@article{WoźnickiLaquaMessmeretal.2022,
  author    = {Wo{\'{z}}nicki, Piotr and Laqua, Fabian Christopher and Messmer, Katharina and Kunz, Wolfgang Gerhard and Stief, Christian and N{\"o}renberg, Dominik and Schreier, Andrea and W{\´o}jcik, Jan and Ruebenthaler, Johannes and Ingrisch, Michael and Ricke, Jens and Buchner, Alexander and Schulz, Gerald Bastian and Gresser, Eva},
  title     = {Radiomics for the prediction of overall survival in patients with bladder cancer prior to radical cystectomy},
  series = {Cancers},
  volume    = {14},
  journal   = {Cancers},
  number    = {18},
  issn      = {2072-6694},
  doi       = {10.3390/cancers14184449},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-288098},
  year      = {2022},
  abstract  = {(1) Background: To evaluate radiomics features as well as a combined model with clinical parameters for predicting overall survival in patients with bladder cancer (BCa). (2) Methods: This retrospective study included 301 BCa patients who received radical cystectomy (RC) and pelvic lymphadenectomy. Radiomics features were extracted from the regions of the primary tumor and pelvic lymph nodes as well as the peritumoral regions in preoperative CT scans. Cross-validation was performed in the training cohort, and a Cox regression model with an elastic net penalty was trained using radiomics features and clinical parameters. The models were evaluated with the time-dependent area under the ROC curve (AUC), Brier score and calibration curves. (3) Results: The median follow-up time was 56 months (95\% CI: 48-74 months). In the follow-up period from 1 to 7 years after RC, radiomics models achieved comparable predictive performance to validated clinical parameters with an integrated AUC of 0.771 (95\% CI: 0.657-0.869) compared to an integrated AUC of 0.761 (95\% CI: 0.617-0.874) for the prediction of overall survival (p = 0.98). A combined clinical and radiomics model stratified patients into high-risk and low-risk groups with significantly different overall survival (p < 0.001). (4) Conclusions: Radiomics features based on preoperative CT scans have prognostic value in predicting overall survival before RC. Therefore, radiomics may guide early clinical decision-making.},
  language  = {en}
}
@article{WoznickiLaquaAlHajetal.2023,
  author    = {Woznicki, Piotr and Laqua, Fabian Christopher and Al-Haj, Adam and Bley, Thorsten and Baeßler, Bettina},
  title     = {Addressing challenges in radiomics research: systematic review and repository of open-access cancer imaging datasets},
  series = {Insights into Imaging},
  volume    = {14},
  journal   = {Insights into Imaging},
  issn      = {1869-4101},
  doi       = {10.1186/s13244-023-01556-w},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-357936},
  year      = {2023},
  abstract  = {Objectives Open-access cancer imaging datasets have become integral for evaluating novel AI approaches in radiology. However, their use in quantitative analysis with radiomics features presents unique challenges, such as incomplete documentation, low visibility, non-uniform data formats, data inhomogeneity, and complex preprocessing. These issues may cause problems with reproducibility and standardization in radiomics studies. Methods We systematically reviewed imaging datasets with public copyright licenses, published up to March 2023 across four large online cancer imaging archives. We included only datasets with tomographic images (CT, MRI, or PET), segmentations, and clinical annotations, specifically identifying those suitable for radiomics research. Reproducible preprocessing and feature extraction were performed for each dataset to enable their easy reuse. Results We discovered 29 datasets with corresponding segmentations and labels in the form of health outcomes, tumor pathology, staging, imaging-based scores, genetic markers, or repeated imaging. We compiled a repository encompassing 10,354 patients and 49,515 scans. Of the 29 datasets, 15 were licensed under Creative Commons licenses, allowing both non-commercial and commercial usage and redistribution, while others featured custom or restricted licenses. Studies spanned from the early 1990s to 2021, with the majority concluding after 2013. Seven different formats were used for the imaging data. Preprocessing and feature extraction were successfully performed for each dataset. Conclusion RadiomicsHub is a comprehensive public repository with radiomics features derived from a systematic review of public cancer imaging datasets. By converting all datasets to a standardized format and ensuring reproducible and traceable processing, RadiomicsHub addresses key reproducibility and standardization challenges in radiomics. Critical relevance statement This study critically addresses the challenges associated with locating, preprocessing, and extracting quantitative features from open-access datasets, to facilitate more robust and reliable evaluations of radiomics models. Key points - Through a systematic review, we identified 29 cancer imaging datasets suitable for radiomics research. - A public repository with collection overview and radiomics features, encompassing 10,354 patients and 49,515 scans, was compiled. - Most datasets can be shared, used, and built upon freely under a Creative Commons license. - All 29 identified datasets have been converted into a common format to enable reproducible radiomics feature extraction.},
  language  = {en}
}
@article{LaquaWoznickiBleyetal.2023,
  author    = {Laqua, Fabian Christopher and Woznicki, Piotr and Bley, Thorsten A. and Sch{\"o}neck, Mirjam and Rinneburger, Miriam and Weisthoff, Mathilda and Schmidt, Matthias and Persigehl, Thorsten and Iuga, Andra-Iza and Baeßler, Bettina},
  title     = {Transfer-learning deep radiomics and hand-crafted radiomics for classifying lymph nodes from contrast-enhanced computed tomography in lung cancer},
  series = {Cancers},
  volume    = {15},
  journal   = {Cancers},
  number    = {10},
  issn      = {2072-6694},
  doi       = {10.3390/cancers15102850},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-319231},
  year      = {2023},
  abstract  = {Objectives: Positron emission tomography (PET) is currently considered the non-invasive reference standard for lymph node (N-)staging in lung cancer. However, not all patients can undergo this diagnostic procedure due to high costs, limited availability, and additional radiation exposure. The purpose of this study was to predict the PET result from traditional contrast-enhanced computed tomography (CT) and to test different feature extraction strategies. Methods: In this study, 100 lung cancer patients underwent a contrast-enhanced \(^{18}\)F-fluorodeoxyglucose (FDG) PET/CT scan between August 2012 and December 2019. We trained machine learning models to predict FDG uptake in the subsequent PET scan. Model inputs were composed of (i) traditional "hand-crafted" radiomics features from the segmented lymph nodes, (ii) deep features derived from a pretrained EfficientNet-CNN, and (iii) a hybrid approach combining (i) and (ii). Results: In total, 2734 lymph nodes [555 (20.3\%) PET-positive] from 100 patients [49\% female; mean age 65, SD: 14] with lung cancer (60\% adenocarcinoma, 21\% plate epithelial carcinoma, 8\% small-cell lung cancer) were included in this study. The area under the receiver operating characteristic curve (AUC) ranged from 0.79 to 0.87, and the scaled Brier score (SBS) ranged from 16 to 36\%. The random forest model (iii) yielded the best results [AUC 0.871 (0.865-0.878), SBS 35.8 (34.2-37.2)] and had significantly higher model performance than both approaches alone (AUC: p < 0.001, z = 8.8 and z = 22.4; SBS: p < 0.001, z = 11.4 and z = 26.6, against (i) and (ii), respectively). Conclusion: Both traditional radiomics features and transfer-learning deep radiomics features provide relevant and complementary information for non-invasive N-staging in lung cancer.},
  language  = {en}
}