Additional file 1 of Enhancing deep learning methods for brain metastasis detection through cross-technique annotations on SPACE MRI
Creators: name: Wald, Tassilo
name: Hamm, Benjamin
name: Holzschuh, Julius C.
name: El Shafie, Rami
name: Kudak, Andreas
name: Kovacs, Balint
name: Pflüger, Irada
name: von Nettelbladt, Bastian
name: Ulrich, Constantin
name: Baumgartner, Michael Anton
name: Vollmuth, Philipp
name: Debus, Jürgen
name: Maier-Hein, Klaus H.
name: Welzel, Thomas
Publisher: name: figshare
Year: 2025
Subjects: subject: Artificial Intelligence and Image Processing
subject: FOS: Computer and information sciences

description: Additional file 1: Table S1 Internal Detection Performance). Detection performance metrics of model A-D on the internal SPACE and MPRAGE test sets. We report mean ± SD (F1-Score, Positive Predictive Value (PPV) and Sensitivity (SN) of each model on each test dataset. Fig. S1. Outline of the selection and preparation process of patient data for deep learning method development utilizing patients from the CYBER-SPACE study. A total of 157 patients were provided, with imaging performed using MPRAGE and SPACE modalities. In the original study patients were randomized into two cohorts to delineate BMs either on MPRAGE or SPACE for later radiation treatment. Of the original study, 76 patients delineated on SPACE and 81 delineated on MPRAGE were received of which 5 were dropped randomly for equal cohort sizes. Due to lower conspicuity of BM on MPRAGE annotations are of normal annotation quality (NAQ), whereas annotations created on SPACE result in high annotation quality (HAQ). Given these images and annotations models were trained in three settings: A) MPRAGE image as input with NAQ; B) MPRAGE image as input with HAQ; and C) SPACE images and HAQ. The flowchart details the distribution of patients at each differentiation step of the study. MPRAGE Magnetization-prepared rapid acquisition gradient echo, SPACE Sampling perfection with application-optimized contrasts using different flip angle evolution. For the classification model training patches were sampled from MPRAGE &amp; SPACE from the patients with HAQ delineation, involving 440 patches (Foreground: 219, Background: 221) for the training set and 82 patches (Foreground: 42, Background: 40) for the test set. The flowchart details the number of patients at each stage and the specific criteria used for their progression through the study. Fig. S2. The box-and-whisker plots illustrate the F1 scores for various convolutional neural network architectures when classifying patches with/without brain metastases. The input sequences for classification tasks are differentiated by color: MPRAGE late contrast in dark blue, MPRAGE with all contrasts in light blue, and SPACE in red. The F1 score served as the performance metric. The SPACE sequence as input demonstrates higher median F1 scores across all architectures, relative to the same architecture trained with MPRAGE images, suggesting better conspicuity of BM on SPACE provides deep learning models a better basis when controlling for annotation quality. Fig. S3. Qualitative examples of the predictive behavior of models A, B, and C applied to the internal MPRAGE and SPACE test sets. True positives (TPs), where the models' predictions (PD) coincide with the ground truth (GT); false positives (FPs), where a PD does not align with an annotated brain metastasis (GT); and false negatives (FNs), where PD is absent despite the presence of GT are visualized. The GT is marked in green, while PD is depicted in red.