A Multi-institutional Analysis of Factors Influencing the Rate of Positive MRI Biopsy Among Women with Early-Stage Breast Cancer.

BACKGROUND: The use of preoperative magnetic resonance imaging (MRI) for early-stage breast cancer (ESBC) is increasing, but its utility in detecting additional malignancy is unclear and delays surgical management (Jatoi and Benson in Future Oncol 9:347-353, 2013. https://doi.org/10.2217/fon.12.186 , Bleicher et al. J Am Coll Surg 209:180-187, 2009. https://doi.org/10.1016/j.jamcollsurg.2009.04.010 , Borowsky et al. J Surg Res 280:114-122, 2022. https://doi.org/10.1016/j.jss.2022.06.066 ). The present study sought to identify ESBC patients most likely to benefit from preoperative MRI by assessing the positive predictive values (PPVs) of ipsilateral and contralateral biopsies. METHODS: A retrospective cohort study included patients with cTis-T2N0-N1 breast cancer from two institutions during 2016-2021. A "positive" biopsy result was defined as additional cancer (PositiveCancer) or cancer with histology often excised (PositiveSurg). The PPV of MRI biopsies was calculated with respect to age, family history, breast density, and histology. Uni- and multivariate logistic regression determined whether combinations of age younger than 50 years, dense breasts, family history, and pure ductal carcinoma in situ (DCIS) histology led to higher biopsy yield. RESULTS: Of the included patients, 447 received preoperative MRI and 131 underwent 149 MRI-guided biopsies (96 ipsilateral, 53 contralateral [18 bilateral]). PositiveCancer for ipsilateral biopsy was 54.2%, and PositiveCancer for contralateral biopsy was 17.0%. PositiveSurg for ipsilateral biopsy was 62.5%, and PositiveSurg for contralateral biopsy was 24.5%. Among the contralateral MRI biopsies, patients younger than 50 years were less likely to have PositiveSurg (odds ratio, 0.02; 95% confidence interval, 0.00-0.84; p = 0.041). The combinations of age, density, family history, and histology did not lead to a higher biopsy yield. CONCLUSION: Historically accepted factors for recommending preoperative MRI did not appear to confer a higher MRI biopsy yield. To prevent delays to surgical management, MRI should be carefully selected for individual patients most likely to benefit from additional imaging.

Power calculation for detecting interaction effect in cross-sectional stepped-wedge cluster randomized trials: an important tool for disparity research.

BACKGROUND: The stepped-wedge cluster randomized trial (SW-CRT) design has become popular in healthcare research. It is an appealing alternative to traditional cluster randomized trials (CRTs) since the burden of logistical issues and ethical problems can be reduced. Several approaches for sample size determination for the overall treatment effect in the SW-CRT have been proposed. However, in certain situations we are interested in examining the heterogeneity in treatment effect (HTE) between groups instead. This is equivalent to testing the interaction effect. An important example includes the aim to reduce racial disparities through healthcare delivery interventions, where the focus is the interaction between the intervention and race. Sample size determination and power calculation for detecting an interaction effect between the intervention status variable and a key covariate in the SW-CRT study has not been proposed yet for binary outcomes. METHODS: We utilize the generalized estimating equation (GEE) method for detecting the heterogeneity in treatment effect (HTE). The variance of the estimated interaction effect is approximated based on the GEE method for the marginal models. The power is calculated based on the two-sided Wald test. The Kauermann and Carroll (KC) and the Mancl and DeRouen (MD) methods along with GEE (GEE-KC and GEE-MD) are considered as bias-correction methods. RESULTS: Among three approaches, GEE has the largest simulated power and GEE-MD has the smallest simulated power. Given cluster size of 120, GEE has over 80% statistical power. When we have a balanced binary covariate (50%), simulated power increases compared to an unbalanced binary covariate (30%). With intermediate effect size of HTE, only cluster sizes of 100 and 120 have more than 80% power using GEE for both correlation structures. With large effect size of HTE, when cluster size is at least 60, all three approaches have more than 80% power. When we compare an increase in cluster size and increase in the number of clusters based on simulated power, the latter has a slight gain in power. When the cluster size changes from 20 to 40 with 20 clusters, power increases from 53.1% to 82.1% for GEE; 50.6% to 79.7% for GEE-KC; and 48.1% to 77.1% for GEE-MD. When the number of clusters changes from 20 to 40 with cluster size of 20, power increases from 53.1% to 82.1% for GEE; 50.6% to 81% for GEE-KC; and 48.1% to 79.8% for GEE-MD. CONCLUSIONS: We propose three approaches for cluster size determination given the number of clusters for detecting the interaction effect in SW-CRT. GEE and GEE-KC have reasonable operating characteristics for both intermediate and large effect size of HTE.

Patterns in use of palliative care in older patients with metastatic breast cancer: A National Cancer Database analysis.

INTRODUCTION: Timely incorporation of palliative care (PC) during treatment of patients with metastatic cancers can improve symptom management and quality of life. Older age has been associated with lower PC use in patients with cancer. The frequency by which older patients with metastatic breast cancer (MBC) receive PC is unknown. The goal of this study was to use the National Cancer Database (NCDB) to describe national patterns in PC use in older adults over 75 years of age with MBC. MATERIALS AND METHODS: Females with a diagnosis of MBC at age ≥ 75 years from 2010 to 2019 were identified from the NCDB. The NCDB defined PC as any surgery, radiation, systemic therapy, and/or pain management that was administered with noncurative intent. Multivariable logistic regression models were performed to assess associations between PC receipt and study covariates. RESULTS: Of 17,325 eligible participants included in the final analysis, 39.4% were 75-79, 30.1% 80-84, and 30.4% ≥ 85 years of age. Overall, 22.1% (N = 3824) of patients utilized PC, of whom 14.3% received pain management, while the remainder received palliative intent surgery, radiation, and/or systemic therapy. Patients who were Hispanic were less likely to receive zzPC (AOR: 0.62, 95% CI: 0.48-0.79), p < 0.001). In the overall population, the use of PC increased from 19.2% in 2010 to 25.3% in 2019, though this was primarily driven by the statistically significant increase in the 75-79 age group (19.9% to 28.1%, p = 0.001). DISCUSSION: In this patient population from the NCDB, we observed an increase in PC utilization over the last decade in older adults with MBC, though the increase was lowest in patients who were 85 years and older. Barriers to PC in older adults with cancer need to be further explored.

Radical prostatectomy cancer grade and percentage of Gleason pattern 4 estimated by global vs individual tumor grading correlate differently with the risk of biochemical recurrence in Grade Group 2 and 3 cancers.

OBJECTIVES: There are 2 grading approaches to radical prostatectomy (RP) in multifocal cancer: Grade Group (GG) and percentage of Gleason pattern 4 (GP4%). We investigated whether RP GG and GP4% generated by global vs individual tumor grading correlate differently with biochemical recurrence. METHODS: We reviewed 531 RP specimens with GG2 or GG3 cancer. Each tumor was scored separately with assessment of tumor volume and GP4%. Global grade and GP4% were assigned by combining Gleason pattern 3 and 4 volumes for all tumors. Correlation of GG and GP4% generated by 2 methods with biochemical recurrence was assessed by Cox proportional hazard regression and receiver operating characteristic curves, with optimism adjustment using a bootstrap analysis. RESULTS: Median age was 63 (range, 42-79) years. Median prostate-specific antigen was 6.3 (range, 0.3-62.9) ng/mL. In total, the highest-grade tumor in 371 (36.9%) men was GG2 and in 160 (30.1%) men was GG3. Global grading was downgraded from GG3 to GG2 in 37 of 121 (30.6%) specimens with multifocal disease, and 145 of 404 (35.9%) specimens had GP4% decreased by at least 10%. Ninety-eight men experienced biochemical recurrence within a median of 13 (range, 3-119) months. Men without biochemical recurrence were followed up for a median of 47 (range, 12-205) months. Grade Group, GP4%, and margin status correlated with the risk of biochemical recurrence using highest-grade tumor and global grading, but the degrees of these correlations varied and were statistically significantly different between the 2 grading approaches. CONCLUSIONS: Grade Group, GP4%, and margin status derived by global vs individual tumor grading predict postoperative biochemical recurrence statistically significantly differently. This difference has important implications if results derived from cohorts graded using different methods are compared.

A Day 14 Endpoint for Acute GVHD Clinical Trials.

The overall response rate (ORR) 28 days after treatment has been adopted as the primary endpoint for clinical trials of acute graft versus host disease (GVHD). However, physicians often need to modify immunosuppression earlier than day (D) 28, and non-relapse mortality (NRM) does not always correlate with ORR at D28. We studied 1144 patients that received systemic treatment for GVHD in the Mount Sinai Acute GVHD International Consortium (MAGIC) and divided them into a training set (n=764) and a validation set (n=380). We used a recursive partitioning algorithm to create a Mount Sinai model that classifies patients into favorable or unfavorable groups that predicted 12 month NRM according to overall GVHD grade at both onset and D14. In the Mount Sinai model grade II GVHD at D14 was unfavorable for grade III/IV GVHD at onset and predicted NRM as well as the D28 standard response model. The MAGIC algorithm probability (MAP) is a validated score that combines the serum concentrations of suppression of tumorigenicity 2 (ST2) and regenerating islet-derived 3-alpha (REG3α) to predict NRM. Inclusion of the D14 MAP biomarker score with the D14 Mount Sinai model created three distinct groups (good, intermediate, poor) with strikingly different NRM (8%, 35%, 76% respectively). This D14 MAGIC model displayed better AUC, sensitivity, positive and negative predictive value, and net benefit in decision curve analysis compared to the D28 standard response model. We conclude that this D14 MAGIC model could be useful in therapeutic decisions and may offer an improved endpoint for clinical trials of acute GVHD treatment.

Amphiregulin, ST2, and REG3α biomarker risk algorithms as predictors of nonrelapse mortality in patients with acute GVHD.

ABSTRACT: Graft-versus-host disease (GVHD) is a major cause of nonrelapse mortality (NRM) after allogeneic hematopoietic cell transplantation. Algorithms containing either the gastrointestinal (GI) GVHD biomarker amphiregulin (AREG) or a combination of 2 GI GVHD biomarkers (suppressor of tumorigenicity-2 [ST2] + regenerating family member 3 alpha [REG3α]) when measured at GVHD diagnosis are validated predictors of NRM risk but have never been assessed in the same patients using identical statistical methods. We measured the serum concentrations of ST2, REG3α, and AREG by enzyme-linked immunosorbent assay at the time of GVHD diagnosis in 715 patients divided by the date of transplantation into training (2004-2015) and validation (2015-2017) cohorts. The training cohort (n = 341) was used to develop algorithms for predicting the probability of 12-month NRM that contained all possible combinations of 1 to 3 biomarkers and a threshold corresponding to the concordance probability was used to stratify patients for the risk of NRM. Algorithms were compared with each other based on several metrics, including the area under the receiver operating characteristics curve, proportion of patients correctly classified, sensitivity, and specificity using only the validation cohort (n = 374). All algorithms were strong discriminators of 12-month NRM, whether or not patients were systemically treated (n = 321). An algorithm containing only ST2 + REG3α had the highest area under the receiver operating characteristics curve (0.757), correctly classified the most patients (75%), and more accurately risk-stratified those who developed Minnesota standard-risk GVHD and for patients who received posttransplant cyclophosphamide-based prophylaxis. An algorithm containing only AREG more accurately risk-stratified patients with Minnesota high-risk GVHD. Combining ST2, REG3α, and AREG into a single algorithm did not improve performance.