دورية أكاديمية
أعرض في EDS

Predicting breast cancer response to neoadjuvant treatment using multi-feature MRI: results from the I-SPY 2 TRIAL.

التفاصيل البيبلوغرافية
العنوان: Predicting breast cancer response to neoadjuvant treatment using multi-feature MRI: results from the I-SPY 2 TRIAL.
المؤلفون: Li W; University of California, San Francisco, CA, USA., Newitt DC; University of California, San Francisco, CA, USA., Gibbs J; University of California, San Francisco, CA, USA., Wilmes LJ; University of California, San Francisco, CA, USA., Jones EF; University of California, San Francisco, CA, USA., Arasu VA; University of California, San Francisco, CA, USA., Strand F; University of California, San Francisco, CA, USA.; Karolinska Institute, Stockholm, Sweden., Onishi N; University of California, San Francisco, CA, USA., Nguyen AA; University of California, San Francisco, CA, USA., Kornak J; University of California, San Francisco, CA, USA., Joe BN; University of California, San Francisco, CA, USA., Price ER; University of California, San Francisco, CA, USA., Ojeda-Fournier H; University of California, San Diego, CA, USA., Eghtedari M; University of California, San Diego, CA, USA., Zamora KW; University of Alabama, Birmingham, AL, USA., Woodard SA; University of Alabama, Birmingham, AL, USA., Umphrey H; University of Alabama, Birmingham, AL, USA., Bernreuter W; University of Alabama, Birmingham, AL, USA., Nelson M; University of Minnesota, Minneapolis, MN, USA., Church AL; University of Minnesota, Minneapolis, MN, USA., Bolan P; University of Minnesota, Minneapolis, MN, USA., Kuritza T; Loyola University, Maywood, IL, USA., Ward K; Loyola University, Maywood, IL, USA., Morley K; Loyola University, Maywood, IL, USA., Wolverton D; University of Colorado, Denver, CO, USA., Fountain K; University of Colorado, Denver, CO, USA., Lopez-Paniagua D; University of Colorado, Denver, CO, USA., Hardesty L; University of Colorado, Denver, CO, USA., Brandt K; Mayo Clinic, Rochester, NY, USA., McDonald ES; University of Pennsylvania, Philadelphia, PA, USA., Rosen M; University of Pennsylvania, Philadelphia, PA, USA., Kontos D; University of Pennsylvania, Philadelphia, PA, USA., Abe H; University of Chicago, Chicago, IL, USA., Sheth D; University of Chicago, Chicago, IL, USA., Crane EP; Georgetown University, Georgetown, DC, USA., Dillis C; Georgetown University, Georgetown, DC, USA., Sheth P; University of Southern California, Los Angeles, CA, USA., Hovanessian-Larsen L; University of Southern California, Los Angeles, CA, USA., Bang DH; Swedish Cancer Institute, Seattle, WA, USA., Porter B; Swedish Cancer Institute, Seattle, WA, USA., Oh KY; Oregon Health & Science University, Portland, OR, USA., Jafarian N; Oregon Health & Science University, Portland, OR, USA., Tudorica A; Oregon Health & Science University, Portland, OR, USA., Niell BL; Moffitt Cancer Center, Tampa, FL, USA., Drukteinis J; Moffitt Cancer Center, Tampa, FL, USA., Newell MS; Emory University, Atlanta, GA, USA., Cohen MA; Emory University, Atlanta, GA, USA., Giurescu M; Mayo Clinic, Scottsdale, AZ, USA., Berman E; Inova Health System, Falls Church, VA, USA., Lehman C; University of Washington, Seattle, WA, USA., Partridge SC; University of Washington, Seattle, WA, USA., Fitzpatrick KA; University of Arizona, Tucson, AZ, USA., Borders MH; University of Arizona, Tucson, AZ, USA., Yang WT; University of Texas, M.D. Anderson Cancer Center, Houston, TX, USA., Dogan B; University of Texas, M.D. Anderson Cancer Center, Houston, TX, USA., Goudreau S; University of Texas Southwestern, Dallas, TX, USA., Chenevert T; University of Michigan, Ann Arbor, MI, USA., Yau C; University of California, San Francisco, CA, USA., DeMichele A; University of Pennsylvania, Philadelphia, PA, USA., Berry D; Berry Consultants, LLC, Austin, TX, USA., Esserman LJ; University of California, San Francisco, CA, USA., Hylton NM; University of California, San Francisco, CA, USA. nola.hylton@ucsf.edu.
المصدر: NPJ breast cancer [NPJ Breast Cancer] 2020 Nov 27; Vol. 6 (1), pp. 63. Date of Electronic Publication: 2020 Nov 27.
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: Breast Cancer Research Foundation Country of Publication: United States NLM ID: 101674891 Publication Model: Electronic Cited Medium: Print ISSN: 2374-4677 (Print) Linking ISSN: 23744677 NLM ISO Abbreviation: NPJ Breast Cancer Subsets: PubMed not MEDLINE
أسماء مطبوعة: Original Publication: New York, NY : Breast Cancer Research Foundation : Nature Publishing Group, [2015]-
مستخلص: Dynamic contrast-enhanced (DCE) MRI provides both morphological and functional information regarding breast tumor response to neoadjuvant chemotherapy (NAC). The purpose of this retrospective study is to test if prediction models combining multiple MRI features outperform models with single features. Four features were quantitatively calculated in each MRI exam: functional tumor volume, longest diameter, sphericity, and contralateral background parenchymal enhancement. Logistic regression analysis was used to study the relationship between MRI variables and pathologic complete response (pCR). Predictive performance was estimated using the area under the receiver operating characteristic curve (AUC). The full cohort was stratified by hormone receptor (HR) and human epidermal growth factor receptor 2 (HER2) status (positive or negative). A total of 384 patients (median age: 49 y/o) were included. Results showed analysis with combined features achieved higher AUCs than analysis with any feature alone. AUCs estimated for the combined versus highest AUCs among single features were 0.81 (95% confidence interval [CI]: 0.76, 0.86) versus 0.79 (95% CI: 0.73, 0.85) in the full cohort, 0.83 (95% CI: 0.77, 0.92) versus 0.73 (95% CI: 0.61, 0.84) in HR-positive/HER2-negative, 0.88 (95% CI: 0.79, 0.97) versus 0.78 (95% CI: 0.63, 0.89) in HR-positive/HER2-positive, 0.83 (95% CI not available) versus 0.75 (95% CI: 0.46, 0.81) in HR-negative/HER2-positive, and 0.82 (95% CI: 0.74, 0.91) versus 0.75 (95% CI: 0.64, 0.83) in triple negatives. Multi-feature MRI analysis improved pCR prediction over analysis of any individual feature that we examined. Additionally, the improvements in prediction were more notable when analysis was conducted according to cancer subtype.
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معلومات مُعتمدة: U01 CA225427 United States CA NCI NIH HHS; R01 CA227763 United States CA NCI NIH HHS; R01 CA197000 United States CA NCI NIH HHS; R01 CA132870 United States CA NCI NIH HHS; U01 CA151235 United States CA NCI NIH HHS; P01 CA210961 United States CA NCI NIH HHS
تواريخ الأحداث: Date Created: 20201210 Latest Revision: 20231218
رمز التحديث: 20231218
مُعرف محوري في PubMed: PMC7695723
DOI: 10.1038/s41523-020-00203-7
PMID: 33298938
قاعدة البيانات: MEDLINE
الوصف
تدمد:2374-4677
DOI:10.1038/s41523-020-00203-7