Additional file 1:Figure S1. of Heterogeneity of aquaporin-4 localization and expression after focal cerebral ischemia underlies differences in white versus grey matter swelling

Validation of Aqp4 antibody. (PDF 1092 kb

Fitting of the lognormal model estimation to the Kaplan-Meier curve for 1975–1977

<p><b>Copyright information:</b></p><p>Taken from "Short- and long-term cause-specific survival of patients with inflammatory breast cancer"</p><p>BMC Cancer 2005;5():137-137.</p><p>Published online 22 Oct 2005</p><p>PMCID:PMC1283744.</p><p>Copyright © 2005 Tai et al; licensee BioMed Central Ltd.</p

Fitting of the lognormal model estimation to the Kaplan-Meier curve for 1993–1995

<p><b>Copyright information:</b></p><p>Taken from "Short- and long-term cause-specific survival of patients with inflammatory breast cancer"</p><p>BMC Cancer 2005;5():137-137.</p><p>Published online 22 Oct 2005</p><p>PMCID:PMC1283744.</p><p>Copyright © 2005 Tai et al; licensee BioMed Central Ltd.</p

Fitting of the lognormal model estimation to the Kaplan-Meier curve for 1990–1992

<p><b>Copyright information:</b></p><p>Taken from "Short- and long-term cause-specific survival of patients with inflammatory breast cancer"</p><p>BMC Cancer 2005;5():137-137.</p><p>Published online 22 Oct 2005</p><p>PMCID:PMC1283744.</p><p>Copyright © 2005 Tai et al; licensee BioMed Central Ltd.</p

Fitting of the lognormal model estimation to the Kaplan-Meier curve for 1978–1980

<p><b>Copyright information:</b></p><p>Taken from "Short- and long-term cause-specific survival of patients with inflammatory breast cancer"</p><p>BMC Cancer 2005;5():137-137.</p><p>Published online 22 Oct 2005</p><p>PMCID:PMC1283744.</p><p>Copyright © 2005 Tai et al; licensee BioMed Central Ltd.</p

Impact of target volume segmentation accuracy and variability on treatment planning for 4D-CT-based non-small cell lung cancer radiotherapy

<div><p></p><p><b>Background.</b> Accurate target volume segmentation is crucial for success in image-guided radiotherapy. However, variability in anatomical segmentation is one of the most significant contributors to uncertainty in radiotherapy treatment planning. This is especially true for lung cancer where target volumes are subject to varying magnitudes of respiratory motion.</p><p><b>Material and methods.</b> This study aims to analyze multiple observer target volume segmentations and subsequent intensity-modulated radiotherapy (IMRT) treatment plans defined by those segmentations against a reference standard for lung cancer patients imaged with four-dimensional computed tomography (4D-CT). Target volume segmentations of 10 patients were performed manually by six physicians, allowing for the calculation of ground truth estimate segmentations via the simultaneous truth and performance level estimation (STAPLE) algorithm. Segmentation variability was assessed in terms of distance- and volume-based metrics. Treatment plans defined by these segmentations were then subject to dosimetric evaluation consisting of both physical and radiobiological analysis of optimized 3D dose distributions.</p><p><b>Results.</b> Significant differences were noticed amongst observers in comparison to STAPLE segmentations and this variability directly extended into the treatment planning stages in the context of all dosimetric parameters used in this study. Mean primary tumor control probability (TCP) ranged from (22.6 ± 11.9)% to (33.7 ± 0.6)%, with standard deviation ranging from 0.5% to 11.9%. However, mean normal tissue complication probabilities (NTCP) based on treatment plans for each physician-derived target volume well as the NTCP derived from STAPLE-based treatment plans demonstrated no discernible trends and variability appeared to be patient-specific. This type of variability demonstrated the large-scale impact that target volume segmentation uncertainty can play in IMRT treatment planning.</p><p><b>Conclusions.</b> Significant target volume segmentation and dosimetric variability exists in IMRT treatment planning amongst experts in the presence of a reference standard for 4D-CT-based lung cancer radiotherapy. Future work is needed to mitigate this uncertainty and ensure highly accurate and effective radiotherapy for lung cancer patients.</p></div