@article{GoekbugetKelshChiaetal.2016,
  author    = {G{\"o}kbuget, N. and Kelsh, M. and Chia, V. and Advani, A. and Bassan, R. and Dombret, H. and Doubek, M. and Fielding, A. K. and Giebel, S. and Haddad, V. and Hoelzer, D. and Holland, C. and Ifrah, N. and Katz, A. and Maniar, T. and Martinelli, G. and Morgades, M. and O'Brien, S. and Ribera, J.-M. and Rowe, J. M. and Stein, A. and Topp, M. and Wadleigh, M. and Kantarjian, H.},
  title     = {Blinatumomab vs historical standard therapy of adult relapsed/refractory acute lymphoblastic leukemia},
  series = {Blood Cancer Journal},
  volume    = {6},
  journal   = {Blood Cancer Journal},
  doi       = {10.1038/bcj.2016.84},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-164495},
  pages     = {e473},
  year      = {2016},
  abstract  = {We compared outcomes from a single-arm study of blinatumomab in adult patients with B-precursor Ph-negative relapsed/refractory acute lymphoblastic leukemia (R/R ALL) with a historical data set from Europe and the United States. Estimates of complete remission (CR) and overall survival (OS) were weighted by the frequency distribution of prognostic factors in the blinatumomab trial. Outcomes were also compared between the trial and historical data using propensity score methods. The historical cohort included 694 patients with CR data and 1112 patients with OS data compared with 189 patients with CR and survival data in the blinatumomab trial. The weighted analysis revealed a CR rate of 24\% (95\% CI: 20-27\%) and a median OS of 3.3 months (95\% CI: 2.8-3.6) in the historical cohort compared with a CR/CRh rate of 43\% (95\% CI: 36-50\%) and a median OS of 6.1 months (95\% CI: 4.2-7.5) in the blinatumomab trial. Propensity score analysis estimated increased odds of CR/CRh (OR=2.68, 95\% CI: 1.67-4.31) and improved OS (HR=0.536, 95\% CI: 0.394-0.730) with blinatumomab. The analysis demonstrates the application of different study designs and statistical methods to compare novel therapies for R/R ALL with historical data.},
  language  = {en}
}
@article{SahlolKollmannsbergerEwees2020,
  author    = {Sahlol, Ahmed T. and Kollmannsberger, Philip and Ewees, Ahmed A.},
  title     = {Efficient Classification of White Blood Cell Leukemia with Improved Swarm Optimization of Deep Features},
  series = {Scientific Reports},
  volume    = {10},
  journal   = {Scientific Reports},
  number    = {1},
  doi       = {10.1038/s41598-020-59215-9},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-229398},
  year      = {2020},
  abstract  = {White Blood Cell (WBC) Leukaemia is caused by excessive production of leukocytes in the bone marrow, and image-based detection of malignant WBCs is important for its detection. Convolutional Neural Networks (CNNs) present the current state-of-the-art for this type of image classification, but their computational cost for training and deployment can be high. We here present an improved hybrid approach for efficient classification of WBC Leukemia. We first extract features from WBC images using VGGNet, a powerful CNN architecture, pre-trained on ImageNet. The extracted features are then filtered using a statistically enhanced Salp Swarm Algorithm (SESSA). This bio-inspired optimization algorithm selects the most relevant features and removes highly correlated and noisy features. We applied the proposed approach to two public WBC Leukemia reference datasets and achieve both high accuracy and reduced computational complexity. The SESSA optimization selected only 1 K out of 25 K features extracted with VGGNet, while improving accuracy at the same time. The results are among the best achieved on these datasets and outperform several convolutional network models. We expect that the combination of CNN feature extraction and SESSA feature optimization could be useful for many other image classification tasks.},
  language  = {en}
}