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A Simple Automated Method for Detecting Recurrence in High-Grade Glioma.

ABSTRACT: Our aim was to develop an automated multiparametric MR imaging analysis of routinely acquired imaging sequences to identify areas of focally recurrent high-grade glioma. Data from 141 patients treated with radiation therapy with a diagnosis of high-grade glioma were reviewed. Strict inclusion/exclusion criteria identified a homogeneous cohort of 12 patients with a nodular recurrence of high-grade glioma that was amenable to focal re-irradiation (cohort 1). T1WI, FLAIR, and DWI data were used to create subtraction maps across time points. Linear regression was performed to identify the pattern of change in these 3 imaging sequences that best correlated with recurrence. The ability of these parameters to guide treatment decisions in individual patients was assessed in a separate cohort of 4 patients who were treated with radiosurgery for recurrent high-grade glioma (cohort 2). A leave-one-out analysis of cohort 1 revealed that automated subtraction maps consistently predicted the radiologist-identified area of recurrence (median area under the receiver operating characteristic curve = 0.91). The regression model was tested in preradiosurgery MRI in cohort 2 and identified 8 recurrent lesions. Six lesions were treated with radiosurgery and were controlled on follow-up imaging, but the remaining 2 lesions were not treated and progressed, consistent with the predictions of the model. Multiparametric subtraction maps can predict areas of nodular progression in patients with previously treated high-grade gliomas. This automated method based on routine imaging sequences is a valuable tool to be prospectively validated in subsequent studies of treatment planning and posttreatment surveillance.

SUBMITTER: Yanagihara TK

PROVIDER: S-EPMC5237423 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundLike most living organisms, the fruit fly Drosophila melanogaster exhibits strong and diverse behavioural reactions to light. Drosophila is a diurnal animal that displays both short- and long-term responses to light, important for, instance, in avoidance and light wavelength preference, regulation of eclosion, courtship, and activity, and provides an important model organism for understanding the regulation of circadian rhythms both at molecular and circuit levels. However, the assessment and comparison of light-based behaviours is still a challenge, mainly due to the lack of a standardised platform to measure behaviour and different protocols created across studies. Here, we describe the Drosophila Interactive System for Controlled Optical manipulations (DISCO), a low-cost, automated, high-throughput device that records the flies' activity using infrared beams while performing LED light manipulations.ResultsTo demonstrate the effectiveness of this tool and validate its potential as a standard platform, we developed a number of distinct assays, including measuring the locomotor response of flies exposed to sudden darkness (lights-off) stimuli. Both white-eyed and red-eyed wild-type flies exhibit increased activity after the application of stimuli, while no changes can be observed in Fmr1 null allele flies, a model of fragile X syndrome. Next, to demonstrate the use of DISCO in long-term protocols, we monitored the circadian rhythm of the flies for 48 h while performing an alcohol preference test. We show that increased alcohol consumption happens intermittently throughout the day, especially in the dark phases. Finally, we developed a feedback-loop algorithm to implement a place preference test based on the flies' innate aversion to blue light and preference for green light. We show that both white-eyed and red-eyed wild-type flies were able to learn to avoid the blue-illuminated zones.ConclusionsOur results demonstrate the versatility of DISCO for a range of protocols, indicating that this platform can be used in a variety of ways to study light-dependent behaviours in flies.

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A Simple Automated Method for Detecting Recurrence in High-Grade Glioma.

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