Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI.
Background: In this study, we evaluated the ability of radiomic textural analysis of intratumoral and peritumoral regions on pretreatment breast cancer dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to predict pathological complete response (pCR) to neoadjuvant chemotherapy (NAC).Met...
| Published in: | Breast Cancer Research Vol. 19; pp. 1 - 15 |
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| Main Authors: | , , , , , , , , |
| Format: | pictorial research tables/charts Journal Article |
| Published: |
BioMed Central
5/18/2017
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=123155838&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 123155838 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14655411 8UYJ jtl: Breast Cancer Research issn: 14655411 maglogo: N pubinfo: dt: 5/18/2017 vid: 19 pid: 24147 pub: BioMed Central artinfo: ui: 123155838 123155838 NLM28521821 123155838 10.1186/s13058-017-0846-1 NLM28521821 123155838 ppf: 1 ppct: 14 formats: fmt: @attributes: type: P tig: atl: Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI. aug: au: Braman, Nathaniel M. Etesami, Maryam Prasanna, Prateek Dubchuk, Christina Gilmore, Hannah Tiwari, Pallavi Pletcha, Donna Madabhushi, Anant Plecha, Donna affil: Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106, USA sug: subj: Receptors, Cell Surface Magnetic Resonance Imaging Methods Breast Neoplasms Contrast Media Administration and Dosage Female Breast Neoplasms Pathology Neoadjuvant Therapy Adult Breast Pathology Antineoplastic Agents, Combined Administration and Dosage Breast Neoplasms Drug Therapy Middle Age Human Adult: 19-44 years Middle Aged: 45-64 years Female ab: Background: In this study, we evaluated the ability of radiomic textural analysis of intratumoral and peritumoral regions on pretreatment breast cancer dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to predict pathological complete response (pCR) to neoadjuvant chemotherapy (NAC).Methods: A total of 117 patients who had received NAC were retrospectively analyzed. Within the intratumoral and peritumoral regions of T1-weighted contrast-enhanced MRI scans, a total of 99 radiomic textural features were computed at multiple phases. Feature selection was used to identify a set of top pCR-associated features from within a training set (n = 78), which were then used to train multiple machine learning classifiers to predict the likelihood of pCR for a given patient. Classifiers were then independently tested on 39 patients. Experiments were repeated separately among hormone receptor-positive and human epidermal growth factor receptor 2-negative (HR+, HER2-) and triple-negative or HER2+ (TN/HER2+) tumors via threefold cross-validation to determine whether receptor status-specific analysis could improve classification performance.Results: Among all patients, a combined intratumoral and peritumoral radiomic feature set yielded a maximum AUC of 0.78 ± 0.030 within the training set and 0.74 within the independent testing set using a diagonal linear discriminant analysis (DLDA) classifier. Receptor status-specific feature discovery and classification enabled improved prediction of pCR, yielding maximum AUCs of 0.83 ± 0.025 within the HR+, HER2- group using DLDA and 0.93 ± 0.018 within the TN/HER2+ group using a naive Bayes classifier. In HR+, HER2- breast cancers, non-pCR was characterized by elevated peritumoral heterogeneity during initial contrast enhancement. However, TN/HER2+ tumors were best characterized by a speckled enhancement pattern within the peritumoral region of nonresponders. Radiomic features were found to strongly predict pCR independent of choice of classifier, suggesting their robustness as response predictors.Conclusions: Through a combined intratumoral and peritumoral radiomics approach, we could successfully predict pCR to NAC from pretreatment breast DCE-MRI, both with and without a priori knowledge of receptor status. Further, our findings suggest that the radiomic features most predictive of response vary across different receptor subtypes. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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