratumor heterogeneity characterized by textural features on baseline 18F-FDG PET images predicts response to concomitant radiochemotherapy in esophageal cancer. J Nucl Med. 2011 Mar;52(3):369-78. doi: 10.2967/jnumed.110.082404. Epub 2011 Feb 14.
PMID: 21321270BACKGROUNDParikh J, Selmi M, Charles-Edwards G, Glendenning J, Ganeshan B, Verma H, Mansi J, Harries M, Tutt A, Goh V. Changes in primary breast cancer heterogeneity may augment midtreatment MR imaging assessment of response to neoadjuvant chemotherapy. Radiology. 2014 Jul;272(1):100-12. doi: 10.1148/radiol.14130569. Epub 2014 Mar 19.
PMID: 24654970BACKGROUNDCatalano OA, Rosen BR, Sahani DV, Hahn PF, Guimaraes AR, Vangel MG, Nicolai E, Soricelli A, Salvatore M. Clinical impact of PET/MR imaging in patients with cancer undergoing same-day PET/CT: initial experience in 134 patients--a hypothesis-generating exploratory study. Radiology. 2013 Dec;269(3):857-69. doi: 10.1148/radiol.13131306. Epub 2013 Oct 28.
PMID: 24009348BACKGROUND
MeSH Terms
Conditions
Condition Hierarchy (Ancestors)
Study Officials
- PRINCIPAL INVESTIGATOR
Jane Wang, PhD
Department of Radiology,Taipei Veterans General Hospital
Central Study Contacts
Study Design
- Study Type
- observational
- Observational Model
- COHORT
- Time Perspective
- PROSPECTIVE
- Target Duration
- 40 Weeks
- Sponsor Type
- OTHER GOV
- Responsible Party
- SPONSOR
Study Record Dates
First Submitted
June 13, 2022
First Posted
July 20, 2022
Study Start
July 6, 2018
Primary Completion
December 31, 2022
Study Completion
December 31, 2022
Last Updated
July 20, 2022
Record last verified: 2022-07
Data Sharing
- IPD Sharing
- Will not share