Association between background parenchymal enhancement and tumor response in patients with breast cancer receiving neoadjuvant chemotherapy

Rella, Rossella; Bufi, Enida; Belli, Paolo; Petta, Filomena; Serra, Tânia; Masiello, Valeria; Scrofani, Anna Rita; Barone, Roberto; Orlandi, Armando; Valentini, Vincenzo; Manfredi, Riccardo

doi:10.1016/j.diii.2020.05.010

Cited by 16 publications

(42 citation statements)

References 32 publications

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“…The relatively low sample size in the various categories represents our study limitation, even though it is in line with other published works [26,42] A second limit may be the retrospective nature of the study. From having analyzed only two MRI steps in a significant proportion of the sample, this limit could eventually be considered apparent since the most significant changes in BPE occurred in the first treatment phase, as reported in the literature [22][23][24]30]. Our study supported this consideration by highlighting the BPE class's confirmation between the intermediate and final step, showing a significant concordance in the results.…”

Section: Discussionsupporting

confidence: 83%

“…This aspect could be related to the combination of the anti-angiogenetic drug with common chemotherapeutic agents that, in addition to improving the tumor's response during chemotherapy, could operate through the normalization of the tumor vascular function while it converts non-functional vessels into functional ones. This would allow for more chemotherapeutic agents to reach the tumor [30]. This would also explain the lower BPE reduction related to the preserved tissue perfusion and the consequent higher percentage of pCR (18.8%) in HER2+ tumors compared to tumors observed in the study.…”

Section: Discussionmentioning

confidence: 78%

“…The qualitative measurement of these class parameters was also subject to inter-intraobserver variability [25,30,31,[36][37][38] and would require the consensus of three radiologists through BIRADS criteria [20,39]. Therefore, an independent qualitative analysis with three different operators was carried out in order to minimize variability.…”

Section: Discussionmentioning

confidence: 99%

“…However, it could also be related to ovarian suppression caused by chemotherapy and to the better vascularization and vasal permeability of certain types of breasts. This type of assessment is still being studied; particularly, the information concerning pre-treatment BPE intensity [4,6,22,[26][27][28][29][30] appears to be controversial. For the reasons mentioned above, BPE is considered an interesting diagnostic and prognostic indicator in breast oncology, yet some aspects are still not fully defined and are deserving of further studies.…”

Section: Introductionmentioning

confidence: 99%

“…This evaluation can be carried out by qualitative method, i.e., by the visual evaluation of one or more radiologists, or by automatic or semi-automatic quantitative methods [25,30,31]. It is important to note that assessing BPE using an objective and automated evaluation method may achieve opposite results [30,31]. Therefore, more in-depth studies are necessary along with more standardized methods to validate them.…”

Section: Introductionmentioning

confidence: 99%

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Response Predictivity to Neoadjuvant Therapies in Breast Cancer: A Qualitative Analysis of Background Parenchymal Enhancement in DCE-MRI

Forgia

Vestito

Lasciarrea

et al. 2021

JPM

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Background: For assessing the predictability of oncology neoadjuvant therapy results, the background parenchymal enhancement (BPE) parameter in breast magnetic resonance imaging (MRI) has acquired increased interest. This work aims to qualitatively evaluate the BPE parameter as a potential predictive marker for neoadjuvant therapy. Method: Three radiologists examined, in triple-blind modality, the MRIs of 80 patients performed before the start of chemotherapy, after three months from the start of treatment, and after surgery. They identified the portion of fibroglandular tissue (FGT) and BPE of the contralateral breast to the tumor in the basal control pre-treatment (baseline). Results: We observed a reduction of BPE classes in serial MRI checks performed during neoadjuvant therapy, as compared to baseline pre-treatment conditions, in 61.3% of patients in the intermediate step, and in 86.7% of patients in the final step. BPE reduction was significantly associated with sequential anthracyclines/taxane administration in the first cycle of neoadjuvant therapy compared to anti-HER2 containing therapies. The therapy response was also significantly related to tumor size. There were no associations with menopausal status, fibroglandular tissue (FGT) amount, age, BPE baseline, BPE in intermediate, and in the final MRI step. Conclusions: The measured variability of this parameter during therapy could predict therapy effectiveness in early stages, improving decision-making in the perspective of personalized medicine. Our preliminary results suggest that BPE may represent a predictive factor in response to neoadjuvant therapy in breast cancer, warranting future investigations in conjunction with radiomics.

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Section: Discussionsupporting

confidence: 83%

Section: Discussionmentioning

confidence: 78%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Response Predictivity to Neoadjuvant Therapies in Breast Cancer: A Qualitative Analysis of Background Parenchymal Enhancement in DCE-MRI

Forgia

Vestito

Lasciarrea

et al. 2021

JPM

View full text Add to dashboard Cite

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Predicting pathological complete response to neoadjuvant chemotherapy in breast cancer patients: use of MRI radiomics data from three regions with multiple machine learning algorithms

Zheng,

Peng,

Shu

et al. 2024

J Cancer Res Clin Oncol

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Objective To construct a multi-region MRI radiomics model for predicting pathological complete response (pCR) in breast cancer (BCa) patients who received neoadjuvant chemotherapy (NACT) and provide a theoretical basis for the peritumoral microenvironment affecting the efficacy of NACT. Methods A total of 133 BCa patients who received NACT, including 49 with confirmed pCR, were retrospectively analyzed. The radiomics features of the intratumoral region, peritumoral region, and background parenchymal enhancement (BPE) were extracted, and the most relevant features were obtained after dimensional reduction. Then, combining different areas, multivariate logistic regression analysis was used to select the optimal feature set, and six different machine learning models were used to predict pCR. The optimal model was selected, and its performance was evaluated using receiver operating characteristic (ROC) analysis. SHAP analysis was used to examine the relationship between the features of the model and pCR. Results For signatures constructed using three individual regions, BPE provided the best predictions of pCR, and the diagnostic performance of the intratumoral and peritumoral regions improved after adding the BPE signature. The radiomics signature from the combination of all the three regions with the XGBoost machine learning algorithm provided the best predictions of pCR based on AUC (training set: 0.891, validation set: 0.861), sensitivity (training set: 0.882, validation set: 0.800), and specificity (training set: 0.847, validation set: 0.84). SHAP analysis demonstrated that LZ_log.sigma.2.0.mm.3D_glcm_ClusterShade_T12 made the greatest contribution to the predictions of this model. Conclusion The addition of the BPE MRI signature improved the prediction of pCR in BCa patients who received NACT. These results suggest that the features of the peritumoral microenvironment are related to the efficacy of NACT.

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Association between mammographic breast density and outcome in patients with unilateral invasive breast cancer receiving neoadjuvant chemotherapy

Rella,

Belli,

Romanucci

et al. 2024

Breast Cancer Res Treat

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Association between background parenchymal enhancement and tumor response in patients with breast cancer receiving neoadjuvant chemotherapy

Cited by 16 publications

References 32 publications

Response Predictivity to Neoadjuvant Therapies in Breast Cancer: A Qualitative Analysis of Background Parenchymal Enhancement in DCE-MRI

Response Predictivity to Neoadjuvant Therapies in Breast Cancer: A Qualitative Analysis of Background Parenchymal Enhancement in DCE-MRI

Predicting pathological complete response to neoadjuvant chemotherapy in breast cancer patients: use of MRI radiomics data from three regions with multiple machine learning algorithms

Association between mammographic breast density and outcome in patients with unilateral invasive breast cancer receiving neoadjuvant chemotherapy

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