Literature DB >> 33915842

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

Daniele La Forgia1, Angela Vestito2, Maurilia Lasciarrea2, Maria Colomba Comes3, Sergio Diotaiuti4, Francesco Giotta5, Agnese Latorre5, Vito Lorusso5, Raffaella Massafra3, Gennaro Palmiotti6, Lucia Rinaldi6, Rahel Signorile7, Gianluca Gatta8, Annarita Fanizzi3.   

Abstract

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.

Entities:  

Keywords:  background parenchymal enhancement; breast MRI; breast cancer; fibro glandular tissue; neoadjuvant chemotherapy

Year:  2021        PMID: 33915842     DOI: 10.3390/jpm11040256

Source DB:  PubMed          Journal:  J Pers Med        ISSN: 2075-4426


  5 in total

1.  Robustness Evaluation of a Deep Learning Model on Sagittal and Axial Breast DCE-MRIs to Predict Pathological Complete Response to Neoadjuvant Chemotherapy.

Authors:  Raffaella Massafra; Maria Colomba Comes; Samantha Bove; Vittorio Didonna; Gianluca Gatta; Francesco Giotta; Annarita Fanizzi; Daniele La Forgia; Agnese Latorre; Maria Irene Pastena; Domenico Pomarico; Lucia Rinaldi; Pasquale Tamborra; Alfredo Zito; Vito Lorusso; Angelo Virgilio Paradiso
Journal:  J Pers Med       Date:  2022-06-10

Review 2.  Radiomics in Triple Negative Breast Cancer: New Horizons in an Aggressive Subtype of the Disease.

Authors:  Camil Ciprian Mireștean; Constantin Volovăț; Roxana Irina Iancu; Dragoș Petru Teodor Iancu
Journal:  J Clin Med       Date:  2022-01-26       Impact factor: 4.241

3.  A Novel Combined Nomogram Model for Predicting the Pathological Complete Response to Neoadjuvant Chemotherapy in Invasive Breast Carcinoma of No Specific Type: Real-World Study.

Authors:  Xuelin Zhu; Jing Shen; Huanlei Zhang; Xiulin Wang; Huihui Zhang; Jing Yu; Qing Zhang; Dongdong Song; Liping Guo; Dianlong Zhang; Ruiping Zhu; Jianlin Wu
Journal:  Front Oncol       Date:  2022-06-06       Impact factor: 5.738

4.  Delta-Radiomics Based on Dynamic Contrast-Enhanced MRI Predicts Pathologic Complete Response in Breast Cancer Patients Treated with Neoadjuvant Chemotherapy.

Authors:  Liangcun Guo; Siyao Du; Si Gao; Ruimeng Zhao; Guoliang Huang; Feng Jin; Yuee Teng; Lina Zhang
Journal:  Cancers (Basel)       Date:  2022-07-20       Impact factor: 6.575

5.  Early prediction of neoadjuvant chemotherapy response by exploiting a transfer learning approach on breast DCE-MRIs.

Authors:  Maria Colomba Comes; Annarita Fanizzi; Samantha Bove; Vittorio Didonna; Sergio Diotaiuti; Daniele La Forgia; Agnese Latorre; Eugenio Martinelli; Arianna Mencattini; Annalisa Nardone; Angelo Virgilio Paradiso; Cosmo Maurizio Ressa; Pasquale Tamborra; Vito Lorusso; Raffaella Massafra
Journal:  Sci Rep       Date:  2021-07-08       Impact factor: 4.379

  5 in total

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