Citations

Abstract Background: Biopsy diagnosis of benign breast disease (BBD) based on the most severe lesion in a sample predicts future breast cancer risk and has implications for screening and management. Lobules are the functional unit of the breast and the structures from which BBD arises. We developed and preliminarily validated an automated computational pathology algorithm to discriminate normal from BBD breast lobules as a step toward automated comprehensive characterization of benign biopsies. Methods: 152 BBD biopsies (27 training, 125 validation) from the Mayo Clinic were examined using scanned digital images of H&E stained sections. For each image, a pathologist annotated up to 10 representative lobules as normal or BBD using standard pathology criteria. A deep learning algorithm to quantify lobular features was developed using 129 lobules from 27 subjects. Nine features were identified that discriminate normal vs BBD lobules, expressing lobule size, acini size and number, acinar lumen size, proportion of lobular stroma, and capillaries. Here, we validate their performance to discriminate normal vs BBD lobules in a set of 1250 lobules from 125 subjects using area under the ROC curve (AUC) analysis. Random forest analysis was used for multivariable modeling; model performance was assessed with a tenfold cross-validation approach. Results: Median subject age was 52 years. Among the 125 validation subjects, BBD findings were nonproliferative in 39%, proliferative in 45%, and atypical hyperplasia in 16%. Sections included 552 (44%) normal lobules and 698 BBD (56%) lobules, with representation of both lobule types on each section. In univariate analyses, four individual features showed good discrimination between normal vs. BBD lobules, yielding the following AUCs: lobule size= 0.74, mean acini size= 0.75, epithelial area= 0.75, and number of acini with large lumens= 0.76. Lobule size and epithelial area were highly correlated (Spearman rank correlation r = 0.95), but both of these features showed lower correlation with mean acini size and number of acini with large lumens (each r < 0.5). With random forest modeling, number of acini with large lumens was the strongest discriminating feature, followed by mean acini size, lobule size, and epithelial area. With ten-fold cross validation of the multivariable random forest model, the overall AUC was 0.82 (95% CI: 0.79-0.85). Conclusion: Our validation showed that automated quantitative computational pathology assessment of breast lobules can discriminate normal versus BBD on a per lobule basis. This finding supports the feasibility of developing automated algorithms to classify every lesion in breast biopsies, expanding beyond visual assessment. Further studies using deep learning may reveal novel pathology features for classifying BBD biopsies with the potential to strengthen estimation of breast cancer risk. Citation Format: Amy C. Degnim, Thomas de Bel, Mark E. Sherman, Derek C. Radisky, Stacey J. Winham, Tanya L. Hoskin, Melody L. Stallings Mann, Marlene Frost, Robert A. Vierkant, Brendan T. Broderick, Ethan P. Heinzen, Rushin Brahmbhatt, Muhammad Arshad, Celine M. Vachon, Jodi M. Carter, Lori A. Denison, Daniel W. Visscher, Jeroen van der Laak. Discrimination of benign breast disease from normal lobules using an automated computational pathology algorithm [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2113.

Abstract Background Modern-day penile prostheses use infection retardant coating to decrease rates of postoperative infection, subsequently reducing explantation and revision rates as well. The Coloplast Titan models are dipped into antimicrobial solutions right before implantation, and the components used for dipping can be tailored toward the patient. Aim To compare infection, explantation, and revision rates among different dipping solutions used before implantation for patients with diabetes receiving a Coloplast Titan implant. Methods We systematically reviewed 932 patients with diabetes receiving a primary penile implant across 18 different centers from the period April 2003 to August 2018. Of those patients, 473 received a Coloplast device, whereas 459 received an AMS device. Data regarding the type of antimicrobial solution used before implantation were recorded for 468 patients receiving a Coloplast Titan, including whether or not they suffered a postoperative infection and if they underwent explantation and/or revision. Outcome rates were compared using Fisher’s exact and Pearson’s chi-square tests, and logistic regression modeling was performed to account for covariates. Outcomes The main outcome measures of this study were postoperative infection, explantation, and revision rates. Results Of the total 932 patients reviewed, 33 suffered a postoperative infection. Of 468 patients receiving Coloplast implants, there was a 3.4% infection rate. The most commonly used antibiotic combination before dipping was vancomycin + gentamicin (59.0%). There was a significantly lower rate of postoperative infection, explantation, and revision when vancomycin + gentamicin was used than those associated with the use of all other dipping solutions ([1.4% vs 6.4%; P = .004], [1.1% vs 8.3%; P < .001], and [2.5% vs 12.5; P < .001], respectively). After adjusting for age, body mass index, preoperative blood glucose level, and hemoglobin A1c, the use of other dips was an independent predictor of postoperative infection (odds ratio: 0.191; P = .049). The inclusion of rifampin in the dipping solution trended toward being a significant risk factor for infection (P = .057). Including antifungals in the dipping solution did not affect infection (P = .414), explantation (P = .421), or revision (P = .328) rates. Clinical Implications Vancomycin + gentamicin was the most efficacious combination of antibiotics used for dipping in terms of preventing postoperative infection and subsequent explantation and revision. Strengths and Limitations Data were sampled across multiple institutions providing a large sample that may be more representative of the population of interest. A key limitation of the study was its retrospective nature, which prevented us from controlling certain variables. Conclusion The use of rifampin did not provide the same type of protection, possibly representing a shift in resistance patterns of common bacteria responsible for device infection.

The NCCN Guidelines for Survivorship provide screening, evaluation, and treatment recommendations for consequences of adult-onset cancer and its treatment, with the goal of helping healthcare professionals who work with survivors, including those in primary care. The guidelines also provide recommendations to help clinicians promote physical activity, weight management, and proper immunizations in survivors and facilitate care coordination to ensure that all of the survivors’ needs are addressed. These NCCN Guidelines Insights summarize additions and changes made to the guidelines in 2020 regarding cardiovascular disease risk assessment and screening for subsequent primary malignancies.

INTRODUCTION: Nonendoscopic Barrett's esophagus (BE) screening may help improve esophageal adenocarcinoma outcomes. We previously demonstrated promising accuracy of methylated DNA markers (MDMs) for the nonendoscopic diagnosis of BE using samples obtained from a capsule sponge-on-string (SOS) device. We aimed to assess the accuracy of these MDMs in an independent cohort using a commercial grade assay. METHODS: BE cases had ≥ 1 cm of circumferential BE with intestinal metaplasia; controls had no endoscopic evidence of BE. The SOS device was withdrawn 8 minutes after swallowing, followed by endoscopy (the criterion standard). Highest performing MDMs from a previous study were blindly assessed on extracted bisulfite-converted DNA by target enrichment long-probe quantitative amplified signal (TELQAS) assays. Optimal MDM combinations were selected and analyzed using random forest modeling with in silico cross-validation. RESULTS: Of 295 patients consented, 268 (91%) swallowed the SOS device; 112 cases and 89 controls met the pre-established inclusion criteria. The median BE length was 6 cm (interquartile range 4–9), and 50% had no dysplasia. The cross-validated sensitivity and specificity of a 5 MDM random forest model were 92% (95% confidence interval 85%–96%) and 94% (95% confidence interval 87%–98%), respectively. Model performance was not affected by age, gender, or smoking history but was influenced by the BE segment length. SOS administration was well tolerated (median [interquartile range] tolerability 2 [0, 4] on 10 scale grading), and 95% preferred SOS over endoscopy. DISCUSSION: Using a minimally invasive molecular approach, MDMs assayed from SOS samples show promise as a safe and accurate nonendoscopic test for BE prediction.