Houda Bahig

Bio: Houda Bahig is an academic researcher from Université de Montréal. The author has contributed to research in topics: Medicine & Radiation therapy. The author has an hindex of 16, co-authored 83 publications receiving 782 citations. Previous affiliations of Houda Bahig include Hôpital Maisonneuve-Rosemont & University of Texas MD Anderson Cancer Center.

Twitter: A Platform for Dissemination and Discussion of Scientific Papers in Radiation Oncology.

Abstract: OBJECTIVE The objective of this study was to examine the correlation between Twitter mentions and the number of academic citations of radiation oncology articles. MATERIALS AND METHODS We reviewed all 178 clinical manuscripts of the 2 most important radiation oncology journals and "Brachytherapy," and all clinical manuscripts relating to radiation oncology from the top 10 impact factor oncology journals, published between January and February 2018. We collected the record of citations utilizing Scopus and Google Scholar platforms and the number of times an article was tweeted about using the "Altmetric Bookmarklet." χ test was used to compare distributions between groups and the Pearson coefficient was used for correlations between the Twitter metrics and academic citations. RESULTS Overall, 71% of all articles were tweeted about at least once. There was a significant correlation between the number of tweets and the number of citations in Google Scholar (r=0.55, P<0.001) and in Scopus (r=0.59, P<0.001). The 11% of articles with a prepublication Twitter "buzz" (defined as an article with ≥10 tweets before publication) had 3.6 times more citations in Scopus (mean: 14.8 vs. 4.2, P<0.001) and 2.9 times more citations in Google Scholar (17.8 vs. 6.0, P<0.001) when compared with papers with no "buzz." CONCLUSIONS Presence on Twitter was correlated with the number of academic citations of an article in radiation oncology. This suggests that Twitter is being utilized by the oncology community as a platform to discuss and disseminate high impact scientific articles. The correlation between Twitter and increasing the number of citations of an article through larger dissemination and exposure requires further studies.

Predictive Parameters of CyberKnife Fiducial-less (XSight Lung) Applicability for Treatment of Early Non-Small Cell Lung Cancer: A Single-Center Experience

Abstract: Purpose To determine which parameters allow for CyberKnife fiducial-less tumor tracking in stereotactic body radiation therapy (SBRT) for early-stage non-small cell lung cancer. Methods and Materials A total of 133 lung SBRT patients were preselected for direct soft-tissue tracking based on manufacturer recommendations (peripherally located tumors ≥1.5 cm with a dense appearance) and staff experience. Patients underwent a tumor visualization test to verify adequate detection by the tracking system (orthogonal radiographs). An analysis of potential predictors of successful tumor tracking was conducted looking at: tumor stage, size, histology, tumor projection on the vertebral column or mediastinum, distance to the diaphragm, lung-to-soft tissue ratio, and patient body mass index. Results Tumor visualization was satisfactory for 88 patients (66%) and unsatisfactory for 45 patients (34%). Median time to treatment start was 6 days in the success group (range, 2-18 days) and 15 days (range, 3-63 days) in the failure group. A stage T2 ( P =.04), larger tumor size (volume of 15.3 cm 3 vs 6.5 cm 3 in success and failure group, respectively) ( P 3 vs 0.79 g/cm 3 ) were predictive of adequate detection. There was a 63% decrease in failure risk with every 1-cm increase in maximum tumor dimension (relative risk for failure=0.37, CI=0.23-0.60, P =.001). A diameter of 3.6 cm predicted a success probability of 80%. Histology, lung-to-soft tissue ratio, distance to diaphragm, patient's body mass index, and tumor projection on vertebral column and mediastinum were not found to be predictive of success. Conclusions Tumor size, volume, and density were the most predictive factors of a successful XSight Lung tumor tracking. Tumors >3.5 cm have ≥80% chance of being adequately visualized and therefore should all be considered for direct tumor tracking.

Severe radiation pneumonitis after lung stereotactic ablative radiation therapy in patients with interstitial lung disease

Abstract: Purpose To investigate the incidence and predictive factors of severe radiation pneumonitis (RP) after stereotactic ablative radiation therapy (SABR) in early-stage lung cancer patients with preexisting radiological interstitial lung disease (ILD). Methods and materials A retrospective analysis of patients with stage I lung cancer treated with SABR from 2009 to 2014 was conducted. Interstitial lung disease diagnosis and grading was based on pretreatment high-resolution computed tomography imaging. A central review of pretreatment computed tomography by a single experienced thoracic radiologist was conducted. Univariate and multivariate analyses were conducted to determine potential predictors of severe RP in patients with ILD. Results Among 504 patients treated with SABR in this period, 6% were identified as having preexisting ILD. There was a 4% rate of ≥ grade 3 RP in the entire cohort. Interstitial lung disease was associated with increased risk of ≥ grade 3 RP (32% in ILD+ vs 2% in ILD-, P Conclusion Interstitial lung disease is associated with an increased risk of severe RP after SABR. Chest imaging should be reviewed for ILD before SABR, and the risk of fatal RP should be carefully weighed against the benefits of SABR in this subgroup.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Tumour-associated neutrophils in patients with cancer

Abstract: The role and importance of neutrophils in cancer has become increasingly apparent over the past decade. Neutrophils accumulate in the peripheral blood of patients with cancer, especially in those with advanced-stage disease, and a high circulating neutrophil-to-lymphocyte ratio is a robust biomarker of poor clinical outcome in various cancers. To date, most studies investigating the role of neutrophils in cancer have involved animal models or investigated the function of circulating human neutrophils. Thus, only limited information is available on the roles of intratumoural neutrophils (also known as tumour-associated neutrophils (TANs)) in patients with cancer. In this Review, we initially describe the evidence correlating the neutrophil-to-lymphocyte ratio with prognosis, followed by a discussion on the predictive value of TANs, which remains debatable, with conflicting data from different cancer types, including variations based on neutrophil location within and/or around the tumour. We then explore available data on the implications of TAN phenotypes and functions for cancer development and progression, highlighting the reported effects of various treatments on TANs and how neutrophils might affect therapeutic efficacy. Finally, we examine the various compounds capable of modulating neutrophils and suggest future research directions that might ultimately enable the manipulation of TANs in patients with cancer.