We established a multi-institution model (big model) of knowledge-based treatment planning with over 500 treatment plans from five institutions in volumetric modulated arc therapy (VMAT) for prostate cancer. This study aimed to clarify the efficacy of using a large number of registered treatment plans for sharing the big model. The big model was created with 561 clinically approved VMAT plans for prostate cancer from five institutions (A: 150, B: 153, C: 49, D: 60, and E: 149) with different planning strategies. The dosimetric parameters of planning target volume (PTV), rectum, and bladder for two validation VMAT plans generated with the big model were compared with those from each institutional model (single-institution model). The goodness-of-fit of regression lines (R and χ values) and ratios of the outliers of Cook's distance (CD) > 4.0, modified Z-score (mZ) > 3.5, studentized residual (SR) > 3.0, and areal difference of estimate (dA) > 3.0 for regression scatter plots in the big model and single-institution model were also evaluated. The mean ± standard deviation (SD) of dosimetric parameters were as follows (big model vs. single-institution model): 79.0 ± 1.6 vs. 78.7 ± 0.5 (D) and 0.13 ± 0.06 vs. 0.13 ± 0.07 (Homogeneity Index) for the PTV; 6.6 ± 4.0 vs. 8.4 ± 3.6 (V) and 32.4 ± 3.8 vs. 46.6 ± 15.4 (V) for the rectum; and 13.8 ± 1.8 vs. 13.3 ± 4.3 (V) and 39.9 ± 2.0 vs. 38.4 ± 5.2 (V) for the bladder. The R values in the big model were 0.251 and 0.755 for rectum and bladder, respectively, which were comparable to those from each institution model. The respective χ values in the big model were 1.009 and 1.002, which were closer to 1.0 than those from each institution model. The ratios of the outliers in the big model were also comparable to those from each institution model. The big model could generate a comparable VMAT plan quality compared with each single-institution model and therefore could possibly be shared with other institutions.
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http://dx.doi.org/10.1038/s41598-022-19498-6 | DOI Listing |
Accid Anal Prev
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School of Computer Science and Informatics, De Montfort University, Leicester LE1 9BH, UK.
With the continuous development of intelligent transportation systems, traffic safety has become a major societal concern, and vehicle trajectory anomaly detection technology has emerged as a crucial method to ensure safety. However, current technologies face significant challenges in handling spatiotemporal data and multi-feature fusion, including difficulties in big data processing, and have room for improvement in these areas. To address these issues, this paper proposes a novel method that combines autoencoders, Mahalanobis distance, and dynamic Bayesian networks for anomaly detection.
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Background: Concomitant intake of proton pump inhibitors (PPIs) may create drug-drug interactions, potentially impacting efficacy of anticancer agents. In the phase III PALLAS trial, the addition of palbociclib capsules to standard adjuvant endocrine therapy in patients with hormone receptor-positive, human epidermal growth factor receptor 2-negative early breast cancer did not improve invasive disease-free survival (iDFS). We explored whether concomitant use of PPIs affected survival outcomes in patients treated with palbociclib in PALLAS.
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International Research Center for Neurointelligence, The University of Tokyo, Tokyo, Japan.
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