Clinical performance of Bladder EpiCheck™ versus voided urine cytology for detecting recurrence of nonmuscle invasive bladder cancer: Systematic review and meta-analysis.

BACKGROUND: Nonmuscle invasive bladder cancer (NMIBC) has a favorable prognosis but has high propensity for recurrence. Recent development in one of the urinary biomarker tests, Bladder EpiCheck™, offers a noninvasive and accurate method to detect NMIBC recurrence. In this study, we aimed to compare the diagnostic performance of Bladder EpiCheck™ with urine cytology to detect NMIBC recurrence. METHODS: We performed a systematic review search through PubMed, Embase, Cochrane Central Register of Controlled Trials, Web of Science, and Scopus from inception to July 2023. Diagnostic accuracy was defined by sensitivity, negative predictive value (NPV), specificity, and positive predictive value (PPV). RESULTS: A total of 6 studies involving 1588 patients were included. Bladder EpiCheck™ has a sensitivity and specificity of 0.81 (95% CI: 0.63-0.91; I2: 43%) and 0.87 (95% CI: 0.83-0.91; I2: 20%), respectively. On the other hand, urine cytology has a sensitivity and specificity of 0.63 (95% CI: 0.29-0.87; I2: 61%) and 0.97 (95% CI: 0.78-1.00; I2: 79%), respectively. EpiCheck™ has a higher NPV (0.94 (95% CI: 0.87-0.97) vs. 0.84 (95% CI: 0.80-0.87) though a lower PPV (0.62 (95% CI: 0.45-0.76) vs. 0.87 (95% CI: 0.56-0.97) than urine cytology. In our subgroup analysis, the sensitivity of Bladder EpiCheck™ for detecting high-grade tumors improved to 0.90 (95% CI: 0.83-0.94) while that for urine cytology improved to 0.72 (95% CI: 0.50-0.87). CONCLUSION: Bladder EpiCheck™ has a high sensitivity and NPV for detecting recurrence among patients with NMIBC.

Optimising Medications in Older Vascular Surgery Patients Through Geriatric Co-management.

BACKGROUND: Prescribing of potentially inappropriate medications and under-prescribing of guideline-recommended medications for cardiovascular risk modification have both been associated with negative outcomes in older adults. Hospitalisation represents an important opportunity to optimise medication use and may be achieved through geriatrician-led interventions. OBJECTIVE: We aimed to evaluate whether implementation of a novel model of care called Geriatric Comanagement of older Vascular (GeriCO-V) surgery patients is associated with improvements in medication prescribing. METHODS: We used a prospective pre-post study design. The intervention was a geriatric co-management model, where a geriatrician delivered comprehensive geriatric assessment-based interventions including a routine medication review. We included consecutively admitted patients to the vascular surgery unit at a tertiary academic centre aged ≥ 65 years with an expected length of stay of ≥ 2 days and who were discharged from hospital. Outcomes of interest were the prevalence of at least one potentially inappropriate medication as defined by the Beers Criteria at admission and discharge, and rates of cessation of at least one potentially inappropriate medication present on admission. In the subgroup of patients with peripheral arterial disease, the prevalence of guideline-recommended medications on discharge was determined. RESULTS: There were 137 patients in the pre-intervention group (median [interquartile range] age: 80.0 [74.0-85.0] years, 83 [60.6%] with peripheral arterial disease) and 132 patients in the post-intervention group (median [interquartile range] age: 79.0 (73.0-84.0) years, 75 [56.8%] with peripheral arterial disease). There was no change in the prevalence of potentially inappropriate medication use from admission to discharge in either group (pre-intervention: 74.5% on admission vs 75.2% on discharge; post-intervention: 72.0% vs 72.7%, p = 0.65). Forty-five percent of pre-intervention group patients had at least one potentially inappropriate medication present on admission ceased, compared with 36% of post-intervention group patients (p = 0.11). A higher number of patients with peripheral arterial disease in the post-intervention group were discharged on antiplatelet agent therapy (63 [84.0%] vs 53 [63.9%], p = 0.004) and lipid-lowering therapy (58 [77.3%] vs 55 [66.3%], p = 0.12). CONCLUSIONS: Geriatric co-management was associated with an improvement in guideline-recommended antiplatelet agent prescribing aimed at cardiovascular risk modification for older vascular surgery patients. The prevalence of potentially inappropriate medications was high in this population, and was not reduced with geriatric co-management.

An Appraisal of Immunohistochemical Stain Use in Hepatic Metastasis Highlights the Effectiveness of the Individualized, Case-Based Approach: Analysis of Data From a Tertiary Care Medical Center.

CONTEXT.­: Liver biopsy plays an important role in the clinical management of metastases and often requires workup using immunohistochemical (IHC) markers, but the approach varies among institutions. OBJECTIVE.­: To evaluate the utility of a morphologic pattern-based, individualized approach in the workup of hepatic metastases. DESIGN.­: All liver biopsies with metastasis between 2015 and 2018 were identified from our institutional database and were reviewed. The morphologic pattern of the metastasis and IHC markers used in each case were recorded. The final identification of primary site of the tumor was assessed based on all the available clinicopathologic data. The academic ranking and practice pattern of the pathologist signing out the case were also recorded. RESULTS.­: A total of 406 liver biopsies with metastasis were identified, and the cases were classified as adenocarcinoma (253 of 406; 62%), carcinoma not otherwise specified (12 of 406; 3%), neuroendocrine neoplasm (54 of 406; 13%), poorly differentiated carcinoma (43 of 406; 11%), nonepithelial tumor (24 of 406; 6%), and squamous cell carcinoma (20 of 406; 5%). The primary site was unknown in 39% (158 of 406) at the time of liver biopsy. A primary site was determined in 97% (395 of 406) of all cases, and only 3% (11 of 406) remained true carcinoma of unknown primary. The average number of IHC markers/case in patients with known primary was 2.6, compared with 5.9 with an initial unknown primary and 9.5 in cases of true carcinoma of unknown primary. CONCLUSIONS.­: An individualized, case-based approach seems to be highly cost-effective and uses fewer IHC markers compared with preset panels that often comprise 10 or more IHC markers.

Calibrated uncertainty estimation for interpretable proton computed tomography image correction using Bayesian deep learning.

Integrated-type proton computed tomography (pCT) measures proton stopping power ratio (SPR) images for proton therapy treatment planning, but its image quality is degraded due to noise and scatter. Although several correction methods have been proposed, techniques that include estimation of uncertainty are limited. This study proposes a novel uncertainty-aware pCT image correction method using a Bayesian convolutional neural network (BCNN). A DenseNet-based BCNN was constructed to predict both a corrected SPR image and its uncertainty from a noisy SPR image. A total 432 noisy SPR images of 6 non-anthropomorphic and 3 head phantoms were collected with Monte Carlo simulations, while true noise-free images were calculated with known geometric and chemical components. Heteroscedastic loss and deep ensemble techniques were performed to estimate aleatoric and epistemic uncertainties by training 25 unique BCNN models. 200-epoch end-to-end training was performed for each model independently. Feasibility of the predicted uncertainty was demonstrated after applying two post-hoc calibrations and calculating spot-specific path length uncertainty distribution. For evaluation, accuracy of head SPR images and water-equivalent thickness (WET) corrected by the trained BCNN models was compared with a conventional method and non-Bayesian CNN model. BCNN-corrected SPR images represent noise-free images with high accuracy. Mean absolute error in test data was improved from 0.263 for uncorrected images to 0.0538 for BCNN-corrected images. Moreover, the calibrated uncertainty represents accurate confidence levels, and the BCNN-corrected calibrated WET was more accurate than non-Bayesian CNN with high statistical significance. Computation time for calculating one image and its uncertainties with 25 BCNN models is 0.7 s with a consumer grade GPU. Our model is able to predict accurate pCT images as well as two types of uncertainty. These uncertainties will be useful to identify potential cause of SPR errors and develop a spot-specific range margin criterion, toward elaboration of uncertainty-guided proton therapy.

Limbal stem cell deficiency secondary to systemic paclitaxel (Taxol) for breast cancer: a case report.

BACKGROUND: Paclitaxel (PTX) is an antineoplastic drug widely used in treatments for ovarian, breast, and small-cell lung cancer. Although ocular effects associated with PTX have been previously described, very few studies have specifically reported systemic PTX as a contributing factor for limbal stem cell deficiency (LSCD), which is characterized by the loss of stem cell and barrier function of the limbus leading to progressive pain and reduction in visual acuity. Described here is a unique case where a patient was diagnosed with LSCD secondary to PTX use for the treatment of breast cancer, at doses of PTX far lower than what is reported in current literature. CASE PRESENTATION: A 73-year-old woman with a previous diagnosis of breast cancer with liver metastasis presented with a complaint of increasing pain in the left eye more than the right, along with decreasing visual acuity in both eyes following 3 months of PTX therapy for recurrent liver metastases. Upon examination, best-corrected visual acuity was 20/100 in the right eye and counting fingers on the left. Peripheral neovascularization, stromal scarring, and features of limbal stem cell deficiency (LSCD) were noted on the right cornea. A central neurotrophic ulcer with thinning to 50% and 360 degrees of conjunctivalization were noted on the left. After the discontinuation PTX with doxorubicin as the substitute, there was no further progression of her LSCD, and stabilization of her ocular surface was achieved. CONCLUSION: Although chemotherapy induced LSCD is a relatively rare adverse event, it is essential for clinicians starting new chemotherapy agents to consider the potential ocular toxicities that may result in their use. Ophthalmology review is recommended for patients after starting PTX therapy to assess for signs of LSCD, particularly in patients where drug toxicity can be aggravated due to impaired hepatic function.

Fast spot-scanning proton dose calculation method with uncertainty quantification using a three-dimensional convolutional neural network.

This study proposes a near-real-time spot-scanning proton dose calculation method with probabilistic uncertainty estimation using a three-dimensional convolutional neural network (3D-CNN). CT images and clinical target volume contours of 215 head and neck cancer patients were collected from a public database. 1484 and 488 plans were extracted for training and testing the 3D-CNN model, respectively. Spot beam data and single-field uniform dose (SFUD) labels were calculated for each plan using an open-source dose calculation toolkit. Variable spot data were converted into a fixed-size volume hereby called a 'peak map' (PM). 300 epochs of end-to-end training was implemented using sets of stopping power ratio and PM as input. Moreover, transfer learning techniques were used to adjust the trained model to SFUD doses calculated with different beam parameters and calculation algorithm using only 7.95% of training data used for the base model. Finally, accuracy of the 3D-CNN-calculated doses and model uncertainty was reviewed with several evaluation metrics. The 3D-CNN model calculates 3D proton dose distributions accurately with a mean absolute error of 0.778 cGyE. The predicted uncertainty is correlated with dose errors at high contrast edges. Averaged Sørensen-Dice similarity coefficients between binarized outputs and ground truths are mostly above 80%. Once the 3D-CNN model was well-trained, it can be efficiently fine-tuned for different proton doses by transfer learning techniques. Inference time for calculating one dose distribution is around 0.8 s for a plan using 1500 spot beams with a consumer grade GPU. A novel spot-scanning proton dose calculation method using 3D-CNN was developed. The 3D-CNN model is able to calculate 3D doses and uncertainty with any SFUD spot data and beam irradiation angles. Our proposed method should be readily extendable to other setups and plans and be useful for dose verification, image-guided proton therapy, or other applications.

Relationship of Serum Progesterone and Progesterone Metabolites with Mammographic Breast Density and Terminal Ductal Lobular Unit Involution among Women Undergoing Diagnostic Breast Biopsy.

The association of progesterone/progesterone metabolites with elevated mammographic breast density (MBD) and delayed age-related terminal duct lobular unit (TDLU) involution, strong breast cancer risk factors, has received limited attention. Using a reliable liquid chromatography-tandem mass-spectrometry assay, we quantified serum progesterone/progesterone metabolites and explored cross-sectional relationships with MBD and TDLU involution among women, ages 40-65, undergoing diagnostic breast biopsy. Quantitative MBD measures were estimated in pre-biopsy digital mammograms. TDLU involution was quantified in diagnostic biopsies. Adjusted partial correlations and trends across MBD/TDLU categories were calculated. Pregnenolone was positively associated with percent MBD-area (MBD-A, rho: 0.30; p-trend = 0.01) among premenopausal luteal phase women. Progesterone tended to be positively associated with percent MBD-A among luteal phase (rho: 0.26; p-trend = 0.07) and postmenopausal (rho: 0.17; p-trend = 0.04) women. Consistent with experimental data, implicating an elevated 5α-pregnanes/3α-dihydroprogesterone (5αP/3αHP) metabolite ratio in breast cancer, higher 5αP/3αHP was associated with elevated percent MBD-A among luteal phase (rho: 0.29; p-trend = 0.08), but not postmenopausal women. This exploratory analysis provided some evidence that endogenous progesterone and progesterone metabolites might be correlated with MBD, a strong breast cancer risk factor, in both pre- and postmenopausal women undergoing breast biopsy. Additional studies are needed to understand the role of progesterone/progesterone metabolites in breast tissue composition and breast cancer risk.

Application of convolutional neural networks to breast biopsies to delineate tissue correlates of mammographic breast density.

Breast density, a breast cancer risk factor, is a radiologic feature that reflects fibroglandular tissue content relative to breast area or volume. Its histology is incompletely characterized. Here we use deep learning approaches to identify histologic correlates in radiologically-guided biopsies that may underlie breast density and distinguish cancer among women with elevated and low density. We evaluated hematoxylin and eosin (H&E)-stained digitized images from image-guided breast biopsies (n = 852 patients). Breast density was assessed as global and localized fibroglandular volume (%). A convolutional neural network characterized H&E composition. In total 37 features were extracted from the network output, describing tissue quantities and morphological structure. A random forest regression model was trained to identify correlates most predictive of fibroglandular volume (n = 588). Correlations between predicted and radiologically quantified fibroglandular volume were assessed in 264 independent patients. A second random forest classifier was trained to predict diagnosis (invasive vs. benign); performance was assessed using area under receiver-operating characteristics curves (AUC). Using extracted features, regression models predicted global (r = 0.94) and localized (r = 0.93) fibroglandular volume, with fat and non-fatty stromal content representing the strongest correlates, followed by epithelial organization rather than quantity. For predicting cancer among high and low fibroglandular volume, the classifier achieved AUCs of 0.92 and 0.84, respectively, with epithelial organizational features ranking most important. These results suggest non-fatty stroma, fat tissue quantities and epithelial region organization predict fibroglandular volume. The model holds promise for identifying histological correlates of cancer risk in patients with high and low density and warrants further evaluation.

Using Digital Pathology to Understand Epithelial Characteristics of Benign Breast Disease among Women Undergoing Diagnostic Image-Guided Breast Biopsy.

Delayed terminal duct lobular unit (TDLU) involution is associated with elevated mammographic breast density (MD). Both are independent breast cancer risk factors among women with benign breast disease (BBD). Prior digital analyses of normal breast tissues revealed that epithelial nuclear density (END) and TDLU involution are inversely correlated. Accordingly, we examined associations of END, TDLU involution, and MD in BBD clinical biopsies. This study included digitized images of 262 representative image-guided hematoxylin and eosin-stained biopsies from 224 women diagnosed with BBD, enrolled within the cross-sectional BREAST-Stamp project that were visually assessed for TDLU involution (TDLU count/100 mm2, median TDLU span and median acini count per TDLU). A digital algorithm estimated nuclei count per unit epithelial area, or END. Single X-ray absorptiometry of prebiopsy ipsilateral craniocaudal digital mammograms measured global and localized MD surrounding the biopsy region. Adjusted ordinal logistic regression models assessed relationships between tertiles of TDLU and END measures. Analysis of covariance examined mean differences in MD across END tertiles. TDLU measures were positively associated with increasing END tertiles [TDLU count/100 mm2, ORT3vsT1: 3.42, 95% confidence interval (CI), 1.87-6.28; acini count/TDLUT3vsT1, OR: 2.40, 95% CI, 1.39-4.15]. END was significantly associated with localized, but not, global MD. Relationships were most apparent among patients with nonproliferative BBD. These findings suggest that quantitative END reflects different but complementary information to the histologic information captured by visual TDLU and radiologic MD measures and merits continued evaluation in assessing cellularity of breast parenchyma to understand the etiology of BBD.

Proapoptotic protein BIM as a novel prognostic marker in mantle cell lymphoma.

Mantle cell lymphoma (MCL) is an aggressive B-cell lymphoma. Numerous studies have demonstrated many genetic aberrations in MCL in addition to the characteristic t(11:14), including frequent biallelic deletions of Bim, a proapoptotic member of the BCL-2 family. In mice, Bim deletion coupled with cyclin D1 overexpression generates pathologic and molecular features of human MCL. Since the regulation of apoptosis is crucial in MCL pathogenesis, we hypothesize that BIM expression may be associated with tumor cell survival. Clinical data and tissue from 100 nodal MCL cases between 1988 and 2009 were collected from three large academic medical centers. The average patient age of our MCL cohort was 65.5 years old (range, 42-97) with a 2:1 male to female ratio. Immunohistochemistry was performed with a validated anti-BIM antibody. Patients were separated into low and high BIM-expressing categories with a cutoff of 80%. As expected for a proapoptotic tumor suppressor, patients with high BIM expression were less likely to have progressive disease and more likely to have a complete response (P = .022). In addition, high BIM-expressing MCL tumors revealed a trend toward increased overall survival with this trend persisting in sub-analysis of Ann Arbor stages III and IV. No correlation between BIM expression, Ki-67 index, and MIPI score was observed, suggesting a role for BIM as a novel independent prognostic factor. While BIM is only one member of a complex family of apoptosis-regulating proteins, these findings may yield clinically relevant information for the prognosis and therapeutic susceptibility of MCL.

Relationship of circulating insulin-like growth factor-I and binding proteins 1-7 with mammographic density among women undergoing image-guided diagnostic breast biopsy.

BACKGROUND: Mammographic density (MD) is a strong breast cancer risk factor that reflects fibroglandular and adipose tissue composition, but its biologic underpinnings are poorly understood. Insulin-like growth factor binding proteins (IGFBPs) are markers that may be associated with MD given their hypothesized role in breast carcinogenesis. IGFBPs sequester IGF-I, limiting its bioavailability. Prior studies have found positive associations between circulating IGF-I and the IGF-I:IGFBP-3 ratio and breast cancer risk. We evaluated the associations of IGF-I, IGFBP-3, and six other IGFBPs with MD. METHODS: Serum IGF measures were quantified in 296 women, ages 40-65, undergoing diagnostic image-guided breast biopsy. Volumetric density measures (MD-V) were assessed in pre-biopsy digital mammograms using single X-ray absorptiometry. Area density measures (MD-A) were estimated by computer-assisted thresholding software. Age, body mass index (BMI), and BMI2-adjusted linear regression models were used to examine associations of serum IGF measures with MD. Effect modification by BMI was also assessed. RESULTS: IGF-I and IGFBP-3 were not strongly associated with MD after BMI adjustment. In multivariable analyses among premenopausal women, IGFBP-2 was positively associated with both percent MD-V (ß = 1.49, p value = 0.02) and MD-A (ß = 1.55, p value = 0.05). Among postmenopausal women, positive relationships between IGFBP-2 and percent MD-V (ß = 2.04, p = 0.003) were observed; the positive associations between IGFBP-2 and percent MD-V were stronger among lean women (BMI < 25 kg/m2) (ß = 5.32, p = 0.0002; p interaction = 0.0003). CONCLUSIONS: In this comprehensive study of IGFBPs and MD, we observed a novel positive association between IGFBP-2 and MD, particularly among women with lower BMI. In concert with in vitro studies suggesting a dual role of IGFBP-2 on breast tissue, promoting cell proliferation as well as inhibiting tumorigenesis, our findings suggest that further studies assessing the role of IGFBP-2 in breast tissue composition, in addition to IGF-1 and IGFBP-3, are warranted.

Non-Seminomatous Germ Cell Tumor Presenting with Superior Vena Cava Syndrome.

BACKGROUND Primary mediastinal non-seminomatous germ cell tumors (NSGCTs) are aggressive and carry a poor five-year disease free survival rate even with aggressive treatment. We describe a young adult male with primary mediastinal NSGCT presenting with airway obstruction and superior vena cava syndrome (SVCS). CASE REPORT The patient presented with four weeks of nonproductive cough, weight loss, and right-sided pleuritic chest pain. Chest computed topography (CT) imaging demonstrated a right-sided mediastinal mass determined as a yolk sac tumor on biopsy. The patient underwent induction chemotherapy with etoposide and cisplatin for stage III NSGCT. In the interim, he developed SVCS warranting a second cycle of chemotherapy along with intravenous steroids, with notable improvement in symptoms. However, serial alpha-fetoprotein (AFP) measurements showed progressively increasing levels up to a maximum of 18,781 ng/mL indicating treatment failure. He is currently on salvage chemotherapy. CONCLUSIONS Obstruction of the SVC by external compression is often a manifestation of a malignant process in the thorax. SVCS is a medical emergency and occurs in 6% of patients with mediastinal GCTs. Historically, irradiation was initiated without a histologic diagnosis to relieve the life-threatening obstruction. However, newer data suggest that it is acceptable to defer therapy until a full diagnostic workup is completed. This case highlights the malignant nature of primary mediastinal NSGCTs. In addition, inasmuch as SVCS is dramatic in presentation, it is important to recognize that symptomatic obstruction often develops over weeks or longer. In a hemodynamically stable patient, an accurate histologic diagnosis prior to starting treatment is essential in guiding therapy.

Identifying relations between imaging phenotypes and molecular subtypes of breast cancer: Model discovery and external validation.

PURPOSE: To determine whether dynamic contrast enhancement magnetic resonance imaging (DCE-MRI) characteristics of the breast tumor and background parenchyma can distinguish molecular subtypes (ie, luminal A/B or basal) of breast cancer. MATERIALS AND METHODS: In all, 84 patients from one institution and 126 patients from The Cancer Genome Atlas (TCGA) were used for discovery and external validation, respectively. Thirty-five quantitative image features were extracted from DCE-MRI (1.5 or 3T) including morphology, texture, and volumetric features, which capture both tumor and background parenchymal enhancement (BPE) characteristics. Multiple testing was corrected using the Benjamini-Hochberg method to control the false-discovery rate (FDR). Sparse logistic regression models were built using the discovery cohort to distinguish each of the three studied molecular subtypes versus the rest, and the models were evaluated in the validation cohort. RESULTS: On univariate analysis in discovery and validation cohorts, two features characterizing tumor and two characterizing BPE were statistically significant in separating luminal A versus nonluminal A cancers; two features characterizing tumor were statistically significant for separating luminal B; one feature characterizing tumor and one characterizing BPE reached statistical significance for distinguishing basal (Wilcoxon P < 0.05, FDR < 0.25). In discovery and validation cohorts, multivariate logistic regression models achieved an area under the receiver operator characteristic curve (AUC) of 0.71 and 0.73 for luminal A cancer, 0.67 and 0.69 for luminal B cancer, and 0.66 and 0.79 for basal cancer, respectively. CONCLUSION: DCE-MRI characteristics of breast cancer and BPE may potentially be used to distinguish among molecular subtypes of breast cancer. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2017;46:1017-1027.

Automatic Estimation of Volumetric Breast Density Using Artificial Neural Network-Based Calibration of Full-Field Digital Mammography: Feasibility on Japanese Women With and Without Breast Cancer.

Breast cancer is the most common invasive cancer among women and its incidence is increasing. Risk assessment is valuable and recent methods are incorporating novel biomarkers such as mammographic density. Artificial neural networks (ANN) are adaptive algorithms capable of performing pattern-to-pattern learning and are well suited for medical applications. They are potentially useful for calibrating full-field digital mammography (FFDM) for quantitative analysis. This study uses ANN modeling to estimate volumetric breast density (VBD) from FFDM on Japanese women with and without breast cancer. ANN calibration of VBD was performed using phantom data for one FFDM system. Mammograms of 46 Japanese women diagnosed with invasive carcinoma and 53 with negative findings were analyzed using ANN models learned. ANN-estimated VBD was validated against phantom data, compared intra-patient, with qualitative composition scoring, with MRI VBD, and inter-patient with classical risk factors of breast cancer as well as cancer status. Phantom validations reached an R 2 of 0.993. Intra-patient validations ranged from R 2 of 0.789 with VBD to 0.908 with breast volume. ANN VBD agreed well with BI-RADS scoring and MRI VBD with R 2 ranging from 0.665 with VBD to 0.852 with breast volume. VBD was significantly higher in women with cancer. Associations with age, BMI, menopause, and cancer status previously reported were also confirmed. ANN modeling appears to produce reasonable measures of mammographic density validated with phantoms, with existing measures of breast density, and with classical biomarkers of breast cancer. FFDM VBD is significantly higher in Japanese women with cancer.

Novel in vivo mouse model of implant related spine infection.

Post-operative spine infections are a challenge, as hardware must often be retained to prevent destabilization of the spine, and bacteria form biofilm on implants, rendering them inaccessible to antibiotic therapy, and immune cells. A model of posterior-approach spinal surgery was created in which a stainless steel k-wire was transfixed into the L4 spinous process of 12-week-old C57BL/six mice. Mice were then randomized to receive either one of three concentrations (1 × 102 , 1 × 103 , and 1 × 104 colony forming units (CFU)) of a bioluminescent strain of Staphylococcus aureus or normal saline at surgery. The mice were then longitudinally imaged for bacterial bioluminescence to quantify infection. The 1 × 102 CFU group had a decrease in signal down to control levels by POD 25, while the 1 × 103 and 1 × 104 CFU groups maintained a 10-fold higher signal through POD 35. Bacteria were then harvested from the pin and surrounding tissue for confirmatory CFU counts. All mice in the 1 × 104 CFU group experienced wound breakdown, while no mice in the other groups had this complication. Once an optimal bacterial concentration was determined, mice expressing enhanced green fluorescent protein in their myeloid cells (Lys-EGFP) were utilized to contemporaneously quantify bacterial burden, and immune response. Neutrophil fluorescence peaked for both groups on POD 3, and then declined. The infected group continued to have a response above the control group through POD 35. This study, establishes a noninvasive in vivo mouse model of spine implant infection that can quantify bacterial burden and host inflammation longitudinally in real time without requiring animal sacrifice. © 2016 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 35:193-199, 2017.

Relationships between mammographic density, tissue microvessel density, and breast biopsy diagnosis.

BACKGROUND: Women with high levels of mammographic density (MD) have a four- to six-fold increased risk of developing breast cancer; however, most neither have a prevalent tumor nor will they develop one. Magnetic resonance imaging (MRI) studies suggest that background parenchymal enhancement, an indicator of vascularity, is related to increased breast cancer risk. Correlations of microvessel density (MVD) in tissue, MD and biopsy diagnosis have not been defined, and we investigated these relationships among 218 women referred for biopsy. METHODS: MVD was determined by counting CD31-positive vessels in whole sections of breast biopsies in three representative areas; average MVD was transformed to approximate normality. Using digital mammograms, we quantified MD volume with single X-ray absorptiometry. We used linear regression to evaluate associations between MVD and MD adjusted for age and body mass index (BMI) overall, and stratified by biopsy diagnosis: cases (in situ or invasive cancer, n = 44) versus non-cases (non-proliferative or proliferative benign breast disease, n = 174). Logistic regression adjusted for age, BMI, and MD was used to calculate odds ratios (ORs) and 95 % confidence intervals (CIs) for associations between MVD and biopsy diagnosis. We also assessed whether the MVD-breast cancer association varied by MD. RESULTS: MVD and MD were not consistently associated. Higher MVD was significantly associated with higher odds of in situ/invasive disease (ORAdjusted = 1.69, 95 % CI = 1.17-2.44). MVD-breast cancer associations were strongest among women with greater non-dense volume. CONCLUSIONS: Increased MVD in tissues is associated with breast cancer, independently of MD, consistent with MRI findings suggestive of its possible value as a radiological cancer biomarker.

Circulating insulin-like growth factor-I, insulin-like growth factor binding protein-3 and terminal duct lobular unit involution of the breast: a cross-sectional study of women with benign breast disease.

BACKGROUND: Terminal duct lobular units (TDLUs) are the primary structures from which breast cancers and their precursors arise. Decreased age-related TDLU involution and elevated mammographic density are both correlated and independently associated with increased breast cancer risk, suggesting that these characteristics of breast parenchyma might be linked to a common factor. Given data suggesting that increased circulating levels of insulin-like growth factors (IGFs) factors are related to reduced TDLU involution and increased mammographic density, we assessed these relationships using validated quantitative methods in a cross-sectional study of women with benign breast disease. METHODS: Serum IGF-I, IGFBP-3 and IGF-I:IGFBP-3 molar ratios were measured in 228 women, ages 40-64, who underwent diagnostic breast biopsies yielding benign diagnoses at University of Vermont affiliated centers. Biopsies were assessed for three separate measures inversely related to TDLU involution: numbers of TDLUs per unit of tissue area ("TDLU count"), median TDLU diameter ("TDLU span"), and number of acini per TDLU ("acini count"). Regression models, stratified by menopausal status and adjusted for potential confounders, were used to assess the associations of TDLU count, median TDLU span and median acini count per TDLU with tertiles of circulating IGFs. Given that mammographic density is associated with both IGF levels and breast cancer risk, we also stratified these associations by mammographic density. RESULTS: Higher IGF-I levels among postmenopausal women and an elevated IGF-I:IGFBP-3 ratio among all women were associated with higher TDLU counts, a marker of decreased lobular involution (P-trend = 0.009 and <0.0001, respectively); these associations were strongest among women with elevated mammographic density (P-interaction <0.01). Circulating IGF levels were not significantly associated with TDLU span or acini count per TDLU. CONCLUSIONS: These results suggest that elevated IGF levels may define a sub-group of women with high mammographic density and limited TDLU involution, two markers that have been related to increased breast cancer risk. If confirmed in prospective studies with cancer endpoints, these data may suggest that evaluation of IGF signaling and its downstream effects may have value for risk prediction and suggest strategies for breast cancer chemoprevention through inhibition of the IGF system.

Differences in morphological features and minimum apparent diffusion coefficient values among breast cancer subtypes using 3-tesla MRI.

PURPOSE: To compare the morphology and minimum apparent diffusion coefficient (ADC) values among breast cancer subtypes. METHODS: Ninety-three patients, who underwent breast MRI and collectively had 98 pathologically proven invasive carcinomas, were enrolled. Morphology was evaluated according to BIRADS-MRI. Minimum ADC was measured. Morphology and minimum ADC were compared among subtypes. Multivariate logistic regression analyses were used to identify the characteristics associated with different subtypes. RESULTS: Oval/round shape was significantly associated with triple-negative (TN) cancer (TN vs. non-TN: 90.9% vs. 45.2%; p=0.0123). Rim enhancement was significantly less frequent in Luminal A (Luminal A vs. non-Luminal A: 34.2% vs. 76.1%; p=0.0003). The minimum ADC of Luminal A was significantly higher than that of Luminal B (HER2-negative) (834 vs. 748×10(-6)mm(2)/s; p<0.025). The minimum ADC of the TN-special type was significantly higher than that of TN-ductal (997 vs. 702×10(-6)mm(2)/s; p<0.025). On the multivariate analysis comparing the characteristics associated with Luminal A vs. Luminal B (HER2-negative), the internal enhancement characteristics of the mass and minimum ADC were significant factors. CONCLUSION: Morphology and minimum ADC would be useful in distinguishing breast cancer subtypes.

Relationship of Terminal Duct Lobular Unit Involution of the Breast with Area and Volume Mammographic Densities.

Elevated mammographic density (MD) is an established breast cancer risk factor. Reduced involution of terminal duct lobular units (TDLU), the histologic source of most breast cancers, has been associated with higher MD and breast cancer risk. We investigated relationships of TDLU involution with area and volumetric MD, measured throughout the breast and surrounding biopsy targets (perilesional). Three measures inversely related to TDLU involution (TDLU count/mm(2), median TDLU span, median acini count/TDLU) assessed in benign diagnostic biopsies from 348 women, ages 40-65, were related to MD area (quantified with thresholding software) and volume (assessed with a density phantom) by analysis of covariance, stratified by menopausal status and adjusted for confounders. Among premenopausal women, TDLU count was directly associated with percent perilesional MD (P trend = 0.03), but not with absolute dense area/volume. Greater TDLU span was associated with elevated percent dense area/volume (P trend<0.05) and absolute perilesional MD (P = 0.003). Acini count was directly associated with absolute perilesional MD (P = 0.02). Greater TDLU involution (all metrics) was associated with increased nondense area/volume (P trend ≤ 0.04). Among postmenopausal women, TDLU measures were not significantly associated with MD. Among premenopausal women, reduced TDLU involution was associated with higher area and volumetric MD, particularly in perilesional parenchyma. Data indicating that TDLU involution and MD are correlated markers of breast cancer risk suggest that associations of MD with breast cancer may partly reflect amounts of at-risk epithelium. If confirmed, these results could suggest a prevention paradigm based on enhancing TDLU involution and monitoring efficacy by assessing MD reduction.

Prognostic Imaging Biomarkers in Glioblastoma: Development and Independent Validation on the Basis of Multiregion and Quantitative Analysis of MR Images.

PURPOSE: To develop and independently validate prognostic imaging biomarkers for predicting survival in patients with glioblastoma on the basis of multiregion quantitative image analysis. MATERIALS AND METHODS: This retrospective study was approved by the local institutional review board, and informed consent was waived. A total of 79 patients from two independent cohorts were included. The discovery and validation cohorts consisted of 46 and 33 patients with glioblastoma from the Cancer Imaging Archive (TCIA) and the local institution, respectively. Preoperative T1-weighted contrast material-enhanced and T2-weighted fluid-attenuation inversion recovery magnetic resonance (MR) images were analyzed. For each patient, we semiautomatically delineated the tumor and performed automated intratumor segmentation, dividing the tumor into spatially distinct subregions that demonstrate coherent intensity patterns across multiparametric MR imaging. Within each subregion and for the entire tumor, we extracted quantitative imaging features, including those that fully capture the differential contrast of multimodality MR imaging. A multivariate sparse Cox regression model was trained by using TCIA data and tested on the validation cohort. RESULTS: The optimal prognostic model identified five imaging biomarkers that quantified tumor surface area and intensity distributions of the tumor and its subregions. In the validation cohort, our prognostic model achieved a concordance index of 0.67 and significant stratification of overall survival by using the log-rank test (P = .018), which outperformed conventional prognostic factors, such as age (concordance index, 0.57; P = .389) and tumor volume (concordance index, 0.59; P = .409). CONCLUSION: The multiregion analysis presented here establishes a general strategy to effectively characterize intratumor heterogeneity manifested at multimodality imaging and has the potential to reveal useful prognostic imaging biomarkers in glioblastoma.