Publications by authors named "Beatriz Adrada"
Molecular breast imaging (MBI) is a functional imaging modality that utilizes technetium 99m sestamibi radiotracer uptake to evaluate the biology of breast tumors. Molecular breast imaging can be a useful tool for supplemental screening of women with dense breasts, for breast cancer diagnosis and staging, and for evaluation of treatment response in patients with breast cancer undergoing neoadjuvant systemic therapy. In addition, MBI is useful in problem-solving when mammography and US imaging are insufficient to arrive at a definite diagnosis and for patients who cannot undergo breast MRI.
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- The BI-RADS category 3 assessment indicates findings that are "probably benign," with a less than 2% chance of being cancerous, helping to reduce unnecessary breast biopsies.
- Despite its established guidelines for mammography, breast ultrasound, and emerging MRI uses, there is still confusion and misuse surrounding this category.
- Category 3 findings should be followed up with short-term imaging to monitor for changes, and it is not appropriate to use in screening studies without further diagnostic evaluations.
Article Synopsis
- The study investigates the effectiveness of the anti-EGFR monoclonal antibody panitumumab combined with carboplatin and paclitaxel for treating chemotherapy-resistant triple-negative breast cancer (TNBC) patients.
- It included 43 patients who had not sufficiently responded to prior doxorubicin and cyclophosphamide treatment, achieving a combined pathological complete response/residual cancer burden class I rate of 30.2%.
- The results indicate that panitumumab shows promise as part of neoadjuvant therapy for TNBC, warranting further evaluation in larger clinical trials.
Article Synopsis
- Triple-negative breast cancer (TNBC) patients often undergo neoadjuvant systemic therapy (NAST) to improve treatment outcomes.
- A study analyzed multiparametric MRI scans from 163 TNBC patients at different stages of NAST to see if radiomic models could predict the likelihood of achieving a pathologic complete response (pCR).
- The best predictive model, based on changes in MRI features after two cycles of treatment, showed a strong ability to forecast pCR with high accuracy, indicating that MRI could be useful for early treatment response assessments in TNBC.
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- Hereditary breast cancers are caused by specific genetic mutations that increase the risk of developing the disease, known as penetrance.
- While BRCA1 and BRCA2 are the most recognized mutations linked to breast cancer, there are several other genes that also significantly contribute to breast cancer risk.
- This review discusses the latest in genetic testing, highlights the important genes related to breast cancer risk, and outlines current screening practices for patients with these genetic mutations.
Article Synopsis
- - A clinical trial evaluated the use of dual PD-L1/CTLA-4 checkpoint inhibition (durvalumab and tremelimumab) in 8 patients with Stage II or III HR+/HER2-negative breast cancer before they received neoadjuvant chemotherapy (NACT).
- - Patient responses varied after the immunotherapy; 3 showed significant tumor volume reduction, 3 increased, and 1 remained stable, but only 1 patient achieved a complete pathological response (pCR) at surgery.
- - The trial was halted due to toxicity issues in 3 patients and limited positive effects on the tumor microenvironment, indicating little benefit from this combined treatment approach before NACT.
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- The study investigates how the PI3K pathway is altered in different subtypes of triple-negative breast cancer (TNBC), focusing on those with mesenchymal (M) and luminal androgen receptor (LAR) characteristics.
- Using tumor samples from patients undergoing neoadjuvant therapy, researchers analyzed alterations in 32 genes related to the PI3K pathway, finding significant differences in gene alterations across seven TNBC subtypes.
- Results indicated that LAR subtype had the highest incidence of pathway alterations and that these alterations may influence treatment responses, suggesting that targeted therapies could benefit patients with M and LAR TNBC.
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- - The nipple-areolar complex (NAC) is a crucial part of breast anatomy that can be affected by various benign and malignant diseases, often presenting overlapping symptoms and imaging findings.
- - Understanding NAC's unique structure and the various conditions affecting it is key for accurate diagnosis; this includes assessing nipple discharge, which can signal serious issues like breast cancer.
- - A multimodal imaging approach, particularly utilizing breast MRI alongside other techniques, is essential for evaluating NAC diseases and guiding appropriate clinical management, including biopsy options for testing identified issues.
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- Breast implant-associated anaplastic large cell lymphoma is classified as a distinct type of cancer linked to textured breast implants, prompting new challenges for medical professionals handling patients with these devices.
- While much focus has been on this more serious lymphoma, benign issues related to breast implants also affect 20-30% of patients, necessitating careful assessment.
- The review discusses a variety of benign complications, detailing their clinical presentations and imaging features, and outlines a structured method for diagnosing and managing breast implant-related specimens.
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- The study investigates the use of diffusion tensor imaging (DTI) to assess treatment response in women with triple-negative breast cancer (TNBC) undergoing neoadjuvant systemic treatment (NAST).
- Out of 86 participants, 47% achieved a pathologic complete response (pCR), and DTI parameters showed significant differences between pCR and non-pCR patients during treatment.
- Findings suggest that DTI measurements, particularly of the peritumoral region, could be valuable in predicting treatment outcomes for TNBC patients.
Article Synopsis
- A deep learning model was trained to predict how well patients with triple negative breast cancer (TNBC) respond to neoadjuvant systemic therapy (NAST) using MRI scans taken before and after treatment.
- The model showed strong predictive performance, achieving high accuracy scores (AUCs) for different testing groups, indicating it can reliably identify patients who have a pathologic complete response (pCR).
- This technology could lead to more personalized treatment strategies for TNBC patients by allowing early identification of those likely to benefit from NAST based on MRI data.
Article Synopsis
- Early prediction of response to neoadjuvant systemic therapy (NAST) in patients with triple-negative breast cancer (TNBC) can help tailor treatments and prevent unnecessary side effects from ineffective therapies.
- The study analyzed 163 TNBC patients using dynamic contrast-enhanced MRI to identify radiomic features that could indicate treatment response, focusing on areas around and within the tumors at different treatment stages.
- Results showed promising predictive capabilities with certain radiomic features, as well as multivariate models, demonstrating significant accuracy in distinguishing between patients who achieved pathologic complete response (pCR) and those who did not, potentially enhancing early, non-invasive treatment assessments.
Article Synopsis
- Accurate tumor segmentation is essential for analyzing tumors in quantitative imaging studies, particularly for triple-negative breast cancer.
- A new automated deep learning model was developed that uses a comprehensive set of dynamic contrast-enhanced MRI images taken at different stages of treatment.
- This advanced model achieved a high Dice similarity coefficient of 93% and sensitivity of 96%, demonstrating its effectiveness in producing precise tumor measurements for clinical use.
Article Synopsis
- Triple-negative breast cancer (TNBC) is an aggressive type of breast cancer that does not express estrogen or progesterone receptors and lacks overexpression of the HER2 protein, affecting 8%-13% of breast cancer patients and more common in younger and non-Hispanic Black women.
- TNBC often shows benign imaging features, making detection through mammography challenging; ultrasound (US) is better for detection, but breast MRI is the most sensitive method.
- Treatment usually involves neoadjuvant chemotherapy followed by surgery and radiation, with lower 5-year survival rates compared to other breast cancer types; recent advances in immunotherapy and imaging may improve outcomes and personalized treatment strategies in the future.*
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- Recent research focuses on the phosphoinositide 3-kinase pathway in breast cancer, highlighting the role of PTEN as a crucial component.
- The study aimed to investigate PTEN expression changes in triple-negative breast cancer (TNBC) patients and assess if next-generation sequencing (NGS) could effectively identify PTEN loss, serving as an alternative to traditional immunohistochemistry (IHC).
- Findings revealed inconsistencies in PTEN expression between pretreatment and post-treatment samples, with some tumors showing intratumoral heterogeneity and overlapping copy numbers, suggesting that testing multiple specimens may be necessary for accurate assessment in clinical trials.
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- The study aimed to evaluate if a radiomics model using synthetic MRI (SyMRI) can predict responses to neoadjuvant systemic therapy (NAST) in women with triple-negative breast cancer (TNBC).
- It involved 181 women who underwent SyMRI scans at the start and mid-treatment, analyzing tumor features extracted from the imaging to identify differences between patients who achieved pathologic complete response (pCR) and those who did not.
- Results indicated that the radiomic features from mid-treatment scans were better at predicting pCR, with the model achieving an area under the receiver operating characteristic curve (AUC) of up to 0.78 in training and 0.72 in testing cohorts, suggesting potential usefulness
Article Synopsis
- High stromal tumor-infiltrating lymphocytes (sTILs) positively correlate with improved pathologic complete response (pCR) in triple-negative breast cancer (TNBC), suggesting their potential as a predictive marker for treatment outcomes.
- A study involving 408 TNBC patients assessed various clinical and biomarker features, identifying thresholds for sTILs and Ki-67 to predict pCR, resulting in specific groups of patients likely benefiting from treatment de-escalation.
- The research demonstrated that combining high sTILs with high Ki-67 significantly increased the accuracy of predicting pCR rates, indicating a promising approach for refining patient selection in neoadjuvant clinical trials.
Article Synopsis
- There is an increasing interest in using functional imaging techniques for breast imaging because they can detect changes that standard imaging misses.
- Technologies like nuclear medicine and breast-specific imaging systems are being used for various purposes, such as screening and checking treatment effectiveness for breast cancer.
- These imaging methods may be particularly beneficial for certain patient groups who don’t get adequate information from conventional imaging.
Article Synopsis
- Neoadjuvant anti-PD-(L)1 therapy, specifically atezolizumab combined with nab-paclitaxel, shows improved pathological complete response (pCR) rates in patients with treatment-resistant triple-negative breast cancer (TNBC).
- A clinical study included 37 patients who had minimal or no response to prior chemotherapy, and found a pCR/RCB-I rate of 46%, significantly higher than the historical rate of 5%.
- The study concluded that an adaptive approach using neoadjuvant immunotherapy based on initial response should be further investigated in randomized trials, as it suggests a promising method for treating high-risk TNBC patients.
Article Synopsis
- Early assessment of neoadjuvant systemic therapy (NAST) response is important for triple-negative breast cancer (TNBC) patients to prevent harmful side effects from ineffective treatments.
- The study evaluated functional tumor volumes (FTVs) using dynamic contrast-enhanced (DCE) MRI after the 2nd and 4th cycles of NAST in 100 patients, finding FTVs at these points could indicate treatment response.
- Results showed that 49% of patients achieved a pathologic complete response (pCR), with FTV at the 4th cycle having the best predictive accuracy (AUC = 0.84), while baseline FTV did not distinguish between pCR and non-pCR.
Article Synopsis
- Triple-negative breast cancer (TNBC) is a particularly aggressive form of breast cancer, and standard treatment involves neoadjuvant systemic therapy (NAST) followed by surgery, with 50-60% of patients achieving a pathologic complete response (pCR).
- Researchers used deep learning (DL) techniques on dynamic contrast-enhanced (DCE) MRI and diffusion-weighted imaging during early NAST in 130 TNBC patients, achieving high predictive accuracy for pCR status.
- The DL model showed robust performance in separate testing groups, with AUC scores ranging from 0.83 to 0.97, suggesting that multiparametric MRI combined with DL can effectively identify TNBC patients likely to achieve pCR early
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- Patient-derived xenograft (PDX) models of breast cancer offer a powerful method for drug testing and discovering biomarkers, especially in triple-negative breast cancer (TNBC).
- The research involved creating PDX models from breast cancer patients before and after neoadjuvant chemotherapy, resulting in 62 successful models from a total of 269 samples, with better success rates from treatment-resistant tumors.
- A predictive model for PDX engraftment was established, focusing on key patient tumor characteristics, and these PDX models are now a valuable resource for advancing treatment strategies in TNBC.
Article Synopsis
- - Contrast-enhanced mammography (CEM) is a new imaging method for breast evaluation that uses iodine-based contrast material to enhance visibility during mammograms, allowing for better identification of suspicious areas.
- - CEM-guided biopsy technology was introduced in 2019 and gained FDA approval in 2020, making it possible to directly sample the areas of concern highlighted by CEM that might not be visible through traditional imaging.
- - The article shares insights from the authors' initial experiences with challenging CEM-guided biopsies and provides a detailed procedural algorithm to help manage potential technical difficulties during the biopsy process.
Article Synopsis
- - The study aims to find biomarkers that can predict how triple-negative breast cancer (TNBC) patients will respond to neoadjuvant chemotherapy (NACT), focusing on blood polyamine levels.
- - Researchers discovered that high levels of acetylated polyamines in pre-treatment plasma were linked to TNBC patients with a worse response to NACT, indicating a moderate to extensive tumor burden.
- - By using artificial intelligence, a deep learning model was created to identify a panel of metabolites, including polyamines, which can help predict which TNBC patients are less likely to benefit from NACT and may require alternative treatments.