Publications by authors named "Rosalind P Candelaria"
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.
Rationale And Objectives: Recent ICE3 trial of breast cryoablation for breast cancer demonstrated 98% success rate, similar to breast-conserving surgery. However, ICE3 and other published studies did not differentiate curative from palliative treatment nor define patient-specific treatment objectives. We sought to define treatment success of curative and palliative breast cryoablation for breast cancer in meeting procedure objectives and patient-specific treatment objectives.
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- Axillary dissection is commonly performed for breast cancer patients with positive sentinel lymph nodes after receiving neoadjuvant therapy, but there is limited data on using frozen section in clinically node-negative cases.
- A study involving 662 patients with non-inflammatory, clinically node-negative HER2-positive or triple-negative breast cancer found a 6.6% prevalence of positive sentinel lymph nodes, with a modest sensitivity of frozen section testing at 57.6%.
- The study concludes that since positive sentinel lymph nodes are rare in these patients, it might be reasonable to rely on final pathology instead of intraoperative frozen section for decisions regarding axillary dissection.
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.
Article Synopsis
- 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
- 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
Surgical treatment for breast cancer has evolved from radical mastectomy to modified radical mastectomy to breast-conserving surgery. As the de-escalation of surgical treatment for breast cancer continues, nonsurgical treatment for early-stage breast cancer with favorable ancillary features (low grade, positivity for hormone receptors) is being explored. Of the nonsurgical treatment options, cryoablation has demonstrated the greatest appeal, proven to be effective, safe, well tolerated, and feasible in an outpatient setting with local anesthetic alone.
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- 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
Article Synopsis
- Triple-negative breast cancer (TNBC) is hard to treat, and researchers are looking for better ways to target and evaluate treatment responses.
- A study involving 56 TNBC patients used MRI data combined with mathematical modeling to predict individual responses to neoadjuvant systemic therapy (NAST).
- The results showed that the model had high accuracy in predicting patient-specific changes in tumor characteristics, outperforming traditional methods in distinguishing those who achieved a complete response to treatment.
Purpose: Metaplastic breast cancer (MpBC) is a rare subtype of breast cancer that is commonly triple-negative and poorly responsive to neoadjuvant therapy in retrospective studies.
Experimental Design: To better define clinical outcomes and correlates of response, we analyzed the rate of pathologic complete response (pCR) to neoadjuvant therapy, survival outcomes, and genomic and transcriptomic profiles of the pretreatment tumors in a prospective clinical trial (NCT02276443). A total of 211 patients with triple-negative breast cancer (TNBC), including 39 with MpBC, received doxorubicin-cyclophosphamide-based neoadjuvant therapy.
Background: Pathologic complete response (pCR) to neoadjuvant systemic therapy (NAST) in triple-negative breast cancer (TNBC) is a strong predictor of patient survival. Edema in the peritumoral region (PTR) has been reported to be a negative prognostic factor in TNBC.
Purpose: To determine whether quantitative apparent diffusion coefficient (ADC) features from PTRs on reduced field-of-view (rFOV) diffusion-weighted imaging (DWI) predict the response to NAST in TNBC.
This study aimed to investigate mid-treatment breast tumor ultrasound characteristics that may predict eventual pathologic complete response (pCR) in triple-negative breast cancer; specifically, we examined associations between pCR and two parameters: tumor response pattern and tumor appearance. Ultrasound was performed at mid-treatment, defined as the completion of four cycles of anthracycline-based chemotherapy and before receiving taxane-based chemotherapy. Consensus imaging review was performed while blinded to pathology results (i.
View Article and Find Full Text PDFA model combining pretreatment MRI radiomic features and tumor-infiltrating lymphocytes to predict response to neoadjuvant systemic therapy in triple-negative breast cancer.
Eur J Radiol
April 2022
Purpose: We aimed to develop a predictive model based on pretreatment MRI radiomic features (MRIRF) and tumor-infiltrating lymphocyte (TIL) levels, an established prognostic marker, to improve the accuracy of predicting pathologic complete response (pCR) to neoadjuvant systemic therapy (NAST) in triple-negative breast cancer (TNBC) patients.
Methods: This Institutional Review Board (IRB) approved retrospective study included a preliminary set of 80 women with biopsy-proven TNBC who underwent NAST, pretreatment dynamic contrast enhanced MRI, and biopsy-based pathologic assessment of TIL. A threshold of ≥ 20% was used to define high TIL.
Rationale And Objectives: Synthesized mammography with digital breast tomosynthesis (SM+DBT) and full-field digital mammography with DBT were prospectively evaluated for recall rate (RR), cancer detection rate (CDR), positive predictive value 1 (PPV1), lesion recall differences, and disagreements in recall for additional imaging.
Materials And Methods: From December 15, 2015 to January 15, 2017, after informed consent was obtained for this Health Insurance Portability and Accountability Act compliant study, each enrolled patient's SM+DBT and FFDM+DBT were interpreted sequentially by one of eight radiologists. RR, CDR, PPV1, and imaging findings (asymmetry, focal asymmetry, mass, architectural distortion, and calcifications) recalled were reviewed.
Purpose: Increased levels of stromal tumor-infiltrating lymphocytes (sTILs) have recently been considered a favorable independent prognostic and predictive biomarker in triple-negative breast cancer (TNBC). The purpose of this study was to determine the relationship between BI-RADS (Breast Imaging Reporting and Data System) ultrasound lexicon descriptors and sTILs in TNBC.
Materials And Methods: Patients with stage I-III TNBC were evaluated within a single-institution neoadjuvant clinical trial.
Background: Dynamic contrast-enhanced (DCE) MRI is useful for diagnosis and assessment of treatment response in breast cancer. Fast DCE MRI offers a higher sampling rate of contrast enhancement curves in comparison to conventional DCE MRI, potentially characterizing tumor perfusion kinetics more accurately for measurement of functional tumor volume (FTV) as a predictor of treatment response.
Purpose: To investigate FTV by fast DCE MRI as a predictor of neoadjuvant systemic therapy (NAST) response in triple-negative breast cancer (TNBC).
Pseudoangiomatous stromal hyperplasia (PASH), a rare, noncancerous lesion, is often an incidental finding on magnetic resonance imaging (MRI)-guided biopsy analysis of other breast lesions. We sought to describe the characteristics of PASH on MRI and identify the extent to which these characteristics are correlated with the amount of PASH in the pathology specimens. We identified 69 patients who underwent MRI-guided biopsies yielding a final pathological diagnosis of PASH between 2008 and 2015.
View Article and Find Full Text PDFThe imaging appearance of implantable loop recorders (ILR's) have been described in literature (Steinberger and Margolies, 2017; Mayo and Leung, 2017; Tsau and Berger, 2004); however, the mammographic appearance of the BioMonitor ILR produced by BIOTRONIK has not been described. It is important for radiologists interpreting breast imaging to become familiar with the appearance of different implantable cardiac devices on mammograms in order to create accurate reports and adjust imaging protocols to improve imaging quality and lessen patient discomfort as needed.
View Article and Find Full Text PDFMammary Paget disease (MPD) comprises 1.45% all male breast cancers, compared with only 0.68% of all female breast cancers.
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