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.

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.

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.

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.

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.

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.

State of the Art in Lung Nodule Localization.

Lung nodule and ground-glass opacity localization for diagnostic and therapeutic purposes is often a challenge for thoracic surgeons. While there are several adjuncts and techniques in the surgeon's armamentarium that can be helpful, accurate localization persists as a problem without a perfect solution. The last several decades have seen tremendous improvement in our ability to perform major operations with minimally invasive procedures and resulting lower morbidity. However, technological advances have not been as widely realized for lung nodule localization to complement minimally invasive surgery. This review describes the latest advances in lung nodule localization technology while also demonstrating that more efforts in this area are needed.

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.

Isoforms of Neuropilin-2 Denote Unique Tumor-Associated Macrophages in Breast Cancer.

Tumor-associated macrophages (TAMs) exert profound influence over breast cancer progression, promoting immunosuppression, angiogenesis, and metastasis. Neuropilin-2 (NRP2), consisting of the NRP2a and NRP2b isoforms, is a co-receptor for heparin-binding growth factors including VEGF-C and Class 3 Semaphorins. Selective upregulation in response to environmental stimuli and independent signaling pathways endow the NRP2 isoforms with unique functionality, with NRP2b promoting increased Akt signaling via receptor tyrosine kinases including VEGFRs, MET, and PDGFR. Although NRP2 has been shown to regulate macrophage/TAM biology, the role of the individual NRP2a/NRP2b isoforms in TAMs has yet to be evaluated. Using transcriptional profiling and spectral flow cytometry, we show that NRP2 isoform expression was significantly higher in TAMs from murine mammary tumors. NRP2a/NRP2b levels in human breast cancer metastasis were dependent upon the anatomic location of the tumor and significantly correlated with TAM infiltration in both primary and metastatic breast cancers. We define distinct phenotypes of NRP2 isoform-expressing TAMs in mouse models of breast cancer and within malignant pleural effusions from breast cancer patients which were exclusive of neuropilin-1 expression. Genetic depletion of either NRP2 isoform in macrophages resulted in a dramatic reduction of LPS-induced IL-10 production, defects in phagosomal processing of apoptotic breast cancer cells, and increase in cancer cell migration following co-culture. By contrast, depletion of NRP2b, but not NRP2a, inhibited production of IL-6. These results suggest that NRP2 isoforms regulate both shared and unique functionality in macrophages and are associated with distinct TAM subsets in breast cancer.

Estimating 3-D whole-body composition from a chest CT scan.

BACKGROUND: Estimating whole-body composition from limited region-computed tomography (CT) scans has many potential applications in clinical medicine; however, it is challenging. PURPOSE: To investigate if whole-body composition based on several tissue types (visceral adipose tissue [VAT], subcutaneous adipose tissue [SAT], intermuscular adipose tissue [IMAT], skeletal muscle [SM], and bone) can be reliably estimated from a chest CT scan only. METHODS: A cohort of 97 lung cancer subjects who underwent both chest CT scans and whole-body positron emission tomography-CT scans at our institution were collected. We used our in-house software to automatically segment and quantify VAT, SAT, IMAT, SM, and bone on the CT images. The field-of-views of the chest CT scans and the whole-body CT scans were standardized, namely, from vertebra T1 to L1 and from C1 to the bottom of the pelvis, respectively. Multivariate linear regression was used to develop the computer models for estimating the volumes of whole-body tissues from chest CT scans. Subject demographics (e.g., gender and age) and lung volume were included in the modeling analysis. Ten-fold cross-validation was used to validate the performance of the prediction models. Mean absolute difference (MAD) and R-squared (R2 ) were used as the performance metrics to assess the model performance. RESULTS: The R2 values when estimating volumes of whole-body SAT, VAT, IMAT, total fat, SM, and bone from the regular chest CT scans were 0.901, 0.929, 0.900, 0.933, 0.928, and 0.918, respectively. The corresponding MADs (percentage difference) were 1.44 ± 1.21 L (12.21% ± 11.70%), 0.63 ± 0.49 L (29.68% ± 61.99%), 0.12 ± 0.09 L (16.20% ± 18.42%), 1.65 ± 1.40 L (10.43% ± 10.79%), 0.71 ± 0.68 L (5.14% ± 4.75%), and 0.17 ± 0.15 L (4.32% ± 3.38%), respectively. CONCLUSION: Our algorithm shows promise in its ability to estimate whole-body compositions from chest CT scans. Body composition measures based on chest CT scans are more accurate than those based on vertebra third lumbar.

Postprocedural Cognitive Load Measurement With Immediate Feedback to Guide Curriculum Development.

BACKGROUND: Methods to assess competency in cardiothoracic training are essential. Here, we report a system that allows us to better assess competency from the perspective of both the trainee and educator. We hypothesized that postprocedural cognitive burden measurement (by the trainee) with immediate feedback (from the educator) could aid in identifying barriers to the acquisition of skills and knowledge so that training curricula can be individualized. METHODS: The National Aeronautics and Space Administration Task Load Index (NASA-TLX), a validated instrument to measure cognitive load, was administered with an online platform after bronchoscopy, esophagogastroduodenoscopy, and video-assisted thoracoscopic surgery for 11 residents. Immediate postprocedure feedback and standardized debriefing occurred for each procedure. RESULTS: Mean NASA-TLX scores were highest (indicating greater cognitive load) for esophagogastroduodenoscopy and video-assisted thoracoscopic surgery (P < .001). When comparing subscale measures, mental demand was significantly higher for video-assisted thoracoscopic surgery (P = .026) compared with the other procedures, whereas physical demand was highest for esophagogastroduodenoscopy (P = .018). Self-reported frustration was similar for all case types (P = .247). Cognitive burden decreased with a greater number of procedures for bronchoscopy (P = .027). Significant improvement was noted by the trainee at the end of the rotation in self-assessed procedural competency and preparedness for thoracic board topics (all P < .05). Postprocedure feedback by the attending surgeon correlated with more frequent completion of self-evaluations by the residents. CONCLUSIONS: Longitudinal assessment of cognitive load in combination with postprocedural feedback identified barriers to skill acquisition for both residents and educators. This information allows for individualized rotation development as a step toward a competency-based curriculum.

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.

Adjuvant chemotherapy for pulmonary sarcomatoid carcinoma: A retrospective analysis of the National Cancer Database.

OBJECTIVES: Pulmonary sarcomatoid carcinoma (PSC) is a rarely occurring variant of non-small cell lung cancer with sarcoma-like features. Compared with traditional non-small cell lung cancer, PSC patients typically present later and have poorer prognoses, irrespective of stage. The standard of care is resection, but guidelines for the use of adjuvant chemotherapy have not been established. To advance the development of evidence-based management algorithms for PSC after resection, a statistical analysis on a nationwide representative sample of patients was performed. METHODS: A retrospective cohort study was performed by querying the National Cancer Database for patients with a diagnosis of PSC between 2004 and 2015. Patients who received complete anatomical resection with or without adjuvant chemotherapy were included. Multivariable regression was used to detect factors associated with the receipt of adjuvant chemotherapy. Multivariable Cox regression of overall survival and Kaplan-Meier survival analysis on propensity-matched groups was conducted to study the association between adjuvant chemotherapy and prognosis. RESULTS: We included 1497 patients with PSC in the final analysis. Factors associated with receiving adjuvant chemotherapy were age, histology, and receipt of adjuvant radiation. The results of multivariable Cox analysis and Kaplan-Meier analysis on propensity matched groups yielded similar trends: adjuvant chemotherapy was associated with improved 5-year overall survival for stage II and III disease, but not for stage I disease. CONCLUSIONS: Multiple factors are associated with receipt of adjuvant chemotherapy for PSC, and this treatment appears to be associated with improved survival in stage II and stage III, but not stage I patients.

Intrapleural interleukin-2-expressing oncolytic virotherapy enhances acute antitumor effects and T-cell receptor diversity in malignant pleural disease.

OBJECTIVE: The mainstay of treatment for patients with malignant pleural disease is fluid drainage and systemic therapy. A tumor-specific oncolytic virus or T-cell-activating interleukin-2 immunotherapy may provide an opportunity for local control. We previously developed a vaccinia virus-expressing interleukin-2, an oncolytic virus that mediated tumor regression in preclinical peritoneal tumor models with expansion of tumor-infiltrating lymphocytes. We evaluated the antitumor efficacy and immune modulatory effects of vaccinia virus-expressing interleukin-2 in malignant pleural disease. METHODS: A murine model of malignant pleural disease was established with percutaneous intrapleural deposition of the Lewis lung carcinoma cell line and monitored with bioluminescent imaging. After intrapleural or systemic administration of vaccinia viruses (vaccinia virus yellow fluorescent protein control, vaccinia virus-expressing interleukin-2), systemic anti-programmed cell death-1 antibody, or combination therapy (vaccinia virus-expressing interleukin-2 and anti-programmed cell death-1), tumor mass, immune cell infiltration, T-cell receptor diversity, and survival were assessed. RESULTS: Intrapleural vaccinia virus resulted in significant tumor regression compared with phosphate-buffered saline control (P < .05). Inclusion of the interleukin-2 transgene further increased intratumoral CD8+ T cells (P < .01) and programmed cell death-1 expression on CD8+ tumor-infiltrating lymphocytes (P < .001). Intrapleural vaccinia virus-expressing interleukin-2 was superior to systemic vaccinia virus-expressing interleukin-2, with reduced tumor burden (P < .0001) and improved survival (P < .05). Intrapleural vaccinia virus-expressing interleukin-2 alone or combined treatment with systemic anti-programmed cell death-1 reduced tumor burden (P < .01), improved survival (P < .01), and increased intratumoral αß T-cell receptor diversity (P < .05) compared with systemic anti-programmed cell death-1 monotherapy. CONCLUSIONS: Intrapleural vaccinia virus-expressing interleukin-2 reduced tumor burden and enhanced survival in a murine malignant pleural disease model. Increased CD8+ tumor-infiltrating lymphocytes and αß T-cell receptor diversity are associated with enhanced response. Clinical trials will enable assessment of intrapleural vaccinia virus-expressing interleukin-2 therapy in patients with malignant pleural disease.

Prognostic Difference of Pleural versus Distant Metastasis after Surgery for Lung Cancer.

BACKGROUND: Pleural metastasis in lung cancer found at diagnosis has a poor prognosis, with 5-11 months' survival. We hypothesized that prognosis might be different for patients who have had curative-intent surgery and subsequent pleural recurrence and that survival might differ based on the location of the first metastasis (distant versus pleural). This may clarify if pleural recurrence is a local event or due to systemic disease. METHODS: A database of 5089 patients who underwent curative-intent surgery for lung cancer was queried, and 85 patients were found who had biopsy-proven pleural metastasis during surveillance. We examined survival based on pattern of metastasis (pleural first versus distant first/simultaneously). RESULTS: Median survival was 34 months (range: 1-171) from the time of surgery and 13 months (range: 0-153) from the time of recurrence. The shortest median survival after recurrence was in patients with adenocarcinoma and pleural metastasis as the first site (6 months). For patients with pleural metastasis as the first site, those with adenocarcinoma had a significantly shorter post-recurrence survival when compared with squamous cell carcinoma (6 vs. 12 months; HR = 0.34) and a significantly shorter survival from the time of surgery when compared with distant metastases first/simultaneously (25 vs. 52 months; HR = 0.49). CONCLUSIONS: Patients who undergo curative-intent surgery for lung adenocarcinoma that have pleural recurrence as the first site have poor survival. This may indicate that pleural recurrence after lung surgery is not likely due to a localized event but rather indicates systemic disease; however, this would require further study.

Lung Nodule Evaluation Using Robotic-Assisted Bronchoscopy at a Veteran's Affairs Hospital.

The incidence of lung nodules has increased with improved diagnostic imaging and screening protocols. Despite improvements for diagnosing pulmonary nodules with technologies such as electromagnetic navigational bronchoscopy (ENB), several limitations still exist including adequate visualization, localization, and diagnostic yield. Robotic-assisted bronchoscopy with ENB has been introduced as a method to overcome these shortcomings. We describe our initial experience in evaluating lung nodules with robotic assisted bronchoscopy. We retrospectively reviewed data on the first 25 patients that underwent robotic-assisted bronchoscopy and biopsy. We analyzed success with localization, diagnostic yield, and post procedural morbidity. Diagnostic yield was 96% (24/25) with no periprocedural morbidity. The majority of nodules were malignant or atypical (76%) and were located in the right upper lobe. Diameter ranged between 0.8-6.9 cm (median size 1-2 cm). Seventy-five percent of patients underwent subsequent treatment for cancer based on these results, with 25% having continued surveillance. Robotic assisted bronchoscopy is safe and accurate. Studies with larger numbers will allow better understanding of the diagnostic yield and clinical utility of this approach in comparison to other diagnostic tools for lung nodules.