Temperature-dependent Development, Survival, and Oviposition of Laricobius osakensis (Coleoptera: Derodontidae): A Specialist Predator of Adelges tsugae (Hemiptera: Adelgidae).

A predator, Laricobius osakensis Montgomery and Shiyake (Coleoptera: Derodontidae), is being mass-produced and released for the biological control of the invasive hemlock woolly adelgid (HWA), Adelges tsugae Annand (Hemiptera: Adelgidae). To better understand and predict the seasonality of this predator in North America, the development and reproduction of L. osakensis were evaluated at constant temperatures ranging from 5 to 22°C. The predicted seasonal biology was compared with data from field collections. L. osakensis did not complete development from egg to adult at the two lowest temperatures tested, 5 and 8°C, but did so at the highest temperature of 22°C. The minimum development thresholds were estimated for eggs (4.2°C), first (1.8°C), second (5.5°C), third (4.6°C), and fourth instar (4.1°C), prepupa (3.6°C), and pupa (7.5°C). Oviposition rates were significantly greater at 5 and 10°C than at 20 and 25°C. Head capsule width significantly increased for each of the four larval instars with a mean of 0.19, 0.26, 0.35, and 0.44 mm, respectively. Laboratory and field data were used to develop a phenology forecasting model to predict the occurrence of all developmental stages of L. osakensis. This model will allow land managers to more accurately predict the optimal timing for L. osakensis larval sampling throughout its established range.

Subterranean Survivorship and Seasonal Emergence of Laricobius spp. (Coleoptera: Derodontidae), Biological Control Agents for the Hemlock Woolly Adelgid.

Following the adventive arrival, subsequent spread, and ensuing impact of Adelges tsugae Annand (Hemiptera: Adelgidae), the hemlock woolly adelgid (HWA) in the eastern United States, a robust initiative was launched with the goal of decreasing ecosystem impacts from the loss of eastern hemlock (Pinales: Pinaceae). This initiative includes the use of biological control agents, including Laricobius spp. (Insecta: Coleoptera). Laboratory production of these agents is limited by subterranean mortality and early emergence. Therefore, the subterranean survivorship and timing of emergence of a mixture of Laricobius spp. was investigated. PVC traps internally lined with a sticky card and covered with a mesh screen were inserted into the soil to measure the percent emergence of adults based on the number of larvae placed within. The number of emerged adults in the field and laboratory-reared larval treatments was adjusted based on emergence numbers in the control and used as the response variable. Independent variables included in the final model were: treatment (field-collected vs. laboratory-reared), organic layer depth (cm), soil pH, and April-to-December mean soil moisture. No differences were found in survivorship between field-collected and laboratory-reared treatments. As pH and organic layer increased survivorship decreased, significantly. Although the majority of emergence occurred in the fall, emergence also occurred in spring and summer. The occurrence of spring and summer emergence and low survivorship (17.1 ± 0.4%) in the field across all treatments suggests that these are characteristics of Laricobius spp. field biology in their introduced range and not artifacts of the laboratory rearing process.

Pre- and post-operative antibiotics in conjunction with cytoreductive surgery and heated intraperitoneal chemotherapy (HIPEC) should be considered for pseudomyxoma peritonei (PMP) treatment.

Pseudomyxoma peritonei (PMP) is a subtype of peritoneal carcinomatosis that is traditionally treated by cytoreductive surgery (CRS) followed by hyperthermic intraperitoneal chemotherapy (HIPEC). A growing body of evidence suggests that microbes are associated with various tumor types and have been found in organs and cavities that were once considered sterile. Prior and ongoing research from our consortium of PMP researchers strongly suggests that bacteria are associated with PMP tumors. While the significance of this association is unclear, in our opinion, further research is warranted to understand whether these bacteria contribute to the development, maintenance and/or progression of PMP. Elucidation of a possible causal role for bacteria in PMP could suggest a benefit for supplementation of antibiotics to current treatment protocols.

Maintaining tumor targeting accuracy in real-time motion compensation systems for respiration-induced tumor motion.

PURPOSE: To determine how best to time respiratory surrogate-based tumor motion model updates by comparing a novel technique based on external measurements alone to three direct measurement methods. METHODS: Concurrently measured tumor and respiratory surrogate positions from 166 treatment fractions for lung or pancreas lesions were analyzed. Partial-least-squares regression models of tumor position from marker motion were created from the first six measurements in each dataset. Successive tumor localizations were obtained at a rate of once per minute on average. Model updates were timed according to four methods: never, respiratory surrogate-based (when metrics based on respiratory surrogate measurements exceeded confidence limits), error-based (when localization error ≥ 3 mm), and always (approximately once per minute). RESULTS: Radial tumor displacement prediction errors (mean ± standard deviation) for the four schema described above were 2.4 ± 1.2, 1.9 ± 0.9, 1.9 ± 0.8, and 1.7 ± 0.8 mm, respectively. The never-update error was significantly larger than errors of the other methods. Mean update counts over 20 min were 0, 4, 9, and 24, respectively. CONCLUSIONS: The same improvement in tumor localization accuracy could be achieved through any of the three update methods, but significantly fewer updates were required when the respiratory surrogate method was utilized. This study establishes the feasibility of timing image acquisitions for updating respiratory surrogate models without direct tumor localization.

A core microbiome associated with the peritoneal tumors of pseudomyxoma peritonei.

BACKGROUND: Pseudomyxoma peritonei (PMP) is a malignancy characterized by dissemination of mucus-secreting cells throughout the peritoneum. This disease is associated with significant morbidity and mortality and despite effective treatment options for early-stage disease, patients with PMP often relapse. Thus, there is a need for additional treatment options to reduce relapse rate and increase long-term survival. A previous study identified the presence of both typed and non-culturable bacteria associated with PMP tissue and determined that increased bacterial density was associated with more severe disease. These findings highlighted the possible role for bacteria in PMP disease. METHODS: To more clearly define the bacterial communities associated with PMP disease, we employed a sequenced-based analysis to profile the bacterial populations found in PMP tumor and mucin tissue in 11 patients. Sequencing data were confirmed by in situ hybridization at multiple taxonomic depths and by culturing. A pilot clinical study was initiated to determine whether the addition of antibiotic therapy affected PMP patient outcome. MAIN RESULTS: We determined that the types of bacteria present are highly conserved in all PMP patients; the dominant phyla are the Proteobacteria, Actinobacteria, Firmicutes and Bacteroidetes. A core set of taxon-specific sequences were found in all 11 patients; many of these sequences were classified into taxonomic groups that also contain known human pathogens. In situ hybridization directly confirmed the presence of bacteria in PMP at multiple taxonomic depths and supported our sequence-based analysis. Furthermore, culturing of PMP tissue samples allowed us to isolate 11 different bacterial strains from eight independent patients, and in vitro analysis of subset of these isolates suggests that at least some of these strains may interact with the PMP-associated mucin MUC2. Finally, we provide evidence suggesting that targeting these bacteria with antibiotic treatment may increase the survival of PMP patients. CONCLUSIONS: Using 16S amplicon-based sequencing, direct in situ hybridization analysis and culturing methods, we have identified numerous bacterial taxa that are consistently present in all PMP patients tested. Combined with data from a pilot clinical study, these data support the hypothesis that adding antimicrobials to the standard PMP treatment could improve PMP patient survival.

Antibiotic treatment decreases microbial burden associated with pseudomyxoma peritonei and affects ß-catenin distribution.

PURPOSE: Pseudomyxoma peritonei is an understudied cancer in which an appendiceal neoplasm invades the peritoneum and forms tumor foci on abdominal organs. Previous studies have shown that bacteria reside within pseudomyxoma peritonei tumors and mucin. Thus, we sought to analyze the effect of antibiotics on bacterial density and ß-catenin expression within pseudomyxoma peritonei samples. EXPERIMENTAL DESIGN: The study included 48 patients: 19 with disseminated peritoneal adenomucinosis (DPAM) and 29 with peritoneal mucinous carcinomatosis (PMCA). Fourteen patients were given antibiotics (30 mg lansoprazole, 1 g amoxicillin, and 500 mg clarithromycin) twice a day for 14 days. One week after completion of therapy, surgery was conducted and specimens were harvested for pathology, bacterial culture, ISH, and immunohistochemistry. RESULTS: ISH showed the presence of bacteria in 83% of the patient samples, with a higher Helicobacter pylori density observed in PMCA versus DPAM. PMCA patients treated with antibiotics had a significantly lower bacterial density and decreased ß-catenin levels in the cytoplasm, the cell nuclei, and mucin-associated cells. Although not significant, similar trends were observed in DPAM patients. Cell membrane ß-catenin was significantly increased in both DPAM and PMCA patients receiving antibiotics. CONCLUSIONS: Bacteria play an important role in pseudomyxoma peritonei. Antibiotic treatment improved the histopathology of tissue, particularly in PMCA patients. In PMCA, antibiotics decreased bacterial density and were associated with a significant ß-catenin decrease in the cytoplasm, cell nuclei, and mucin along with a small membrane increase. These results suggest that antibiotics offer potential protection against cell detachment, cellular invasion, and metastasis.

Online monitoring and error detection of real-time tumor displacement prediction accuracy using control limits on respiratory surrogate statistics.

PURPOSE: To evaluate Hotelling's T(2) statistic and the input variable squared prediction error (Q((X))) for detecting large respiratory surrogate-based tumor displacement prediction errors without directly measuring the tumor's position. METHODS: Tumor and external marker positions from a database of 188 Cyberknife Synchrony™ lung, liver, and pancreas treatment fractions were analyzed. The first ten measurements of tumor position in each fraction were used to create fraction-specific models of tumor displacement using external surrogates as input; the models were used to predict tumor position from subsequent external marker measurements. A partial least squares (PLS) model with four scores was developed for each fraction to determine T(2) and Q((X)) confidence limits based on the first ten measurements in a fraction. The T(2) and Q((X)) statistics were then calculated for every set of external marker measurements. Correlations between model error and both T(2) and Q((X)) were determined. Receiver operating characteristic analysis was applied to evaluate sensitivities and specificities of T(2), Q((X)), and T(2)∪Q((X)) for predicting real-time tumor localization errors >3 mm over a range of T(2) and Q((X)) confidence limits. RESULTS: Sensitivity and specificity of detecting errors >3 mm varied with confidence limit selection. At 95% sensitivity, T(2)∪Q((X)) specificity was 15%, 2% higher than either T(2) or Q((X)) alone. The mean time to alarm for T(2)∪Q((X)) at 95% sensitivity was 5.3 min but varied with a standard deviation of 8.2 min. Results did not differ significantly by tumor site. CONCLUSIONS: The results of this study establish the feasibility of respiratory surrogate-based online monitoring of real-time respiration-induced tumor motion model accuracy for lung, liver, and pancreas tumors. The T(2) and Q((X)) statistics were able to indicate whether inferential model errors exceeded 3 mm with high sensitivity. Modest improvements in specificity were achieved by combining T(2) and Q((X)) results.

Mitigating errors in external respiratory surrogate-based models of tumor position.

PURPOSE: To investigate the effect of tumor site, measurement precision, tumor-surrogate correlation, training data selection, model design, and interpatient and interfraction variations on the accuracy of external marker-based models of tumor position. METHODS AND MATERIALS: Cyberknife Synchrony system log files comprising synchronously acquired positions of external markers and the tumor from 167 treatment fractions were analyzed. The accuracy of Synchrony, ordinary-least-squares regression, and partial-least-squares regression models for predicting the tumor position from the external markers was evaluated. The quantity and timing of the data used to build the predictive model were varied. The effects of tumor-surrogate correlation and the precision in both the tumor and the external surrogate position measurements were explored by adding noise to the data. RESULTS: The tumor position prediction errors increased during the duration of a fraction. Increasing the training data quantities did not always lead to more accurate models. Adding uncorrelated noise to the external marker-based inputs degraded the tumor-surrogate correlation models by 16% for partial-least-squares and 57% for ordinary-least-squares. External marker and tumor position measurement errors led to tumor position prediction changes 0.3-3.6 times the magnitude of the measurement errors, varying widely with model algorithm. The tumor position prediction errors were significantly associated with the patient index but not with the fraction index or tumor site. Partial-least-squares was as accurate as Synchrony and more accurate than ordinary-least-squares. CONCLUSIONS: The accuracy of surrogate-based inferential models of tumor position was affected by all the investigated factors, except for the tumor site and fraction index.

Incidence of changes in respiration-induced tumor motion and its relationship with respiratory surrogates during individual treatment fractions.

PURPOSE: To determine how frequently (1) tumor motion and (2) the spatial relationship between tumor and respiratory surrogate markers change during a treatment fraction in lung and pancreas cancer patients. METHODS AND MATERIALS: A Cyberknife Synchrony system radiographically localized the tumor and simultaneously tracked three respiratory surrogate markers fixed to a form-fitting vest. Data in 55 lung and 29 pancreas fractions were divided into successive 10-min blocks. Mean tumor positions and tumor position distributions were compared across 10-min blocks of data. Treatment margins were calculated from both 10 and 30 min of data. Partial least squares (PLS) regression models of tumor positions as a function of external surrogate marker positions were created from the first 10 min of data in each fraction; the incidence of significant PLS model degradation was used to assess changes in the spatial relationship between tumors and surrogate markers. RESULTS: The absolute change in mean tumor position from first to third 10-min blocks was >5 mm in 13% and 7% of lung and pancreas cases, respectively. Superior-inferior and medial-lateral differences in mean tumor position were significantly associated with the lobe of lung. In 61% and 54% of lung and pancreas fractions, respectively, margins calculated from 30 min of data were larger than margins calculated from 10 min of data. The change in treatment margin magnitude for superior-inferior motion was >1 mm in 42% of lung and 45% of pancreas fractions. Significantly increasing tumor position prediction model error (mean ± standard deviation rates of change of 1.6 ± 2.5 mm per 10 min) over 30 min indicated tumor-surrogate relationship changes in 63% of fractions. CONCLUSIONS: Both tumor motion and the relationship between tumor and respiratory surrogate displacements change in most treatment fractions for patient in-room time of 30 min.

Inferring positions of tumor and nodes in Stage III lung cancer from multiple anatomical surrogates using four-dimensional computed tomography.

PURPOSE: To investigate the feasibility of modeling Stage III lung cancer tumor and node positions from anatomical surrogates. METHODS AND MATERIALS: To localize their centroids, the primary tumor and lymph nodes from 16 Stage III lung cancer patients were contoured in 10 equal-phase planning four-dimensional (4D) computed tomography (CT) image sets. The centroids of anatomical respiratory surrogates (carina, xyphoid, nipples, mid-sternum) in each image set were also localized. The correlations between target and surrogate positions were determined, and ordinary least-squares (OLS) and partial least-squares (PLS) regression models based on a subset of respiratory phases (three to eight randomly selected) were created to predict the target positions in the remaining images. The three-phase image sets that provided the best predictive information were used to create models based on either the carina alone or all surrogates. RESULTS: The surrogate most correlated with target motion varied widely. Depending on the number of phases used to build the models, mean OLS and PLS errors were 1.0 to 1.4 mm and 0.8 to 1.0 mm, respectively. Models trained on the 0%, 40%, and 80% respiration phases had mean (+/- standard deviation) PLS errors of 0.8 +/- 0.5 mm and 1.1 +/- 1.1 mm for models based on all surrogates and carina alone, respectively. For target coordinates with motion >5 mm, the mean three-phase PLS error based on all surrogates was 1.1 mm. CONCLUSIONS: Our results establish the feasibility of inferring primary tumor and nodal motion from anatomical surrogates in 4D CT scans of Stage III lung cancer. Using inferential modeling to decrease the processing time of 4D CT scans may facilitate incorporation of patient-specific treatment margins.

Investigation of motion sickness and inertial stability on a moving couch for intra-fraction motion compensation.

BACKGROUND. Respiration-induced tumor motion compensation using a treatment couch requires moving the patient at non-trivial speeds. The purpose of this work was to investigate motion sickness and stability of the patient's external surface due to a moving couch with respiration-comparable velocities and accelerations. MATERIAL AND METHODS. A couch was designed to move with a peak-peak displacement of 5 cm and 1 cm in the S-I and A-P directions, respectively, and a period of 3.6 s. Fifty patients completed a 16-question motion sickness assessment questionnaire (MSAQ) prior to, during, and after the study. Seven optical reflectors affixed to the abdomen of each patient were monitored by infrared cameras. The relationship between reflector positions under stationary and moving conditions was evaluated to assess the stability of the patient's external surface. RESULTS AND DISCUSSION. Among the 4800 responses, 95% were 1 (no discomfort) of 9, and there were no scores of 6 or higher. Mild discomfort (scores of 4-5) was similar during couch motion and before couch motion (p = 0.39). Mild discomfort was less common after couch motion (p = 0.039) than before or during couch movement. There was a near 1:1 correspondence between marker-pair regression coefficients and phase offset values during couch-stationary and couch-moving conditions. Our results show that patients do not suffer motion sickness or external surface instability on a moving couch.

Inferential modeling and predictive feedback control in real-time motion compensation using the treatment couch during radiotherapy.

Tumor motion induced by respiration presents a challenge to the reliable delivery of conformal radiation treatments. Real-time motion compensation represents the technologically most challenging clinical solution but has the potential to overcome the limitations of existing methods. The performance of a real-time couch-based motion compensation system is mainly dependent on two aspects: the ability to infer the internal anatomical position and the performance of the feedback control system. In this paper, we propose two novel methods for the two aspects respectively, and then combine the proposed methods into one system. To accurately estimate the internal tumor position, we present partial-least squares (PLS) regression to predict the position of the diaphragm using skin-based motion surrogates. Four radio-opaque markers were placed on the abdomen of patients who underwent fluoroscopic imaging of the diaphragm. The coordinates of the markers served as input variables and the position of the diaphragm served as the output variable. PLS resulted in lower prediction errors compared with standard multiple linear regression (MLR). The performance of the feedback control system depends on the system dynamics and dead time (delay between the initiation and execution of the control action). While the dynamics of the system can be inverted in a feedback control system, the dead time cannot be inverted. To overcome the dead time of the system, we propose a predictive feedback control system by incorporating forward prediction using least-mean-square (LMS) and recursive least square (RLS) filtering into the couch-based control system. Motion data were obtained using a skin-based marker. The proposed predictive feedback control system was benchmarked against pure feedback control (no forward prediction) and resulted in a significant performance gain. Finally, we combined the PLS inference model and the predictive feedback control to evaluate the overall performance of the feedback control system. Our results show that, with the tumor motion unknown but inferred by skin-based markers through the PLS model, the predictive feedback control system was able to effectively compensate intra-fraction motion.

An analysis of the treatment couch and control system dynamics for respiration-induced motion compensation.

Sophisticated methods for real-time motion compensation include using the linear accelerator, MLC, or treatment couch. To design such a couch, the required couch and control system dynamics need to be investigated. We used an existing treatment couch known as the Hexapod to gain insight into couch dynamics and an internal model controller to simulate feedback control of respiration-induced motion. The couch dynamics, described using time constants and dead times, were investigated using step inputs. The resulting data were modeled as first and second order systems with dead time. The couch was determined to have a linear response for step inputs < or = 1 cm. Motion data from 12 patients were obtained using a skin marker placed on the abdomen of the patient and the marker data were assumed to be an exact surrogate of tumor motion. The feedback system was modeled with the couch as a second-ordersystem and the controller as a first order system. The time constants of the couch and controller and the dead times were varied starting with parameters obtained from the Hexapod couch and the performance of the feedback system was evaluated. The resulting residual motion under feedback control was generally <0.3 cm when a fast enough couch was simulated.