Fighting Fire With Fire: Oncolytic Virotherapy for Thoracic Malignancies.

Thoracic malignancies are associated with high mortality rates. Conventional therapy for many of the patients with thoracic malignancies is obviated by a high incidence of locoregional recurrence and distant metastasis. Fortunately, developments in immunotherapy provide effective strategies for both local and systemic treatments that have rapidly advanced during the last decade. One promising approach to cancer immunotherapy is to use oncolytic viruses, which have the advantages of relatively high tumor specificity, selective replication-mediated oncolysis, enhanced antigen presentation, and potential for delivery of immunogenic payloads such as cytokines, with subsequent elicitation of effective antitumor immunity. Several oncolytic viruses including adenovirus, coxsackievirus B3, herpes virus, measles virus, reovirus, and vaccinia virus have been developed and applied to thoracic cancers in preclinical murine studies and clinical trials. This review discusses the current state of oncolytic virotherapy in lung cancer, esophageal cancer, and metastatic malignant pleural effusions and considers its potential as an emergent therapeutic for these patients.

Higher lymph node harvest in patients with a pathologic complete response after neoadjuvant therapy for esophageal cancer is associated with improved survival.

BACKGROUND AND OBJECTIVES: Lymph node harvest during esophagectomy has been associated with improved survival for esophageal cancer but the value of enhanced lymph node harvest following complete pathologic response (pCR) is debated. This study investigated if increasing lymph node harvest in esophageal cancer patients with a pCR after neoadjuvant therapy and esophagectomy is associated with improved survival. METHODS: We queried the National Cancer Data Base for patients with esophageal cancer between 2004 and 2014 who underwent neoadjuvant chemotherapy or chemoradiation therapy followed by esophagectomy found to have pCR. Multivariable Cox modeling was utilized to evaluate the impact of increasing lymph node counts on overall survival (OS). RESULTS: A total of 1373 patients met inclusion criteria. A National Comprehensive Cancer Network compliant lymphadenectomy of ≥15 nodes was associated with improved survival (66.7% vs 51.1%; P < .001). Cox modeling showed that the first node cutoff to demonstrate a statistically significant improvement in OS was ≥7 nodes (hazard ratio [HR], 95% confidence interval [CI]: 0.81, 0.68-0.97; 5-year OS: 54.2%) with a trend of decreasing and statistically significant HRs until ≥25 nodes (HR, 95% CI: 0.52, 0.37-0.72; 5-year OS: 68.4%). CONCLUSIONS: High negative node counts after neoadjuvant therapy and esophagectomy are associated with improved survival in patients with pCR.

Racial disparities in local therapy for early stage non-small-cell lung cancer.

OBJECTIVES: This study investigated disparities in the delivery of definitive therapy for early stage non-small-cell lung cancer (ESNSCLC) between Caucasian (CS) and African American (AA) populations. METHODS: The National Cancer Data Base was queried for AA and CS patients, diagnosed with c stage I Non small cell lung cancer between 2004 and 2015. Trends in surgery, stereotactic ablative radiotherapy (SABR), or external beam radiation therapy (EBRT) were compared. Kaplan-Meier and Cox hazards models were used to compare 5-year overall survival (5YOS). RESULTS: A total of 174,338 (90.6%) patients were CS and 18,077 (9.4%) patients were AA. AA patients were less likely to receive surgery (60.3% vs. 66.9%; p < .001) and more likely to receive EBRT (12.4% vs. 10.6%; p < .001); however, there was no significant difference in rates of SABR (8.8% vs. 9.2%; p = .066). From 2004 to 2015, the surgery rates increased for AA patients from 44.4% to 61.8% and for CS patients from 57.6% to 65.6%. AA patients had worse 5YOS on an unadjusted analysis (46.7% vs. 47.9%; p = .009). When adjusted for definitive treatment, AA patients had improved survival (hazard ratio = 0.97, 95% confidence interval = 0.94-0.99). CONCLUSION: Improvements in the delivery of surgery and equal utilization of definitive radiation therapy are at least partially responsible for closing the survival gap between AA and CS patients with ESNSCLC.

Resident perception of standardization and credentialing for high-risk bedside procedures in cardiothoracic surgery: Results from an institutional pilot study.

OBJECTIVES: Though clear-guidelines are set by the American Board of Thoracic Surgery (ABTS) for the operative cases that cardiothoracic surgery residents must perform to be board-eligible, no such recommendations exist to assess competency for the wide range of high-risk bedside procedures. Our department created and implemented a multidisciplinary course designed to standardize common high-risk bedside procedures and credential our trainees. The aim of this study was to survey the attitudes of residents towards and query the efficacy of such a course. METHODS: The course was designed with the goal of standardizing endotracheal intubation, arterial line insertion (radial and femoral), central venous line insertion, pigtail tube thoracostomy, thoracentesis and nasogastric tube placement. The course consisted of an online module followed by a 4-hour hands-on simulation session. Knowledge-based pre- and post-evaluations were administered as well as a Likert-based survey regarding multiple aspects of the residents' perceptions of the course and the procedures. RESULTS: Twenty-three (7 traditional and 16 integrated) cardiothoracic surgical residents participated in the course. Residents reported that 48% of the time, bedside procedures were historically taught by other trainees rather than by faculty. All residents endorsed increased standardization of all procedures after the course. Likewise, residents showed increased confidence in all procedures except for pigtail tube thoracostomy, thoracentesis as well as nasogastric tube placement. 43.5% of the participants demonstrated improvement in the pretest and posttest knowledge-based evaluations. CONCLUSION: Cardiothoracic residents have favorable attitudes towards standardization and credentialing for high-risk bedside procedures and utilizing such courses may help standardize procedural techniques.

Characteristics of Malignant Pleural Effusion Resident CD8+ T Cells from a Heterogeneous Collection of Tumors.

While T cell-based cancer immunotherapies have shown great promise, there remains a need to understand how individual metastatic tumor environments impart local T cell dysfunction. At advanced stages, cancers that metastasize to the pleural space can result in a malignant pleural effusion (MPE) that harbors abundant tumor and immune cells, often exceeding 108 leukocytes per liter. Unlike other metastatic sites, MPEs are readily and repeatedly accessible via indwelling catheters, providing an opportunity to study the interface between tumor dynamics and immunity. In the current study, we examined CD8+ T cells within MPEs collected from patients with heterogeneous primary tumors and at various stages in treatment to determine (1) if these cells possess anti-tumor activity following removal from the MPE, (2) factors in the MPE that may contribute to their dysfunction, and (3) the phenotypic changes in T cell populations that occur following ex vivo expansion. Co-cultures of CD8+ T cells with autologous CD45- tumor containing cells demonstrated cytotoxicity (p = 0.030) and IFNγ production (p = 0.003) that inversely correlated with percent of myeloid derived suppressor cells, lactate, and lactate dehydrogenase (LDH) within the MPE. Ex vivo expansion of CD8+ T cells resulted in progressive differentiation marked by distinct populations expressing decreased CD45RA, CCR7, CD127, and increased inhibitory receptors. These findings suggest that MPEs may be a source of tumor-reactive T cells and that the cellular and acellular components suppress optimal function.

Graphical modeling of causal factors associated with the postoperative survival of esophageal cancer subjects.

PURPOSE: To clarify the causal relationship between factors contributing to the postoperative survival of patients with esophageal cancer. METHODS: A cohort of 195 patients who underwent surgery for esophageal cancer between 2008 and 2021 was used in the study. All patients had preoperative chest computed tomography (CT) and positron emission tomography-CT (PET-CT) scans prior to receiving any treatment. From these images, high throughput and quantitative radiomic features, tumor features, and various body composition features were automatically extracted. Causal relationships among these image features, patient demographics, and other clinicopathological variables were analyzed and visualized using a novel score-based directed graph called "Grouped Greedy Equivalence Search" (GGES) while taking prior knowledge into consideration. After supplementing and screening the causal variables, the intervention do-calculus adjustment (IDA) scores were calculated to determine the degree of impact of each variable on survival. Based on this IDA score, a GGES prediction formula was generated. Ten-fold cross-validation was used to assess the performance of the models. The prediction results were evaluated using the R-Squared Score (R2 score). RESULTS: The final causal graphical model was formed by two PET-based image variables, ten body composition variables, four pathological variables, four demographic variables, two tumor variables, and one radiological variable (Percentile 10). Intramuscular fat mass was found to have the most impact on overall survival month. Percentile 10 and overall TNM (T: tumor, N: nodes, M: metastasis) stage were identified as direct causes of overall survival (month). The GGES casual model outperformed GES in regression prediction (R2  = 0.251) (p < 0.05) and was able to avoid unreasonable causality that may contradict common sense. CONCLUSION: The GGES causal model can provide a reliable and straightforward representation of the intricate causal relationships among the variables that impact the postoperative survival of patients with esophageal cancer.

Orchestrating Resilience: How Neuropilin-2 and Macrophages Contribute to Cardiothoracic Disease.

Immunity has evolved to balance the destructive nature of inflammation with wound healing to overcome trauma, infection, environmental insults, and rogue malignant cells. The inflammatory response is marked by overlapping phases of initiation, resolution, and post-resolution remodeling. However, the disruption of these events can lead to prolonged tissue damage and organ dysfunction, resulting long-term disease states. Macrophages are the archetypic phagocytes present within all tissues and are important contributors to these processes. Pleiotropic and highly plastic in their responses, macrophages support tissue homeostasis, repair, and regeneration, all while balancing immunologic self-tolerance with the clearance of noxious stimuli, pathogens, and malignant threats. Neuropilin-2 (Nrp2), a promiscuous co-receptor for growth factors, semaphorins, and integrins, has increasingly been recognized for its unique role in tissue homeostasis and immune regulation. Notably, recent studies have begun to elucidate the role of Nrp2 in both non-hematopoietic cells and macrophages with cardiothoracic disease. Herein, we describe the unique role of Nrp2 in diseases of the heart and lung, with an emphasis on Nrp2 in macrophages, and explore the potential to target Nrp2 as a therapeutic intervention.

Automated segmentation of five different body tissues on computed tomography using deep learning.

PURPOSE: To develop and validate a computer tool for automatic and simultaneous segmentation of five body tissues depicted on computed tomography (CT) scans: visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), intermuscular adipose tissue (IMAT), skeletal muscle (SM), and bone. METHODS: A cohort of 100 CT scans acquired on different subjects were collected from The Cancer Imaging Archive-50 whole-body positron emission tomography-CTs, 25 chest, and 25 abdominal. Five different body tissues (i.e., VAT, SAT, IMAT, SM, and bone) were manually annotated. A training-while-annotating strategy was used to improve the annotation efficiency. The 10-fold cross-validation method was used to develop and validate the performance of several convolutional neural networks (CNNs), including UNet, Recurrent Residual UNet (R2Unet), and UNet++. A grid-based three-dimensional patch sampling operation was used to train the CNN models. The CNN models were also trained and tested separately for each body tissue to see if they could achieve a better performance than segmenting them jointly. The paired sample t-test was used to statistically assess the performance differences among the involved CNN models RESULTS: When segmenting the five body tissues simultaneously, the Dice coefficients ranged from 0.826 to 0.840 for VAT, from 0.901 to 0.908 for SAT, from 0.574 to 0.611 for IMAT, from 0.874 to 0.889 for SM, and from 0.870 to 0.884 for bone, which were significantly higher than the Dice coefficients when segmenting the body tissues separately (p < 0.05), namely, from 0.744 to 0.819 for VAT, from 0.856 to 0.896 for SAT, from 0.433 to 0.590 for IMAT, from 0.838 to 0.871 for SM, and from 0.803 to 0.870 for bone. CONCLUSION: There were no significant differences among the CNN models in segmenting body tissues, but jointly segmenting body tissues achieved a better performance than segmenting them separately.

A Graph-Based Approach to Identify Factors Contributing to Postoperative Lung Cancer Recurrence among Patients with Non-Small-Cell Lung Cancer.

The accurate identification of the preoperative factors impacting postoperative cancer recurrence is crucial for optimizing neoadjuvant and adjuvant therapies and guiding follow-up treatment plans. We modeled the causal relationship between radiographical features derived from CT scans and the clinicopathologic factors associated with postoperative lung cancer recurrence and recurrence-free survival. A retrospective cohort of 363 non-small-cell lung cancer (NSCLC) patients who underwent lung resections with a minimum 5-year follow-up was analyzed. Body composition tissues and tumor features were quantified based on preoperative whole-body CT scans (acquired as a component of PET-CT scans) and chest CT scans, respectively. A novel causal graphical model was used to visualize the causal relationship between these factors. Variables were assessed using the intervention do-calculus adjustment (IDA) score. Direct predictors for recurrence-free survival included smoking history, T-stage, height, and intramuscular fat mass. Subcutaneous fat mass, visceral fat volume, and bone mass exerted the greatest influence on the model. For recurrence, the most significant variables were visceral fat volume, subcutaneous fat volume, and bone mass. Pathologic variables contributed to the recurrence model, with bone mass, TNM stage, and weight being the most important. Body composition, particularly adipose tissue distribution, significantly and causally impacted both recurrence and recurrence-free survival through interconnected relationships with other variables.

CT-derived body composition associated with lung cancer recurrence after surgery.

OBJECTIVES: To evaluate the impact of body composition derived from computed tomography (CT) scans on postoperative lung cancer recurrence. METHODS: We created a retrospective cohort of 363 lung cancer patients who underwent lung resections and had verified recurrence, death, or at least 5-year follow-up without either event. Five key body tissues and ten tumor features were automatically segmented and quantified based on preoperative whole-body CT scans (acquired as part of a PET-CT scan) and chest CT scans, respectively. Time-to-event analysis accounting for the competing event of death was performed to analyze the impact of body composition, tumor features, clinical information, and pathological features on lung cancer recurrence after surgery. The hazard ratio (HR) of normalized factors was used to assess individual significance univariately and in the combined models. The 5-fold cross-validated time-dependent receiver operating characteristics analysis, with an emphasis on the area under the 3-year ROC curve (AUC), was used to characterize the ability to predict lung cancer recurrence. RESULTS: Body tissues that showed a standalone potential to predict lung cancer recurrence include visceral adipose tissue (VAT) volume (HR = 0.88, p = 0.047), subcutaneous adipose tissue (SAT) density (HR = 1.14, p = 0.034), inter-muscle adipose tissue (IMAT) volume (HR = 0.83, p = 0.002), muscle density (HR = 1.27, p < 0.001), and total fat volume (HR = 0.89, p = 0.050). The CT-derived muscular and tumor features significantly contributed to a model including clinicopathological factors, resulting in an AUC of 0.78 (95% CI: 0.75-0.83) to predict recurrence at 3 years. CONCLUSIONS: Body composition features (e.g., muscle density, or muscle and inter-muscle adipose tissue volumes) can improve the prediction of recurrence when combined with clinicopathological factors.

Combined exploratory laparotomy, transpsoas, and thoracic approach to resection of a giant spinal ganglioneuroma: illustrative case.

BACKGROUND: Ganglioneuromas are rare peripheral nervous system tumors of neural crest origin. Most are often asymptomatic and incidentally found, but large tumors can cause mass effect. Herein, the authors report a case of a giant ganglioneuroma that arose from the lumbar foramina into the retroperitoneal and thoracic cavities. OBSERVATIONS: A 62-year-old female presented with low back pain, left lower extremity swelling, and increased sensation of an abdominal mass. Surgical treatment options were reviewed with the patient and coordinated care was planned by surgical oncological specialists. The patient opted for multistage exploratory laparotomy for abdominal mobilization, diaphragm resection, and en bloc resection with neuromonitoring. After surgery, the patient experienced significant improvement in symptoms. LESSONS: A combined surgical exposure involving gastrointestinal, thoracic, and neurological surgeons can be important in the safe resection of ganglioneuromas that span multiple body cavities. Hence, a thorough preoperative assessment could help plan surgery accordingly.

Assessing retinal vein occlusion based on color fundus photographs using neural understanding network (NUN).

OBJECTIVE: To develop and validate a novel deep learning architecture to classify retinal vein occlusion (RVO) on color fundus photographs (CFPs) and reveal the image features contributing to the classification. METHODS: The neural understanding network (NUN) is formed by two components: (1) convolutional neural network (CNN)-based feature extraction and (2) graph neural networks (GNN)-based feature understanding. The CNN-based image features were transformed into a graph representation to encode and visualize long-range feature interactions to identify the image regions that significantly contributed to the classification decision. A total of 7062 CFPs were classified into three categories: (1) no vein occlusion ("normal"), (2) central RVO, and (3) branch RVO. The area under the receiver operative characteristic (ROC) curve (AUC) was used as the metric to assess the performance of the trained classification models. RESULTS: The AUC, accuracy, sensitivity, and specificity for NUN to classify CFPs as normal, central occlusion, or branch occlusion were 0.975 (± 0.003), 0.911 (± 0.007), 0.983 (± 0.010), and 0.803 (± 0.005), respectively, which outperformed available classical CNN models. CONCLUSION: The NUN architecture can provide a better classification performance and a straightforward visualization of the results compared to CNNs.

CT-Derived Body Composition Is a Predictor of Survival after Esophagectomy.

BACKGROUND: Body composition can be accurately quantified based on computed tomography (CT) and typically reflects an individual's overall health status. However, there is a dearth of research examining the relationship between body composition and survival following esophagectomy. METHODS: We created a cohort consisting of 183 patients who underwent esophagectomy for esophageal cancer without neoadjuvant therapy. The cohort included preoperative PET-CT scans, along with pathologic and clinical data, which were collected prospectively. Radiomic, tumor, PET, and body composition features were automatically extracted from the images. Cox regression models were utilized to identify variables associated with survival. Logistic regression and machine learning models were developed to predict one-, three-, and five-year survival rates. Model performance was evaluated based on the area under the receiver operating characteristics curve (ROC/AUC). To test for the statistical significance of the impact of body composition on survival, body composition features were excluded for the best-performing models, and the DeLong test was used. RESULTS: The one-year survival model contained 10 variables, including three body composition variables (bone mass, bone density, and visceral adipose tissue (VAT) density), and demonstrated an AUC of 0.817 (95% CI: 0.738-0.897). The three-year survival model incorporated 14 variables, including three body composition variables (intermuscular adipose tissue (IMAT) volume, IMAT mass, and bone mass), with an AUC of 0.693 (95% CI: 0.594-0.792). For the five-year survival model, 10 variables were included, of which two were body composition variables (intramuscular adipose tissue (IMAT) volume and visceral adipose tissue (VAT) mass), with an AUC of 0.861 (95% CI: 0.783-0.938). The one- and five-year survival models exhibited significantly inferior performance when body composition features were not incorporated. CONCLUSIONS: Body composition features derived from preoperative CT scans should be considered when predicting survival following esophagectomy.

Intraoperative Evaluation of Soft Tissue Sarcoma Surgical Margins with Indocyanine Green Fluorescence Imaging.

Soft tissue sarcomas (STS) are rare malignant tumors often associated with poor outcomes and high local recurrence rates. Current tools for intraoperative and definitive margin assessment include intraoperative frozen section and permanent pathology, respectively. Indocyanine green dye (ICG) is a historically safe fluorophore dye that has demonstrated efficacy for intraoperative margin assessment in the surgical management of both breast and gastrointestinal cancers. The utility of ICG in the surgical management of sarcoma surgery has primarily been studied in pre-clinical mouse models and warrants further investigation as a potential adjunct to achieving negative margins. This study is a prospective, non-randomized clinical study conducted on patients with confirmed or suspected STS. Patients younger than 18 years, with a prior adverse reaction to iodine or fluorescein, or with renal disease were excluded from the study. Intravenous ICG was infused approximately three hours prior to surgery at a dosage of 2.0-2.5 mg/kg, and following tumor resection, the excised tumor and tumor bed were imaged for fluorescence intensity. When scanning the tumor bed, a threshold of 77% calibrated to the region of maximum intensity in the resected tumor was defined as a positive ICG margin, according to published protocols from the breast cancer literature. ICG results were then compared with the surgeon's clinical impression of margin status and permanent pathology results. Out of 26 subjects recruited for the original study, 18 soft tissue sarcomas (STS) were included for analysis. Three subjects were excluded for having bone sarcomas, and five subjects were excluded due to final pathology, which was ultimately inconsistent with sarcoma. The average age of patients was 64.1 years old (range: 28-83), with an average ICG dose of 201.8 mg. In 56% (10/18) of patients, ICG margins were consistent with the permanent pathology margins, with 89% specificity. The use of ICG as an intraoperative adjunct to obtaining negative margins in soft tissue sarcoma surgery is promising. However, studies with larger sample sizes are warranted to further delineate the accuracy, optimal dosage, timing, and types of sarcoma in which this diagnostic tool may be most useful.

Comparison of robotic-assisted minimally invasive esophagectomy versus minimally invasive esophagectomy: A propensity-matched study from a single high-volume institution.

OBJECTIVE: Robotic-assisted minimally invasive esophagectomy accounts for a growing proportion of esophagectomies, potentially due to improved technical capabilities simplifying the challenging aspects of standard minimally invasive esophagectomy. However, there is limited evidence directly comparing both operations. The objective is to evaluate the short-term and long-term outcomes of robotic-assisted minimally invasive esophagectomy in comparison with the minimally invasive esophagectomy approach for patients with esophageal cancer over a 7-year period at a high-volume center. The primary end points of this study were overall survival and disease-free survival. Secondary end points included operation-specific morbidity, lymph node yield, readmission status, and in-hospital, 30-day, and 90-day mortality. METHODS: Patients who underwent robotic-assisted minimally invasive esophagectomy or standard minimally invasive esophagectomy over a 7-year period were identified from a prospectively maintained database. Inclusion criteria were patients with stage I to III disease, operations performed past the learning curve, and no evidence of scleroderma or cirrhosis. A 1:3 propensity match (robotic-assisted minimally invasive esophagectomy:minimally invasive esophagectomy) for multiple clinical covariates was performed to identify the final study cohort. Perioperative outcomes were compared between the 2 operations. RESULTS: A total of 734 patients undergoing minimally invasive esophagectomy (n = 630) or robotic-assisted minimally invasive esophagectomy (n = 104) for esophageal cancer were identified. After exclusions and matching, a total cohort of 246 patients undergoing robotic-assisted minimally invasive esophagectomy (n = 65) or minimally invasive esophagectomy (n = 181) were identified. There was no difference in overall survival (P = .69) or disease-free survival (P = .70). There were no significant differences in rates of major morbidity: pneumonia (17% vs 17%, P = .34), chylothorax (8% vs 9%, P = .95), recurrent laryngeal nerve injury (0% vs 1.5%, P = 1), anastomotic leak (5% vs 4%, P = .49), intraoperative complications (9% vs 8%, P = .73), or complete resection rates (99% vs 96%, P = .68). There was no difference in in-hospital (P = .89), 30-day (P = .66) or 90-day mortality (P = .73) between both cohorts. The robotic-assisted minimally invasive esophagectomy cohort yielded a higher median lymph node harvest in comparison with the minimally invasive esophagectomy cohort (32 vs 29, P = .02). CONCLUSIONS: Robotic-assisted minimally invasive esophagectomy may improve lymphadenectomy in patients undergoing esophagectomy for cancer. Minimally invasive esophagectomy and robotic-assisted minimally invasive esophagectomy are otherwise associated with similar mortality, morbidity, and perioperative outcomes. Further prospective study is required to investigate whether improved lymph node resection may translate to improved oncologic outcomes.

Interferon regulatory factor-2 is protective against hepatic ischemia-reperfusion injury.

Interferon regulatory factor (IRF)-1 is a nuclear transcription factor that induces inflammatory cytokine mediators and contributes to hepatic ischemia-reperfusion (I/R) injury. No strategies to mitigate IRF1-mediated liver damage exist. IRF2 is a structurally similar endogenous protein that competes with IRF1 for DNA binding sites in IRF-responsive target genes and acts as a competitive inhibitor. However, the role of IRF2 in hepatic injury during hypoxic or inflammatory conditions is unknown. We hypothesize that IRF2 overexpression may mitigate IRF1-mediated I/R damage. Endogenous IRF2 is basally expressed in normal livers and is mildly increased by ischemia alone. Overexpression of IRF2 protects against hepatic warm I/R injury. Furthermore, we demonstrate that IRF2 overexpression limits production of IRF1-dependent proinflammatory genes, such as IL-12, IFNß, and inducible nitric oxide synthase, even in the presence of IRF1 induction. Additionally, isograft liver transplantation with IRF2 heterozygote knockout (IRF2(+/-)) donor grafts that have reduced endogenous IRF2 levels results in worse injury following cold I/R during murine orthotopic liver transplantation. These findings indicate that endogenous intrahepatic IRF2 protein is protective, because the IRF2-deficient liver donor grafts exhibited increased liver damage compared with the wild-type donor grafts. In summary, IRF2 overexpression protects against I/R injury by decreasing IRF1-dependent injury and may represent a novel therapeutic strategy.

A critical role for IFN regulatory factor 1 in NKT cell-mediated liver injury induced by alpha-galactosylceramide.

NKT cells are remarkably abundant in mouse liver. Compelling experimental evidence has suggested that NKT cells are involved in the pathogenesis of many liver diseases. Activation of NKT cells with alpha-galactosylceramide (alpha-GalCer) causes liver injury through mechanisms that are not well understood. We undertook studies to characterize the key pathways involved in alpha-GalCer-induced liver injury. We found that expression of the transcription factor IFN regulatory factor 1 (IRF-1) in mouse liver was dramatically upregulated by alpha-GalCer treatment. Neutralization of either TNF-alpha or IFN-gamma inhibited alpha-GalCer-mediated IRF-1 upregulation. alpha-GalCer-induced liver injury was significantly suppressed in IRF-1 knockout mice or in wild-type C56BL/6 mice that received a microRNA specifically targeting IRF-1. In contrast, overexpression of IRF-1 greatly potentiated alpha-GalCer-induced liver injury. alpha-GalCer injection also induced a marked increase in hepatic inducible NO synthase expression in C56BL/6 mice, but not in IRF-1 knockout mice. Inducible NO synthase knockout mice exhibited significantly reduced liver injury following alpha-GalCer treatment. Finally, we demonstrated that both NKT cells and hepatocytes expressed IRF-1 in response to alpha-GalCer. However, it appeared that the hepatocyte-derived IRF-1 was mainly responsible for alpha-GalCer-induced liver injury, based on the observation that inhibition of IRF-1 by RNA interference did not affect alpha-GalCer-induced NKT cell activation. Our findings revealed a novel mechanism of NKT cell-mediated liver injury in mice, which has implications in the development of human liver diseases.

A Program to Reduce Post-Operative Opioid Prescribing at a Veteran's Affairs Hospital.

Variability in surgeon prescribing patterns is common in the post-operative period and can be the nidus for dependence and addiction. This project aims to reduce opioid overprescribing at the Veteran's Affairs Pittsburgh Healthcare System (VAPHS). The VAPHS Opioid Stewardship Committee collaborated to create prescribing guidelines for inpatient and outpatient general, thoracic, and vascular surgery procedures. We incorporated bundled order sets into the provider workflow in the electronic medical system and performed a retrospective cohort study comparing opioid prescription patterns for Veterans who underwent any surgical procedure for a three-month period pre- and post- guideline implementation. After implementation of opioid prescribing guidelines, morphine milligram equivalents (MME), quantity of pills prescribed, and days prescribed were statistically significantly reduced for procedures with associated guidelines, including cholecystectomy (MME 140.8 vs. 57.5, p = 0.002; quantity 18.8 vs. 8, p = 0.002; days 5.1 vs. 2.8, p = 0.021), inguinal hernia repair (MME 129.9 vs. 45.3, p = 0.002; quantity 17.3 vs. 6.1, p = 0.002; days 5.0 vs. 2.4, p = 0.002), and umbilical hernia repair (MME 128.8 vs. 53.8, p = 0.002; quantity 17.1 vs. 7.8, p = 0.002; days 5.1 vs. 2.5, p = 0.022). Procedures without associated recommendations also preceded a decrease in overall opioid prescribing. Post-operative opioid prescribing guidelines can steer clinicians toward more conscientious opioid disbursement. There may also be reductions in prescribing opioids for procedures without guidelines as an indirect effect of practice change.

Predicting benign, preinvasive, and invasive lung nodules on computed tomography scans using machine learning.

OBJECTIVE: The study objective was to investigate if machine learning algorithms can predict whether a lung nodule is benign, adenocarcinoma, or its preinvasive subtype from computed tomography images alone. METHODS: A dataset of chest computed tomography scans containing lung nodules was collected with their pathologic diagnosis from several sources. The dataset was split randomly into training (70%), internal validation (15%), and independent test sets (15%) at the patient level. Two machine learning algorithms were developed, trained, and validated. The first algorithm used the support vector machine model, and the second used deep learning technology: a convolutional neural network. Receiver operating characteristic analysis was used to evaluate the performance of the classification on the test dataset. RESULTS: The support vector machine/convolutional neural network-based models classified nodules into 6 categories resulting in an area under the curve of 0.59/0.65 when differentiating atypical adenomatous hyperplasia versus adenocarcinoma in situ, 0.87/0.86 with minimally invasive adenocarcinoma versus invasive adenocarcinoma, 0.76/0.72 atypical adenomatous hyperplasia + adenocarcinoma in situ versus minimally invasive adenocarcinoma, 0.89/0.87 atypical adenomatous hyperplasia + adenocarcinoma in situ versus minimally invasive adenocarcinoma + invasive adenocarcinoma, and 0.93/0.92 atypical adenomatous hyperplasia + adenocarcinoma in situ + minimally invasive adenocarcinoma versus invasive adenocarcinoma. Classifying benign versus atypical adenomatous hyperplasia + adenocarcinoma in situ + minimally invasive adenocarcinoma versus invasive adenocarcinoma resulted in a micro-average area under the curve of 0.93/0.94 for the support vector machine/convolutional neural network models, respectively. The convolutional neural network-based methods had higher sensitivities than the support vector machine-based methods but lower specificities and accuracies. CONCLUSIONS: The machine learning algorithms demonstrated reasonable performance in differentiating benign versus preinvasive versus invasive adenocarcinoma from computed tomography images alone. However, the prediction accuracy varies across its subtypes. This holds the potential for improved diagnostic capabilities with less-invasive means.