Dataset Information

Assessment of Response to Neoadjuvant Systemic Treatment in Triple-Negative Breast Cancer Using Functional Tumor Volumes from Longitudinal Dynamic Contrast-Enhanced MRI.

ABSTRACT: Early assessment of neoadjuvant systemic therapy (NAST) response for triple-negative breast cancer (TNBC) is critical for patient care in order to avoid the unnecessary toxicity of an ineffective treatment. We assessed functional tumor volumes (FTVs) from dynamic contrast-enhanced (DCE) MRI after 2 cycles (C2) and 4 cycles (C4) of NAST as predictors of response in TNBC. A group of 100 patients with stage I-III TNBC who underwent DCE MRI at baseline, C2, and C4 were included in this study. Tumors were segmented on DCE images of 1 min and 2.5 min post-injection. FTVs were measured using the optimized percentage enhancement (PE) and signal enhancement ratio (SER) thresholds. The Mann-Whitney test was used to compare the performance of the FTVs at C2 and C4. Of the 100 patients, 49 (49%) had a pathologic complete response (pCR) and 51 (51%) had a non-pCR. The maximum area under the receiving operating characteristic curve (AUC) for predicting the treatment response was 0.84 (p < 0.001) for FTV at C4 followed by FTV at C2 (AUC = 0.82, p < 0.001). The FTV measured at baseline was not able to discriminate pCR from non-pCR. FTVs measured on DCE MRI at C2, as well as at C4, of NAST can potentially predict pCR and non-pCR in TNBC patients.

SUBMITTER: Panthi B

PROVIDER: S-EPMC9953797 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature

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Assessment of Response to Neoadjuvant Systemic Treatment in Triple-Negative Breast Cancer Using Functional Tumor Volumes from Longitudinal Dynamic Contrast-Enhanced MRI.

Panthi Bikash B Adrada Beatriz E BE Candelaria Rosalind P RP Guirguis Mary S MS Yam Clinton C Boge Medine M Chen Huiqin H Hunt Kelly K KK Huo Lei L Hwang Ken-Pin KP Korkut Anil A Lane Deanna L DL Le-Petross Huong C HC Leung Jessica W T JWT Litton Jennifer K JK Mohamed Rania M RM Musall Benjamin C BC Pashapoor Sanaz S Patel Miral M MM Perez Frances F Son Jong Bum JB Thompson Alastair A Valero Vicente V Wei Peng P White Jason J Xu Zhan Z Pinsky Lawrence L Tripathy Debu D Yang Wei W Ma Jingfei J Rauch Gaiane M GM

Cancers 20230206 4

Early assessment of neoadjuvant systemic therapy (NAST) response for triple-negative breast cancer (TNBC) is critical for patient care in order to avoid the unnecessary toxicity of an ineffective treatment. We assessed functional tumor volumes (FTVs) from dynamic contrast-enhanced (DCE) MRI after 2 cycles (C2) and 4 cycles (C4) of NAST as predictors of response in TNBC. A group of 100 patients with stage I-III TNBC who underwent DCE MRI at baseline, C2, and C4 were included in this study. Tumors ...[more]

PMID: 36831368

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Project description:ObjectivesTo determine the added discriminative value of detailed quantitative characterization of background parenchymal enhancement in addition to the tumor itself on dynamic contrast-enhanced (DCE) MRI at 3.0 Tesla in identifying "triple-negative" breast cancers.Materials and methodsIn this Institutional Review Board-approved retrospective study, DCE-MRI of 84 women presenting 88 invasive carcinomas were evaluated by a radiologist and analyzed using quantitative computer-aided techniques. Each tumor and its surrounding parenchyma were segmented semi-automatically in 3-D. A total of 85 imaging features were extracted from the two regions, including morphologic, densitometric, and statistical texture measures of enhancement. A small subset of optimal features was selected using an efficient sequential forward floating search algorithm. To distinguish triple-negative cancers from other subtypes, we built predictive models based on support vector machines. Their classification performance was assessed with the area under receiver operating characteristic curve (AUC) using cross-validation.ResultsImaging features based on the tumor region achieved an AUC of 0.782 in differentiating triple-negative cancers from others, in line with the current state of the art. When background parenchymal enhancement features were included, the AUC increased significantly to 0.878 (p<0.01). Similar improvements were seen in nearly all subtype classification tasks undertaken. Notably, amongst the most discriminating features for predicting triple-negative cancers were textures of background parenchymal enhancement.ConclusionsConsidering the tumor as well as its surrounding parenchyma on DCE-MRI for radiomic image phenotyping provides useful information for identifying triple-negative breast cancers. Heterogeneity of background parenchymal enhancement, characterized by quantitative texture features on DCE-MRI, adds value to such differentiation models as they are strongly associated with the triple-negative subtype. Prospective validation studies are warranted to confirm these findings and determine potential implications.

Dataset Information

Assessment of Response to Neoadjuvant Systemic Treatment in Triple-Negative Breast Cancer Using Functional Tumor Volumes from Longitudinal Dynamic Contrast-Enhanced MRI.

Publications

Assessment of Response to Neoadjuvant Systemic Treatment in Triple-Negative Breast Cancer Using Functional Tumor Volumes from Longitudinal Dynamic Contrast-Enhanced MRI.

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