Quantitative assessment of inter-observer variability in target volume delineation on stereotactic radiotherapy treatment for pituitary adenoma and meningioma near optic tract

<p>Abstract</p> <p>Background</p> <p>To assess inter-observer variability in delineating target volume and organs at risk in benign tumor adjacent to optic tract as a quality assurance exercise.</p> <p>Methods</p> <p>We quantitatively analyzed 21 plans made by 11 clinicians in seven CyberKnife centers. The clinicians were provided with a raw data set (pituitary adenoma and meningioma) including clinical information, and were asked to delineate the lesions and create a treatment plan. Their contouring and plans (10 adenoma and 11 meningioma plans), were then compared. In addition, we estimated the influence of differences in contouring by superimposing the respective contours onto a default plan.</p> <p>Results</p> <p>The median planning target volume (PTV) and the ratio of the largest to the smallest contoured volume were 9.22 cm³(range, 7.17 - 14.3 cm³) and 1.99 for pituitary adenoma, and 6.86 cm³(range 6.05 - 14.6 cm³) and 2.41 for meningioma. PTV volume was 10.1 ± 1.74 cm³for group 1 with a margin of 1 -2 mm around the CTV (n = 3) and 9.28 ± 1.8 cm³(p = 0.51) for group 2 with no margin (n = 7) in pituitary adenoma. In meningioma, group 1 showed larger PTV volume (10.1 ± 3.26 cm³) than group 2 (6.91 ± 0.7 cm³, p = 0.03). All submitted plan keep the irradiated dose to optic tract within the range of 50 Gy (equivalent total doses in 2 Gy fractionation). However, contours superimposed onto the dose distribution of the default plan indicated that an excessive dose 23.64 Gy (up to 268% of the default plan) in pituitary adenoma and 24.84 Gy (131% of the default plan) in meningioma to the optic nerve in the contours from different contouring.</p> <p>Conclusion</p> <p>Quality assurance revealed inter-observer variability in contour delineation and their influences on planning for pituitary adenoma and meningioma near optic tract.</p

Integrated-boost IMRT or 3-D-CRT using FET-PET based auto-contoured target volume delineation for glioblastoma multiforme - a dosimetric comparison

<p>Abstract</p> <p>Background</p> <p>Biological brain tumor imaging using O-(2-[¹⁸F]fluoroethyl)-L-tyrosine (FET)-PET combined with inverse treatment planning for locally restricted dose escalation in patients with glioblastoma multiforme seems to be a promising approach.</p> <p>The aim of this study was to compare inverse with forward treatment planning for an integrated boost dose application in patients suffering from a glioblastoma multiforme, while biological target volumes are based on FET-PET and MRI data sets.</p> <p>Methods</p> <p>In 16 glioblastoma patients an intensity-modulated radiotherapy technique comprising an integrated boost (IB-IMRT) and a 3-dimensional conventional radiotherapy (3D-CRT) technique were generated for dosimetric comparison. FET-PET, MRI and treatment planning CT (P-CT) were co-registrated. The integrated boost volume (PTV1) was auto-contoured using a cut-off tumor-to-brain ratio (TBR) of ≥ 1.6 from FET-PET. PTV2 delineation was MRI-based. The total dose was prescribed to 72 and 60 Gy for PTV1 and PTV2, using daily fractions of 2.4 and 2 Gy.</p> <p>Results</p> <p>After auto-contouring of PTV1 a marked target shape complexity had an impact on the dosimetric outcome. Patients with 3-4 PTV1 subvolumes vs. a single volume revealed a significant decrease in mean dose (67.7 vs. 70.6 Gy). From convex to complex shaped PTV1 mean doses decreased from 71.3 Gy to 67.7 Gy. The homogeneity and conformity for PTV1 and PTV2 was significantly improved with IB-IMRT. With the use of IB-IMRT the minimum dose within PTV1 (61.1 vs. 57.4 Gy) and PTV2 (51.4 vs. 40.9 Gy) increased significantly, and the mean EUD for PTV2 was improved (59.9 vs. 55.3 Gy, p < 0.01). The EUD for PTV1 was only slightly improved (68.3 vs. 67.3 Gy). The EUD for the brain was equal with both planning techniques.</p> <p>Conclusion</p> <p>In the presented planning study the integrated boost concept based on inversely planned IB-IMRT is feasible. The FET-PET-based automatically contoured PTV1 can lead to very complex geometric configurations, limiting the achievable mean dose in the boost volume. With IB-IMRT a better homogeneity and conformity, compared to 3D-CRT, could be achieved.</p

Diffusion and perfusion weighted magnetic resonance imaging for tumor volume definition in radiotherapy of brain tumors

Abstract Accurate target volume delineation is crucial for the radiotherapy of tumors. Diffusion and perfusion magnetic resonance imaging (MRI) can provide functional information about brain tumors, and they are able to detect tumor volume and physiological changes beyond the lesions shown on conventional MRI. This review examines recent studies that utilized diffusion and perfusion MRI for tumor volume definition in radiotherapy of brain tumors, and it presents the opportunities and challenges in the integration of multimodal functional MRI into clinical practice. The results indicate that specialized and robust post-processing algorithms and tools are needed for the precise alignment of targets on the images, and comprehensive validations with more clinical data are important for the improvement of the correlation between histopathologic results and MRI parameter images

Development and Optimization of a Machine-Learning Prediction Model for Acute Desquamation After Breast Radiation Therapy in the Multicenter REQUITE Cohort.

Some patients with breast cancer treated by surgery and radiation therapy experience clinically significant toxicity, which may adversely affect cosmesis and quality of life. There is a paucity of validated clinical prediction models for radiation toxicity. We used machine learning (ML) algorithms to develop and optimise a clinical prediction model for acute breast desquamation after whole breast external beam radiation therapy in the prospective multicenter REQUITE cohort study. Using demographic and treatment-related features (m = 122) from patients (n = 2058) at 26 centers, we trained 8 ML algorithms with 10-fold cross-validation in a 50:50 random-split data set with class stratification to predict acute breast desquamation. Based on performance in the validation data set, the logistic model tree, random forest, and naïve Bayes models were taken forward to cost-sensitive learning optimisation. One hundred and ninety-two patients experienced acute desquamation. Resampling and cost-sensitive learning optimisation facilitated an improvement in classification performance. Based on maximising sensitivity (true positives), the "hero" model was the cost-sensitive random forest algorithm with a false-negative: false-positive misclassification penalty of 90:1 containing m = 114 predictive features. Model sensitivity and specificity were 0.77 and 0.66, respectively, with an area under the curve of 0.77 in the validation cohort. ML algorithms with resampling and cost-sensitive learning generated clinically valid prediction models for acute desquamation using patient demographic and treatment features. Further external validation and inclusion of genomic markers in ML prediction models are worthwhile, to identify patients at increased risk of toxicity who may benefit from supportive intervention or even a change in treatment plan. [Abstract copyright: © 2022 The Authors.

Fine-Scale Mapping of the 5q11.2 Breast Cancer Locus Reveals at Least Three Independent Risk Variants Regulating MAP3K1

Peer reviewe

Second Language Processing Shows Increased Native-Like Neural Responses after Months of No Exposure

Although learning a second language (L2) as an adult is notoriously difficult, research has shown that adults can indeed attain native language-like brain processing and high proficiency levels. However, it is important to then retain what has been attained, even in the absence of continued exposure to the L2—particularly since periods of minimal or no L2 exposure are common. This event-related potential (ERP) study of an artificial language tested performance and neural processing following a substantial period of no exposure. Adults learned to speak and comprehend the artificial language to high proficiency with either explicit, classroom-like, or implicit, immersion-like training, and then underwent several months of no exposure to the language. Surprisingly, proficiency did not decrease during this delay. Instead, it remained unchanged, and there was an increase in native-like neural processing of syntax, as evidenced by several ERP changes—including earlier, more reliable, and more left-lateralized anterior negativities, and more robust P600s, in response to word-order violations. Moreover, both the explicitly and implicitly trained groups showed increased native-like ERP patterns over the delay, indicating that such changes can hold independently of L2 training type. The results demonstrate that substantial periods with no L2 exposure are not necessarily detrimental. Rather, benefits may ensue from such periods of time even when there is no L2 exposure. Interestingly, both before and after the delay the implicitly trained group showed more native-like processing than the explicitly trained group, indicating that type of training also affects the attainment of native-like processing in the brain. Overall, the findings may be largely explained by a combination of forgetting and consolidation in declarative and procedural memory, on which L2 grammar learning appears to depend. The study has a range of implications, and suggests a research program with potentially important consequences for second language acquisition and related fields