Ignite Creation Date:
2024-05-06 @ 8:25 PM
Last Modification Date:
2024-10-26 @ 3:27 PM
Study NCT ID:
NCT06370234
Status:
COMPLETED
Last Update Posted:
2024-04-17
First Post:
2016-07-01
Brief Title:
The Prediction Model of NAC Response for Breast Cancer Based on The Parametric Dynamics Features
Sponsor:
National Taiwan University Hospital
Organization:
National Taiwan University Hospital
Study Overview
Official Title:
The Prediction Model of Neoadjuvant Chemotherapy Response for Breast Cancer Based on The Parametric Dynamics Features of The Pretreatment and Early-Treatment MR-PET and QDS-IR Images
Status:
COMPLETED
Status Verified Date:
2024-04
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
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 PETCT 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 PETMRI and prediction model can be achieved in accordance with the features extracted from dynamic features extraction using longitudinal images of PETMRI
Detailed Description:
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 PETCT 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 PETMRI and prediction model can be achieved in accordance with the features extracted from dynamic features extraction using longitudinal images of PETMRI 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 PETCT and DCE MRI new types of features namely the parametric dynamics features from PETMRI 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 PETMR 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 n1 so that they were not eligible for surgery b patients unable to complete two sequential PETMR scans for personal reasons n10 c patients refused further NAC after the first cycle n2 and d post-treatment scan could not be performed because of elevated creatinine level n1 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 ECT 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 ECTH or taxanes with trastuzumab and pertuzumab THP One triple-negative patient received paclitaxel with afatinib
PETMR image acquisition MR examinations were performed on a 3T PETMR 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 37-555 MBqkg 01-015 mCikg of 18F-fluorodeoxyglucose 18F-FDG PETMR 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 TRecho timeTE50098 ms flip angle 150 slice thickness 35 mm matrix field of view FOV 320 320 T2-weighted short tau inversion recovery STIR images TRTETI 300078230 ms matrix size 320 320 slice thickness 40 mm matrix FOV 330 330 mm diffusion-weighted images DWIs TRTE 750083 ms matrix size 192 77 slice thickness 40 mm FOV 360 1830 mm b value 50 600 and 1000 secmm2 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 TRTE 4115 ms slice thickness and gap 100 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 01mmolkg gadobutrol Gadovist Bayer Pharma AG Berlin Germany with an injection rate of 3 mLs
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 YFL and NC 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 value1000 secmm2 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 PETMR 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 PETCT 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 PETMRI and prediction model can be achieved in accordance with the features extracted from dynamic features extraction using longitudinal images of PETMRI 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 PETCT and DCE MRI new types of features namely the parametric dynamics features from PETMRI 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 PETMR 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 n1 so that they were not eligible for surgery b patients unable to complete two sequential PETMR scans for personal reasons n10 c patients refused further NAC after the first cycle n2 and d post-treatment scan could not be performed because of elevated creatinine level n1 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 ECT 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 ECTH or taxanes with trastuzumab and pertuzumab THP One triple-negative patient received paclitaxel with afatinib
PETMR image acquisition MR examinations were performed on a 3T PETMR 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 37-555 MBqkg 01-015 mCikg of 18F-fluorodeoxyglucose 18F-FDG PETMR 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 TRecho timeTE50098 ms flip angle 150 slice thickness 35 mm matrix field of view FOV 320 320 T2-weighted short tau inversion recovery STIR images TRTETI 300078230 ms matrix size 320 320 slice thickness 40 mm matrix FOV 330 330 mm diffusion-weighted images DWIs TRTE 750083 ms matrix size 192 77 slice thickness 40 mm FOV 360 1830 mm b value 50 600 and 1000 secmm2 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 TRTE 4115 ms slice thickness and gap 100 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 01mmolkg gadobutrol Gadovist Bayer Pharma AG Berlin Germany with an injection rate of 3 mLs
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 YFL and NC 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 value1000 secmm2 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 PETMR scans performed pretreatment R0 and midtreatment R1 and before surgery R2 are analyzed
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
None
Is a FDA Regulated Device?:
None
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None