The Prediction Model of NAC Response for Breast Cancer Based on The Parametric Dynamics Features.
NCT ID: NCT06370234
Last Updated: 2024-04-17
Study Results
Results pending
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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Recruitment Status
COMPLETED
Clinical Phase
NA
Total Enrollment
60 participants
Study Classification
INTERVENTIONAL
Study Start Date
2015-04-21
Study Completion Date
2020-03-03
Brief Summary
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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.
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Detailed Description
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Breast cancer is the most frequently diagnosed cancer and remains the fourth leading cause of cancer deaths in Taiwan women over the past decade. Decisions about the best treatment for breast cancer is based on the result of estrogen (ER) and progesterone receptor (PR) test, human epidermal growth factor type 2 receptor (HER2) test, and TNM staging using biopsy. After evaluation of menopause status and response of ER, PR and HER2, the treatments for stage 2 or above breast cancer may consider neoadjuvant chemotherapy (NAC) for the benefits of (1) converting an inoperable to a surgical resectable cancer, (2) metastasis management, (3) shrink the tumor, (4) improved overall survival and recurrence free survival rate (5) histologic parameters predictive. It is known that patients with pathological complete response (pCR) after NAC are associated with better disease-free survival and improved overall survival. Therefore, it is essential to develop more effective regimens and stratify patients based on computer assisted prediction model to evaluate the response of NAC.
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. The followings are the expected contributions:
To propose a novel parametric dynamics features for overcoming the issues with traditional thresholding method.
To develop segmentation algorithms for breast tissue and tumor region on DCE MRI in order to improve treatment response prediction.
To develop trajectory analysis for non-invasive QDS-IR image To develop segmentation algorithm for metabolic tumor volume by registering PET uptake boundary with CT tumor boundary in order to improve reliability and reproducibility of morphology feature.
By combining preceding techniques developed for PET/CT and DCE MRI, new types of features, namely the parametric dynamics features from PET/MRI can be served as dynamic radiomics for predicting NAC response.
To develop a Deep-Learning algorithm, which is the essential of the project in terms of self-learning methodology, for exploiting the high dimensional features space in search of the prediction model.
Subject Eligibility and Enrollment This prospective study was approved by the local Institutional Ethics Committee who waived the requirement for patient approval and written informed consent for the review of records and images.
From April 2015 to June 2019, 60 women with breast cancer who underwent NAC were screened for eligibility. The eligibility criteria were patients who: (a) were \> 18 years of age, (b) with pathologically confirmed breast cancer with core needle biopsy, (c) were willing to undergo NAC, (d) were eligible for surgery after NAC, and (e) were willing to undergo at least two PET/MR scans during NAC: the first \[R0\], pre-treatment; and the second \[R1\], after two cycles of chemotherapy (post-treatment). Of the 60 women, 14 were excluded for the following reasons: (a) distant metastases found after the first scan (n=1), so that they were not eligible for surgery; (b) patients unable to complete two sequential PET/MR scans for personal reasons (n=10); (c) patients refused further NAC after the first cycle (n=2); and (d) post-treatment scan could not be performed because of elevated creatinine level (n=1). The study inclusion flow diagram is shown in Figure 1. All patients received 6 to 8 cycles of NAC including the following options. For human epidermal growth factor receptor 2 (HER2)-negative patients, NAC included the concomitant use of epirubicin with cyclophosphamide (EC based) or epirubicin with cyclophosphamide followed by taxanes (docetaxel or paclitaxel; EC+T). For HER2-positive patients, the NAC protocols included the concomitant use of taxanes with trastuzumab (TH based), or a concurrent regimen of taxanes and cyclophosphamide followed by trastuzumab (EC+TH), or taxanes with trastuzumab and pertuzumab (THP). One triple-negative patient received paclitaxel with afatinib.
PET/MR image acquisition MR examinations were performed on a 3T PET/MR scanner (Biograph mMR, Siemens Healthcare, Erlangen, Germany). The midtreatment scan was arranged after two cycles of NAC. The last scan was arranged before surgery. The patients were scanned in the prone position using a dedicated four-channel breast coil (Noras GmbH, Höchberg, Germany). After fasting for at least 6 hours, the patients were injected intravenously with 3.7-5.55 MBq/kg (0.1-0.15 mCi/kg) of 18F-fluorodeoxyglucose (18F-FDG). PET/MR scanning was performed approximately 60 minutes later. A 10-minute breast PET was performed along with precontrast breast MRI in prone position. The PET images were reconstructed with an ordered-subset, expectation-maximization iterative algorithm (3 iterations, 21 subsets), with a 4mm post reconstruction Gaussian filter and an image matrix of 172 x 172. Attenuation correction of PET data was obtained by using a 4-tissue-class filter (air, lung, fat, soft tissue) segmented attenuation correction map , which was reconstructed from a 2-point Dixon MR pulse sequence.
The preconstrat breast MRI protocol included 2D gradient echo T1-weighted (repetition time \[TR\]/echo time\[TE\],500/9.8 ms, flip angle 150°, slice thickness 3.5 mm; matrix field of view \[FOV\] 320 × 320), T2-weighted short tau inversion recovery (STIR) images (TR/TE/TI = 3000/78/230 ms, matrix size 320 × 320, slice thickness 4.0 mm, matrix FOV 330 × 330 mm), diffusion-weighted images (DWIs) (TR/TE = 7500/83 ms, matrix size 192 × 77, slice thickness 4.0 mm, FOV 360 × 1830 mm, b value = 50, 600 and 1000 sec/mm2, average = 2) with apparent diffusion coefficient (ADC) maps were acquired.
Seven dynamic contrast-enhanced images, including one before and six after contrast agent administration, were acquired using a fat-saturated T1-weighted fast low angle shot (FLASH) 3D gradient echo sequence (TR/TE 4.1/1.5 ms, slice thickness and gap 1.0/0 mm, matrix size 352 × 282, flip angle 10°, 146 slices, FOV 330 × 330 mm) with spectral attenuated inversion recovery (SPAIR), each set took approximately 60 seconds. The postcontrast DCE-MRI was started 10 seconds after intravenous injection of 0.1mmol/kg gadobutrol (Gadovist; Bayer Pharma AG, Berlin, Germany) with an injection rate of 3 mL/s.
Histopathology assessment The histologic type and grade were based on histopathological reports of ultrasound-guided core biopsies performed before NAC. The expressions of estrogen receptor (ER), progesterone receptor (PR), and HER2 were assessed using immunohistochemical staining. ER and PR positivity were assessed using the Allred score, in which a score \> 3 was considered to be positive. Tumors were considered to be HER2 positive if the immunohistochemical score was 3+. In cases with an equivocal HER2 status (score 2+) in immunohistochemistry, fluorescence in situ hybridization analysis (FISH) was performed to confirm the diagnosis.
A pCR was defined as the absence of residual invasive cancer except for ductal carcinoma in situ (DCIS) in the surgical breast specimen after NAC and the absence of axillary lymph node involvement, as previous reported by Pinder et al.
Image analysis Images were interpreted by two radiologists (Y.F.L. and N.C., both with 12 years of experience in breast imaging). Tumor appearance on MRI (mass, non-mass enhancement) was visually assessed by the two radiologists. The regions of interest (ROIs) were manually defined by selecting a two-dimensional region enclosing the largest cross-sectional area of high signal intensity of enhancement that differed from normal background parenchyma on the fifth contrast-enhanced series, while avoiding normal parenchyma and marking clip artifacts . If the cancer was multifocal or multicentric, the ROI was measured at the tumor area with the largest size.
After the tumor ROIs had been segmented, PET and MR images including DWI, and apparent diffusion coefficient (ADC) image ROI measurements were aligned using the ROIs drawn on dynamic contrast enhanced (DCE) images using semi-automated non-rigid registration and manual alignment where necessary. The ROIs were simultaneously evaluated on six different phases of DCE images (coded as P1 to P6). The ROIs were also registered onto other series including DWIs at b value=1000 sec/mm2, ADC map and PET images . From each ROI, six first-order and four second-order texture features were automatically computed. Histogram analysis was used to assess first-order textural features including mean, standard deviation (SD), median, 5th and 95th percentile values, kurtosis, and skewness. For second-order textural analysis, a gray-level co-occurrence matrix (GLCM) was used as the parent matrix, and a four heterogeneous textural features were extracted from the GLCM, namely difference entropy (DiffEntropy), difference variance (DiffVariance), contrast, and entropy \[18\]. These textural parameters were calculated using MR Multiparametric Analysis prototype software (Siemens Healthcare, Erlangen, Germany). All imaging parameters were evaluated using PET/MR scans performed pretreatment (R0) and midtreatment (R1), and before surgery (R2) are analyzed.
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. The followings are the expected contributions:
To propose a novel parametric dynamics features for overcoming the issues with traditional thresholding method.
To develop segmentation algorithms for breast tissue and tumor region on DCE MRI in order to improve treatment response prediction.
To develop trajectory analysis for non-invasive QDS-IR image To develop segmentation algorithm for metabolic tumor volume by registering PET uptake boundary with CT tumor boundary in order to improve reliability and reproducibility of morphology feature.
By combining preceding techniques developed for PET/CT and DCE MRI, new types of features, namely the parametric dynamics features from PET/MRI can be served as dynamic radiomics for predicting NAC response.
To develop a Deep-Learning algorithm, which is the essential of the project in terms of self-learning methodology, for exploiting the high dimensional features space in search of the prediction model.
Subject Eligibility and Enrollment This prospective study was approved by the local Institutional Ethics Committee who waived the requirement for patient approval and written informed consent for the review of records and images.
From April 2015 to June 2019, 60 women with breast cancer who underwent NAC were screened for eligibility. The eligibility criteria were patients who: (a) were \> 18 years of age, (b) with pathologically confirmed breast cancer with core needle biopsy, (c) were willing to undergo NAC, (d) were eligible for surgery after NAC, and (e) were willing to undergo at least two PET/MR scans during NAC: the first \[R0\], pre-treatment; and the second \[R1\], after two cycles of chemotherapy (post-treatment). Of the 60 women, 14 were excluded for the following reasons: (a) distant metastases found after the first scan (n=1), so that they were not eligible for surgery; (b) patients unable to complete two sequential PET/MR scans for personal reasons (n=10); (c) patients refused further NAC after the first cycle (n=2); and (d) post-treatment scan could not be performed because of elevated creatinine level (n=1). The study inclusion flow diagram is shown in Figure 1. All patients received 6 to 8 cycles of NAC including the following options. For human epidermal growth factor receptor 2 (HER2)-negative patients, NAC included the concomitant use of epirubicin with cyclophosphamide (EC based) or epirubicin with cyclophosphamide followed by taxanes (docetaxel or paclitaxel; EC+T). For HER2-positive patients, the NAC protocols included the concomitant use of taxanes with trastuzumab (TH based), or a concurrent regimen of taxanes and cyclophosphamide followed by trastuzumab (EC+TH), or taxanes with trastuzumab and pertuzumab (THP). One triple-negative patient received paclitaxel with afatinib.
PET/MR image acquisition MR examinations were performed on a 3T PET/MR scanner (Biograph mMR, Siemens Healthcare, Erlangen, Germany). The midtreatment scan was arranged after two cycles of NAC. The last scan was arranged before surgery. The patients were scanned in the prone position using a dedicated four-channel breast coil (Noras GmbH, Höchberg, Germany). After fasting for at least 6 hours, the patients were injected intravenously with 3.7-5.55 MBq/kg (0.1-0.15 mCi/kg) of 18F-fluorodeoxyglucose (18F-FDG). PET/MR scanning was performed approximately 60 minutes later. A 10-minute breast PET was performed along with precontrast breast MRI in prone position. The PET images were reconstructed with an ordered-subset, expectation-maximization iterative algorithm (3 iterations, 21 subsets), with a 4mm post reconstruction Gaussian filter and an image matrix of 172 x 172. Attenuation correction of PET data was obtained by using a 4-tissue-class filter (air, lung, fat, soft tissue) segmented attenuation correction map , which was reconstructed from a 2-point Dixon MR pulse sequence.
The preconstrat breast MRI protocol included 2D gradient echo T1-weighted (repetition time \[TR\]/echo time\[TE\],500/9.8 ms, flip angle 150°, slice thickness 3.5 mm; matrix field of view \[FOV\] 320 × 320), T2-weighted short tau inversion recovery (STIR) images (TR/TE/TI = 3000/78/230 ms, matrix size 320 × 320, slice thickness 4.0 mm, matrix FOV 330 × 330 mm), diffusion-weighted images (DWIs) (TR/TE = 7500/83 ms, matrix size 192 × 77, slice thickness 4.0 mm, FOV 360 × 1830 mm, b value = 50, 600 and 1000 sec/mm2, average = 2) with apparent diffusion coefficient (ADC) maps were acquired.
Seven dynamic contrast-enhanced images, including one before and six after contrast agent administration, were acquired using a fat-saturated T1-weighted fast low angle shot (FLASH) 3D gradient echo sequence (TR/TE 4.1/1.5 ms, slice thickness and gap 1.0/0 mm, matrix size 352 × 282, flip angle 10°, 146 slices, FOV 330 × 330 mm) with spectral attenuated inversion recovery (SPAIR), each set took approximately 60 seconds. The postcontrast DCE-MRI was started 10 seconds after intravenous injection of 0.1mmol/kg gadobutrol (Gadovist; Bayer Pharma AG, Berlin, Germany) with an injection rate of 3 mL/s.
Histopathology assessment The histologic type and grade were based on histopathological reports of ultrasound-guided core biopsies performed before NAC. The expressions of estrogen receptor (ER), progesterone receptor (PR), and HER2 were assessed using immunohistochemical staining. ER and PR positivity were assessed using the Allred score, in which a score \> 3 was considered to be positive. Tumors were considered to be HER2 positive if the immunohistochemical score was 3+. In cases with an equivocal HER2 status (score 2+) in immunohistochemistry, fluorescence in situ hybridization analysis (FISH) was performed to confirm the diagnosis.
A pCR was defined as the absence of residual invasive cancer except for ductal carcinoma in situ (DCIS) in the surgical breast specimen after NAC and the absence of axillary lymph node involvement, as previous reported by Pinder et al.
Image analysis Images were interpreted by two radiologists (Y.F.L. and N.C., both with 12 years of experience in breast imaging). Tumor appearance on MRI (mass, non-mass enhancement) was visually assessed by the two radiologists. The regions of interest (ROIs) were manually defined by selecting a two-dimensional region enclosing the largest cross-sectional area of high signal intensity of enhancement that differed from normal background parenchyma on the fifth contrast-enhanced series, while avoiding normal parenchyma and marking clip artifacts . If the cancer was multifocal or multicentric, the ROI was measured at the tumor area with the largest size.
After the tumor ROIs had been segmented, PET and MR images including DWI, and apparent diffusion coefficient (ADC) image ROI measurements were aligned using the ROIs drawn on dynamic contrast enhanced (DCE) images using semi-automated non-rigid registration and manual alignment where necessary. The ROIs were simultaneously evaluated on six different phases of DCE images (coded as P1 to P6). The ROIs were also registered onto other series including DWIs at b value=1000 sec/mm2, ADC map and PET images . From each ROI, six first-order and four second-order texture features were automatically computed. Histogram analysis was used to assess first-order textural features including mean, standard deviation (SD), median, 5th and 95th percentile values, kurtosis, and skewness. For second-order textural analysis, a gray-level co-occurrence matrix (GLCM) was used as the parent matrix, and a four heterogeneous textural features were extracted from the GLCM, namely difference entropy (DiffEntropy), difference variance (DiffVariance), contrast, and entropy \[18\]. These textural parameters were calculated using MR Multiparametric Analysis prototype software (Siemens Healthcare, Erlangen, Germany). All imaging parameters were evaluated using PET/MR scans performed pretreatment (R0) and midtreatment (R1), and before surgery (R2) are analyzed.
Conditions
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Breast Cancer
Chemotherapy Effect
Diffusion Weighted MRI
PET Imaging
Multiparametric Magnetic Resonance Imaging
Study Design
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Allocation Method
NA
Intervention Model
SINGLE_GROUP
Primary Study Purpose
DIAGNOSTIC
Blinding Strategy
NONE
Study Groups
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PET/MR scanning for neoadjuvant chemotherapy breast cancer patients
From April 2015 to June 2019, women with breast cancer who underwent neoadjuvant chemotherapy were enrolled.
Arranged for at least three PET/MR scans during NAC: the first \[R0\], pre-treatment; and the second\[R1\], after two cycles of chemotherapy (post-treatment) and the third \[R2\] before surgery.
Group Type
OTHER
Whole body 18F-FDG Positron Emission Tomography
Intervention Type
RADIATION
The subjects enrolling and participating this study will have done PET/MR during pre-operation chemotherapy. But, in normal procedure, they will not have done.
Interventions
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Whole body 18F-FDG Positron Emission Tomography
The subjects enrolling and participating this study will have done PET/MR during pre-operation chemotherapy. But, in normal procedure, they will not have done.
Intervention Type
RADIATION
Eligibility Criteria
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Inclusion Criteria
* (a) were \> 20 years of age,
* (b) with pathologically confirmed breast cancer with core needle biopsy
* (c) were willing to undergo NAC
* (d) were eligible for surgery after NAC
* (e) were willing to undergo at least three PET/MR scans during NAC: the first \[R0\], pre-treatment; and the second \[R1\], after two cycles of chemotherapy (post-treatment) and before surgery \[R2\]
* (b) with pathologically confirmed breast cancer with core needle biopsy
* (c) were willing to undergo NAC
* (d) were eligible for surgery after NAC
* (e) were willing to undergo at least three PET/MR scans during NAC: the first \[R0\], pre-treatment; and the second \[R1\], after two cycles of chemotherapy (post-treatment) and before surgery \[R2\]
Exclusion Criteria
* (a) distant metastases or recurrent breast cancer.
* (b) unable to comply with sequential PET/MR scanning schedule.
* (c) Impaired renal function, CCR\>30ml/min.
* (d) Known aller
* (b) unable to comply with sequential PET/MR scanning schedule.
* (c) Impaired renal function, CCR\>30ml/min.
* (d) Known aller
Minimum Eligible Age
20 Years
Eligible Sex
FEMALE
Accepts Healthy Volunteers
No
Sponsors
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Ministry of Science and Technology, Taiwan
OTHER_GOV
Sponsor Role
collaborator
National Taiwan University Hospital
OTHER
Sponsor Role
lead
Responsible Party
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Responsibility Role
SPONSOR
Principal Investigators
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Yeun-Chung Chang, M.D., PhD.
Role: STUDY_CHAIR
National Taiwan University Hospital
References
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Houssami N, Macaskill P, von Minckwitz G, Marinovich ML, Mamounas E. Meta-analysis of the association of breast cancer subtype and pathologic complete response to neoadjuvant chemotherapy. Eur J Cancer. 2012 Dec;48(18):3342-54. doi: 10.1016/j.ejca.2012.05.023. Epub 2012 Jul 3.
Reference Type
BACKGROUND
Yang Z, Tang LH, Klimstra DS. Effect of tumor heterogeneity on the assessment of Ki67 labeling index in well-differentiated neuroendocrine tumors metastatic to the liver: implications for prognostic stratification. Am J Surg Pathol. 2011 Jun;35(6):853-60. doi: 10.1097/PAS.0b013e31821a0696.
Reference Type
BACKGROUND
Gerlinger M, Rowan AJ, Horswell S, Math M, Larkin J, Endesfelder D, Gronroos E, Martinez P, Matthews N, Stewart A, Tarpey P, Varela I, Phillimore B, Begum S, McDonald NQ, Butler A, Jones D, Raine K, Latimer C, Santos CR, Nohadani M, Eklund AC, Spencer-Dene B, Clark G, Pickering L, Stamp G, Gore M, Szallasi Z, Downward J, Futreal PA, Swanton C. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med. 2012 Mar 8;366(10):883-892. doi: 10.1056/NEJMoa1113205.
Reference Type
BACKGROUND
Davnall F, Yip CS, Ljungqvist G, Selmi M, Ng F, Sanghera B, Ganeshan B, Miles KA, Cook GJ, Goh V. Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice? Insights Imaging. 2012 Dec;3(6):573-89. doi: 10.1007/s13244-012-0196-6. Epub 2012 Oct 24.
Reference Type
BACKGROUND
Gillies RJ, Schornack PA, Secomb TW, Raghunand N. Causes and effects of heterogeneous perfusion in tumors. Neoplasia. 1999 Aug;1(3):197-207. doi: 10.1038/sj.neo.7900037.
Reference Type
BACKGROUND
von Minckwitz G, Rezai M, Loibl S, Fasching PA, Huober J, Tesch H, Bauerfeind I, Hilfrich J, Eidtmann H, Gerber B, Hanusch C, Kuhn T, du Bois A, Blohmer JU, Thomssen C, Dan Costa S, Jackisch C, Kaufmann M, Mehta K, Untch M. Capecitabine in addition to anthracycline- and taxane-based neoadjuvant treatment in patients with primary breast cancer: phase III GeparQuattro study. J Clin Oncol. 2010 Apr 20;28(12):2015-23. doi: 10.1200/JCO.2009.23.8303. Epub 2010 Mar 22.
Reference Type
BACKGROUND
Kaufmann M, von Minckwitz G, Bear HD, Buzdar A, McGale P, Bonnefoi H, Colleoni M, Denkert C, Eiermann W, Jackesz R, Makris A, Miller W, Pierga JY, Semiglazov V, Schneeweiss A, Souchon R, Stearns V, Untch M, Loibl S. Recommendations from an international expert panel on the use of neoadjuvant (primary) systemic treatment of operable breast cancer: new perspectives 2006. Ann Oncol. 2007 Dec;18(12):1927-34. doi: 10.1093/annonc/mdm201. Epub 2007 Nov 12.
Reference Type
BACKGROUND
Pinker K, Helbich TH, Morris EA. The potential of multiparametric MRI of the breast. Br J Radiol. 2017 Jan;90(1069):20160715. doi: 10.1259/bjr.20160715. Epub 2016 Nov 2.
Reference Type
BACKGROUND
Yoon HJ, Kim Y, Chung J, Kim BS. Predicting neo-adjuvant chemotherapy response and progression-free survival of locally advanced breast cancer using textural features of intratumoral heterogeneity on F-18 FDG PET/CT and diffusion-weighted MR imaging. Breast J. 2019 May;25(3):373-380. doi: 10.1111/tbj.13032. Epub 2018 Mar 30.
Reference Type
BACKGROUND
Wang J, Shih TT, Yen RF. Multiparametric Evaluation of Treatment Response to Neoadjuvant Chemotherapy in Breast Cancer Using Integrated PET/MR. Clin Nucl Med. 2017 Jul;42(7):506-513. doi: 10.1097/RLU.0000000000001684.
Reference Type
BACKGROUND
Erratum: Predicting Response to Neoadjuvant Chemotherapy in Patients With Breast Cancer: Combined Statistical Modeling Using Clinicopathological Factors and FDG PET/CT Texture Parameters. Clin Nucl Med. 2021 Jun 1;46(6):525. doi: 10.1097/RLU.0000000000003704. No abstract available.
Reference Type
BACKGROUND
Lim I, Noh WC, Park J, Park JA, Kim HA, Kim EK, Park KW, Lee SS, You EY, Kim KM, Byun BH, Kim BI, Choi CW, Lim SM. The combination of FDG PET and dynamic contrast-enhanced MRI improves the prediction of disease-free survival in patients with advanced breast cancer after the first cycle of neoadjuvant chemotherapy. Eur J Nucl Med Mol Imaging. 2014 Oct;41(10):1852-60. doi: 10.1007/s00259-014-2797-4. Epub 2014 Jun 14.
Reference Type
BACKGROUND
Hylton NM, Blume JD, Bernreuter WK, Pisano ED, Rosen MA, Morris EA, Weatherall PT, Lehman CD, Newstead GM, Polin S, Marques HS, Esserman LJ, Schnall MD; ACRIN 6657 Trial Team and I-SPY 1 TRIAL Investigators. Locally advanced breast cancer: MR imaging for prediction of response to neoadjuvant chemotherapy--results from ACRIN 6657/I-SPY TRIAL. Radiology. 2012 Jun;263(3):663-72. doi: 10.1148/radiol.12110748.
Reference Type
BACKGROUND
Yuan Y, Chen XS, Liu SY, Shen KW. Accuracy of MRI in prediction of pathologic complete remission in breast cancer after preoperative therapy: a meta-analysis. AJR Am J Roentgenol. 2010 Jul;195(1):260-8. doi: 10.2214/AJR.09.3908.
Reference Type
BACKGROUND
Choi JH, Kim HA, Kim W, Lim I, Lee I, Byun BH, Noh WC, Seong MK, Lee SS, Kim BI, Choi CW, Lim SM, Woo SK. Early prediction of neoadjuvant chemotherapy response for advanced breast cancer using PET/MRI image deep learning. Sci Rep. 2020 Dec 3;10(1):21149. doi: 10.1038/s41598-020-77875-5.
Reference Type
BACKGROUND
Romeo V, Clauser P, Rasul S, Kapetas P, Gibbs P, Baltzer PAT, Hacker M, Woitek R, Helbich TH, Pinker K. AI-enhanced simultaneous multiparametric 18F-FDG PET/MRI for accurate breast cancer diagnosis. Eur J Nucl Med Mol Imaging. 2022 Jan;49(2):596-608. doi: 10.1007/s00259-021-05492-z. Epub 2021 Aug 10.
Reference Type
BACKGROUND
Other Identifiers
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201412166MINA
Identifier Type: -
Identifier Source: org_study_id
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