Framework for Radiation Oncology Department-wide Evaluation and Implementation of Commercial Artificial Intelligence Autocontouring.

PURPOSE: Artificial intelligence (AI)-based autocontouring in radiation oncology has potential benefits such as standardization and time savings. However, commercial AI solutions require careful evaluation before clinical integration. We developed a multidimensional evaluation method to test pretrained AI-based automated contouring solutions across a network of clinics. METHODS AND MATERIALS: Curated data included 121 patient planning computed tomography (CT) scans with a total of 859 clinically approved contours used for treatment from 4 clinics. Regions of interest (ROIs) were generated with 3 commercial AI-based automated contouring software solutions (AI1, AI2, AI3) spanning the following disease sites: brain, head and neck (H&N), thorax, abdomen, and pelvis. Quantitative agreement between AI-generated and clinical contours was measured by Dice similarity coefficient (DSC) and Hausdorff distance (HD). Qualitative assessment was performed by multiple experts scoring blinded AI-contours using a Likert scale. Workflow and usability surveying was also conducted. RESULTS: AI1, AI2, and AI3 contours had high quantitative agreement in 27.8%, 32.8%, and 34.1% of cases (DSC >0.9), performing well in pelvis (median DSC = 0.86/0.88/0.91) and thorax (median DSC = 0.91/0.89/0.91). All 3 solutions had low quantitative agreement in 7.4%, 8.8%, and 6.1% of cases (DSC <0.5), performing worse in brain (median DSC = 0.65/0.78/0.75) and H&N (median DSC = 0.76/0.80/0.81). Qualitatively, AI1 and AI2 contours were acceptable (rated 1-2) with at most minor edits in 70.7% and 74.6% of ROIs (2906 ratings), higher for abdomen (AI1: 79.2%) and thorax (AI2: 90.2%), and lower for H&N (29.0/35.6%). An end-user survey showed strong user preference for full automation and mixed preferences for accuracy versus total number of structures generated. CONCLUSIONS: Our evaluation method provided a comprehensive analysis of both quantitative and qualitative measures of commercially available pretrained AI autocontouring algorithms. The evaluation framework served as a roadmap for clinical integration that aligned with user workflow preference.

Artificial intelligence (AI) and machine learning (ML) in precision oncology: a review on enhancing discoverability through multiomics integration.

Multiomics data including imaging radiomics and various types of molecular biomarkers have been increasingly investigated for better diagnosis and therapy in the era of precision oncology. Artificial intelligence (AI) including machine learning (ML) and deep learning (DL) techniques combined with the exponential growth of multiomics data may have great potential to revolutionize cancer subtyping, risk stratification, prognostication, prediction and clinical decision-making. In this article, we first present different categories of multiomics data and their roles in diagnosis and therapy. Second, AI-based data fusion methods and modeling methods as well as different validation schemes are illustrated. Third, the applications and examples of multiomics research in oncology are demonstrated. Finally, the challenges regarding the heterogeneity data set, availability of omics data, and validation of the research are discussed. The transition of multiomics research to real clinics still requires consistent efforts in standardizing omics data collection and analysis, building computational infrastructure for data sharing and storing, developing advanced methods to improve data fusion and interpretability, and ultimately, conducting large-scale prospective clinical trials to fill the gap between study findings and clinical benefits.

Increased preoperative aspartate aminotransferase-to-platelet ratio index predicts complications following total shoulder arthroplasty.

Background: This study investigates the association between aspartate aminotransferase-to-platelet ratio index (APRI), a noninvasive measure of liver function, and 30-day postoperative complications following total shoulder arthroplasty (TSA). Methods: The American College of Surgeons National Surgical Quality Improvement Program database was queried for all patients who underwent TSA between 2015 and 2021. The study population was divided into 4 groups based on preoperative APRI: normal/reference (APRI ≤ 0.5), mild fibrosis (0.5 < APRI ≤ 0.7), significant fibrosis (0.7 < APRI ≤ 1), and cirrhosis (APRI > 1). Multivariate logistic regression analysis was conducted to investigate the connection between preoperative APRI and postoperative complications. Results: Compared to the reference group, significant fibrosis was independently associated with a greater likelihood of major complications (odds ratio [OR]: 1.82, 95% confidence interval [CI]: 1.11-2.99; P = .017), minor complications (OR: 2.70, 95% CI: 1.67-4.37; P < .001), pneumonia (OR: 5.78, 95% CI: 2.58-12.95; P < .001), blood transfusions (OR: 2.89, 95% CI: 1.57-5.32; P < .001), readmission (OR: 1.88, 95% CI: 1.10-3.21; P = .022), and non-home discharge (OR: 1.83, 95% CI: 1.23-2.73; P = .003). Cirrhosis was independently associated with a greater likelihood of minor complications (OR: 3.96, 95% CI: 2.67-5.88; P < .001), blood transfusions (OR: 5.85, 95% CI: 3.79-9.03; P < .001), failure to wean off a ventilator (OR: 9.10, 95% CI: 1.98-41.82; P = .005), and non-home discharge (OR: 2.06, 95% CI: 1.43-2.96; P < .001). Conclusion: Increasing preoperative APRI was associated with an increasing rate of postoperative complications following TSA.

Automated treatment planning for proton pencil beam scanning using deep learning dose prediction and dose-mimicking optimization.

PURPOSE: The purpose of this study is to investigate the use of a deep learning architecture for automated treatment planning for proton pencil beam scanning (PBS). METHODS: A 3-dimensional (3D) U-Net model has been implemented in a commercial treatment planning system (TPS) that uses contoured regions of interest (ROI) binary masks as model inputs with a predicted dose distribution as the model output. Predicted dose distributions were converted to deliverable PBS treatment plans using a voxel-wise robust dose mimicking optimization algorithm. This model was leveraged to generate machine learning (ML) optimized plans for patients receiving proton PBS irradiation of the chest wall. Model training was carried out on a retrospective set of 48 previously-treated chest wall patient treatment plans. Model evaluation was carried out by generating ML-optimized plans on a hold-out set of 12 contoured chest wall patient CT datasets from previously treated patients. Clinical goal criteria and gamma analysis were used to compare dose distributions of the ML-optimized plans against the clinically approved plans across the test patients. RESULTS: Statistical analysis of mean clinical goal criteria indicates that compared to the clinical plans, the ML optimization workflow generated robust plans with similar dose to the heart, lungs, and esophagus while achieving superior dosimetric coverage to the PTV chest wall (clinical mean V95 = 97.6% vs. ML mean V95 = 99.1%, p < 0.001) across the 12 test patients. CONCLUSIONS: ML-based automated treatment plan optimization using the 3D U-Net model can generate treatment plans of similar clinical quality compared to human-driven optimization.

Thirty day low-dose versus regular-dose aspirin for venous thromboembolism prophylaxis in primary total joint arthroplasty.

BACKGROUND: The optimal dosing of aspirin (ASA) monotherapy for prophylaxis after total joint arthroplasty is debatable. The objective of this study was to compare two ASA regimens with regards to symptomatic deep venous thrombosis (DVT), pulmonary embolism (PE), bleeding, and infection 90 days after primary total hip arthroplasty (THA) and total knee arthroplasty (TKA). METHODS: We retrospectively identified 625 primary THA and TKA surgeries in 483 patients who received ASA for 4 weeks post-op. 301 patients received 325 mg once daily (QD) and 324 patients received 81 mg twice daily (BID). Patients were excluded if they were minors, had a prior venous thromboembolism (VTE), had ASA allergy, or received other VTE prophylaxis drugs. RESULTS: There was a significant difference in rate of bleeding and suture reactions between the two groups. Bleeding was 7.6% for 325 mg QD and 2.5% for 81 mg BID (p = .0029 Χ2, p = .004 on multivariate logistic regression analysis). Suture reactions were 3.3% for 325 mg QD and 1.2% for 81 mg BID (p = .010 Χ2, p = .027 on multivariate logistic regression analysis). Rates of VTE, symptomatic DVT, and PE were not significantly different. The incidence of VTE was 2.7% for 325 mg QD and 1.5% for 81 mg BID (p = .4056). Symptomatic DVT rates were 1.6% for 325 mg QD and 0.9% for 81 mg BID (p = .4139). Deep infection was 1.0% for 325 mg QD and 0.31% for 81 mg BID (p = .3564). CONCLUSION: Low-dose ASA in patients with limited comorbidities undergoing primary THA and TKA is associated with significant lower rates of bleeding and suture reactions than high dose ASA. Low-dose ASA was not inferior to higher dose ASA for the prevention of VTE, wound complications, and infection 90 days postoperatively.

Independent association of metabolic tumor response on FDG-PET with pulmonary toxicity following risk-adaptive chemoradiation for unresectable non-small cell lung cancer: Inherent radiosensitivity or immune response?

BACKGROUND: In the context of a phase II trial of risk-adaptive chemoradiation, we evaluated whether tumor metabolic response could serve as a correlate of treatment sensitivity and toxicity. METHODS: Forty-five patients with AJCCv7 stage IIB-IIIB NSCLC enrolled on the FLARE-RT phase II trial (NCT02773238). [18F]fluorodeoxyglucose (FDG) PET-CT images were acquired prior to treatment and after 24 Gy during week 3. Patients with unfavorable on-treatment tumor response received concomitant boosts to 74 Gy total over 30 fractions rather than standard 60 Gy. Metabolic tumor volume and mean standardized uptake value (SUVmean) were calculated semi-automatically. Risk factors of pulmonary toxicity included concurrent chemotherapy regimen, adjuvant anti-PDL1 immunotherapy, and lung dosimetry. Incidence of CTCAE v4 grade 2+ pneumonitis was analyzed using the Fine-Gray method with competing risks of metastasis or death. Peripheral germline DNA microarray sequencing measured predefined candidate genes from distinct pathways: 96 DNA repair, 53 immunology, 38 oncology, 27 lung biology. RESULTS: Twenty-four patients received proton therapy, 23 received ICI, 26 received carboplatin-paclitaxel, and 17 pneumonitis events were observed. Pneumonitis risk was significantly higher for patients with COPD (HR 3.78 [1.48, 9.60], p = 0.005), those treated with immunotherapy (HR 2.82 [1.03, 7.71], p = 0.043) but not with carboplatin-paclitaxel (HR 1.98 [0.71, 5.54], p = 0.19). Pneumonitis rates were similar among selected patients receiving 74 Gy radiation vs 60 Gy (p = 0.33), proton therapy vs photon (p = 0.60), or with higher lung dosimetric V20 (p = 0.30). Patients in the upper quartile decrease in SUVmean (>39.7%) were at greater risk for pneumonitis (HR 4.00 [1.54, 10.44], p = 0.005) and remained significant in multivariable analysis (HR 3.34 [1.23, 9.10], p = 0.018). Germline DNA gene alterations in immunology pathways were most frequently associated with pneumonitis. CONCLUSION: Tumor metabolic response as measured by mean SUV is associated with increased pneumonitis risk in a clinical trial cohort of NSCLC patients independent of treatment factors. This may be partially attributed to patient-specific differences in immunogenicity.

Gradient Boosted Trees for Spatial Data and Its Application to Medical Imaging Data.

Boosting Trees are one of the most successful statistical learning approaches that involve sequentially growing an ensemble of simple regression trees ("weak learners"). This paper proposes a gradient Boosted Trees algorithm for Spatial Data (Boost-S) with covariate information. Boost-S integrates the spatial correlation into the classical framework of eXtreme Gradient Boosting. Each tree is constructed by solving a regularized optimization problem, where the objective function takes into account the underlying spatial correlation and involves two penalty terms on tree complexity. A computationally-efficient greedy heuristic algorithm is proposed to obtain an ensemble of trees. The proposed Boost-S is applied to the spatially-correlated FDG-PET (fluorodeoxyglucose-positron emission tomography) imaging data collected from clinical trials of cancer chemoradiotherapy. Our numerical investigations successfully demonstrate the advantages of the proposed Boost-S over existing approaches for this particular application.

Radiation and immune checkpoint inhibitor-mediated pneumonitis risk stratification in patients with locally advanced non-small cell lung cancer: role of functional lung radiomics?

BACKGROUND: Patients undergoing chemoradiation and immune checkpoint inhibitor (ICI) therapy for locally advanced non-small cell lung cancer (NSCLC) experience pulmonary toxicity at higher rates than historical reports. Identifying biomarkers beyond conventional clinical factors and radiation dosimetry is especially relevant in the modern cancer immunotherapy era. We investigated the role of novel functional lung radiomics, relative to functional lung dosimetry and clinical characteristics, for pneumonitis risk stratification in locally advanced NSCLC. METHODS: Patients with locally advanced NSCLC were prospectively enrolled on the FLARE-RT trial (NCT02773238). All received concurrent chemoradiation using functional lung avoidance planning, while approximately half received consolidation durvalumab ICI. Within tumour-subtracted lung regions, 110 radiomics features (size, shape, intensity, texture) were extracted on pre-treatment [99mTc]MAA SPECT/CT perfusion images using fixed-bin-width discretization. The performance of functional lung radiomics for pneumonitis (CTCAE v4 grade 2 or higher) risk stratification was benchmarked against previously reported lung dosimetric parameters and clinical risk factors. Multivariate least absolute shrinkage and selection operator Cox models of time-varying pneumonitis risk were constructed, and prediction performance was evaluated using optimism-adjusted concordance index (c-index) with 95% confidence interval reporting throughout. RESULTS: Thirty-nine patients were included in the study and pneumonitis occurred in 16/39 (41%) patients. Among clinical characteristics and anatomic/functional lung dosimetry variables, only the presence of baseline chronic obstructive pulmonary disease (COPD) was significantly associated with the development of pneumonitis (HR 4.59 [1.69-12.49]) and served as the primary prediction benchmark model (c-index 0.69 [0.59-0.80]). Discrimination of time-varying pneumonitis risk was numerically higher when combining COPD with perfused lung radiomics size (c-index 0.77 [0.65-0.88]) or shape feature classes (c-index 0.79 [0.66-0.91]) but did not reach statistical significance compared to benchmark models (p > 0.26). COPD was associated with perfused lung radiomics size features, including patients with larger lung volumes (AUC 0.75 [0.59-0.91]). Perfused lung radiomic texture features were correlated with lung volume (adj R2 = 0.84-1.00), representing surrogates rather than independent predictors of pneumonitis risk. CONCLUSIONS: In patients undergoing chemoradiation with functional lung avoidance therapy and optional consolidative immune checkpoint inhibitor therapy for locally advanced NSCLC, the strongest predictor of pneumonitis was the presence of baseline chronic obstructive pulmonary disease. Results from this novel functional lung radiomics exploratory study can inform future validation studies to refine pneumonitis risk models following combinations of radiation and immunotherapy. Our results support functional lung radiomics as surrogates of COPD for non-invasive monitoring during and after treatment. Further study of clinical, dosimetric, and radiomic feature combinations for radiation and immune-mediated pneumonitis risk stratification in a larger patient population is warranted.

Assessment of Cardiac, Vascular, and Pulmonary Pathobiology In Vivo During Acute COVID-19.

Background Acute COVID-19-related myocardial, pulmonary, and vascular pathology and how these relate to each other remain unclear. To our knowledge, no studies have used complementary imaging techniques, including molecular imaging, to elucidate this. We used multimodality imaging and biochemical sampling in vivo to identify the pathobiology of acute COVID-19. Specifically, we investigated the presence of myocardial inflammation and its association with coronary artery disease, systemic vasculitis, and pneumonitis. Methods and Results Consecutive patients presenting with acute COVID-19 were prospectively recruited during hospital admission in this cross-sectional study. Imaging involved computed tomography coronary angiography (identified coronary disease), cardiac 2-deoxy-2-[fluorine-18]fluoro-D-glucose positron emission tomography/computed tomography (identified vascular, cardiac, and pulmonary inflammatory cell infiltration), and cardiac magnetic resonance (identified myocardial disease) alongside biomarker sampling. Of 33 patients (median age 51 years, 94% men), 24 (73%) had respiratory symptoms, with the remainder having nonspecific viral symptoms. A total of 9 patients (35%, n=9/25) had cardiac magnetic resonance-defined myocarditis. Of these patients, 53% (n=5/8) had myocardial inflammatory cell infiltration. A total of 2 patients (5%) had elevated troponin levels. Cardiac troponin concentrations were not significantly higher in patients with and without myocarditis (8.4 ng/L [interquartile range, IQR: 4.0-55.3] versus 3.5 ng/L [IQR: 2.5-5.5]; P=0.07) or myocardial cell infiltration (4.4 ng/L [IQR: 3.4-8.3] versus 3.5 ng/L [IQR: 2.8-7.2]; P=0.89). No patients had obstructive coronary artery disease or vasculitis. Pulmonary inflammation and consolidation (percentage of total lung volume) was 17% (IQR: 5%-31%) and 11% (IQR: 7%-18%), respectively. Neither were associated with the presence of myocarditis. Conclusions Myocarditis was present in a third patients with acute COVID-19, and the majority had inflammatory cell infiltration. Pneumonitis was ubiquitous, but this inflammation was not associated with myocarditis. The mechanism of cardiac pathology is nonischemic and not attributable to a vasculitic process. Registration URL: https://www.isrctn.com; Unique identifier: ISRCTN12154994.

The Management of an Open Biopsy Tract Site Infection and Ulceration in the Setting of Osteosarcoma.

Biopsy site infection in the setting of osteosarcoma is a potentially devastating complication. We present the case of a 16-year-old adolescent girl with a distal femur osteosarcoma who developed an open biopsy site ulceration and infection after initiation of neoadjuvant chemotherapy. This was treated with careful local excision of the ulcerated biopsy site and systemic antibiotic therapy throughout the duration of her chemotherapy course. She subsequently underwent wide resection of the tumor en bloc with a generous ellipse around the biopsy scar and reconstruction with cemented knee megaprosthesis. No recurrence of either infection or malignancy was observed. This case represents the successful treatment of a biopsy site ulceration and infection in a patient with osteosarcoma and may merit adoption in future instances of this complication.

Prognostic Value of Early Fluorodeoxyglucose-Positron Emission Tomography Response Imaging and Peripheral Immunologic Biomarkers: Substudy of a Phase II Trial of Risk-Adaptive Chemoradiation for Unresectable Non-Small Cell Lung Cancer.

Purpose: We sought to examine the prognostic value of fluorodeoxyglucose-positron emission tomography (PET) imaging during chemoradiation for unresectable non-small cell lung cancer for survival and hypothesized that tumor PET response is correlated with peripheral T-cell function. Methods and Materials: Forty-five patients with American Joint Committee on Cancer version 7 stage IIB-IIIB non-small cell lung cancer enrolled in a phase II trial and received platinum-doublet chemotherapy concurrent with 6 weeks of radiation (NCT02773238). Fluorodeoxyglucose-PET was performed before treatment start and after 24 Gy of radiation (week 3). PET response status was prospectively defined by multifactorial radiologic interpretation. PET responders received 60 Gy in 30 fractions, while nonresponders received concomitant boosts to 74 Gy in 30 fractions. Peripheral blood was drawn synchronously with PET imaging, from which germline DNA sequencing, T-cell receptor sequencing, and plasma cytokine analysis were performed. Results: Median follow-up was 18.8 months, 1-year overall survival (OS) 82%, 1-year progression-free survival 53%, and 1-year locoregional control 88%. Higher midtreatment PET total lesion glycolysis was detrimental to OS (1 year 87% vs 63%, P < .001), progression-free survival (1 year 60% vs 26%, P = .044), and locoregional control (1 year 94% vs 65%, P = .012), even after adjustment for clinical/treatment factors. Twenty-nine of 45 patients (64%) were classified as PET responders based on a priori definition. Higher tumor programmed death-ligand 1 expression was correlated with response on PET (P = .017). Higher T-cell receptor richness and clone distribution slope were associated with improved OS (P = .018-0.035); clone distribution slope was correlated with PET response (P = .031). Conclusions: Midchemoradiation PET imaging is prognostic for survival; PET response may be linked to tumor and peripheral T-cell biomarkers.

A Prospective Study of a Resorbable Intravesical Fiducial Marker for Bladder Cancer Radiation Therapy.

Purpose: We conducted a prospective pilot study to evaluate safety and feasibility of TraceIT, a resorbable radiopaque hydrogel, to improve image guidance for bladder cancer radiation therapy (RT). Methods and Materials: Patients with muscle invasive bladder cancer receiving definitive RT were eligible. TraceIT was injected intravesically around the tumor bed during maximal transurethral resection of bladder tumor. The primary endpoint was the difference between radiation treatment planning margin on daily cone beam computed tomography based on alignment to TraceIT versus standard-of-care pelvic bone anatomy. The Van Herk margin formula was used to determine the optimal planning target volume margin. TraceIT visibility, recurrence rates, and survival were estimated by Kaplan-Meier method. Toxicity was measured by Common Terminology Criteria for Adverse Events version 4.03. Results: The trial was fully accrued and 15 patients were analyzed. TraceIT was injected in 4 sites/patient (range, 4-6). Overall, 94% (95% confidence interval [CI], 90%-98%) of injection sites were radiographically visible at RT initiation versus 71% (95% CI, 62%-81%) at RT completion. The median duration of radiographic visibility for injection sites was 106 days (95% CI, 104-113). Most patients were treated with a standard split-course approach with initial pelvic radiation fields, then midcourse repeat transurethral resection of bladder tumor followed by bladder tumor bed boost fields, and 14/15 received concurrent chemotherapy. Alignment to fiducials could allow for reduced planning target volume margins (0.67 vs 1.56 cm) for the initial phase of RT, but not for the boost (1.01 vs 0.96 cm). This allowed for improved target coverage (D95% 80%-83% to 91%-94%) for 2 patients retrospectively planned with both volumetric-modulated arc therapy and 3-dimensional conformal RT. At median follow-up of 22 months, no acute or late complications attributable to TraceIT placement occurred. No patients required salvage cystectomy. Conclusions: TraceIT intravesical fiducial placement is safe and feasible and may facilitate tumor bed delineation and targeting in patients undergoing RT for localized muscle invasive bladder cancer. Improved image guided treatment may facilitate strategies to improve local control and minimize toxicity.

Multitask Learning Radiomics on Longitudinal Imaging to Predict Survival Outcomes following Risk-Adaptive Chemoradiation for Non-Small Cell Lung Cancer.

Medical imaging provides quantitative and spatial information to evaluate treatment response in the management of patients with non-small cell lung cancer (NSCLC). High throughput extraction of radiomic features on these images can potentially phenotype tumors non-invasively and support risk stratification based on survival outcome prediction. The prognostic value of radiomics from different imaging modalities and time points prior to and during chemoradiation therapy of NSCLC, relative to conventional imaging biomarker or delta radiomics models, remains uncharacterized. We investigated the utility of multitask learning of multi-time point radiomic features, as opposed to single-task learning, for improving survival outcome prediction relative to conventional clinical imaging feature model benchmarks. Survival outcomes were prospectively collected for 45 patients with unresectable NSCLC enrolled on the FLARE-RT phase II trial of risk-adaptive chemoradiation and optional consolidation PD-L1 checkpoint blockade (NCT02773238). FDG-PET, CT, and perfusion SPECT imaging pretreatment and week 3 mid-treatment was performed and 110 IBSI-compliant pyradiomics shape-/intensity-/texture-based features from the metabolic tumor volume were extracted. Outcome modeling consisted of a fused Laplacian sparse group LASSO with component-wise gradient boosting survival regression in a multitask learning framework. Testing performance under stratified 10-fold cross-validation was evaluated for multitask learning radiomics of different imaging modalities and time points. Multitask learning models were benchmarked against conventional clinical imaging and delta radiomics models and evaluated with the concordance index (c-index) and index of prediction accuracy (IPA). FDG-PET radiomics had higher prognostic value for overall survival in test folds (c-index 0.71 [0.67, 0.75]) than CT radiomics (c-index 0.64 [0.60, 0.71]) or perfusion SPECT radiomics (c-index 0.60 [0.57, 0.63]). Multitask learning of pre-/mid-treatment FDG-PET radiomics (c-index 0.71 [0.67, 0.75]) outperformed benchmark clinical imaging (c-index 0.65 [0.59, 0.71]) and FDG-PET delta radiomics (c-index 0.52 [0.48, 0.58]) models. Similarly, the IPA for multitask learning FDG-PET radiomics (30%) was higher than clinical imaging (26%) and delta radiomics (15%) models. Radiomics models performed consistently under different voxel resampling conditions. Multitask learning radiomics for outcome modeling provides a clinical decision support platform that leverages longitudinal imaging information. This framework can reveal the relative importance of different imaging modalities and time points when designing risk-adaptive cancer treatment strategies.

Digestion and assimilation of benthic biofilm by the Sábalo, Prochilodus lineatus.

The Sábalo, Prochilodus lineatus (Valenciennes, 1837), is one of 270 fish species in the Rio Paraná system, and it comprises >50% of the fish biomass. Its diet is flocculant benthic biofilm comprised of algae, bacteria and non-living organic matter: a food resource apparently of little value to other fishes. Digestion and assimilation of key nutrients from its biofilm diet by P. lineatus were described and quantified in an attempt to discover how this species is so successful. The fish begin a feeding period with empty digestive tracts, accumulate food during the feeding period and then void the gut content at the end of the feeding period. Early in a feeding period, sand accumulates in the gizzard-like pyloric stomach where it serves as both a grinding medium and a sieve. After sufficient sand has been acquired, food particles passed from the pyloric stomach to the intestine are dramatically reduced in size to <20 µ maximum dimension, whereas larger particles including mineral matter and plant fibres are retained. Total ash, hydrolysis-resistant-ash and hydrolysis-resistant-organic-matter were tested as reference materials against which to measure assimilation and hydrolysis-resistant-organic matter best met the assumptions of the technique. Comparison of the first food ingested to food ingested later in the feeding period shows that grinding of food and selective retention of larger particles results in a three-fold increase in the assimilation of ash-free-dry-mass (to 56%) and hydrolysis-labile-organic-matter (to 67%), and a six-fold increase in the assimilation of amino acids (AAs; to 74%). When food quality is assessed in terms of g AA assimilated · kJ-1 energy assimilated, the quality of food ingested by P. lineatus ranges from a maintenance level of 5 to 12 mg AA · kJ-1 , a level expected to produce near maximum growth. Thus, the processing of food in the pyloric stomach is integral to the success of P. lineatus in establishing large populations on a diet of flocculent benthic biofilm.

A system for equitable workload distribution in clinical medical physics.

BACKGROUND: Clinical medical physics duties include routine tasks, special procedures, and development projects. It can be challenging to distribute the effort equitably across all team members, especially in large clinics or systems where physicists cover multiple sites. The purpose of this work is to study an equitable workload distribution system in radiotherapy physics that addresses the complex and dynamic nature of effort assignment. METHODS: We formed a working group that defined all relevant clinical tasks and estimated the total time spent per task. Estimates used data from the oncology information system, a survey of physicists, and group consensus. We introduced a quantitative workload unit, "equivalent workday" (eWD), as a common unit for effort. The sum of all eWD values adjusted for each physicist's clinical full-time equivalent yields a "normalized total effort" (nTE) metric for each physicist, that is, the fraction of the total effort assigned to that physicist. We implemented this system in clinical operation. During a trial period of 9 months, we made adjustments to include tasks previously unaccounted for and refined the system. The workload distribution of eight physicists over 12 months was compared before and after implementation of the nTE system. RESULTS: Prior to implementation, differences in workload of up to 50% existed between individual physicists (nTE range of 10.0%-15.0%). During the trial period, additional categories were added to account for leave and clinical projects that had previously been assigned informally. In the 1-year period after implementation, the individual workload differences were within 5% (nTE range of 12.3%-12.8%). CONCLUSION: We developed a system to equitably distribute workload and demonstrated improvements in the equity of workload. A quantitative approach to workload distribution improves both transparency and accountability. While the system was motivated by the complexities within an academic medical center, it may be generally applicable for other clinics.

Framework for Machine Learning of CT and PET Radiomics to Predict Local Failure after Radiotherapy in Locally Advanced Head and Neck Cancers.

CONTEXT: Cancer Radiomics is an emerging field in medical imaging and refers to the process of converting routine radiological images that are typically qualitatively interpreted to quantifiable descriptions of the tumor phenotypes and when combined with statistical analytics can improve the accuracy of clinical outcome prediction models. However, to understand the radiomic features and their correlation to molecular changes in the tumor, first, there is a need for the development of robust image analysis methods, software tools and statistical prediction models which is often limited in low- and middle-income countries (LMIC). AIMS: The aim is to build a framework for machine learning of radiomic features of planning computed tomography (CT) and positron emission tomography (PET) using open source radiomics and data analytics platforms to make it widely accessible to clinical groups. The framework is tested in a small cohort to predict local disease failure following radiation treatment for head-and-neck cancer (HNC). The predictors were also compared with the existing Aerts HNC radiomics signature. SETTINGS AND DESIGN: Retrospective analysis of patients with locally advanced HNC between 2017 and 2018 and 31 patients with both pre- and post-radiation CT and evaluation PET were selected. SUBJECTS AND METHODS: Tumor volumes were delineated on baseline PET using the semi-automatic adaptive-threshold algorithm and propagated to CT; PyRadiomics features (total of 110 under shape/intensity/texture classes) were extracted. Two feature-selection methods were tested for model stability. Models were built based on least absolute shrinkage and selection operator-logistic and Ridge regression of the top pretreatment radiomic features and compared to Aerts' HNC-signature. Average model performance across all internal validation test folds was summarized by the area under the receiver operator curve (ROC). RESULTS: Both feature selection methods selected CT features MCC (GLCM), SumEntropy (GLCM) and Sphericity (Shape) that could predict the binary failure status in the cross-validated group and achieved an AUC >0.7. However, models using Aerts' signature features (Energy, Compactness, GLRLM-GrayLevelNonUniformity and GrayLevelNonUniformity-HLH wavelet) could not achieve a clear separation between outcomes (AUC = 0.51-0.54). CONCLUSIONS: Radiomics pipeline included open-source workflows which makes it adoptable in LMIC countries. Additional independent validation of data is crucial for the implementation of radiomic models for clinical risk stratification.

One Affectionate Puppy: A Case of Septic Arthritis Due to Staphylococcus Pseudintermedius.

We describe a case of septic arthritis caused by Staphylococcus pseudintermedius, a common colonizer of dogs that has emerged as a rare human pathogen. Our patient presented with ankle pain and swelling and was treated adequately with cefazolin/cephalexin and arthrotomy. S. pseudintermedius is often misidentified as other coagulase-positive staphylococcal species and has high rates of methicillin and nonpenicillin antibiotic resistance.

Reliability of Quantitative 18F-FDG PET/CT Imaging Biomarkers for Classifying Early Response to Chemoradiotherapy in Patients With Locally Advanced Non-Small Cell Lung Cancer.

PURPOSE OF THE REPORT: We evaluated the reliability of 18F-FDG PET imaging biomarkers to classify early response status across observers, scanners, and reconstruction algorithms in support of biologically adaptive radiation therapy for locally advanced non-small cell lung cancer. PATIENTS AND METHODS: Thirty-one patients with unresectable locally advanced non-small cell lung cancer were prospectively enrolled on a phase 2 trial (NCT02773238) and underwent 18F-FDG PET on GE Discovery STE (DSTE) or GE Discovery MI (DMI) PET/CT systems at baseline and during the third week external beam radiation therapy regimens. All PET scans were reconstructed using OSEM; GE-DMI scans were also reconstructed with BSREM-TOF (block sequential regularized expectation maximization reconstruction algorithm incorporating time of flight). Primary tumors were contoured by 3 observers using semiautomatic gradient-based segmentation. SUVmax, SUVmean, SUVpeak, MTV (metabolic tumor volume), and total lesion glycolysis were correlated with midtherapy multidisciplinary clinical response assessment. Dice similarity of contours and response classification areas under the curve were evaluated across observers, scanners, and reconstruction algorithms. LASSO logistic regression models were trained on DSTE PET patient data and independently tested on DMI PET patient data. RESULTS: Interobserver variability of PET contours was low for both OSEM and BSREM-TOF reconstructions; intraobserver variability between reconstructions was slightly higher. ΔSUVpeak was the most robust response predictor across observers and image reconstructions. LASSO models consistently selected ΔSUVpeak and ΔMTV as response predictors. Response classification models achieved high cross-validated performance on the DSTE cohort and more variable testing performance on the DMI cohort. CONCLUSIONS: The variability FDG PET lesion contours and imaging biomarkers was relatively low across observers, scanners, and reconstructions. Objective midtreatment PET response assessment may lead to improved precision of biologically adaptive radiation therapy.

Hip Fractures Before and During the COVID-19 Pandemic: Comparative Demographics and Outcomes.

INTRODUCTION: During the height of the COVID-19 pandemic in New York, hip fractures requiring operative management continued to present to Stony Brook University Hospital. Given the novelty of SARS-CoV-2, there is recent interest in the pandemic and its relationship to orthopedic operative outcomes. This retrospective cohort study compared outcomes for operative hip fractures in patients prior to and during the COVID-19 pandemic at a level 1 academic center. Materials and Methods: Data was collected on patients age 18 years or older who underwent operative management for hip fractures performed from January 21, 2019 to July 1, 2019 (pre-pandemic) or from January 21, 2020 to July 1, 2020 (pandemic). COVID-19 status, demographics and outcomes were analyzed. RESULTS: Overall, 159 patients with hip fractures were included in this study, 103 in the 2019 group and 56 in the 2020 group. Within the 2019 group, there was a significantly greater proportion of female patients compared to 2020 (p = 0.0128). The length of hospital stay was shorter for the 2020 group by 1.84 days (p = 0.0138). COVID-19 testing was positive in 4 (7.1%) patients in the 2020 group, negative for 22 patients (39.3%), and the remaining 30 patients in the 2020 group (53.7%) were not tested during their admission. There were no other significant differences in demographics or outcomes between the 2019 and 2020 groups. DISCUSSION: The COVID-19 pandemic did not significantly alter most aspects of care for hip fracture patients at our institution. Interestingly, postoperative pulmonary outcomes were not affected by the pandemic. CONCLUSIONS: In this study, a significantly higher proportion of males presented with hip fractures in the pandemic group. In addition, the average length of hospital stay was shorter during the COVID-19 pandemic. Further research is needed to understand the nuances that may lead to improved care for patients with hip fractures during a pandemic.

Treatment Intensification in Locally Advanced/Unresectable NSCLC Through Combined Modality Treatment and Precision Dose Escalation.

The best survival for patients with unresectable, locally advanced NSCLC is currently achieved through concurrent chemoradiation followed by durvalumab for a year. Despite the best standard of care treatment, the majority of patients still develop disease recurrence, which could be distant and/or local. Trials continue to try and improve outcomes for patients with unresectable NSCLC, typically through treatment intensification, with the addition of more systemic agents, or more radiation dose to the tumor. Although RTOG 0617 showed that uniform dose escalation across an unselected population of patients undergoing chemoradiation is not beneficial, efforts continue to select patients and tumor subsets that are likely to benefit from dose escalation. This review describes some of the ongoing therapeutic trials in unresectable NSCLC, with an emphasis on quantitative imaging and precision radiation dose escalation.