Design study of a high power in-pile nuclear thermionic space powerplant final report

Thermionic nuclear spacecraft power plan

Historical cohort study examining comparative effectiveness of albuterol inhalers with and without integrated dose counter for patients with asthma or chronic obstructive pulmonary disease

This study was supported financially by an unrestricted grant from Teva Pharmaceuticals, Frazer, PA, USA. The authors thank Jenny Fanstone of Fanstone Medical Communications Ltd., UK, and Elizabeth V Hillyer for medical writing support, funded by Research in Real-Life. We acknowledge with gratitude Dr Ruchir Parikh for his review of and contributions to the manuscript.Peer reviewedPublisher PD

gcDLSeg: Integrating Graph-cut into Deep Learning for Binary Semantic Segmentation

Binary semantic segmentation in computer vision is a fundamental problem. As a model-based segmentation method, the graph-cut approach was one of the most successful binary segmentation methods thanks to its global optimality guarantee of the solutions and its practical polynomial-time complexity. Recently, many deep learning (DL) based methods have been developed for this task and yielded remarkable performance, resulting in a paradigm shift in this field. To combine the strengths of both approaches, we propose in this study to integrate the graph-cut approach into a deep learning network for end-to-end learning. Unfortunately, backward propagation through the graph-cut module in the DL network is challenging due to the combinatorial nature of the graph-cut algorithm. To tackle this challenge, we propose a novel residual graph-cut loss and a quasi-residual connection, enabling the backward propagation of the gradients of the residual graph-cut loss for effective feature learning guided by the graph-cut segmentation model. In the inference phase, globally optimal segmentation is achieved with respect to the graph-cut energy defined on the optimized image features learned from DL networks. Experiments on the public AZH chronic wound data set and the pancreas cancer data set from the medical segmentation decathlon (MSD) demonstrated promising segmentation accuracy, and improved robustness against adversarial attacks.Comment: 12 page

Presentation, Prognostic Factors and Patterns of Failure in Adult Rhabdomyosarcoma

Purpose: The purpose of our study is to retrospectively review our institutional experience with adult rhabdomyosarcoma (RMS) to determine presentation, prognostic factors and patterns of failure in this disease

Deep segmentation networks predict survival of non-small cell lung cancer

Non-small-cell lung cancer (NSCLC) represents approximately 80-85% of lung cancer diagnoses and is the leading cause of cancer-related death worldwide. Recent studies indicate that image-based radiomics features from positron emission tomography-computed tomography (PET/CT) images have predictive power on NSCLC outcomes. To this end, easily calculated functional features such as the maximum and the mean of standard uptake value (SUV) and total lesion glycolysis (TLG) are most commonly used for NSCLC prognostication, but their prognostic value remains controversial. Meanwhile, convolutional neural networks (CNN) are rapidly emerging as a new premise for cancer image analysis, with significantly enhanced predictive power compared to other hand-crafted radiomics features. Here we show that CNN trained to perform the tumor segmentation task, with no other information than physician contours, identify a rich set of survival-related image features with remarkable prognostic value. In a retrospective study on 96 NSCLC patients before stereotactic-body radiotherapy (SBRT), we found that the CNN segmentation algorithm (U-Net) trained for tumor segmentation in PET/CT images, contained features having strong correlation with 2- and 5-year overall and disease-specific survivals. The U-net algorithm has not seen any other clinical information (e.g. survival, age, smoking history) than the images and the corresponding tumor contours provided by physicians. Furthermore, through visualization of the U-Net, we also found convincing evidence that the regions of progression appear to match with the regions where the U-Net features identified patterns that predicted higher likelihood of death. We anticipate our findings will be a starting point for more sophisticated non-intrusive patient specific cancer prognosis determination

Phase 1b/2a trial of the superoxide dismutase mimetic GC4419 to reduce chemoradiotherapy-induced oral mucositis in patients with oral cavity or oropharyngeal carcinoma

PURPOSE: To assess the safety of the superoxide dismutase mimetic GC4419 in combination with radiation and concurrent cisplatin for patients with oral cavity or oropharyngeal cancer (OCC) and to assess the potential of GC4419 to reduce severe oral mucositis (OM). PATIENTS AND METHODS: Patients with locally advanced OCC treated with definitive or postoperative intensity modulated radiation therapy (IMRT) plus cisplatin received GC4419 by 60-minute intravenous infusion, ending \u3c60 minutes before IMRT, Monday through Friday for 3 to 7 weeks, in a dose and duration escalation study. Oral mucositis was assessed twice weekly during and weekly after IMRT. RESULTS: A total of 46 patients received GC4419 in 11 separate dosing and duration cohorts: dose escalation occurred in 5 cohorts receiving 15 to 112 mg/d over 3 weeks (n=20), duration escalation in 3 cohorts receiving 112 mg/d over 4 to 6 weeks (n=12), and then 3 additional cohorts receiving 30 or 90 mg/d over 6 to 7 weeks (n=14). A maximum tolerated dose was not reached. One dose-limiting toxicity (grade 3 gastroenteritis and vomiting with hyponatremia) occurred in each of 2 separate cohorts at 112 mg. Nausea/vomiting and facial paresthesia during infusion seemed to be GC4419 dose-related. Severe OM occurred through 60 Gy in 4 of 14 patients (29%) dosed for 6 to 7 weeks, with median duration of only 2.5 days. CONCLUSIONS: The safety of GC4419 concurrently with chemoradiation for OCC was acceptable. Toxicities included nausea/vomiting and paresthesia. Doses of 30 and 90 mg/d administered for 7 weeks were selected for further study. In an exploratory analysis, severe OM seemed less frequent and briefer than expected

The Effect of Concurrent Stereotactic Body Radiation and Anti-PD-1 Therapy for Recurrent Metastatic Sarcoma.

Patients diagnosed with metastatic sarcoma have limited options for achieving both local and distant tumor control. While SBRT can achieve local control, distant response rates remain low. There is limited evidence demonstrating the safety and efficacy for combining SBRT with concurrent PD-1 checkpoint blockade in metastatic sarcoma. In this prospective case-series, we examined five patients with metastatic sarcoma on pembrolizumab treated concurrently with SBRT from July 1, 2016-October 30, 2018. Acute and chronic toxicity were recorded using Common Terminology Criteria for Adverse Events (CTCAE, version 5.0). SBRT-treated tumor control was assessed using Response Evaluation Criteria in Solid Tumors (RECIST version 1.1). With median follow-up of 14.9 months, three patients with undifferentiated pleomorphic sarcoma, one with intimal, and one with chondroblastic osteosarcoma received SBRT with concurrent pembrolizumab to 10 sites of metastatic disease. No grade 5 toxicities were observed. There was a single incidence of transient grade 4 lymphopenia which resolved without intervention. Grade 3 toxicities included anemia, thrombocytopenia, lymphopenia and colitis. One tumor demonstrated local progression after SBRT, and all others remained stable or with response. In conclusion, combining SBRT with PD-1 inhibition appeared to be safe in this patient population. Expected high rates of treated-tumor local control after SBRT were observed. Two of five patients demonstrated either enhanced local tumor regression, or possible abscopal effect

A Recursive Partitioning Analysis Demonstrating Risk Subsets for 8-Year Biochemical Relapse After Margin-Positive Radical Prostatectomy Without Adjuvant Hormone or Radiation Therapy.

PURPOSE: The cohort of patients with locally advanced prostate cancer (PC) and positive surgical margin(s) at radical prostatectomy (RP) who would benefit from salvage or adjuvant treatment is unclear. This study examines the risk of prostate-specific antigen (PSA) relapse in a large population of men with PC after margin-positive RP. METHODS AND MATERIALS: Using a multi-institutional database, patients with clinically localized PC who underwent RP between 2002 and 2010 with recorded follow-up PSA were retrospectively selected. Patients were excluded for pathologic seminal vesicle or lymph node involvement, metastatic disease, pre-RP PSA ≥ 30, or adjuvant (nonsalvage) radiation therapy or hormone therapy. The primary endpoint was biochemical relapse free survival (bRFS), where PSA failure was defined as PSA > 0.10 ng/mL and rising, or at salvage intervention. The Kaplan-Meier method was employed for bRFS estimates; recursive partitioning analysis using cumulative or single maximal margin extent (ME) and Gleason grade (GG) at RP was applied to identify variables associated with bRFS. RESULTS: At median follow-up of 105 months, 210 patients with positive margins at RP were eligible for analysis, and 89 had experienced PSA relapse. Median age was 61 years (range, 43-76), and median pre-RP PSA 5.8 ng/mL (1.6-26.0). Recursive partitioning analysis yielded 5 discrete risk groups, with the lowest risk group (GG1, ≤ 2 mm ME) demonstrating a bRFS of 92% at 8 years compared with the highest risk group (GG3-5, ≥ 3 mm ME) of 11%. CONCLUSIONS: This retrospective study suggests that it may be possible to risk-stratify patients undergoing margin-positive RP using commonly acquired clinical and pathologic variables. Patients with low-grade tumors and minimally involved margins have a very low recurrence risk and may be able to forego postprostatectomy radiation. Meanwhile, those with higher grade and greater involvement could benefit from adjuvant or early salvage radiation therapy