TY  - JOUR
A1  - Laqua, Fabian Christopher
A1  - Woznicki, Piotr
A1  - Bley, Thorsten A.
A1  - Schöneck, Mirjam
A1  - Rinneburger, Miriam
A1  - Weisthoff, Mathilda
A1  - Schmidt, Matthias
A1  - Persigehl, Thorsten
A1  - Iuga, Andra-Iza
A1  - Baeßler, Bettina
T1  - Transfer-learning deep radiomics and hand-crafted radiomics for classifying lymph nodes from contrast-enhanced computed tomography in lung cancer
JF  - Cancers
N2  - 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.
KW  - computed tomography
KW  - computational neural networks
KW  - lymphatic metastasis
KW  - carcinoma
KW  - non-small-cell lung
KW  - small-cell lung
Y1  - 2023
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-319231
SN  - 2072-6694
VL  - 15
IS  - 10
ER  - 
TY  - JOUR
A1  - Woźnicki, Piotr
A1  - Laqua, Fabian Christopher
A1  - Messmer, Katharina
A1  - Kunz, Wolfgang Gerhard
A1  - Stief, Christian
A1  - Nörenberg, Dominik
A1  - Schreier, Andrea
A1  - Wójcik, Jan
A1  - Ruebenthaler, Johannes
A1  - Ingrisch, Michael
A1  - Ricke, Jens
A1  - Buchner, Alexander
A1  - Schulz, Gerald Bastian
A1  - Gresser, Eva
T1  - Radiomics for the prediction of overall survival in patients with bladder cancer prior to radical cystectomy
JF  - Cancers
N2  - (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.
KW  - bladder cancer
KW  - radical cystectomy
KW  - radiomics
KW  - outcome prediction
Y1  - 2022
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-288098
SN  - 2072-6694
VL  - 14
IS  - 18
ER  -