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"Jon Feldman"
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Structures and transformations in modern British history
Inspired by the work of Gareth Stedman Jones, this book contains major essays on modern British history by leading scholars in the field. Ranging across core issues in social, cultural, imperial and political history, the collection will prove indispensable for anyone interested in what is new in modern history.
Book
Short report: Plasma based biomarkers detect radiation induced brain injury in cancer patients treated for brain metastasis: A pilot study
Radiotherapy has an important role in the treatment of brain metastases but carries risk of short and/or long-term toxicity, termed radiation-induced brain injury (RBI). As the diagnosis of RBI is crucial for correct patient management, there is an unmet need for reliable biomarkers for RBI. The aim of this proof-of concept study is to determine the utility of brain-derived circulating free DNA (BncfDNA), identified by specific methylation patterns for neurons, astrocytes, and oligodendrocytes, as biomarkers brain injury induced by radiotherapy.
Twenty-four patients with brain metastases were monitored clinically and radiologically before, during and after brain radiotherapy, and blood for BncfDNA analysis (98 samples) was concurrently collected. Sixteen patients were treated with whole brain radiotherapy and eight patients with stereotactic radiosurgery.
During follow-up nine RBI events were detected, and all correlated with significant increase in BncfDNA levels compared to baseline. Additionally, resolution of RBI correlated with a decrease in BncfDNA. Changes in BncfDNA were independent of tumor response.
Elevated BncfDNA levels reflects brain cell injury incurred by radiotherapy. further research is needed to establish BncfDNA as a novel plasma-based biomarker for brain injury induced by radiotherapy.
Journal Article
Real world clinical experience using daily intelligence-assisted online adaptive radiotherapy for head and neck cancer
Background
Adaptive radiation therapy (ART) offers a dynamic approach to address structural and spatial changes that occur during radiotherapy (RT) for locally advanced head and neck cancers. The integration of daily ART with Cone-Beam CT (CBCT) imaging presents a solution to enhance the therapeutic ratio by addressing inter-fractional changes.
Methods
We evaluated the initial clinical experience of daily ART for patients with head and neck cancer using an online adaptive platform with intelligence-assisted workflows on daily CBCT. Treatment included auto-contour and structure deformation of Organs at Risk (OARs) and target structures, with adjustments by the treating physician. Two plans were generated: one based on the initial CT simulation with the edited structures (scheduled) and a re-optimized plan (adaptive). Both plans were evaluated and the superior one approved and delivered. Clinical and dosimetric outcomes were reviewed.
Results
Twenty two patients with head and neck cancers (7 Nasopharynx, 6 Oropharynx, 1 oral cavity, 8 larynx) stages I-IVA were treated with daily ART. 770 adaptive and scheduled radiotherapy plans were generated. 703 (91.3%) adaptive plans were chosen. Median time to deliver ART was 20 minutes (range: 18-23). Adaptive compared to scheduled plans demonstrated improved mean V95 values for the PTV70, PTV59.5, and PTV56 by 1.2%, 7.2%, and 6.0% respectively and a mean 1.4% lower maximum dose in PTV70. Fourteen of 17 OARs demonstrated improved dosimetry with adaptation, with select OARs reaching statistical significance. At a median follow up of 14.1 months, local control was 95.5%, two patients developed metastatic disease and four patients died. 9.1% of patients had acute grade 3 dysphagia and 13.6% had grade 2 chronic xerostomia.
Discussion
These findings provide real world evidence of the feasibility and dosimetric benefit of incorporating daily ART on CBCT in the treatment of head and neck cancer. Prospective study is needed to determine if these dosimetric improvements translate into improved outcomes.
Journal Article
Computational quantification and characterization of independently evolving cellular subpopulations within tumors is critical to inhibit anti-cancer therapy resistance
Background
Drug resistance continues to be a major limiting factor across diverse anti-cancer therapies. Contributing to the complexity of this challenge is cancer plasticity, in which one cancer subtype switches to another in response to treatment, for example, triple-negative breast cancer (TNBC) to Her2-positive breast cancer. For optimal treatment outcomes, accurate tumor diagnosis and subsequent therapeutic decisions are vital. This study assessed a novel approach to characterize treatment-induced evolutionary changes of distinct tumor cell subpopulations to identify and therapeutically exploit anticancer drug resistance.
Methods
In this research, an information-theoretic single-cell quantification strategy was developed to provide a high-resolution and individualized assessment of tumor composition for a customized treatment approach.
Briefly, this single-cell quantification strategy computes cell barcodes based on at least 100,000 tumor cells from each experiment and reveals a cell-specific signaling signature (CSSS) composed of a set of ongoing processes in each cell.
Results
Using these CSSS-based barcodes, distinct subpopulations evolving within the tumor in response to an outside influence, like anticancer treatments, were revealed and mapped. Barcodes were further applied to assign targeted drug combinations to each individual tumor to optimize tumor response to therapy.
The strategy was validated using TNBC models and patient-derived tumors known to switch phenotypes in response to radiotherapy (RT).
Conclusions
We show that a barcode-guided targeted drug cocktail significantly enhances tumor response to RT and prevents regrowth of once-resistant tumors. The strategy presented herein shows promise in preventing cancer treatment resistance, with significant applicability in clinical use.
Journal Article
Learning Mixtures of Product Distributions over Discrete Domains
We consider the problem of learning mixtures of product distributions over discrete domains in the distribution learning framework introduced by Kearns et al. [Proceedings of the $26$th Annual Symposium on Theory of Computing (STOC), Montréal, QC, 1994, ACM, New York, pp. 273-282]. We give a $\\operatorname{poly}(n/\\epsilon)$-time algorithm for learning a mixture of $k$ arbitrary product distributions over the $n$-dimensional Boolean cube $\\{0,1\\}^n$ to accuracy $\\epsilon$, for any constant $k$. Previous polynomial-time algorithms could achieve this only for $k = 2$ product distributions; our result answers an open question stated independently in [M. Cryan, Learning and Approximation Algorithms for Problems Motivated by Evolutionary Trees, Ph.D. thesis, University of Warwick, Warwick, UK, 1999] and [Y. Freund and Y. Mansour, Proceedings of the $12$th Annual Conference on Computational Learning Theory, 1999, pp. 183-192]. We further give evidence that no polynomial-time algorithm can succeed when $k$ is superconstant, by reduction from a difficult open problem in PAC (probably approximately correct) learning. Finally, we generalize our $\\operatorname{poly}(n/\\epsilon)$-time algorithm to learn any mixture of $k = O(1)$ product distributions over $\\{0,1, \\dots, b-1\\}^n$, for any $b = O(1)$.
Journal Article
Endovascular brachytherapy for extensive right-heart and pulmonary artery sarcoma – a case report
Primary sarcoma of the heart is a rare but devastating tumor. Median survival with conventional treatment is 8-12 months. When resection is not feasible, patients often succumb to heart failure secondary to obstruction of blood flow, valve dysfunction, chamber compression or conduction abnormalities. Palliative treatment options include systemic chemotherapy and external beam irradiation. We herein describe a novel technique using endovascular brachytherapy, aiming at reducing tumor mass, alleviating right ventricular pressure overload and at the same time keeping the option of R0 resection viable.
A 35-year-old man was diagnosed with a non-resectable high-grade intimal sarcoma of the right ventricle (RV), main pulmonary artery (PA) and right PA. After three cycles of doxorubicin and ifosfamide, the patient's symptoms of right heart failure worsened. Imaging documented tumor progression and supra-systemic pulmonary artery pressure. Through a trans-femoral venous access, a brachytherapy sleeve was placed in the RV and main and right PA. A dose of 20 Gy was delivered over a period of ten minutes.
The patient had an uneventful course and was discharged home 24 hours after the procedure. Ten months after brachytherapy, repeat imaging demonstrated a significant reduction in tumor volume and an increase in pulmonary artery cross-sectional area with a marked reduction of pulmonary artery pressure, leading to a complete resolution of heart failure symptoms.
Endovascular brachytherapy is a novel, safe and effective therapeutic modality for non-resectable primary cardiac sarcomas either for palliation of obstruction, or tumor mass reduction to allow complete resection.
Journal Article
Yield Optimization of Display Advertising with Ad Exchange
It is clear from the growing role of ad exchanges in the real-time sale of advertising slots that Web publishers are considering a new alternative to their more traditional reservation-based ad contracts. To make this choice, the publisher must trade off, in real-time, the short-term revenue from ad exchange with the long-term benefits of delivering good spots to the reservation ads. In this paper we formalize this combined optimization problem as a multiobjective stochastic control problem and derive an efficient policy for online ad allocation in settings with general joint distribution over placement quality and exchange prices. We prove the asymptotic optimality of this policy in terms of any arbitrary trade-off between the quality of delivered reservation ads and revenue from the exchange, and we show that our policy approximates any Pareto-optimal point on the quality-versus-revenue curve. Experimental results on data derived from real publisher inventory confirm that there are significant benefits for publishers if they jointly optimize over both channels.
Data, as supplemental material, are available at
http://dx.doi.org/10.1287/mnsc.2014.2017
.
This paper was accepted by Dimitris Bertsimas, optimization.
Journal Article
The Directed Steiner Network Problem is Tractable for a Constant Number of Terminals
We consider the Directed Steiner Network problem, also called the Point-to-Point Connection problem. Given a directed graph $G$ and $p$ pairs $\\{ (s_1,t_1), \\dotsc, (s_p,t_p) \\}$ of nodes in the graph, one has to find the smallest subgraph $H$ of $G$ that contains paths from $s_i$ to $t_i$ for all $i$. The problem is NP-hard for general $p$, since the Directed Steiner Tree problem is a special case. Until now, the complexity was unknown for constant $p \\geq 3$. We prove that the problem is polynomially solvable if $p$ is any constant number, even if nodes and edges in $G$ are weighted and the goal is to minimize the total weight of the subgraph $H$. In addition, we give an efficient algorithm for the Strongly Connected Steiner Subgraph problem for any constant $p$, where given a directed graph and $p$ nodes in the graph, one has to compute the smallest strongly connected subgraph containing the $p$ nodes.
Journal Article
Yield optimization of display advertising with ad exchange
Tt is clear from the growing role of ad exchanges in the real-time sale of advertising slots that Web publishers are Iconsidering a new alternative to their more traditional reservation-based ad contracts. To make .this choice, the publisher must .trade off, in real-time, the short-term revenue from ad exchange with the long-term benefits of delivering good spots to the reservation ads. In this paper we formalize this combined optimization problem as a multiobjective stochastic control problem and derive an efficient policy for online ad allocation in settings with general joint distribution over placement quality and exchange prices. We prove the asymptotic optimality of this policy in terms of any arbitrary trade-off between the quality of delivered reservation ads and revenue from the exchange, and we show that our policy approximates any Pareto-optimal point on the quality-versus-revenue curve. Experimental results on data derived from real publisher inventory confirm that there are significant benefits for publishers if they jointly optimize over both channels.
Journal Article