Can breast MRI help in the management of women with breast cancer treated by neoadjuvant chemotherapy?
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ABSTRACT:
SUBMITTER: Warren RM
PROVIDER: S-EPMC2409692 | biostudies-other | 2004 Apr
REPOSITORIES: biostudies-other
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Project description:Breast cancer is a complex disease, with heterogeneous clinical evolution. Several analyses have been performed to identify the risk factors for breast cancer progression and the patients who respond best to a specific treatment. We aimed to evaluate whether the hormone receptor expression, HER2 and MYC genes and their protein status, and KRAS codon 12 mutations may be prognostic or predictive biomarkers of breast cancer. Protein, gene and mutation status were concomitantly evaluated in 116 breast tumors from women who underwent neoadjuvant chemotherapy with doxorubicin plus cyclophosphamide. We observed that MYC expression was associated with luminal B and HER2 overexpression phenotypes compared to luminal A (p<0.05). The presence of MYC duplication or polysomy 8, as well as KRAS mutation, were also associated with the HER2 overexpression subtype (p<0.05). MYC expression and MYC gain were more frequently observed in early-onset compared to late-onset tumors (p<0.05). KRAS mutation was a risk factor of grade 3 tumors (p<0.05). A multivariate logistic regression demonstrated that MYC amplification defined as MYC/nucleus ratio of ≥2.5 was a protective factor for chemotherapy resistance. On the other hand, age and grade 2 tumors were a risk factor. Additionally, luminal B, HER2 overexpression, and triple-negative tumors presented increased odds of being resistant to chemotherapy relative to luminal A tumors. Thus, breast tumors with KRAS codon 12 mutations seem to present a worse prognosis. Additionally, MYC amplification may help in the identification of tumors that are sensitive to doxorubicin plus cyclophosphamide treatment. If confirmed in a large set of samples, these markers may be useful for clinical stratification and prognosis.
| S-EPMC3608596 | biostudies-literature
Project description:Simple Summary Neoadjuvant chemotherapy (NAC) followed with surgery is the standard strategy in the treatment of locally advanced breast cancer, but the individual efficacy varies. Early and accurate prediction of complete responders determines the NAC regimens and prognosis. Breast MRI has been recommended to monitor NAC response before, during, and after treatment. Radiomics has been heralded as a breakthrough in medicine and regarded to have changed the landscape of biomedical research in oncology. Delta-radiomics characterizing the change in feature values by applying radiomics to multiple time points, is a promising strategy for predicting response after NAC. In our study, the delta-radiomics model built with the change of radiomic features before and after one cycle NAC could effectively predict pathological complete response (pCR) in breast cancer. The model provides strong support for clinical decision-making at the earliest stage and helps patients benefit the most from NAC. Abstract Objective: To investigate the value of delta-radiomics after the first cycle of neoadjuvant chemotherapy (NAC) using dynamic contrast-enhanced (DCE) MRI for early prediction of pathological complete response (pCR) in patients with breast cancer. Methods: From September 2018 to May 2021, a total of 140 consecutive patients (training, n = 98: validation, n = 42), newly diagnosed with breast cancer who received NAC before surgery, were prospectively enrolled. All patients underwent DCE-MRI at pre-NAC (pre-) and after the first cycle (1st-) of NAC. Radiomic features were extracted from the postcontrast early, peak, and delay phases. Delta-radiomics features were computed in each contrast phases. Least absolute shrinkage and selection operator (LASSO) and a logistic regression model were used to select features and build models. The model performance was assessed by receiver operating characteristic (ROC) analysis and compared by DeLong test. Results: The delta-radiomics model based on the early phases of DCE-MRI showed a highest AUC (0.917/0.842 for training/validation cohort) compared with that using the peak and delay phases images. The delta-radiomics model outperformed the pre-radiomics model (AUC = 0.759/0.617, p = 0.011/0.047 for training/validation cohort) in early phase. Based on the optimal model, longitudinal fusion radiomic models achieved an AUC of 0.871/0.869 in training/validation cohort. Clinical-radiomics model generated good calibration and discrimination capacity with AUC 0.934 (95%CI: 0.882, 0.986)/0.864 (95%CI: 0.746, 0.982) for training and validation cohort. Delta-radiomics based on early contrast phases of DCE-MRI combined clinicopathology information could predict pCR after one cycle of NAC in patients with breast cancer.
| S-EPMC9316501 | biostudies-literature
Project description:Purpose To describe the design, conduct, and results of the Breast Multiparametric MRI for prediction of neoadjuvant chemotherapy Response (BMMR2) challenge. Materials and Methods The BMMR2 computational challenge opened on May 28, 2021, and closed on December 21, 2021. The goal of the challenge was to identify image-based markers derived from multiparametric breast MRI, including diffusion-weighted imaging (DWI) and dynamic contrast-enhanced (DCE) MRI, along with clinical data for predicting pathologic complete response (pCR) following neoadjuvant treatment. Data included 573 breast MRI studies from 191 women (mean age [±SD], 48.9 years ± 10.56) in the I-SPY 2/American College of Radiology Imaging Network (ACRIN) 6698 trial (ClinicalTrials.gov: NCT01042379). The challenge cohort was split into training (60%) and test (40%) sets, with teams blinded to test set pCR outcomes. Prediction performance was evaluated by area under the receiver operating characteristic curve (AUC) and compared with the benchmark established from the ACRIN 6698 primary analysis. Results Eight teams submitted final predictions. Entries from three teams had point estimators of AUC that were higher than the benchmark performance (AUC, 0.782 [95% CI: 0.670, 0.893], with AUCs of 0.803 [95% CI: 0.702, 0.904], 0.838 [95% CI: 0.748, 0.928], and 0.840 [95% CI: 0.748, 0.932]). A variety of approaches were used, ranging from extraction of individual features to deep learning and artificial intelligence methods, incorporating DCE and DWI alone or in combination. Conclusion The BMMR2 challenge identified several models with high predictive performance, which may further expand the value of multiparametric breast MRI as an early marker of treatment response. Clinical trial registration no. NCT01042379 Keywords: MRI, Breast, Tumor Response Supplemental material is available for this article. © RSNA, 2024.
| S-EPMC10825718 | biostudies-literature
Project description:BackgroundAccurate identification of pCR to neoadjuvant chemotherapy (NAC) is essential for determining appropriate surgery strategy and guiding resection extent in breast cancer. However, a non-invasive tool to predict pCR accurately is lacking. Our study aims to develop ensemble learning models using longitudinal multiparametric MRI to predict pCR in breast cancer.MethodsFrom July 2015 to December 2021, we collected pre-NAC and post-NAC multiparametric MRI sequences per patient. We then extracted 14,676 radiomics and 4096 deep learning features and calculated additional delta-value features. In the primary cohort (n = 409), the inter-class correlation coefficient test, U-test, Boruta and the least absolute shrinkage and selection operator regression were used to select the most significant features for each subtype of breast cancer. Five machine learning classifiers were then developed to predict pCR accurately for each subtype. The ensemble learning strategy was used to integrate the single-modality models. The diagnostic performances of models were evaluated in the three external cohorts (n = 343, 170 and 340, respectively).FindingsA total of 1262 patients with breast cancer from four centers were enrolled in this study, and pCR rates were 10.6% (52/491), 54.3% (323/595) and 37.5% (66/176) in HR+/HER2-, HER2+ and TNBC subtype, respectively. Finally, 20, 15 and 13 features were selected to construct the machine learning models in HR+/HER2-, HER2+ and TNBC subtypes, respectively. The multi-Layer Perception (MLP) yields the best diagnostic performances in all subtypes. For the three subtypes, the stacking model integrating pre-, post- and delta-models yielded the highest AUCs of 0.959, 0.974 and 0.958 in the primary cohort, and AUCs of 0.882-0.908, 0.896-0.929 and 0.837-0.901 in the external validation cohorts, respectively. The stacking model had accuracies of 85.0%-88.9%, sensitivities of 80.0%-86.3%, and specificities of 87.4%-91.5% in the external validation cohorts.InterpretationOur study established a novel tool to predict the responses of breast cancer to NAC and achieve excellent performance. The models could help to determine post-NAC surgery strategy for breast cancer.FundingThis study is supported by grants from the National Natural Science Foundation of China (82171898, 82103093), the Deng Feng project of high-level hospital construction (DFJHBF202109), the Guangdong Basic and Applied Basic Research Foundation (grant number, 2020A1515010346, 2022A1515012277), the Science and Technology Planning Project of Guangzhou City (202002030236), the Beijing Medical Award Foundation (YXJL-2020-0941-0758), and the Beijing Science and Technology Innovation Medical Development Foundation (KC2022-ZZ-0091-5). Funding sources were not involved in the study design, data collection, analysis and interpretation, writing of the report, or decision to submit the article for publication.
| S-EPMC10050775 | biostudies-literature
Project description:BACKGROUND:The purpose of this study was to investigate the value of wavelet-transformed radiomic MRI in predicting the pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) for patients with locally advanced breast cancer (LABC). METHODS:Fifty-five female patients with LABC who underwent contrast-enhanced MRI (CE-MRI) examination prior to NAC were collected for the retrospective study. According to the pathological assessment after NAC, patient responses to NAC were categorized into pCR and non-pCR. Three groups of radiomic textures were calculated in the segmented lesions, including (1) volumetric textures, (2) peripheral textures, and (3) wavelet-transformed textures. Six models for the prediction of pCR were Model I: group (1), Model II: group (1)?+?(2), Model III: group (3), Model IV: group (1)?+?(3), Model V: group (2)?+?(3), and Model VI: group (1)?+?(2)?+?(3). The performance of predicting models was compared using the area under the receiver operating characteristic (ROC) curves (AUC). RESULTS:The AUCs of the six models for the prediction of pCR were 0.816?±?0.033 (Model I), 0.823?±?0.020 (Model II), 0.888?±?0.025 (Model III), 0.876?±?0.015 (Model IV), 0.885?±?0.030 (Model V), and 0.874?±?0.019 (Model VI). The performance of four models with wavelet-transformed textures (Models III, IV, V, and VI) was significantly better than those without wavelet-transformed textures (Model I and II). In addition, the inclusion of volumetric textures or peripheral textures or both did not result in any improvements in performance. CONCLUSIONS:Wavelet-transformed textures outperformed volumetric and/or peripheral textures in the radiomic MRI prediction of pCR to NAC for patients with LABC, which can potentially serve as a surrogate biomarker for the prediction of the response of LABC to NAC.
| S-EPMC7003343 | biostudies-literature
Project description:BackgroundTumour-infiltrating lymphocyte (TIL)-high breast tumours have a high rate of pathological complete response (pCR) with neoadjuvant chemotherapy. In our routine pathological diagnoses of biopsy specimens from pCR cases, we have observed a high infiltration of plasma cells (PCs). A positive correlation of PCs with favourable patient outcome has recently been reported, but little is known about how PCs contribute to local tumour immunity.MethodsWe retrospectively examined biopsy specimens from 146 patients with invasive breast cancer who received neoadjuvant chemotherapy. CD138+ PC infiltration was assessed by immunohistochemistry. Multiplexed fluorescent immunohistochemistry (mfIHC) with T and B cell markers was also conducted to elucidate the profile of immune cells.ResultsGreater PC infiltration was observed in the pCR group (p = 0.028) and this trend was confirmed in another patient cohort. With mfIHC, we observed significantly more CD8+, T-bet+CD4+, and CD8+FOXP3+ T cells, total B cells and PCs in pCR cases. Such cases were also characterised by high expression of both PD-1 and PD-L1 on B cells and PCs. In patients with hormone receptor-negative tumours, high PC infiltration was correlated with significantly longer disease-free survival (p = 0.034).ConclusionsWe found that higher PC infiltration in biopsy specimens before neoadjuvant chemotherapy was associated with pCR. With mfIHC, we also revealed that the local cytotoxic immune response was clearly enhanced in pCR cases, as was the infiltration of B cells including PCs. Moreover, higher PC levels were correlated with favourable outcomes in hormone receptor-negative breast cancer patients.
| S-EPMC8555250 | biostudies-literature
Project description:Background: The therapeutic strategy of invasive breast cancer is based on routine histopathological markers (estrogen-, progesterone receptor, HER2, Ki67) routinely evaluated in tumor cells. However, the assessment of cancer stroma could influence therapeutic strategies. Studies have shown that stromal expression of CD10, a zinc-dependent metalloproteinase, is associated with biological aggressiveness of the tumor. In the present retrospective study, we aimed to evaluate stromal CD10 expression and association between CD10 expression and response to neoadjuvant chemotherapy in invasive breast cancer. Methods: CD10 immunohistochemistry was performed on core biopsies taken before the neoadjuvant therapy. Stromal CD10 expression was determined and compared with well-known predictive and prognostic tissue markers as well as with the following groups defined according to the degree of tumor response: no regression, partial regression, and complete regression. Results: A total of 60 locally advanced invasive breast carcinomas of “no special type” were included. The proportion of CD10 positive tumors was significantly higher in the “no regression” group compared to “complete regression” group (p = 0.000). Stromal CD10 expression was found to be significantly associated with decrease in response to neoadjuvant chemotherapy. According to CD10 expression we did not find any difference in hormone receptor status, Ki67, tumor grade or neostromal area. Conclusion: Our data suggest that CD10 expression can serve as a predictive marker of the effect of neoadjuvant chemotherapy in breast cancer patients. Therefore, its inclusion into the routine assessment of biopsies to tailor tumor-specific therapeutic strategies merits consideration.
| S-EPMC9849231 | biostudies-literature
Project description:BACKGROUND: Changing the neoadjuvant chemotherapy regimen in insufficiently responding breast cancer is not a standard policy. We analysed a series of patients with 'luminal'-type breast cancer in whom the second half of neoadjuvant chemotherapy was selected based on the response to the first half. METHODS: Patients with oestrogen receptor-positive (ER+) human epidermal growth factor receptor 2-negative (HER2-) breast cancer received three courses of neoadjuvant dose-dense doxorubicin and cyclophosphamide (ddAC). Three further courses of ddAC were administered in case of a 'favourable response' on the interim magnetic resonance imaging (MRI) and a switch to docetaxel and capecitabine (DC) was made in case of an 'unfavourable response', using previously published response criteria. The efficacy of this approach was evaluated by tumour size reductions on serial contrast-enhanced MRI, pathologic response and relapse-free survival. RESULTS: Two hundred and forty-six patients received three courses of ddAC. One hundred and sixty-four patients (67%) had a favourable response at the interim MRI, with a mean tumour size reduction of 31% after the first three courses and 34% after the second three courses. Patients with unfavourable responsive tumours had a mean tumour size reduction of 12% after three courses and received three courses of DC rather than ddAC. This led to a mean shrinkage of 27%. CONCLUSION: The tumour size reduction of initially less responsive tumours after treatment adaptation adds further evidence that a response-adapted strategy may enhance the efficacy of neoadjuvant chemotherapy.
| S-EPMC3859944 | biostudies-other
Project description:Hyperpolarized 13C-magnetic resonance imaging (MRI) is an emerging tool for probing tissue metabolism by measuring 13C-label exchange between intravenously injected hyperpolarized [1-13C]pyruvate and endogenous tissue lactate. Here we demonstrate that hyperpolarized 13C-MRI can be used to detect early response to neoadjuvant therapy in breast cancer. Seven patients underwent multiparametric 1H-MRI and hyperpolarized 13C-MRI before and 7-11 days after commencing treatment. An increase in the lactate-to-pyruvate ratio of ~20% identified three patients who, following 5-6 cycles of treatment, showed pathological complete response. This ratio correlated with gene expression of the pyruvate transporter MCT1, and lactate dehydrogenase A (LDHA), the enzyme catalyzing label exchange between pyruvate and lactate. Analysis of ~2000 breast tumors showed that overexpression of LDHA and the hypoxia marker CAIX were associated with reduced relapse-free and overall survival. Hyperpolarized 13C-MRI represents a promising method for monitoring very early treatment response in breast cancer and has demonstrated prognostic potential.
| S-EPMC7612070 | biostudies-literature
Project description:The purpose of this study was to evaluate breast MRI radiomics in predicting, prior to any treatment, the response to neoadjuvant chemotherapy (NAC) in patients with invasive lymph node (LN)-positive breast cancer for two tasks: (1) prediction of pathologic complete response and (2) prediction of post-NAC LN status. Our study included 158 patients, with 19 showing post-NAC complete pathologic response (pathologic TNM stage T0,N0,MX) and 139 showing incomplete response. Forty-two patients were post-NAC LN-negative, and 116 were post-NAC LN-positive. We further analyzed prediction of response by hormone receptor subtype of the primary cancer (77 hormone receptor-positive, 39 HER2-enriched, 38 triple negative, and 4 cancers with unknown receptor status). Only pre-NAC MRIs underwent computer analysis, initialized by an expert breast radiologist indicating index cancers and metastatic axillary sentinel LNs on DCE-MRI images. Forty-nine computer-extracted radiomics features were obtained, both for the primary cancers and for the metastatic sentinel LNs. Since the dataset contained MRIs acquired at 1.5 T and at 3.0 T, we eliminated features affected by magnet strength using the Mann-Whitney U-test with the null-hypothesis that 1.5 T and 3.0 T samples were selected from populations having the same distribution. Bootstrapping and ROC analysis were used to assess performance of individual features in the two classification tasks. Eighteen features appeared unaffected by magnet strength. Pre-NAC tumor features generally appeared uninformative in predicting response to therapy. In contrast, some pre-NAC LN features were able to predict response: two pre-NAC LN features were able to predict pathologic complete response (area under the ROC curve (AUC) up to 0.82 [0.70; 0.88]), and another two were able to predict post-NAC LN-status (AUC up to 0.72 [0.62; 0.77]), respectively. In the analysis by a hormone receptor subtype, several potentially useful features were identified for predicting response to therapy in the hormone receptor-positive and HER2-enriched cancers.
| S-EPMC6768440 | biostudies-literature