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| ID | Type | Description | Link |
|---|---|---|---|
| ACRIN-6698 | Other Identifier | NCI CIP | |
| U01CA080098 | U.S. NIH Grant/Contract | View source | |
| U01CA079778 | U.S. NIH Grant/Contract | View source |
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| Name | Class |
|---|---|
| National Cancer Institute (NCI) | NIH |
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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.
OBJECTIVES:
Primary
Secondary
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Diffusion Weighted-MRI | Experimental | 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. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| diffusion-weighted magnetic resonance imaging | Procedure | diffusion-weighted magnetic resonance imaging examination and subsequent radiologist interpretation |
|
| Measure | Description | Time Frame |
|---|---|---|
| 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 | Surgery |
| Measure | Description | Time Frame |
|---|---|---|
| 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 |
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DISEASE CHARACTERISTICS:
Meets I-SPY 2 TRIAL inclusion criteria
PATIENT CHARACTERISTICS:
PRIOR CONCURRENT THERAPY:
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| Name | Affiliation | Role |
|---|---|---|
| Nola M. Hylton, PhD | University of California, San Francisco | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| University of Alabama at Birmingham | Birmingham | Alabama | 35294 | United States | ||
| University of California, San Francisco |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 3203132 | Background | 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. | |
| 38180338 | Result | 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. |
| Label | URL |
|---|---|
| National Cancer Institute's Clinical trial database | View source |
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See ACRIN data sharing Policy https://www.acrin.org/RESEARCHERS/POLICIES/DATAANDIMAGESHARINGPOLICY.aspx
6mo post publication
upon request
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| ID | Title | Description |
|---|---|---|
| FG000 | 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 (pathological Complete Response: pCR) in neoadjuvant treatment for breast cancer. |
| Title | Milestones | Reasons Not Completed | |||||
|---|---|---|---|---|---|---|---|
| Overall Study |
|
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| Type | Includes Protocol | Includes SAP | Includes ICF | Document Label | Document Date | Document Uploaded Date | Document File Name |
|---|---|---|---|---|---|---|---|
| Prot_SAP | Yes | Yes | No | Study Protocol and Statistical Analysis Plan | Apr 30, 2014 |
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|
| Surgery |
| 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. | baseline and mid-treatment |
| 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](streamdown:incomplete-link) | baseline (pre-treatment) or after 3 weeks of taxane-based treatment (early-treatment) |
| 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](streamdown:incomplete-link) | baseline (pre-treatment) or after 3 weeks of taxane-based treatment (early-treatment) |
| 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 | baseline (pre-treatment) or after 3 weeks of taxane-based treatment (early-treatment) |
| 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 | baseline (pre-treatment) or after 3 weeks of taxane-based treatment (early-treatment) |
| 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 |
| 30179110 | Result | 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. |
| 32548294 | Result | 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. |
| 30350329 | Result | 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. |
| 35314635 | Result | 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. |
| 25758543 | Result | 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. |
| Randomized in Parent Study |
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| Acceptable Baseline Imaging |
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| Acceptable Post Baseline Image |
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| Baseline and Early Treatment Usable |
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| Baseline and Mid-treatment Usable |
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| Baseline and Post-Treatment Usable |
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| Usable Re-test Scan |
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| COMPLETED |
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| NOT COMPLETED |
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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
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| ID | Title | Description |
|---|---|---|
| BG000 | Diffusion Weighted-MRI | Participants on all arms of the I-SPY II trial with both a diffusion-weighted magnetic resonance imaging (DWI-MRI) scan at baseline and 1 post-baseline timepoint (early-treatment, mid-treatment, or post-treatment). The experimental component/intervention is whether DW-MRI can predict therapeutic response in women receiving neoadjuvant treatment for breast cancer. |
| Units | Counts |
|---|---|
| Participants |
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| Title | Description | Population Description | Parameter Type | Dispersion Type | Unit of Measure | Calculate Percentage | Denominator Units Selected | Denominators | Classes | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Age, Continuous | Mean | Standard Deviation | years |
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| Sex/Gender, Customized | Count of Participants | Participants |
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| Ethnicity (NIH/OMB) | Count of Participants | Participants |
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| Race (NIH/OMB) | Count of Participants | Participants |
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| Type | Title | Description | Population Description | Reporting Status | Anticipated Posting Date | Parameter Type | Dispersion Type | Unit of Measure | Calculate Percentage | Time Frame | Units Analyzed | Denominator Units Selected | Arm/Group Information | Denominators | Classes | Analyses | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Primary | 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 | Posted | Count of Participants | Participants | Surgery |
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| Secondary | 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 | Posted | Count of Participants | Participants | Surgery |
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| Secondary | 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. | Posted | Number | 95% Confidence Interval | probability | baseline and mid-treatment |
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Secondary | 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](streamdown:incomplete-link) | Posted | Number | 95% Confidence Interval | 10E-3 mm/sec^2 | baseline (pre-treatment) or after 3 weeks of taxane-based treatment (early-treatment) |
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| Secondary | 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](streamdown:incomplete-link) | Posted | Number | 95% Confidence Interval | coefficient of variation | baseline (pre-treatment) or after 3 weeks of taxane-based treatment (early-treatment) |
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| Secondary | 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 | Posted | Number | 95% Confidence Interval | correlation coefficient | baseline (pre-treatment) or after 3 weeks of taxane-based treatment (early-treatment) |
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| Secondary | 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 | Posted | Number | 95% Confidence Interval | probability | baseline (pre-treatment) or after 3 weeks of taxane-based treatment (early-treatment) |
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From registration to surgery or off study, for events occurring within 30 days of each DW-MRI exam
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| ID | Title | Description | Deaths (Affected) | Deaths (At Risk) | Serious Events (Affected) | Serious Events (At Risk) | Other Events (Affected) | Other Events (At Risk) |
|---|---|---|---|---|---|---|---|---|
| EG000 | 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. diffusion-weighted magnetic resonance imaging: diffusion-weighted magnetic resonance imaging examination and subsequent radiologist interpretation | 0 | 406 | 0 | 406 | 0 | 406 |
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| Title | Organization | Phone | Extension | |
|---|---|---|---|---|
| Senior Director Clinical Research Administration | American College of Radiology | 215-574-3150 | info@acr.org |
| Jul 3, 2019 |
| Prot_SAP_000.pdf |
| ID | Term |
|---|---|
| D001943 | Breast Neoplasms |
| ID | Term |
|---|---|
| D009371 | Neoplasms by Site |
| D009369 | Neoplasms |
| D001941 | Breast Diseases |
| D012871 | Skin Diseases |
| D017437 | Skin and Connective Tissue Diseases |
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| Unknown or Not Reported |
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| Native Hawaiian or Other Pacific Islander |
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| Black or African American |
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| White |
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| More than one race |
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| Unknown or Not Reported |
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Receiver operating characteristic curve and AUC were estimated empirically, and its 95% CI was constructed using variance derived from DeLong's method
| Superiority |
| Receiver Operating Characteristic curves and the corresponding Area Under the Curve (AUC), were estimated for the change in Apparent Diffusion Coefficients (ADC2 - ADC0)/ADC0 (Test: %change in ADC; Reference: pCR) detect a difference of 0.15 between the AUC under H0: AUC=0.5 and an AUC under the alternative hypothesis of 0.65. It was assumed that the number of pCR non-responders would be approximately 2.7 times greater than the number of complete responders | Z-test | The empirical AUC was tested by using variance derived from the method of DeLong | 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 | 0.60 | 1-Sided | 95 | 0.68 | Receiver operating characteristic curve and AUC were estimated empirically, and its 95% CI was constructed using variance derived from DeLong's method | Superiority |
| Receiver Operating Characteristic curves and the corresponding Area Under the Curve (AUC), were estimated for the change in Apparent Diffusion Coefficients (ADC3 - ADC0)/ADC0 (Test: %change in ADC; Reference: pCR) detect a difference of 0.15 between the AUC under H0: AUC=0.5 and an AUC under the alternative hypothesis of 0.65. It was assumed that the number of pCR non-responders would be approximately 2.7 times greater than the number of complete responders | Z-test | The empirical AUC was tested by using variance derived from the method of DeLong | 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 | 0.61 | 1-Sided | 95 | 0.69 | Receiver operating characteristic curve and AUC were estimated empirically, and its 95% CI was constructed using variance derived from DeLong's method | Superiority |
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| OG002 | ΔADC Alone | Tumor ADC change (the percentage change from the pretreatment to mid-treatment (ΔADC)) as the sole predictor of pCR |
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