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CompletedNCT01564368ACRIN6698Updated Apr 15, 2024Results posted

DWI in Assessing Treatment Response in Patients With Breast Cancer Receiving Neoadjuvant Chemotherapy

An interventional study of diffusion-weighted magnetic resonance imaging in Breast Cancer, sponsored by American College of Radiology Imaging Network. Completed at 7 sites in United States. Open to female participants aged 18 Years and older. Per ClinicalTrials.gov, last updated 2024-04-15.

Sponsored by American College of Radiology Imaging Network · Not applicable, Interventional, and Diagnostic

Phase
Not applicable
Study type
Interventional
Enrollment
406
Allocation
Not applicable
Ages
18 Years and older
Sex
Female
01

Study summary

RATIONALE: Imaging procedures, such as diffusion-weighted magnetic resonance imaging (DWI) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), may help in evaluating how well patients with breast cancer respond to treatment.

PURPOSE: This research trial studies DWI and DCE-MRI in assessing treatment response in patients with breast cancer undergoing neoadjuvant chemotherapy.

Read the detailed description

OBJECTIVES:

Primary

  • To determine if the change in tumor apparent diffusion coefficient (ADC) value measured from each treatment timepoint to baseline is predictive of pathologic complete response (pCR).

Secondary

  • To determine if the combined measurement of change in tumor ADC value, change in tumor volume, and change in peak signal-enhancement ratio (SER) is predictive of pCR.
  • To investigate the relative effectiveness of the individual measurements, change in tumor ADC value, change in tumor volume, and change in peak SER for predicting pCR in experimental treatment arms.
  • To assess the test-retest reproducibility of ADC metrics applied to breast tumors.

OUTLINE: This is a multicenter study.

Patients undergo diffusion-weighted magnetic resonance imaging (DWI) at baseline, after week 3 of neoadjuvant paclitaxel regimen, and prior to and after completion of 4 courses of neoadjuvant chemotherapy. Patients then undergo surgery. Patients undergo DWI prior to contrast administration for dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).

After completion of treatment procedure, patients are followed up for 5 years on the I-SPY 2 TRIAL.

02

Conditions studied

  • Breast Cancer

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Keywords

  • stage II breast cancer
  • stage IIIA breast cancer
  • stage IIIB breast cancer
  • stage IIIC breast cancer
  • stage IV breast cancer
  • HER2-negative breast cancer
  • HER2-positive breast cancer
03

In context

Breast Neoplasms

12,544 studies on the registry are indexed under Breast Neoplasms; 2,892 are open to participants now.

This study's enrollment of 406 is above the median of 72 across 9,303 interventional studies indexed under Breast Neoplasms.

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Lead sponsor

American College of Radiology Imaging Network is the lead sponsor of 21 studies on the registry; none are open to participants now.

Counted across the registry records on this site, refreshed daily.

04

Who can participate

Ages eligible
18 Years and older
Sexes eligible
Female
Accepts healthy volunteers
No

Eligibility criteria

DISEASE CHARACTERISTICS:

  • Meets I-SPY 2 TRIAL inclusion criteria

    • High-risk for recurrent disease

PATIENT CHARACTERISTICS:

  • Able to tolerate imaging required by protocol

PRIOR CONCURRENT THERAPY:

  • Not specified
05

Study design

Phase
Not applicable
Primary purpose
Diagnostic
Allocation
Not applicable
Intervention model
Single group
Masking
None (open label)
Enrollment
406 participants (actual)

Study arms

  • Experimental
    Diffusion Weighted-MRI

    Participants on all arms of the I-SPY II trial will undergo diffusion-weighted magnetic resonance imaging as described in the ACRIN 6698 protocol. The experimental component/intervention is whether DW-MRI can predict therapeutic response in neoadjuvant treatment for breast cancer.

    Procedure: diffusion-weighted magnetic resonance imaging

Interventions

  • Procedurediffusion-weighted magnetic resonance imaging

    diffusion-weighted magnetic resonance imaging examination and subsequent radiologist interpretation

    Also known as: functional MRI, DWI, diffusion-weighted MRI, DW-MRI

06

What researchers measure

Primary outcomes

  1. Pathologic Complete Response (pCR)

    Pathologic complete response (pCR) is defined as the lack of all signs of cancer in tissue samples removed during surgery after Neoadjuvant treatment for Breast cancer. ie., no residual invasive disease in either breast or axillary lymph nodes after neoadjuvant therapy (ypT0/is, ypN0) Histopathologic analysis was performed using the Residual Cancer Burden system

    Time frame: Surgery

Secondary outcomes

  1. Functional Tumor Volume (FTV) as a Predictor of Pathologic Complete Response (pCR)

    Pathologic complete response (pCR) is defined as the lack of all signs of cancer in tissue samples removed during surgery after Neoadjuvant treatment for Breast cancer. ie., no residual invasive disease in either breast or axillary lymph nodes after neoadjuvant therapy (ypT0/is, ypN0) Histopathologic analysis was performed using the Residual Cancer Burden system Functional tumor volume (FTV) (units cm3) was computed by summing all tumor voxels meeting specific enhancement criteria, with customized thresholds for each site to account for variability in MR imaging systems

    Time frame: Surgery

  2. Determine the Accuracy of Predictive Models Including Covariates for Combined Measurement of Change in Tumor ADC Value, Change in Tumor Volume, and Other Variables

    Accuracy will be measured as the Area under the Receiver Operating Characteristic Curve (AUC) Predictive logistic regression modeling was performed in 207 patients with complete mid-treatment ΔADC and ΔFTV data. To build prediction models with ADC and other variables, a data-splitting approach was used where a randomly selected 60% of participants (124 patients), stratified according to pCR status and tumor subtype, were selected as the training data set and the rest (86 patients) as the test set. Logistic regression with backward variable selection was used to construct the prediction models, which were then applied to the remaining 40% of the data to obtain predictive scores for each participant.

    Time frame: baseline and mid-treatment

  3. Repeatability Coefficient (RC)Test-retest Metric for Reproducibility of ADC as Applied to Breast Tumors

    within-subject standard deviation (wSD) Repeatability coefficient (RC): \[RC = 2.77\*wSD\] (units: 10E-3 mm/sec\^2) Smaller values of RC, bounded \[0, ...), represent agreement

    Time frame: baseline (pre-treatment) or after 3 weeks of taxane-based treatment (early-treatment)

  4. Within-subject Coefficient of Variation (wCV) Test-retest Metric for Reproducibility of ADC as Applied to Breast Tumors

    within-subject standard deviation (wSD) Within-subject coefficient of variation (wCV): \[wCV = 100%\*wSD/mean\] Smaller values of wCV bounded for \[0,...) represent better agreement

    Time frame: baseline (pre-treatment) or after 3 weeks of taxane-based treatment (early-treatment)

  5. ICC Test-retest Metric for Reproducibility of ADC as Applied to Breast Tumors

    Test and retest DWI measurements for a given patient were performed on the same day in a single imaging session. Intraclass correlation coefficient (ICC) is derived from the analysis of variance (ANOVA) model estimates (Barnhart,Haber, Lin 2007), Larger values of ICC (bounded \[-1,1\]) represent agreement

    Time frame: baseline (pre-treatment) or after 3 weeks of taxane-based treatment (early-treatment)

  6. Agreement Index (AI) Test-retest Metric for Reproducibility of ADC as Applied to Breast Tumors

    Test and retest DWI measurements for a given patient were performed on the same day in a single imaging session. Agreement index (AI): (Zhang, Wang, Duan - 2014) is based on the data's overall ranking. AI confidence intervals were obtained via bootstrap method Larger values AI (bounded \[0.5,1\]) represent agreement

    Time frame: baseline (pre-treatment) or after 3 weeks of taxane-based treatment (early-treatment)

07

Results

Posted Feb 10, 2023

Participant flow

Participant flow — Overall Study
MilestoneDiffusion Weighted-MRI
Started406
Randomized in parent study272
Acceptable baseline imaging263
Acceptable post baseline image242
Baseline and early treatment usable227
Baseline and mid-treatment usable210
Baseline and post-treatment usable186
Usable re-test scan71
Completed242
Not completed164
Withdrew: Ineligible18
Withdrew: Not randomized in parent study116
Withdrew: Baseline imaging failed qc requirements9
Withdrew: No acceptable post-baseline imaging21

Outcome measures

PrimaryPathologic Complete Response (pCR)

Pathologic complete response (pCR) is defined as the lack of all signs of cancer in tissue samples removed during surgery after Neoadjuvant treatment for Breast cancer. ie., no residual invasive disease in either breast or axillary lymph nodes after neoadjuvant therapy (ypT0/is, ypN0) Histopathologic analysis was performed using the Residual Cancer Burden system

Time frame:
Surgery
Reported as:
Count of participants · Participants
Pathologic Complete Response (pCR)
ParticipantsEarly Treatment ChangeMid-Treatment ChangePost-Treatment Change
Pathological Complete Responders (pCR)717063
Non-Responders(pCR-)156140123
Statistical analysis
  • Early Treatment Change · Z-test · p = 0.484 (Bonferroni's correction was used for multiple comparisons adjustment, where p\<0.003 (0.05/15) was considered statistically significant.) · Area under the curve (auc): 0.53Receiver operating characteristic curve and AUC were estimated empirically, and its 95% CI was constructed using variance derived from DeLong's method
  • Mid-Treatment Change · Z-test · p = 0.017 (Bonferroni's correction was used for multiple comparisons adjustment, where p\<0.003 (0.05/15) was considered statistically significant.) · Area under the curve: 0.60Receiver operating characteristic curve and AUC were estimated empirically, and its 95% CI was constructed using variance derived from DeLong's method
  • Post-Treatment Change · Z-test · p = 0.013 (Bonferroni's correction was used for multiple comparisons adjustment, where p\<0.003 (0.05/15) is considered statistically significant.) · Area under the curve: 0.61Receiver operating characteristic curve and AUC were estimated empirically, and its 95% CI was constructed using variance derived from DeLong's method
SecondaryFunctional Tumor Volume (FTV) as a Predictor of Pathologic Complete Response (pCR)

Pathologic complete response (pCR) is defined as the lack of all signs of cancer in tissue samples removed during surgery after Neoadjuvant treatment for Breast cancer. ie., no residual invasive disease in either breast or axillary lymph nodes after neoadjuvant therapy (ypT0/is, ypN0) Histopathologic analysis was performed using the Residual Cancer Burden system Functional tumor volume (FTV) (units cm3) was computed by summing all tumor voxels meeting specific enhancement criteria, with customized thresholds for each site to account for variability in MR imaging systems

Time frame:
Surgery
Reported as:
Count of participants · Participants
Functional Tumor Volume (FTV) as a Predictor of Pathologic Complete Response (pCR)
ParticipantsEarly Treatment ChangeMid-Treatment ChangePost-Treatment Change
Pathological Complete Responders (pCR)717063
Non-Responders(pCR-)156140123
Statistical analysis
  • Early Treatment Change · Z-test · p = <0.001 (Bonferroni's correction was used for multiple comparisons adjustment, where p\<0.003 (0.05/15) was considered statistically significant.) · Area under the curve: 0.68Receiver operating characteristic curve and AUC were estimated empirically, and its 95% CI was constructed using variance derived from DeLong's method
  • Mid-Treatment Change · Z-test · p = <0.001 (Bonferroni's correction was used for multiple comparisons adjustment, where p\<0.003 (0.05/15) was considered statistically significant.) · Area under the curve: 0.63Receiver operating characteristic curve and AUC were estimated empirically, and its 95% CI was constructed using variance derived from DeLong's method
  • Post-Treatment Change · Z-test · p = <0.001 (Bonferroni's correction was used for multiple comparisons adjustment, where p\<0.003 (0.05/15) was considered statistically significant.) · Area under the curve: 0.68Receiver operating characteristic curve and AUC were estimated empirically, and its 95% CI was constructed using variance derived from DeLong's method
SecondaryDetermine the Accuracy of Predictive Models Including Covariates for Combined Measurement of Change in Tumor ADC Value, Change in Tumor Volume, and Other Variables

Accuracy will be measured as the Area under the Receiver Operating Characteristic Curve (AUC) Predictive logistic regression modeling was performed in 207 patients with complete mid-treatment ΔADC and ΔFTV data. To build prediction models with ADC and other variables, a data-splitting approach was used where a randomly selected 60% of participants (124 patients), stratified according to pCR status and tumor subtype, were selected as the training data set and the rest (86 patients) as the test set. Logistic regression with backward variable selection was used to construct the prediction models, which were then applied to the remaining 40% of the data to obtain predictive scores for each participant.

Time frame:
baseline and mid-treatment
Reported as:
Number · probability
Determine the Accuracy of Predictive Models Including Covariates for Combined Measurement of Change in Tumor ADC Value, Change in Tumor Volume, and Other Variables
probabilityFull Combined ModelOptimized ModelΔADC Alone
Determine the Accuracy of Predictive Models Including Covariates for Combined Measurement of Change in Tumor ADC Value, Change in Tumor Volume, and Other Variables0.71 (0.59 to 0.84)0.72 (0.61 to 0.83)0.57 (0.44 to 0.70)
Statistical analysis
  • Optimized Model vs ΔADC Alone · z-test · p = 0.032
SecondaryRepeatability Coefficient (RC)Test-retest Metric for Reproducibility of ADC as Applied to Breast Tumors

within-subject standard deviation (wSD) Repeatability coefficient (RC): \[RC = 2.77\*wSD\] (units: 10E-3 mm/sec\^2) Smaller values of RC, bounded \[0, ...), represent agreement

Time frame:
baseline (pre-treatment) or after 3 weeks of taxane-based treatment (early-treatment)
Reported as:
Number · 10E-3 mm/sec^2
Repeatability Coefficient (RC)Test-retest Metric for Reproducibility of ADC as Applied to Breast Tumors
10E-3 mm/sec^2Test-Retest Subjects
Repeatability Coefficient (RC)Test-retest Metric for Reproducibility of ADC as Applied to Breast Tumors0.16 (0.13 to 0.19)
SecondaryWithin-subject Coefficient of Variation (wCV) Test-retest Metric for Reproducibility of ADC as Applied to Breast Tumors

within-subject standard deviation (wSD) Within-subject coefficient of variation (wCV): \[wCV = 100%\*wSD/mean\] Smaller values of wCV bounded for \[0,...) represent better agreement

Time frame:
baseline (pre-treatment) or after 3 weeks of taxane-based treatment (early-treatment)
Reported as:
Number · coefficient of variation
Within-subject Coefficient of Variation (wCV) Test-retest Metric for Reproducibility of ADC as Applied to Breast Tumors
coefficient of variationTest-Retest Subjects
Within-subject Coefficient of Variation (wCV) Test-retest Metric for Reproducibility of ADC as Applied to Breast Tumors4.8 (4.0 to 5.7)
SecondaryICC Test-retest Metric for Reproducibility of ADC as Applied to Breast Tumors

Test and retest DWI measurements for a given patient were performed on the same day in a single imaging session. Intraclass correlation coefficient (ICC) is derived from the analysis of variance (ANOVA) model estimates (Barnhart,Haber, Lin 2007), Larger values of ICC (bounded \[-1,1\]) represent agreement

Time frame:
baseline (pre-treatment) or after 3 weeks of taxane-based treatment (early-treatment)
Reported as:
Number · correlation coefficient
ICC Test-retest Metric for Reproducibility of ADC as Applied to Breast Tumors
correlation coefficientTest-Retest Subjects
ICC Test-retest Metric for Reproducibility of ADC as Applied to Breast Tumors0.97 (0.95 to 0.98)
SecondaryAgreement Index (AI) Test-retest Metric for Reproducibility of ADC as Applied to Breast Tumors

Test and retest DWI measurements for a given patient were performed on the same day in a single imaging session. Agreement index (AI): (Zhang, Wang, Duan - 2014) is based on the data's overall ranking. AI confidence intervals were obtained via bootstrap method Larger values AI (bounded \[0.5,1\]) represent agreement

Time frame:
baseline (pre-treatment) or after 3 weeks of taxane-based treatment (early-treatment)
Reported as:
Number · probability
Agreement Index (AI) Test-retest Metric for Reproducibility of ADC as Applied to Breast Tumors
probabilityTest-Retest Subjects
Agreement Index (AI) Test-retest Metric for Reproducibility of ADC as Applied to Breast Tumors0.83 (0.76 to 0.87)

Adverse events

Collected over From registration to surgery or off study, for events occurring within 30 days of each DW-MRI exam. Non-serious events are listed at a 0% frequency threshold.

Adverse event summary by group
GroupDeathsSeriousOther
Diffusion Weighted-MRI0/406 (0%)0/406 (0%)0/406 (0%)

Baseline characteristics

Eligible randomized participants with a usable Baseline DWI-MRI and at least 1 other usable DWI scan at early-treatment, late-treatment, or pre-surgery

Age, Continuous
Age, Continuous(years)Diffusion Weighted-MRI
Mean48.1 ± 10.4
Sex/Gender, Customized
Sex/Gender, Customized(Participants)Diffusion Weighted-MRI
Female242
Ethnicity (NIH/OMB)
Ethnicity (NIH/OMB)(Participants)Diffusion Weighted-MRI
Hispanic or Latino23
Not Hispanic or Latino154
Unknown or Not Reported65
Race (NIH/OMB)
Race (NIH/OMB)(Participants)Diffusion Weighted-MRI
American Indian or Alaska Native0
Asian16
Native Hawaiian or Other Pacific Islander1
Black or African American26
White173
More than one race0
Unknown or Not Reported26
08

Study locations

7 sites
  • University of Alabama at Birmingham
    Birmingham, Alabama 35294, United States
  • University of California, San Francisco
    San Francisco, California 94143, United States
  • University of Minnesota
    Minneapolis, Minnesota 55455, United States
  • Oregon Health and Science University
    Portland, Oregon 97239, United States
  • University of Pennsylvania
    Philadelphia, Pennsylvania 19104, United States
  • University of Texas M.D. Anderson Cancer Center
    Houston, Texas 77030, United States
  • University of Washington/SCCA
    Seattle, Washington 98195, United States
09

References and documents

Publications

  • DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988 Sep;44(3):837-45. PubMed 3203132 ↗
  • Li W, Partridge SC, Newitt DC, Steingrimsson J, Marques HS, Bolan PJ, Hirano M, Bearce BA, Kalpathy-Cramer J, Boss MA, Teng X, Zhang J, Cai J, Kontos D, Cohen EA, Mankowski WC, Liu M, Ha R, Pellicer-Valero OJ, Maier-Hein K, Rabinovici-Cohen S, Tlusty T, Ozery-Flato M, Parekh VS, Jacobs MA, Yan R, Sung K, Kazerouni AS, DiCarlo JC, Yankeelov TE, Chenevert TL, Hylton NM. Breast Multiparametric MRI for Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer: The BMMR2 Challenge. Radiol Imaging Cancer. 2024 Jan;6(1):e230033. doi: 10.1148/rycan.230033. PubMed 38180338 ↗
  • Partridge SC, Zhang Z, Newitt DC, Gibbs JE, Chenevert TL, Rosen MA, Bolan PJ, Marques HS, Romanoff J, Cimino L, Joe BN, Umphrey HR, Ojeda-Fournier H, Dogan B, Oh K, Abe H, Drukteinis JS, Esserman LJ, Hylton NM; ACRIN 6698 Trial Team and I-SPY 2 Trial Investigators. Diffusion-weighted MRI Findings Predict Pathologic Response in Neoadjuvant Treatment of Breast Cancer: The ACRIN 6698 Multicenter Trial. Radiology. 2018 Dec;289(3):618-627. doi: 10.1148/radiol.2018180273. Epub 2018 Sep 4. PubMed 30179110 ↗
  • Newitt DC, Amouzandeh G, Partridge SC, Marques HS, Herman BA, Ross BD, Hylton NM, Chenevert TL, Malyarenko DI. Repeatability and Reproducibility of ADC Histogram Metrics from the ACRIN 6698 Breast Cancer Therapy Response Trial. Tomography. 2020 Jun;6(2):177-185. doi: 10.18383/j.tom.2020.00008. PubMed 32548294 ↗
  • Newitt DC, Zhang Z, Gibbs JE, Partridge SC, Chenevert TL, Rosen MA, Bolan PJ, Marques HS, Aliu S, Li W, Cimino L, Joe BN, Umphrey H, Ojeda-Fournier H, Dogan B, Oh K, Abe H, Drukteinis J, Esserman LJ, Hylton NM; ACRIN Trial Team and I-SPY 2 TRIAL Investigators. Test-retest repeatability and reproducibility of ADC measures by breast DWI: Results from the ACRIN 6698 trial. J Magn Reson Imaging. 2019 Jun;49(6):1617-1628. doi: 10.1002/jmri.26539. Epub 2018 Oct 22. PubMed 30350329 ↗
  • Partridge SC, Steingrimsson J, Newitt DC, Gibbs JE, Marques HS, Bolan PJ, Boss MA, Chenevert TL, Rosen MA, Hylton NM. Impact of Alternate b-Value Combinations and Metrics on the Predictive Performance and Repeatability of Diffusion-Weighted MRI in Breast Cancer Treatment: Results from the ECOG-ACRIN A6698 Trial. Tomography. 2022 Mar 4;8(2):701-717. doi: 10.3390/tomography8020058. PubMed 35314635 ↗
  • Newitt DC, Tan ET, Wilmes LJ, Chenevert TL, Kornak J, Marinelli L, Hylton N. Gradient nonlinearity correction to improve apparent diffusion coefficient accuracy and standardization in the american college of radiology imaging network 6698 breast cancer trial. J Magn Reson Imaging. 2015 Oct;42(4):908-19. doi: 10.1002/jmri.24883. Epub 2015 Mar 11. PubMed 25758543 ↗

Study documents

  • Protocol and statistical analysis plan · Apr 30, 2014

Documents are hosted by the registry — open the source record to download them.

Individual participant data

Plan to share: Yes — See ACRIN data sharing Policy https://www.acrin.org/RESEARCHERS/POLICIES/DATAANDIMAGESHARINGPOLICY.aspx

Supporting information: Study protocol, Sap

10

Updates

Tracking since Sep 25, 2026
No changes since tracking began. The registry record was last updated on Apr 15, 2024, before this site started recording changes on Sep 25, 2026. Its history is on ClinicalTrials.gov ↗
11

Registry details

Key details

Study ID
NCT01564368
Lead sponsor
American College of Radiology Imaging Network
Collaborators
National Cancer Institute (NCI)
Responsible party
Sponsor
First posted
Mar 27, 2012
Start date
Aug 27, 2012
Primary completion
Jul 19, 2018
Completion
Jan 14, 2020
Results posted
Feb 10, 2023
Last update
Apr 15, 2024

Study contacts

Nola M. Hylton, PhD
principal investigator · University of California, San Francisco

Oversight

Data monitoring committee
Yes
View the source record on ClinicalTrials.gov ↗

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