BACKGROUND: Recent discussions have focused on redefining noninvasive follicular variant of papillary thyroid carcinoma (NI-FVPTC) as a neoplasm rather than a carcinoma. This study assesses the potential impact of such a reclassification on the implied risk of malignancy (ROM) for the diagnostic categories of The Bethesda System for Reporting Thyroid The FNAB cohort consisted of 6943 thyroid nodules representing 5179 women and 1409 men with an average age of 54 years (range, 9-94 years). The combined average ROM and OROM for the diagnostic categories of TBSRTC were as follows: nondiagnostic, 4.4% to 25.3%; benign, 0.9% to 9.3%; atypia of undetermined significance/follicular lesion of undetermined significance (AUS/FLUS), 12.1% to 31.2%; follicular neoplasm (FN), 21.8% to 33.2%; suspicious for malignancy (SM), 62.1% to 82.6%; and malignant, 75.9% to 99.1%. The impact of reclassifying NI-FVPTC on the ROM and OROM was most pronounced and statistically significant in the 3 indeterminate categories: the AUS/FLUS category had a decrease of 5.2% to 13.6%, the FN category had a decrease of 9.9% to 15.1%, and the SM category had a decrease of 17.6% to 23.4% (P < .05), whereas the benign and malignant categories had decreases of 0.3% to 3.5% and 2.5% to 3.3%, respectfully. The trend of the effect on the ROM and OROM was similar for all 5 institutions. CONCLUSIONS: The results from this multiinstitutional cohort indicate that the reclassification of NI-FVPTC will have a significant impact on the ROM for the 3 indeterminate categories of TBSRTC.

Classification of histologic patterns in lung adenocarcinoma is critical for determining tumor grade and treatment for patients. However, this task is often challenging due to the heterogeneous nature of lung adenocarcinoma and the subjective criteria for evaluation. In this study, we propose a deep learning model that automatically classifies the histologic patterns of lung adenocarcinoma on surgical resection slides. Our model uses a convolutional neural network to identify regions of neoplastic cells, then aggregates those classifications to infer predominant and minor histologic patterns for any given whole-slide image. We evaluated our model on an independent set of 143 whole-slide images. It achieved a kappa score of 0.525 and an agreement of 66.6% with three pathologists for classifying the predominant patterns, slightly higher than the inter-pathologist kappa score of 0.485 and agreement of 62.7% on this test set. All evaluation metrics for our model and the three pathologists were within 95% confidence intervals of agreement. If confirmed in clinical practice, our model can assist pathologists in improving classification of lung adenocarcinoma patterns by automatically pre-screening and highlighting cancerous regions prior to review. Our approach can be generalized to any whole-slide image classification task, and code is made publicly available at https://github.com/BMIRDS/deepslide .

Background The Paris System for Urine Cytopathology (the Paris System) has succeeded in making the analysis of liquid‐based urine preparations more reproducible. Any algorithm seeking to automate this system must accurately estimate the nuclear‐to‐cytoplasmic (N:C) ratio and produce a qualitative “atypia score.” The authors propose a hybrid deep‐learning and morphometric model that reliably automates the Paris System. Methods Whole‐slide images (WSI) of liquid‐based urine cytology specimens were extracted from 51 negative, 60 atypical, 52 suspicious, and 54 positive cases. Morphometric algorithms were applied to decompose images to their component parts; and statistics, including the NC ratio, were tabulated using segmentation algorithms to create organized data structures, dubbed rich information matrices (RIMs). These RIM objects were enhanced using deep‐learning algorithms to include qualitative measures. The augmented RIM objects were then used to reconstruct WSIs with filtering criteria and to generate pancellular statistical information. Results The described system was used to calculate the N:C ratio for all cells, generate object classifications (atypical urothelial cell, squamous cell, crystal, etc), filter the original WSI to remove unwanted objects, rearrange the WSI to an efficient, condensed‐grid format, and generate pancellular statistics containing quantitative/qualitative data for every cell in a WSI. In addition to developing novel techniques for managing WSIs, a system capable of automatically tabulating the Paris System criteria also was generated. Conclusions A hybrid deep‐learning and morphometric algorithm was developed for the analysis of urine cytology specimens that could reliably automate the Paris System and provide many avenues for increasing the efficiency of digital screening for urine WSIs and other cytology preparations.

TRX1 displayed nonlinear pharmacokinetic behavior and the CD4 receptors on T cells were saturated and down-modulated following treatment with TRX1. Results from in vitro studies using purified human T cells suggested that CD4-mediated internalization may constitute one pathway by which CD4 is down-modulated and TRX1 is cleared in vivo. The developed receptor-mediated PK/PD model adequately described the data. This PK/PD model was used to simulate PK/PD time profiles after different dosing regimens to help guide the dose selection in future clinical studies.

Background. Malignancy‐related pericardial effusions may represent a terminal event in patients with therapeutically unresponsive disease. However, select patients with malignancies sensitive to available therapies may achieve significant improvement in palliation and long term survival with prompt recognition and appropriate intervention. Methods. From 1968 to 1994, 150 invasive procedures were performed for the treatment or diagnosis of pericardial effusion in 127 patients with underlying malignancies. These cases were reviewed retrospectively to best identify the clinical features, appropriate diagnostic workup, and optimal therapy for this complication of malignancy. Results. Dyspnea (81%) and an abnormal pulsus paradoxus (32%) were the most common symptoms. Echocardiography had a 96% diagnostic accuracy. Cytology and pericardial biopsy had sensitivities of 90% and 56%, respectively. Fifty‐five percent of all effusions were malignant comprising 71% of adenocarcinomas of the lung, breast, esophagus, and unknown primary site. In 57 patients, a malignant effusion could not be determined, and no definitive etiology could be established for 74% of these effusions. Radiation‐induced, infectious, and hemorrhagic pericarditis each were identified in fewer than 5% of cases. Conclusions. Subxyphoid pericardiotomy proved to be a safe and effective intervention that successfully relieved pericardial effusions in 99% of cases with recurrence and reoperation rates of 9% and 7%, respectively. Survival most closely was related to the extent of disease and its inherent chemo‐/radiosensitivity, with 72% of the patients who survived longer than 1 year having breast cancer, leukemia, or lymphoma.

Objective The cause of death in murine models of sepsis remains unclear. The primary purpose of this study was to determine if significant lung injury develops in mice predicted to die following cecal ligation and puncture induced sepsis compared to those predicted to live. Design Prospective, laboratory controlled experiments. Setting University research laboratory. Subjects Adult, female, outbred ICR mice. Interventions Mice underwent cecal ligation and puncture (CLP) to induce sepsis. Two groups of mice were sacrificed at 24 and 48 hours post-CLP and samples were collected. These mice were further stratified into groups predicted to die (Die-P) and predicted to live (Live-P) based on plasma interleukin 6 (IL-6) levels obtained 24 hours post-CLP. Multiple measures of lung inflammation and lung injury were quantified in these two groups. Results from a group of mice receiving intratracheal normal saline without surgical intervention were also included as a negative control. As a positive control, bacterial pneumonia was induced with Pseudomonas aeruginosa to cause definitive lung injury. Separate mice were followed for survival until day 28 post-CLP. These mice were used to verify the IL-6 cut-offs for survival prediction. Measurements and Main Results Following sepsis, both the Die-P and Live-P mice had significantly suppressed measures of respiratory physiology but maintained normal levels of arterial oxygen saturation. Bronchoalveolar lavage (BAL) levels of pro and anti-inflammatory cytokines were not elevated in the Die-P mice compared to the Live-P. Additionally, there was no increase in the recruitment of neutrophils to the lung, pulmonary vascular permeability, or histological evidence of damage. In contrast, all of these pulmonary injury and inflammatory parameters were increased in mice with Pseudomonas pneumonia. Conclusions These data demonstrate that mice predicted to die during sepsis have no significant lung injury. In murine intra-abdominal sepsis, pulmonary injury cannot be considered the etiology of death in the acute phase.

Are deep neural networks trained on data from a single institution for classification of colorectal polyps on digitized histopathology slides generalizable across multiple external institutions? Findings: A new deep neural network was developed based on 326 slide images from our institution to classify the four most common polyp types on digitized histopathology slides. In addition to evaluation on an internal test set of 157 slide images, we evaluated the model on an external test set of 238 slide images from 24 institutions across 13 states in the United States.This model achieved mean accuracies of 93.5% and 87.0% on the internal and external test sets, respectively, which were comparable with the performance of local pathologists on these test sets.Meaning: Deep neural networks could provide a generalizable approach for the classification of colorectal polyps on digitized histopathology slides and if confirmed in clinical trials, could potentially improve the efficiency, reproducibility, and accuracy of one of the most common cancer screening procedures.

BACKGROUND:The estimation of the nuclear-to-cytoplasmic ratio (N:C ratio) is an important factor in diagnosing atypia and malignancy in pathological specimens, particularly in cytology. Many algorithms for determining malignant potential make reference to specific, decimal N:C ratios without specifying how the ratio should be measured, with the implication that the observer is intended to estimate this ratio by eye. The authors wanted to determine how accurate trained morphologists (including attending pathologists, pathology residents, and cytotechnologists) are at estimating the N:C ratio without a measuring device. METHODS: Two surveys were prepared containing ideal and real cell images of various N:C ratios. Participants were instructed to select their best estimate from a list of decimal ratios. The data were tabulated and analyzed to determine how accurate the estimates were and whether there was any performance difference between ideal and real images. RESULTS: The absolute and percentage deviation from the actual N:C ratio decreased steadily with increasing N:C ratio. Aggregate performance was found to be closely correlated between real and ideal images, although interobserver variation was not significantly different among participants in the real images quiz, but was significantly different on the ideal images quiz. CONCLUSIONS: Trained morphologists make relatively accurate estimations of the N:C ratio and become increasingly more accurate as the depicted N:C ratio increases. This suggests that including N:C ratio decimals as a criteria for the diagnosis of atypia is valid for high N:C ratios. Cancer (Cancer Cytopathol) 2015;123:524-30. V C 2015 American Cancer Society.