OncoLog Volume 52, Number 12, December 2007

Worth More than a Thousand Words\ A Step Forward for Stents House Call: Getting the Most From Your Doctor Visit DiaLog: Is MRI Better for Breast Screening?, by Huong Le-Petross, MDhttps://openworks.mdanderson.org/oncolog/1164/thumbnail.jp

Deep Learning for Automatic Detection and Segmentation from CT Angiography of Deep Inferior Epigastric Vascular Structures for Preoperative Planning of TRAM Flap Surgeries

https://openworks.mdanderson.org/sumexp23/1131/thumbnail.jp

Mammography in asymptomatic women aged 40-49 years

OBJECTIVE To assess findings of mammography of and interventions resulting from breast cancer screening in women aged 40-49 years with no increased risk (typical risk) of breast cancer. METHODS This cross-sectional study evaluated women aged 40-49 years who underwent mammography screening in a mastology reference center in Recife, PE, Northeastern Brazil, between January 2010 and October 2011. Women with breast-related complaints, positive findings in the physical examination, or high risk of breast cancer were excluded. RESULTS The 1,000 mammograms performed were classified into the following Breast Imaging-Reporting and Data System (BI-RADS) categories BI-RADS 0, 232; BI-RADS 1, 294; BI-RADS 2, 294; BI-RADS 3, 16; BI-RADS 4A, 2; BI-RADS 5, 1. There was one case of grade II invasive ductal carcinoma and various interventions, including 469 ultrasound scans, 53 referrals to mastologists, 11 cytological examinations, and 8 biopsies. CONCLUSIONS Mammography screening in women aged 40-49 years with typical risk of breast cancer led to the performance of other interventions. However, it also resulted in increased costs without demonstrable efficacy in decreasing mortality

Inflammatory breast cancer appearance at presentation is associated with overall survival

Background: Inflammatory breast cancer (IBC) is a clinical diagnosis. Here, we examined the association of a "classic" triad of clinical signs, swollen involved breast, nipple change, and diffuse skin change, with overall survival (OS). Method: Breast medical photographs from patients enrolled on a prospective IBC registry were scored by two independent reviewers as classic (triad above), not classic, and difficult to assign. Chi-squared test, Fisher's exact test, and Wilcoxon rank-sum test were used to assess differences between patient groups. Kaplan-Meier estimates and the log-rank test and Cox proportional hazard regression were used to assess the OS. Results: We analyzed 245 IBC patients with median age 54 (range 26-81), M0 versus M1 status (157 and 88 patients, respectively). The classic triad was significantly associated with smoking, post-menopausal status, and metastatic disease at presentation (p = 0.002, 0.013, and 0.035, respectively). Ten-year actuarial OS for not classic and difficult to assign were not significantly different and were grouped for further analyses. Ten-year OS was 29.7% among patients with the classic sign triad versus 57.2% for non-classic (p < 0.0001). The multivariate Cox regression model adjusting for clinical staging (p < 0.0001) and TNBC status (<0.0001) demonstrated classic presentation score significantly associated with poorer OS time (HR 2.6, 95% CI 1.7-3.9, p < 0.0001). Conclusions: A triad of classic IBC signs independently predicted OS in patients diagnosed with IBC. Further work is warranted to understand the biology related to clinical signs and further extend the understanding of physical examination findings in IBC

Longitudinal Dynamic Contrast-Enhanced MRI Radiomic Models for Early Prediction of Response to Neoadjuvant Systemic Therapy in Triple-Negative Breast Cancer

Early prediction of neoadjuvant systemic therapy (NAST) response for triple-negative breast cancer (TNBC) patients could help oncologists select individualized treatment and avoid toxic effects associated with ineffective therapy in patients unlikely to achieve pathologic complete response (pCR). The objective of this study is to evaluate the performance of radiomic features of the peritumoral and tumoral regions from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) acquired at different time points of NAST for early treatment response prediction in TNBC. This study included 163 Stage I-III patients with TNBC undergoing NAST as part of a prospective clinical trial (NCT02276443). Peritumoral and tumoral regions of interest were segmented on DCE images at baseline (BL) and after two (C2) and four (C4) cycles of NAST. Ten first-order (FO) radiomic features and 300 gray-level-co-occurrence matrix (GLCM) features were calculated. Area under the receiver operating characteristic curve (AUC) and Wilcoxon rank sum test were used to determine the most predictive features. Multivariate logistic regression models were used for performance assessment. Pearson correlation was used to assess intrareader and interreader variability. Seventy-eight patients (48%) had pCR (52 training, 26 testing), and 85 (52%) had non-pCR (57 training, 28 testing). Forty-six radiomic features had AUC at least 0.70, and 13 multivariate models had AUC at least 0.75 for training and testing sets. The Pearson correlation showed significant correlation between readers. In conclusion, Radiomic features from DCE-MRI are useful for differentiating pCR and non-pCR. Similarly, predictive radiomic models based on these features can improve early noninvasive treatment response prediction in TNBC patients undergoing NAST

Cost-effectiveness of MRI compared to mammography for breast cancer screening in a high risk population

<p>Abstract</p> <p>Background</p> <p>Breast magnetic resonance imaging (MRI) is a sensitive method of breast imaging virtually uninfluenced by breast density. Because of the improved sensitivity, breast MRI is increasingly being used for detection of breast cancer among high risk young women. However, the specificity of breast MRI is variable and costs are high. The purpose of this study was to determine if breast MRI is a cost-effective approach for the detection of breast cancer among young women at high risk.</p> <p>Methods</p> <p>A Markov model was created to compare annual breast cancer screening over 25 years with either breast MRI or mammography among young women at high risk. Data from published studies provided probabilities for the model including sensitivity and specificity of each screening strategy. Costs were based on Medicare reimbursement rates for hospital and physician services while medication costs were obtained from the Federal Supply Scale. Utilities from the literature were applied to each health outcome in the model including a disutility for the temporary health state following breast biopsy for a false positive test result. All costs and benefits were discounted at 5% per year. The analysis was performed from the payer perspective with results reported in 2006 U.S. dollars. Univariate and probabilistic sensitivity analyses addressed uncertainty in all model parameters.</p> <p>Results</p> <p>Breast MRI provided 14.1 discounted quality-adjusted life-years (QALYs) at a discounted cost of $18,167 while mammography provided 14.0 QALYs at a cost of$4,760 over 25 years of screening. The incremental cost-effectiveness ratio of breast MRI compared to mammography was $179,599/QALY. In univariate analysis, breast MRI screening became <$50,000/QALY when the cost of the MRI was < $315. In the probabilistic sensitivity analysis, MRI screening produced a net health benefit of -0.202 QALYs (95$50,000/QALY. Breast MRI screening was superior in 0%, < $50,000/QALY in 22$50,000/QALY in 34%, and inferior in 44% of trials.</p> <p>Conclusion</p> <p>Although breast MRI may provide health benefits when compared to mammographic screening for some high risk women, it does not appear to be cost-effective even at willingness to pay thresholds above $120,000/QALY.</p

Assessment of Response to Neoadjuvant Systemic Treatment in Triple-Negative Breast Cancer Using Functional Tumor Volumes from Longitudinal Dynamic Contrast-Enhanced MRI

Early assessment of neoadjuvant systemic therapy (NAST) response for triple-negative breast cancer (TNBC) is critical for patient care in order to avoid the unnecessary toxicity of an ineffective treatment. We assessed functional tumor volumes (FTVs) from dynamic contrast-enhanced (DCE) MRI after 2 cycles (C2) and 4 cycles (C4) of NAST as predictors of response in TNBC. A group of 100 patients with stage I-III TNBC who underwent DCE MRI at baseline, C2, and C4 were included in this study. Tumors were segmented on DCE images of 1 min and 2.5 min post-injection. FTVs were measured using the optimized percentage enhancement (PE) and signal enhancement ratio (SER) thresholds. The Mann-Whitney test was used to compare the performance of the FTVs at C2 and C4. Of the 100 patients, 49 (49%) had a pathologic complete response (pCR) and 51 (51%) had a non-pCR. The maximum area under the receiving operating characteristic curve (AUC) for predicting the treatment response was 0.84 (p \u3c 0.001) for FTV at C4 followed by FTV at C2 (AUC = 0.82, p \u3c 0.001). The FTV measured at baseline was not able to discriminate pCR from non-pCR. FTVs measured on DCE MRI at C2, as well as at C4, of NAST can potentially predict pCR and non-pCR in TNBC patients

Deep Learning for Fully Automatic Tumor Segmentation on Serially Acquired Dynamic Contrast-Enhanced MRI Images of Triple-Negative Breast Cancer

Accurate tumor segmentation is required for quantitative image analyses, which are increasingly used for evaluation of tumors. We developed a fully automated and high-performance segmentation model of triple-negative breast cancer using a self-configurable deep learning framework and a large set of dynamic contrast-enhanced MRI images acquired serially over the patients\u27 treatment course. Among all models, the top-performing one that was trained with the images across different time points of a treatment course yielded a Dice similarity coefficient of 93% and a sensitivity of 96% on baseline images. The top-performing model also produced accurate tumor size measurements, which is valuable for practical clinical applications