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MRI-based response patterns during neoadjuvant chemotherapy can predict pathological (complete) response in patients with breast cancer.

ABSTRACT: BACKGROUND:The main purpose was to investigate the correlation between magnetic resonance imaging (MRI)-based response patterns halfway through neoadjuvant chemotherapy and immunotherapy (NAC) and pathological tumor response in patients with breast cancer. Secondary purposes were to compare the predictive value of MRI-based response patterns measured halfway through NAC and after NAC and to measure interobserver variability. METHODS:All consecutive patients treated with NAC for primary invasive breast cancer from 2012 to 2015 and who underwent breast MRI before, halfway through (and after) NAC were included. All breast tumors were reassessed on MRI by two experienced breast radiologists and classified into six patterns: type 0 (complete radiologic response); type 1 (concentric shrinkage); type 2 (crumbling); type 3 (diffuse enhancement); type 4 (stable disease); type 5 (progressive disease). Percentages of tumors showing pathological complete response (pCR), >?50% tumor reduction and >?50% tumor diameter reduction per MRI-based response pattern were calculated. Correlation between MRI-based response patterns and pathological tumor reduction was studied with Pearson's correlation coefficient, and interobserver agreement was tested with Cohen's Kappa. RESULTS:Patients (n = 76; mean age 53, range 29-72 years) with 80 tumors (4 bilateral) were included. There was significant correlation between these MRI-based response patterns halfway through NAC and tumor reduction on pathology assessment (reader 1 r = 0.33; p = 0.003 and reader 2 r = 0.45; p??50% tumor reduction in 90%, 78% and 65% of cases, respectively. In 83% of tumors with type 0 halfway through NAC, pathology assessment showed pCR. There was no significant correlation between MRI-based response patterns after NAC and tumor reduction rates on pathology assessment (reader 1 r = - 0.17; p = 0.145 and reader 2 r = - 0.17; p = 0.146). In 41% of tumors with type 0 after NAC, pathology assessment showed pCR. CONCLUSION:MRI-based response patterns halfway through NAC can predict pathologic response more accurately than MRI-based response patterns after NAC. Complete radiological response halfway NAC is associated with 83% pCR, while complete radiological response after NAC seems to be correct in only 41% of cases.

SUBMITTER: Goorts B

PROVIDER: S-EPMC5907188 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature

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MRI-based response patterns during neoadjuvant chemotherapy can predict pathological (complete) response in patients with breast cancer.

Goorts Briete B Dreuning Kelly M A KMA Houwers Janneke B JB Kooreman Loes F S LFS Boerma Evert-Jan G EG Mann Ritse M RM Lobbes Marc B I MBI Smidt Marjolein L ML

Breast cancer research : BCR 20180418 1

<h4>Background</h4>The main purpose was to investigate the correlation between magnetic resonance imaging (MRI)-based response patterns halfway through neoadjuvant chemotherapy and immunotherapy (NAC) and pathological tumor response in patients with breast cancer. Secondary purposes were to compare the predictive value of MRI-based response patterns measured halfway through NAC and after NAC and to measure interobserver variability.<h4>Methods</h4>All consecutive patients treated with NAC for pr ...[more]

PMID: 29669584

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Project description:BackgroundBreast cancer with pathological non-complete response (non-pCR) after neoadjuvant chemotherapy (NAC) has a worse prognosis. Despite Neo-Bioscore has been validated as an independent prognostic model for breast cancer submitted to NAC, non-pCR carcinoma was not assessed in this setting.MethodsThis is a retrospective trial that included women with localized breast cancer who underwent NAC and had non-pCR carcinoma in surgical specimen between 01/01/2013 to 12/31/2015 with a three-year follow-up. Survival analysis was performed by Kaplan-Meier estimator and hazard ratio (HR) set by log-rank test for the primary and secondary endpoints, respectively Disease-Free Survival (DFS) and Overall Survival (OS). According to Neo-Bioscore, the proposed prognostic model named Clustered Neo-Bioscore was classified into low (0-3), low-intermediate (4-5), high-intermediate (6) and high (7) risk. The prognostic accuracy for recurrence risk was assessed by time-dependent receiver operating characteristic (time-ROC) methodology. Multivariate Cox regression assessed the menopausal status, histological grade, Ki-67, estrogen receptor, HER2, tumor subtype, pathological and clinical stages. Confidence interval at 95% (CI95%) and statistical significance at set 2-sided p-value less than 0.05 were adopted.ResultsAmong the 310 women enrolled, 267 patients (86.2%) had non-pCR carcinoma presenting size T3/T4 (63.3%), node-positive axilla (74.9%), stage III (62.9%), Ki-67???20% (71.9%) and non-luminal A (78.3%). Non-pCR carcinoma presented worse DFS-3y (HR?=?3.88, CI95%?=?1.18-11.95) but not OS-3y (HR?=?2.73, CI95%?=?0.66-11.40). Clustered Neo-Bioscore discerned the recurrence risk for non-pCR carcinoma: low (DFS-3y?=?0.86; baseline), low-intermediate (DFS-3y?=?0.70; HR?=?2.61), high-intermediate (DFS-3y?=?0.13, HR?=?14.05), and high (DFS-3y?=?not achieved; HR?=?22.19). The prognostic accuracy was similar between Clustered Neo-Bioscore and Neo-Bioscore (0.76 vs 0.78, p?>?0.05). Triple-negative subtype (HR?=?3.6, CI95%?=?1.19-10.92) and pathological stages II (HR?=?5.35, CI95%?=?1.19-24.01) and III (HR?=?6.56, CI95%?=?1.29-33.32) were prognoses for low-intermediate risk, whereas pathological stage III (HR?=?13.0, CI95%?=?1.60-106.10) was prognosis for low risk.ConclusionsClustered Neo-Bioscore represents a novel prognostic model of non-pCR carcinoma undergoing NAC with a more simplified and appropriate score pattern in the assessment of prognostic factors.

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MRI-based response patterns during neoadjuvant chemotherapy can predict pathological (complete) response in patients with breast cancer.

Publications

MRI-based response patterns during neoadjuvant chemotherapy can predict pathological (complete) response in patients with breast cancer.

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