Introduction: Spondyloarthritis (SpA) belongs to the chronic inflammatory rheumatic diseases, and primarily affects the axial skeleton. Quantitative 18F-NaF PET/CT is a new imaging approach that shows promise for accurate diagnosis and treatment assessment. Manual segmentation of low-dose computed tomography (LDCT) for quantitative feature extraction is time-consuming and subjective, and can be replaced by automatic methods. This study aims to develop and validate an automatic algorithm for the segmentation of the spinal joints and intervertebral disks (IVD’s) on LDCT using two different approaches.
Methods: Two methods for spinal structure segmentation were developed and compared. Both methods used segmentations of bony structures obtained from the TotalSegmentator algorithm. The first method employed morphological dilation and erosion operations to localise the joints and IVD’s, while the second method used a multi-atlas-based method approach with partial atlases and corresponding manually segmented labelmaps. The performance of the methods was assessed on ten manually segmented LDCT’s using sensitivity, and maximum and average Hausdorff distance (HD) for IVD’s and the sacroiliac joints (SIJ) and mean error distance for the smaller joints. The reproducibility of the methods was evaluated using a set of 20 LDCT test-retest images.
Results: The atlas-based method achieved significantly better maximum HD (8.45 (1.80) vs. 9.64 (5.83) (p = 0.002)) and sensitivity (0.79 (0.22) vs. 0.61 (0.30) (p < 0.001)) for all IVD’s combined compared to the morphological method. The atlas-based method also outperformed the morphological method for the facet joints, costovertebral joints and costotransverse joints with a mean error distance of 4.71 mm (2.72) vs 6.90 mm (4.80) (p < 0.001). For the thoracic IVD’s the morphological method showed significantly better average HD (1.48 (1.03) vs. 1.72 (0.53) (p = 0.018)) and maximum HD (6.97 (3.36) vs. 8.22 (1.66) (p < 0.001)) than the atlas-based method. In the reproducibility assessment on the test-retest scans, the atlas-based method outperformed the morphological method for all metrics and structures, with average HD’s well below the voxel resolution (< 2 mm).
Conclusion: We present the first methods for automatic segmentation of the spinal structures on LDCT. The atlas-based method seems to be the most suitable algorithm, achieving average HD’s below the voxel size, and maximum errors below one centimetre. However, it is dependent on accurate segmentation by the TotalSegmentator algorithm. Further research is warranted to investigate the influence of the segmentation results on the extraction of quantitative PET information.

Purpose: 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) is used in the diagnostic process and management of patients with metastatic colorectal cancer (mCRC). Also, 18F-FDG PET radiomic features have been found to hold prognostic value for clinical outcome in mCRC. However, no prognostic model has yet been developed to predict clinical outcome in mCRC using 18F-FDG PET images. Computer-aided pattern recognition can be helpful in this process but needs to be validated. The aim of this work was to develop and evaluate a medical-based explainable artificial intelligence (XAI) framework for discriminating between dichotomous progression free survival (PFS) in patients with mCRC undergoing anti-epidermal growth factor receptor (anti-EGFR) monoclonal antibody (mAb) treatment using pre-treatment 18F-FDG PET images.
Methods: We conducted an analysis of 18F-FDG PET images, expressed in standardized uptake values (SUV), obtained from 80 patients with mCRC who were eligible for third-line treatment with an anti-EGFR mAb as part of the IMPACT study. A coronal 2.5D Convolutional Neural Network (CNN) was built to capture features of the 18F-FDG PET images specific for the two patient groups and a medical-based XAI framework was developed to extract the 18F-FDG PET features used by the CNN. The images were randomly divided into a training and a validation set (10-fold cross-validation). Performance of the CNN was evaluated based on the average area under the curve (AUC), accuracy, sensitivity and specificity from the cross-validation. A statistical analysis was performed to assess the predictive value of the 18F-FDG PET features extracted by the XAI framework.
Results: The coronal 2.5D-CNN was able to discriminate between dichotomous PFS (median PFS: 152 days) in patients with mCRC undergoing anti-EGFR mAb treatment using pre-treatment 18F-FDG PET images, with an average AUC of 0.95  ± 0.11 (SD), accuracy of 94% ± 12, sensitivity of 91% ± 21 and specificity of 94% ± 21 %. The XAI framework showed that especially low 18F-FDG PET uptake volume features hold significant differences between the two patient groups.
Conclusion: The coronal 2.5D-CNN showed good performance to predict dichotomous PFS from pre-treatment 18F-FDG PET images in patients with mCRC undergoing anti-EGFR mAb treatment. Low 18F-FDG PET uptake volume features seem to have potential as IB in this patient cohort, but further validation is required.

Introduction Breast cancer is the most frequently diagnosed type of cancer in women in 2020. Treatment and prognosis of breast cancer is highly dependent on early and accurate diagnosis. In recent years, many studies have evaluated the use of artificial intelligence for the detection of breast cancer in mammographic images. Another imaging modality, breast-specific gamma imaging (BSGI), or molecular breast imaging, has not yet been subject to AI algorithms to detect breast cancer. In this paper, we aim to develop and evaluate convolutional neural networks (CNNs) that can detect malignancies in breast-specific gamma imaging, and evaluate the efficacy of different machine learning classifiers to classify breast tumors based on estrogen receptor (ER) status, progesterone receptor (PR) status and human epidermal growth factor receptor 2 (HER2-neu) status. Methods Three CNNs were created and trained and tested on a total of 3,503 BSGI images. The models varied in complexity in terms of convolutional layers and filter sizes. A semi quantitative lesion segmentation was created based on adaptive thresholding and shape and location analysis. Radiomics features were extracted, and univariate feature selection was applied to disregard redundant features. Different machine learning classifiers, which are widely used in literature for binary classification problems, were evaluated. Results In detecting malignancies in a dataset containing clean breasts and breast with malignant lesions, the best performing network reached an area under the receiving operating characteristic (AUROC) of 0.93, while an AUROC of 0.88 was achieved when using the same networks in the classification of malignant versus benign lesions. The best performing machine learning classifiers were the linear discriminating analysis (LDA) classifier for ER and PR status, reaching accuracies of 75% in both receptors. In Her2-neu prediction using machine learning, the best accuracy of 69% was achieved by the RF classifier. Discussion & conclusions Based on the results presented in this paper, CNNs can accurately detect malignancies in BSGI images, and discriminate malignancies from benign lesions to a certain extent. The combination of radiomics and machine learning, however, is in its current implementation not accurate enough to predict the ER, PR and Her2-neu status in BSGI images. Future research, however, could focus on using a combination of imaging modalities, such as BSGI, MRI and mammography to improve the predictive accuracy of machine learning.