Differentiation of benign and malignant vertebral fractures using a convolutional neural network to extract CT-based texture features

CT-based texture analysis (TA) is a growing and representative subfield of radiomics and represents both a non-invasive and quantitative method for the assessment of medical images. Texture features (TFs) allow for quantitative characterization of image properties, such as uniformity, heterogeneity, and randomness, as well as repetitive image patterns [1]. Thus, TA enables the extraction of additional diagnostic, predictive, and prognostic information beyond what is visually perceptive [2, 3]. Recently, TA methods have been used in different radiological subfields, such as neuroimaging or musculoskeletal imaging providing additional information regarding diagnosis or patients’ outcomes in various diseases [2, 4].

The differentiation between benign (osteoporotic) vertebral fractures (VFs) on the one hand, and malignant VFs due to underlying metastases on the other hand, based on CT imaging only is a frequent challenge in clinical practice and of emerging importance within the aging population. Up to 10% of all cancer patients develop symptomatic bone metastases during the course of the illness [5], and vertebral metastases account for up to 39% of all bone metastases [6]. Benign and malignant VFs are characterized by microarchitectural deterioration of osseous tissue and decreased bone mineral density (BMD) with associated fracture risk, since the three-dimensional (3D) trabecular bone architecture is impaired in both entities [7‐11]. All the more, the differentiation between benign and malignant VFs is crucial due to the very different clinical work-up pathways. Making this even more challenging, a history of malignancy does not necessarily imply a tumorous osseous infiltration with subsequent malignant fracture. Malignant entities are frequently associated with either cancer- or treatment-induced BMD reduction, and both the primary condition and antitumoral therapies may thus lead to increased bone fragility and as a consequence can cause benign VFs in patients with a history of cancer [12, 13].

Standard morphological CT is the most suitable imaging technique for clinical routine work-up of bony tissues in general as it provides high spatial resolution and the possibility of reformation in three dimensions [13], while CT-based TA enables a more detailed assessment of the trabecular bone microarchitecture [1]. Thus, the differentiation of benign and malignant VFs represents a promising clinical application of CT-based TA.

In recent years, the deep learning (DL) approach using layers of convolutional neural networks (CNNs) has become frequently applied in many different settings and was able to increase both efficiency and accuracy in segmentation tasks. A fully automated framework (https://​anduin.​bonescreen.​de), which enables an instant segmentation of vertebrae in any CT data set, has recently been introduced, particularly for opportunistic osteoporosis screening and related applications [9, 14‐18].

The aim of this study was to investigate the diagnostic performance of 3D TFs derived from clinical routine CT using a CNN-based segmentation framework in order to differentiate benign and malignant thoracolumbar VFs. We hypothesized that the use of CT-based 3D TFs could improve the differentiation between benign and malignant VFs.

The utilized workflow of this study is illustrated in Fig. 1.

Standard of reference for malignant VFs was either the histological analysis if a biopsy of the vertebra was performed or PET-CT, scintigraphy, and MRI confirming metastatic bone disease. VFs were considered benign if fulfilling all of the following criteria: no positive biopsy result (biopsy could be absent or negative) and no malignancy criteria found at baseline and imaging follow-up (≥ 3 months). All images were assessed by three board-certified radiologists (SSG, ASG, and JSK with 4, 11, and 15 years of radiological experience, respectively). In unclear cases, consensus readings were performed.

An automated CNN-based spine segmentation and extraction of TFs of thoracolumbar vertebral bodies in routine MDCT scans was performed, showing statistically higher values in benign VFs compared to malignant VFs analyzing the global TF Skewness. This finding suggests differences in microstructural bone changes between benign and malignant fractured vertebrae.

Dimension reduction and feature selection are commonly performed steps in TA. During the selection process, it needs to be ensured that the selected TFs fulfill certain criteria and are of high relevance [1]. GLCM- and GLRLM-derived TFs may be restricted to or averaged across directions and distances [22, 23]. The selection of reproducible TFs with high inter- and intra-reader agreement is a further common approach chosen [22, 24]. Furthermore, correlation analysis can be performed in order to identify and consecutively exclude highly correlating TFs for redundancy reduction purposes [22]. With the rise of machine learning capabilities, further feature reduction methods emerged, such as using a random forest classifier to identify VFs to optimize the number of TFs based on the Gini importance and classification performance in an exponential search [25]. In another study, a more clinically driven approach was chosen, selecting TFs based on a preceding long-term reproducibility analysis to identify TFs, which are particularly suitable for long-term comparisons. With this background, we consciously selected the most promising TFs as well as vertebral levels based on the following relevant findings from three previous studies: in a first study, TFs from the histogram as well as second-order TFs applied to CT scans were able to predict radiation-induced insufficiency fractures in patients undergoing radiation therapy for pelvic malignancies [20]. Analyzing three ROIs (L5, sacrum, both femoral heads), the authors identified L5-energy, and femoral head-skewness, as the significant parameters to stratify the risk of patients to develop radiation-induced insufficiency fractures in logistic regression analysis [20]. A second relevant study identified a total of six TFs of the thoracolumbar spine (Variance_global, entropy, SRE, LRE, RLN, and RP), which were long-term reproducible when characterizing gender-, age-, and region-specific vertebral bone microstructure on routine abdominal MDCT [1]. A third previous study found that level-specific volumetric BMD using opportunistic QCT is a significant classifier of incident VF status for both single vertebral levels from T1 to L5 as well as for all analyzed combinations of four consecutive vertebral bodies. [19].

To the best of our knowledge, this is the first study analyzing the diagnostic performance of 3D CT-based TFs using a CNN-based framework to differentiate benign and malignant VFs. A recently published study on CT radiomics developed and validated an automated algorithm for segmentation of fractured vertebrae on CT and evaluated the applicability of this algorithm in a radiomics prediction model to differentiate a total of 341 benign and malignant VFs from 158 patients [26]. The authors validated their algorithm on independent test sets and constructed a radiomics model predicting fracture malignancy on CT. Further, they compared the prediction performance between automated and human segmentations. An automated segmentation algorithm that showed comparable performance to human expert segmentations in a CT radiomics model was developed and validated in order to predict fracture malignancy [26]. Accordingly, the CNN-based segmentation algorithm that we used in the present study also has been proven to be valid and accurate in several previous studies [9, 14‐18]. However, the study by Park et al. and our study have key differences. Firstly, the TFs chosen by the authors [26] are only to a small extent comparable to the TFs we chose in our study. Secondly, we also analyzed non-fractured vertebrae, as the CNN-based segmentation pipeline we used labells and segments both fractured and non-fractured vertebrae, while only fractured vertebrae were analyzed in this previous study [26]. Thirdly, our patient cohort was significantly larger (409 vs. 158 patients) [26].

Based on current literature, TF extraction using a DL-based pipeline may add important value to future routine clinical practice in cases in which VFs need to be classified further into benign or malignant fractures [1, 19, 26, 27]. This becomes particularly important for the individual diagnostic work-up of the patient, potentially resulting in further MRI, PET-CT imaging, or biopsy to initiate the adequate therapy without any delay.

Analyzing fractured vertebrae, the global TF skewness, which reflects the shape of gray-level distribution, showed a significant difference between the benign and malignant fracture group with higher values in benign fractures. Interestingly, in a previous study, this TF—analyzing the femoral head—was able to stratify patients into those who developed and those who did not develop an insufficiency fracture [20]. In summary, there are indications that the global TF skewness might be useful both in the field of differentiating benign, respectively, malignant VFs and in fracture prediction, e.g., the development of insufficiency fractures as discussed above.

This study has several limitations. Firstly, we used a retrospective study design with related potential selection and referral bias. However, we used a high-quality standard of reference for the differentiation between benign and malignant VFs. Secondly, we did not evaluate the potential impact of contrast agent on texture analysis, which of course might have an influence and needs to be analyzed in bigger study cohorts. Thirdly, we did not analyze any bone-related health data such as weight and height, body mass index, corticosteroid therapy, or smoking status in our study, which are known to have an influence on bone quality. Combined radiomics–clinical models might be superior compared with radiomics models alone, e.g., in predicting malignancy of VFs on CT [27]. Yet, the purpose of this study was to evaluate the diagnostic performance of CT-based 3D TFs using a CNN-based segmentation framework for the differentiation of benign and malignant VFs and not to investigate whether the use of clinical information might improve the algorithm’s performance. And as a last point, the analyzed set of TFs was limited in order to avoid statistical errors due to multiple testing.

In conclusion, DL-based extraction of global CT-based 3D TFs was feasible and the global TF skewness showed significant differences between patients with benign and those with malignant VFs of the thoracolumbar spine. This suggests that DL-based TF extraction for the classification of benign versus malignant VFs might add relevant diagnostic information and therefore enhance the clinical work-up of diagnostics of VFs.