Ignite Creation Date:
2025-12-25 @ 2:10 AM
Ignite Modification Date:
2026-01-04 @ 10:15 PM
Study NCT ID:
NCT05466760
Status:
UNKNOWN
Last Update Posted:
2022-07-20
First Post:
2022-06-13
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
PET/MR Radiomics for Breast Cancer Diagnosis
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D001943', 'term': 'Breast Neoplasms'}], 'ancestors': [{'id': 'D009371', 'term': 'Neoplasms by Site'}, {'id': 'D009369', 'term': 'Neoplasms'}, {'id': 'D001941', 'term': 'Breast Diseases'}, {'id': 'D012871', 'term': 'Skin Diseases'}, {'id': 'D017437', 'term': 'Skin and Connective Tissue Diseases'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'PROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 120}, 'targetDuration': '40 Weeks', 'patientRegistry': True}, 'statusModule': {'overallStatus': 'UNKNOWN', 'lastKnownStatus': 'RECRUITING', 'startDateStruct': {'date': '2018-07-06', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2022-07', 'completionDateStruct': {'date': '2022-12-31', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2022-07-18', 'studyFirstSubmitDate': '2022-06-13', 'studyFirstSubmitQcDate': '2022-07-18', 'lastUpdatePostDateStruct': {'date': '2022-07-20', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2022-07-20', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2022-12-31', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'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.'}], 'secondaryOutcomes': [{'measure': 'Comparison of PET/MR imaging metrics among patients with different histologic grades', 'timeFrame': '2 weeks', 'description': 'The histologic grades will be categorized as grade I, II, III. The difference of PET/MR imaging metrics among patients of non-high grade (grades I, II) and high grade (grade III) will be compared by Mann-Whitney U test.'}, {'measure': 'Comparison of PET/MR imaging metrics among patients with different molecular subtypes.', 'timeFrame': '2 weeks', 'description': 'The molecular subtypes will be categorized as luminal, HER2-enriched, triple negative breast cancers (TNBC). The difference of PET/MR imaging metrics among patients of different subtypes will be compared by Kruskal-Wallis test.'}, {'measure': 'Performance of PET/MR imaging metrics to predict the recurrence status.', 'timeFrame': '5 years', 'description': 'Determination of the of PET/MR imaging metrics to predict recurrence status at 5 years after breast cancer diagnosis. The Cox proportional hazards regression model will be performed with the recurrence status (recurrence or not) as dependent variable and the different PET/MR imaging metrics as independent variables, the hazards ratios from the different PET/MR imaging metrics estimated from the models will be compared.'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Breast cancer', 'radiomics', 'hybrid PET/MRI'], 'conditions': ['Breast Cancer']}, 'referencesModule': {'references': [{'pmid': '27904837', 'type': 'BACKGROUND', 'citation': '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': '22438441', 'type': 'BACKGROUND', 'citation': 'Tateishi 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': '26579733', 'type': 'BACKGROUND', 'citation': 'Gillies 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': '26663695', 'type': 'BACKGROUND', 'citation': 'Grimm 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': '25777181', 'type': 'BACKGROUND', 'citation': 'Grimm 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': '26835491', 'type': 'BACKGROUND', 'citation': 'Guo 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': '27144536', 'type': 'BACKGROUND', 'citation': 'Li 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': '27853751', 'type': 'BACKGROUND', 'citation': 'Li 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': '26639025', 'type': 'BACKGROUND', 'citation': 'Zhu 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': '25875904', 'type': 'BACKGROUND', 'citation': 'Wu 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': '22265426', 'type': 'BACKGROUND', 'citation': "Asselin 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': '26945421', 'type': 'BACKGROUND', 'citation': 'Choi 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': '21321270', 'type': 'BACKGROUND', 'citation': 'Tixier 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': '24654970', 'type': 'BACKGROUND', 'citation': 'Parikh 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': '24009348', 'type': 'BACKGROUND', 'citation': 'Catalano 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.'}]}, 'descriptionModule': {'briefSummary': '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.', 'detailedDescription': 'Purposes and background introduction:\n\nBreast 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. In recent years, the concept of "Radiomics" is emerging. Radiomics is a process involving the conversion of images (imaging phenotypes) 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. Radiomics is applied for the diagnostic, prognostic, and predictive purposes of diseases. There are two main approaches to radiomics: First, the semantic approach, which uses the usual radiological lexicon derived from regions of interest; second, the agnostic approach is higher-order, mathematically computed data derived from images instead of the commonly used radiologists\' lexicon. MRI and PET can be used in breast radiomics studies. Hybrid PET/MR is a machine that the PET and MRI can be performed on the same table at the same time slot, therefore, the imaging data of MRI and PET can be obtained at the same examination, with less radiation dosage, more reliable lesion mapping than separate examinations of PET/CT and MRI.\n\nMaterial and methods:\n\nThere is a total of 120 patients would like to be included in the study. Our study is to use semantic and agnostic features of radiomics by hybrid PET/MR for:\n\n1. The pre-operative breast cancer patients (without neoadjuvant chemotherapy before operation): to investigate the association of PET/MR imaging data with molecular subtypes, cell proliferation (Ki-67), tumor aggressiveness (by histologic grade).\n2. The patients who will receive neoadjuvant chemotherapy (NAC): PET/MR study will be performed for 3 times: pre-MAC (study 1), PET/MR after 1st dose of NAC (study 2), and PET/MR after 3rd or 4th NAC (study 3). We will investigate the predictive ability of PET/MR imaging data for NAC response. And we will investigate which parameters at which series of examinations are more predictive of the final NAC response. Therefore, we can adjust the NAC regimen as early as possible.\n3. The breast cancer patients mentioned above: we will investigate the association of PET/MR radiomics data with the probability of metastasis or risk of recurrences and survival.\n4. 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.\n\nData analysis:\n\n1. For the breast cancer patients who will go directly to surgical treatment: We will analyze the association of MRI and PET semantic radiomics features (including DCE MRI, DWI/ADC, CEST, MRS and SUVmax, MTV, TLG) and agnostic radiomics ( texture) features with molecular subtypes, proliferation index (Ki-67), histology type and grades, and tumor size, lymph node status. And we will also investigate whether the semantic or agnostic/ texture analysis, or combination of both can be predictive of the factors associated with clinical outcome (that is, molecular subtype, Ki-67, histology type and grade, size, LN status).\n2. For the patients who will receive NAC: As stated previously, each participant is designed to undergo three examinations:\n\n 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.\n3. For long-term follow-up:The 5-year survival of each study participant will be obtained, and we will investigate the association of all aforementioned PET or MRI-related radiomics parameters with overall survival, disease-free survival by Cox proportional hazards model. Kaplan-Meier analysis for survival curves and comparison of survival by log-rank test will also be estimated.\n4. The association of BPE and breast density (BD) with long term outcome:\n5. The Statistical analysis will be performed by Stata 13 (Stata Corp., College Station, Texas, USA) and SAS 9.3 (SAS Institute Inc., Cary, NC, USA). A P value \\<0.05 will be regarded as statistical significance.'}, 'eligibilityModule': {'sex': 'FEMALE', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'maximumAge': '75 Years', 'minimumAge': '25 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'We will recruit two types of study participants in our study:\n\n1. Patients with recently diagnosed breast cancer who will undergo surgery (without NAC, but may undergo postoperative adjuvant chemotherapy and other regimens after surgery)\n2. Patients with recently diagnosed breast cancer who will undergo NAC.', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* 1\\. Women aged 25-75 years old.\n* 2\\. Women with recently diagnosed breast cancer.\n\nExclusion Criteria:\n\n* 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.\n* 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.\n* 3\\. Past history of claustrophobia.\n* 4\\. Women who are pregnant or who are planning to be pregnant, or who are lactating\n* 5\\. Past history of breast cancer within recent 5 years\n* 6\\. Women undergoing chemotherapy for other disease entity in recent 1 year.\n* 7\\. Women who cannot cooperate with the examinations.'}, 'identificationModule': {'nctId': 'NCT05466760', 'briefTitle': 'PET/MR Radiomics for Breast Cancer Diagnosis', 'organization': {'class': 'OTHER_GOV', 'fullName': 'Taipei Veterans General Hospital, Taiwan'}, 'officialTitle': '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', 'orgStudyIdInfo': {'id': '2017-03-002A'}}, 'armsInterventionsModule': {'armGroups': [{'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']}], 'interventions': [{'name': 'PET/MR', 'type': 'RADIATION', 'description': '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.', 'armGroupLabels': ['Patients with recently diagnosed breast cancer who will undergo NAC.']}]}, 'contactsLocationsModule': {'locations': [{'zip': '112304', 'city': 'Taipei', 'status': 'RECRUITING', 'country': 'Taiwan', 'contacts': [{'name': 'Jane Wang, PhD', 'role': 'CONTACT', 'email': 'jwang2@vghtpe.gov.tw', 'phone': '+886-2-28712121', 'phoneExt': '2970'}, {'name': 'Ling-Ming Tseng, MD', 'role': 'SUB_INVESTIGATOR'}, {'name': 'Ko-Han Lin, MD', 'role': 'SUB_INVESTIGATOR'}], 'facility': 'Department of Radiology,Taipei Veterans General Hospital', 'geoPoint': {'lat': 25.05306, 'lon': 121.52639}}], 'centralContacts': [{'name': 'Jane Wang, PhD', 'role': 'CONTACT', 'email': 'jwang2@vghtpe.gov.tw', 'phone': '+886-2-28712121', 'phoneExt': '2970'}], 'overallOfficials': [{'name': 'Jane Wang, PhD', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'Department of Radiology,Taipei Veterans General Hospital'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Taipei Veterans General Hospital, Taiwan', 'class': 'OTHER_GOV'}, 'responsibleParty': {'type': 'SPONSOR'}}}}