Lytic Metastases in Thoracolumbar Spine: Computer-aided Detection at CT—Preliminary Study

Oncologic Applications of Artificial Intelligence and Deep Learning Methods in CT Spine Imaging-A Systematic Review

In spinal oncology, integrating deep learning with computed tomography (CT) imaging has shown promise in enhancing diagnostic accuracy, treatment planning, and patient outcomes. This systematic review synthesizes evidence on artificial intelligence (AI) applications in CT imaging for spinal tumors. A PRISMA-guided search identified 33 studies: 12 (36.4%) focused on detecting spinal malignancies, 11 (33.3%) on classification, 6 (18.2%) on prognostication, 3 (9.1%) on treatment planning, and 1 (3.0%) on both detection and classification. Of the classification studies, 7 (21.2%) used machine learning to distinguish between benign and malignant lesions, 3 (9.1%) evaluated tumor stage or grade, and 2 (6.1%) employed radiomics for biomarker classification. Prognostic studies included three (9.1%) that predicted complications such as pathological fractures and three (9.1%) that predicted treatment outcomes. AI's potential for improving workflow efficiency, aiding decision-making, and reducing complications is discussed, along with its limitations in generalizability, interpretability, and clinical integration. Future directions for AI in spinal oncology are also explored. In conclusion, while AI technologies in CT imaging are promising, further research is necessary to validate their clinical effectiveness and optimize their integration into routine practice.

Artificial intelligence: a new cutting-edge tool in spine surgery

The purpose of this narrative review was to comprehensively elaborate the various components of artificial intelligence (AI), their applications in spine surgery, practical concerns, and future directions. Over the years, spine surgery has been continuously transformed in various aspects, including diagnostic strategies, surgical approaches, procedures, and instrumentation, to provide better-quality patient care. Surgeons have also augmented their surgical expertise with rapidly growing technological advancements. AI is an advancing field that has the potential to revolutionize many aspects of spine surgery. We performed a comprehensive narrative review of the various aspects of AI and machine learning in spine surgery. To elaborate on the current role of AI in spine surgery, a review of the literature was performed using PubMed and Google Scholar databases for articles published in English in the last 20 years. The initial search using the keywords "artificial intelligence" AND "spine," "machine learning" AND "spine," and "deep learning" AND "spine" extracted a total of 78, 60, and 37 articles and 11,500, 4,610, and 2,270 articles on PubMed and Google Scholar. After the initial screening and exclusion of unrelated articles, duplicates, and non-English articles, 405 articles were identified. After the second stage of screening, 93 articles were included in the review. Studies have shown that AI can be used to analyze patient data and provide personalized treatment recommendations in spine care. It also provides valuable insights for planning surgeries and assisting with precise surgical maneuvers and decisionmaking during the procedures. As more data become available and with further advancements, AI is likely to improve patient outcomes.

Artificial Intelligence in Bone Metastasis Imaging: Recent Progresses from Diagnosis to Treatment - A Narrative Review

The introduction of artificial intelligence (AI) represents an actual revolution in the radiological field, including bone lesion imaging. Bone lesions are often detected both in healthy and oncological patients and the differential diagnosis can be challenging but decisive, because it affects the diagnostic and therapeutic process, especially in case of metastases. Several studies have already demonstrated how the integration of AI-based tools in the current clinical workflow could bring benefits to patients and to healthcare workers. AI technologies could help radiologists in early bone metastases detection, increasing the diagnostic accuracy and reducing the overdiagnosis and the number of unnecessary deeper investigations. In addition, radiomics and radiogenomics approaches could go beyond the qualitative features, visible to the human eyes, extrapolating cancer genomic and behavior information from imaging, in order to plan a targeted and personalized treatment. In this article, we want to provide a comprehensive summary of the most promising AI applications in bone metastasis imaging and their role from diagnosis to treatment and prognosis, including the analysis of future challenges and new perspectives.

Hybrid CNN-transformer network for interactive learning of challenging musculoskeletal images

BACKGROUND AND OBJECTIVES

Segmentation of regions of interest (ROIs) such as tumors and bones plays an essential role in the analysis of musculoskeletal (MSK) images. Segmentation results can help with orthopaedic surgeons in surgical outcomes assessment and patient's gait cycle simulation. Deep learning-based automatic segmentation methods, particularly those using fully convolutional networks (FCNs), are considered as the state-of-the-art. However, in scenarios where the training data is insufficient to account for all the variations in ROIs, these methods struggle to segment the challenging ROIs that with less common image characteristics. Such characteristics might include low contrast to the background, inhomogeneous textures, and fuzzy boundaries.

METHODS

we propose a hybrid convolutional neural network - transformer network (HCTN) for semi-automatic segmentation to overcome the limitations of segmenting challenging MSK images. Specifically, we propose to fuse user-inputs (manual, e.g., mouse clicks) with high-level semantic image features derived from the neural network (automatic) where the user-inputs are used in an interactive training for uncommon image characteristics. In addition, we propose to leverage the transformer network (TN) - a deep learning model designed for handling sequence data, in together with features derived from FCNs for segmentation; this addresses the limitation of FCNs that can only operate on small kernels, which tends to dismiss global context and only focus on local patterns.

RESULTS

We purposely selected three MSK imaging datasets covering a variety of structures to evaluate the generalizability of the proposed method. Our semi-automatic HCTN method achieved a dice coefficient score (DSC) of 88.46 ± 9.41 for segmenting the soft-tissue sarcoma tumors from magnetic resonance (MR) images, 73.32 ± 11.97 for segmenting the osteosarcoma tumors from MR images and 93.93 ± 1.84 for segmenting the clavicle bones from chest radiographs. When compared to the current state-of-the-art automatic segmentation method, our HCTN method is 11.7%, 19.11% and 7.36% higher in DSC on the three datasets, respectively.

CONCLUSION

Our experimental results demonstrate that HCTN achieved more generalizable results than the current methods, especially with challenging MSK studies.

Artificial intelligence-aided lytic spinal bone metastasis classification on CT scans

PURPOSE

Spinal bone metastases directly affect quality of life, and patients with lytic-dominant lesions are at high risk for neurological symptoms and fractures. To detect and classify lytic spinal bone metastasis using routine computed tomography (CT) scans, we developed a deep learning (DL)-based computer-aided detection (CAD) system.

METHODS

We retrospectively analyzed 2125 diagnostic and radiotherapeutic CT images of 79 patients. Images annotated as tumor (positive) or not (negative) were randomized into training (1782 images) and test (343 images) datasets. YOLOv5m architecture was used to detect vertebra on whole CT scans. InceptionV3 architecture with the transfer-learning technique was used to classify the presence/absence of lytic lesions on CT images showing the presence of vertebra. The DL models were evaluated via fivefold cross-validation. For vertebra detection, bounding box accuracy was estimated using intersection over union (IoU). We evaluated the area under the curve (AUC) of a receiver operating characteristic curve to classify lesions. Moreover, we determined the accuracy, precision, recall, and F1 score. We used the gradient-weighted class activation mapping (Grad-CAM) technique for visual interpretation.

RESULTS

The computation time was 0.44 s per image. The average IoU value of the predicted vertebra was 0.923 ± 0.052 (0.684-1.000) for test datasets. In the binary classification task, the accuracy, precision, recall, F1-score, and AUC value for test datasets were 0.872, 0.948, 0.741, 0.832, and 0.941, respectively. Heat maps constructed using the Grad-CAM technique were consistent with the location of lytic lesions.

CONCLUSION

Our artificial intelligence-aided CAD system using two DL models could rapidly identify vertebra bone from whole CT images and detect lytic spinal bone metastasis, although further evaluation of diagnostic accuracy is required with a larger sample size.

Correlation analysis between morphologic characteristics of the thoracolumbar basivertebral foramen and Kummell's disease in patients with osteoporosis using imaging techniques

BACKGROUND

The aging of the population is a social problem faced by many countries in the world. With the increase in the elderly population, the number of patients with Kummell's disease is also gradually increasing. No study has demonstrated that Kummell's disease has a clear correlation with the foramen of a vertebrobasilar vein.

OBJECTIVES

The research was conducted to describe and evaluate the morphological characteristics of a basivertebral foramen in patients with osteoporosis and Kummell's disease by CT; to infer whether the specific morphological characteristics of basivertebral foramen may be one of the risk factors of Kummell's disease; to provide clinical suggestions for the treatment of Kummell's disease.

DESIGN

Retrospective analysis from January 2020 to December 2021 on 83 patients with 83 vertebral bodies (T8-L5) diagnosed with senile osteoporosis and Kummell's disease hospitalized in our hospital due to chronic low back pain, including 57 women and 23 men. Group A was assigned for the following patients: the age ranged from 59 to 86 years old, with the average age of 67.30 ± 7.32 years old; the body mass index ranged from 20.01 to 29.46 kg/m², with the average body mass index of 23.51 ± 3.03 kg/m².Group B was assigned for the following patients: 83 patients diagnosed with senile osteoporosis in our outpatient department from January 2020 to December 2021, including 41 males and 42 females; the age ranged from 60 to 85 years, with an average age of 68.52 ± 4.68 years old; the height to weight ratio met the normal reference standard (except 20% above or 10% below the standard weight). Through the lanwon PACS imaging system, the related parameters of the vertebrobasilar foramen in patients with osteoporosis and Kummell's disease were measured to evaluate and analyze the correlation between the morphological characteristics of the vertebrobasilar foramen in patients with osteoporosis and Kummell's disease.

RESULTS

In patients with osteoporosis, the distribution of incidence rate of Kummell's disease in the spine was consistent with that of osteoporotic compression fractures. Sagittal view of the vertebral body on CT scan and the triangular-shaped, trapezoidal-shaped, and irregular-shaped basivertebral foramen in group A accounted for 18%,57%,and 36%,respectively. In group B, triangular-shaped, trapezoidal-shaped, and irregular-shaped foramen accounted for 51%,17%,and 26%,respectively.The distribution of triangular-shaped, trapezoidal-shaped, and irregular-shaped foramen was compared between groups A and B, and the difference was recorded as statistically significant (P < 0.05). Additionally, the difference in the distribution of triangular-shaped, trapezoidal-shaped, and irregular-shaped foramen in group A was found statistically significant (P < 0.05),while that of Group B was found statistically insignificant (P > 0.05).On a horizontal CT scan of the vertebra of group A, triangles, trapezoids, and irregularities accounted for 28%, 26%, and 47%, respectively. In group B, triangles,trapezoids,and irregularities accounted for 31%, 37%, and 30%, respectively. The difference in the distribution of the triangular-shaped and trapezoidal-shaped foramen in groups A and B was statistically insignificant (P > 0.05), while that of irregular-shaped was statistically significant (P < 0.05). Additionally, there was no statistical significance (P > 0.05) in the difference in the morphological distribution of triangular-shaped and trapezoidal-shaped foramen in group A, while that of irregular-shaped was found to be statistically significant (P < 0.05). Further, the difference in the morphological distribution of triangular-shaped, trapezoidal-shaped, and irregular-shaped foramen in group B was not statistically significant (P > 0.05).In general, about 8% of the vertebral body of BF has an osseous septum. In group A, 97% are single-holed while the remaining 3% are porous; in group B, those with single holes accounted for 76%, while the remaining 24% are porous. In groups A and B, the difference in the morphological distribution of single-holed and multi-holed T8, T11, T12, L1, L2, L4, and L5 vertebral bodies was statistically significant (P < 0.05). In group A, the difference in the distribution of single-holed and multi-holed L1 and L5 vertebral bodies was statistically significant (P < 0.05). Similarly, the difference in the distribution of single-holed and multi-holed T8, T11, T12, L1, L2, and L4 basivertebral foramen was statistically significant (P < 0.05).

CONCLUSIONS

In patients with osteoporosis, the incidence of vertebral Kummell's disease can be associated with the morphological characteristics of the basivertebral foramen, as observed in the CT scan. Furthermore, the vertebral body with trapezoidal-shaped and irregular-shaped basivertebral foramen and boneless septum in the foramen is highly susceptible to Kummell's disease.

Original research: utilization of a convolutional neural network for automated detection of lytic spinal lesions on body CTs

OBJECTIVE

To develop, train, and test a convolutional neural network (CNN) for detection of spinal lytic lesions in chest, abdomen, and pelvis CT scans.

MATERIALS AND METHODS

Cases of malignant spinal lytic lesions in CT scans were identified. Images were manually segmented for the following classes: (i) lesion, (ii) normal bone, (iii) background. If more than one lesion was on a single slice, all lesions were segmented. Images were stored as 128×128 pixel grayscale, with 10% segregated for testing. The training pipeline of the dataset included histogram equalization and data augmentation. A model was trained on Keras/Tensorflow using an 80/20 training/validation split, based on U-Net architecture. Additional testing of the model was performed on 1106 images of healthy controls. Global sensitivity measured detection of any lesion on a single image. Local sensitivity and positive predictive value (PPV) measured detection of all lesions on an image. Global specificity measured false positive rate in non-pathologic bone.

RESULTS

Six hundred images were obtained for model creation. The training set consisted of 540 images, which was augmented to 20,000. The test set consisted of 60 images. Model training was performed in triplicate. Mean Dice scores were 0.61 for lytic lesion, 0.95 for normal bone, and 0.99 for background. Mean global sensitivity was 90.6%, local sensitivity was 74.0%, local PPV was 78.3%, and global specificity was 63.3%. At least one false positive lesion was noted in 28.8-44.9% of control images.

CONCLUSION

A task-trained CNN showed good sensitivity in detecting spinal lytic lesions in axial CT images.

Artificial intelligence, machine learning and deep learning in musculoskeletal imaging: Current applications

Artificial intelligence is rapidly expanding in all technological fields. The medical field, and especially diagnostic imaging, has been showing the highest developmental potential. Artificial intelligence aims at human intelligence simulation through the management of complex problems. This review describes the technical background of artificial intelligence, machine learning, and deep learning. The first section illustrates the general potential of artificial intelligence applications in the context of request management, data acquisition, image reconstruction, archiving, and communication systems. In the second section, the prospective of dedicated tools for segmentation, lesion detection, automatic diagnosis, and classification of musculoskeletal disorders is discussed.

Application of Artificial Intelligence Methods for Imaging of Spinal Metastasis

Spinal metastasis is the most common malignant disease of the spine. Recently, major advances in machine learning and artificial intelligence technology have led to their increased use in oncological imaging. The purpose of this study is to review and summarise the present evidence for artificial intelligence applications in the detection, classification and management of spinal metastasis, along with their potential integration into clinical practice. A systematic, detailed search of the main electronic medical databases was undertaken in concordance with the PRISMA guidelines. A total of 30 articles were retrieved from the database and reviewed. Key findings of current AI applications were compiled and summarised. The main clinical applications of AI techniques include image processing, diagnosis, decision support, treatment assistance and prognostic outcomes. In the realm of spinal oncology, artificial intelligence technologies have achieved relatively good performance and hold immense potential to aid clinicians, including enhancing work efficiency and reducing adverse events. Further research is required to validate the clinical performance of the AI tools and facilitate their integration into routine clinical practice.

Computer-Aided Diagnosis for Determining Sagittal Spinal Curvatures Using Deep Learning and Radiography

Analyzing spinal curvatures manually is time-consuming and tedious for clinicians, and intra-observer and inter-observer variability can affect manual measurements. In this study, we developed and evaluated the performance of an automated deep learning-based computer-aided diagnosis (CAD) tool for measuring the sagittal alignment of the spine from X-ray images. The CAD system proposed here performs two functions: deep learning-based lateral spine segmentation and automatic analysis of thoracic kyphosis and lumbar lordosis angles. We utilized 322 datasets with data augmentation for learning and fivefold cross-validation. The segmentation model was based on U-Net, which has multiple applications in medical image processing. Here, we utilized parameter equations and trigonometric functions to design spinal angle measurement algorithms. The kyphosis (T4-T12) and lordosis angle (L1-S1, L1-L5) were automatically measured to help diagnose kyphosis and lordosis. The segmentation model had precision, sensitivity, and dice similarity coefficient values of 90.53 ± 4.61%, 89.53 ± 1.8%, and 90.22 ± 0.62%, respectively. The performance of the CAD algorithm was also verified with the Pearson correlation, Bland-Altman, and intra-class correlation coefficient (ICC) analysis. The proposed angle measurement algorithm exhibited high similarity and reliability during verification. Therefore, CAD can help clinicians in reaching a diagnosis by analyzing the sagittal spinal curvatures while reducing observer-based variability and the required time or effort.

Effectiveness of temporal subtraction computed tomography images using deep learning in detecting vertebral bone metastases

PURPOSE

To assess the clinical effectiveness of temporal subtraction computed tomography (TS CT) using deep learning to improve vertebral bone metastasis detection.

METHOD

This retrospective study used TS CT comprising bony landmark detection, bone segmentation with a multi-atlas-based method, and spatial registration of two images by a log-domain diffeomorphic Demons algorithm. Paired current and past CT images of 50 patients without vertebral metastasis, recorded during June 2011-September 2016, were included as training data. A deep learning-based method estimated registration errors and suppressed false positives. Thereafter, paired CT images of 40 cancer patients with newly developed vertebral metastases and 40 control patients without vertebral metastases were evaluated. Six board-certified radiologists and five radiology residents independently interpreted 80 paired CT images with and without TS CT.

RESULTS

Records of 40 patients in the metastasis group (median age: 64.5 years; 20 males) and 40 patients in the control group (median age: 64.0 years; 20 males) were evaluated. With TS CT, the overall figure of merit (FOM) of the board-certified radiologist and resident groups improved from 0.848 to 0.876 (p = 0.01) and from 0.752 to 0.799 (p = 0.02), respectively. The sub-analysis focusing on attenuation changes in lesions revealed that the FOM of osteoblastic lesions significantly improved in both the board-certified radiologist and resident groups using TS CT. The sub-analysis focusing on lesion location showed that the FOM of the resident group significantly improved in the vertebral arch (p = 0.04).

CONCLUSIONS

TS CT was effective in detecting bone metastasis by both board-certified radiologists and radiology residents.

State-of-the-Art Imaging Techniques in Metastatic Spinal Cord Compression

Metastatic Spinal Cord Compression (MSCC) is a debilitating complication in oncology patients. This narrative review discusses the strengths and limitations of various imaging modalities in diagnosing MSCC, the role of imaging in stereotactic body radiotherapy (SBRT) for MSCC treatment, and recent advances in deep learning (DL) tools for MSCC diagnosis. PubMed and Google Scholar databases were searched using targeted keywords. Studies were reviewed in consensus among the co-authors for their suitability before inclusion. MRI is the gold standard of imaging to diagnose MSCC with reported sensitivity and specificity of 93% and 97% respectively. CT Myelogram appears to have comparable sensitivity and specificity to contrast-enhanced MRI. Conventional CT has a lower diagnostic accuracy than MRI in MSCC diagnosis, but is helpful in emergent situations with limited access to MRI. Metal artifact reduction techniques for MRI and CT are continually being researched for patients with spinal implants. Imaging is crucial for SBRT treatment planning and three-dimensional positional verification of the treatment isocentre prior to SBRT delivery. Structural and functional MRI may be helpful in post-treatment surveillance. DL tools may improve detection of vertebral metastasis and reduce time to MSCC diagnosis. This enables earlier institution of definitive therapy for better outcomes.

Benign and malignant diagnosis of spinal tumors based on deep learning and weighted fusion framework on MRI

BACKGROUND

The application of deep learning has allowed significant progress in medical imaging. However, few studies have focused on the diagnosis of benign and malignant spinal tumors using medical imaging and age information at the patient level. This study proposes a multi-model weighted fusion framework (WFF) for benign and malignant diagnosis of spinal tumors based on magnetic resonance imaging (MRI) images and age information.

METHODS

The proposed WFF included a tumor detection model, sequence classification model, and age information statistic module based on sagittal MRI sequences obtained from 585 patients with spinal tumors (270 benign, 315 malignant) between January 2006 and December 2019 from the cooperative hospital. The experimental results of the WFF were compared with those of one radiologist (D1) and two spine surgeons (D2 and D3).

RESULTS

In the case of reference age information, the accuracy (ACC) (0.821) of WFF was higher than three doctors' ACC (D1: 0.686; D2: 0.736; D3: 0.636). Without age information, the ACC (0.800) of the WFF was also higher than that of the three doctors (D1: 0.750; D2: 0.664; D3:0.614).

CONCLUSIONS

The proposed WFF is effective in the diagnosis of benign and malignant spinal tumors with complex histological types on MRI.

Segmentation Performance Comparison Considering Regional Characteristics in Chest X-ray Using Deep Learning

Chest radiography is one of the most widely used diagnostic methods in hospitals, but it is difficult to read clearly because several human organ tissues and bones overlap. Therefore, various image processing and rib segmentation methods have been proposed to focus on the desired target. However, it is challenging to segment ribs elaborately using deep learning because they cannot reflect the characteristics of each region. Identifying which region has specific characteristics vulnerable to deep learning is an essential indicator of developing segmentation methods in medical imaging. Therefore, it is necessary to compare the deep learning performance differences based on regional characteristics. This study compares the differences in deep learning performance based on the rib region to verify whether deep learning reflects the characteristics of each part and to demonstrate why this regional performance difference has occurred. We utilized 195 normal chest X-ray datasets with data augmentation for learning and 5-fold cross-validation. To compare segmentation performance, the rib image was divided vertically and horizontally based on the spine, clavicle, heart, and lower organs, which are characteristic indicators of the baseline chest X-ray. Resultingly, we found that the deep learning model showed a 6-7% difference in the segmentation performance depending on the regional characteristics of the rib. We verified that the performance differences in each region cannot be ignored. This study will enable a more precise segmentation of the ribs and the development of practical deep learning algorithms.

Automated detection and segmentation of sclerotic spinal lesions on body CTs using a deep convolutional neural network

PURPOSE

To develop a deep convolutional neural network capable of detecting spinal sclerotic metastases on body CTs.

MATERIALS AND METHODS

Our study was IRB-approved and HIPAA-compliant. Cases of confirmed sclerotic bone metastases in chest, abdomen, and pelvis CTs were identified. Images were manually segmented for 3 classes: background, normal bone, and sclerotic lesion(s). If multiple lesions were present on a slice, all lesions were segmented. A total of 600 images were obtained, with a 90/10 training/testing split. Images were stored as 128 × 128 pixel grayscale and the training dataset underwent a processing pipeline of histogram equalization and data augmentation. We trained our model from scratch on Keras/TensorFlow using an 80/20 training/validation split and a U-Net architecture (64 batch size, 100 epochs, dropout 0.25, initial learning rate 0.0001, sigmoid activation). We also tested our model's true negative and false positive rate with 1104 non-pathologic images. Global sensitivity measured model detection of any lesion on a single image, local sensitivity and positive predictive value (PPV) measured model detection of each lesion on a given image, and local specificity measured the false positive rate in non-pathologic bone.

RESULTS

Dice scores were 0.83 for lesion, 0.96 for non-pathologic bone, and 0.99 for background. Global sensitivity was 95% (57/60), local sensitivity was 92% (89/97), local PPV was 97% (89/92), and local specificity was 87% (958/1104).

CONCLUSION

A deep convolutional neural network has the potential to assist in detecting sclerotic spinal metastases.

Differentiation Between Osteoporotic And Neoplastic Vertebral Fractures: State Of The Art And Future Perspectives

Vertebral fractures are a common condition, occurring in the context of osteoporosis and malignancy. These entities affect a group of patients in the same age range; clinical features may be indistinct and symptoms non-existing, and thus present challenges to diagnosis. In this article, we review the use and accuracy of different imaging modalities available to characterize vertebral fracture etiology, from well-established classical techniques, to the role of new and advanced imaging techniques, and the prospective use of artificial intelligence. We also address the role of imaging on treatment. In the context of osteoporosis, the importance of opportunistic diagnosis is highlighted. In the near future, the use of automated computer-aided diagnostic algorithms applied to different imaging techniques may be really useful to aid on diagnosis.

Applications of Machine Learning to Imaging of Spinal Disorders: Current Status and Future Directions

STUDY DESIGN

Narrative review.

OBJECTIVES

We aim to describe current progress in the application of artificial intelligence and machine learning technology to provide automated analysis of imaging in patients with spinal disorders.

METHODS

A literature search utilizing the PubMed database was performed. Relevant studies from all the evidence levels have been included.

RESULTS

Within spine surgery, artificial intelligence and machine learning technologies have achieved near-human performance in narrow image classification tasks on specific datasets in spinal degenerative disease, spinal deformity, spine trauma, and spine oncology.

CONCLUSION

Although substantial challenges remain to be overcome it is clear that artificial intelligence and machine learning technology will influence the practice of spine surgery in the future.

Temporal subtraction of low-dose and relatively thick-slice CT images with large deformation diffeomorphic metric mapping and adaptive voxel matching for detection of bone metastases: A STARD-compliant article

To evaluate the improvement of radiologist performance in detecting bone metastases at follow up low-dose computed tomography (CT) by using a temporal subtraction (TS) technique based on an advanced nonrigid image registration algorithm.Twelve patients with bone metastases (males, 5; females, 7; mean age, 64.8 ± 7.6 years; range 51-81 years) and 12 control patients without bone metastases (males, 5; females, 7; mean age, 64.8 ± 7.6 years; 51-81 years) were included, who underwent initial and follow-up CT examinations between December 2005 and July 2016. Initial CT images were registered to follow-up CT images by the algorithm, and TS images were created. Three radiologists independently assessed the bone metastases with and without the TS images. The reader averaged jackknife alternative free-response receiver operating characteristics figure of merit was used to compare the diagnostic accuracy.The reader-averaged values of the jackknife alternative free-response receiver operating characteristics figures of merit (θ) significantly improved from 0.687 for the readout without TS and 0.803 for the readout with TS (P value = .031. F statistic = 5.24). The changes in the absolute value of CT attenuations in true-positive lesions were significantly larger than those in false-negative lesions (P < .001). Using TS, segment-based sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of the readout with TS were 66.7%, 98.9%, 94.4%, 90.9%, and 94.8%, respectively.The TS images can significantly improve the radiologist's performance in the detection of bone metastases on low-dose and relatively thick-slice CT.

Artificial Intelligence in Musculoskeletal Imaging: A Paradigm Shift

Artificial intelligence is upending many of our assumptions about the ability of computers to detect and diagnose diseases on medical images. Deep learning, a recent innovation in artificial intelligence, has shown the ability to interpret medical images with sensitivities and specificities at or near that of skilled clinicians for some applications. In this review, we summarize the history of artificial intelligence, present some recent research advances, and speculate about the potential revolutionary clinical impact of the latest computer techniques for bone and muscle imaging. © 2019 American Society for Bone and Mineral Research. Published 2019. This article is a U.S. Government work and is in the public domain in the USA.

Clinical usefulness of temporal subtraction CT in detecting vertebral bone metastases

PURPOSE

The purpose of this study was to determine whether temporal subtraction (TS) computed tomography (CT) contributes to the detection of vertebral bone metastases.

METHOD

The calculation of TS CT was composed of bony landmark detection, bone segmentation with a multiatlas-based method, and spatial registration. Temporal increase and decrease of the CT values were visualized in blue and red, respectively. Paired CT images of 20 patients with cancer and newly-developed vertebral metastases were analyzed. Control CT examinations of 20 different patients were also included. The presence of vertebral metastases on the TS CT was evaluated by two board-certified radiologists. Five additional board-certified radiologists and five radiology residents independently interpreted the 40 paired CT images with and without TS CT.

RESULTS

In the lesion conspicuity evaluation, 96% of vertebral metastases were scored as excellent or good. In the image interpretation examination, according to free-response receiver operating characteristics analysis, the overall figure of merit (FOM) of the board-certified radiologist group was 0.892 and 0.898 with and without TS CT, respectively. The FOM of the resident group improved from 0.849 to 0.902 with viewing TS CT. In the sub-analysis focusing on the location of the lesion, the FOM of the resident group significantly improved from 0.75 to 0.92 in vertebral arch lesions (p = 0.001).

CONCLUSIONS

The TS CT may be useful to detect vertebral metastases because almost all the vertebral metastases were shown to be favorable visualization. The TS CT was proven to be especially helpful for radiology residents in detecting vertebral arch metastases.

Are we at a crossroads or a plateau? Radiomics and machine learning in abdominal oncology imaging

Advances in radiomics and machine learning have driven a technology boom in the automated analysis of radiology images. For the past several years, expectations have been nearly boundless for these new technologies to revolutionize radiology image analysis and interpretation. In this editorial, I compare the expectations with the realities with particular attention to applications in abdominal oncology imaging. I explore whether these technologies will leave us at a crossroads to an exciting future or to a sustained plateau and disillusionment.

Radiological images and machine learning: Trends, perspectives, and prospects

The application of machine learning to radiological images is an increasingly active research area that is expected to grow in the next five to ten years. Recent advances in machine learning have the potential to recognize and classify complex patterns from different radiological imaging modalities such as x-rays, computed tomography, magnetic resonance imaging and positron emission tomography imaging. In many applications, machine learning based systems have shown comparable performance to human decision-making. The applications of machine learning are the key ingredients of future clinical decision making and monitoring systems. This review covers the fundamental concepts behind various machine learning techniques and their applications in several radiological imaging areas, such as medical image segmentation, brain function studies and neurological disease diagnosis, as well as computer-aided systems, image registration, and content-based image retrieval systems. Synchronistically, we will briefly discuss current challenges and future directions regarding the application of machine learning in radiological imaging. By giving insight on how take advantage of machine learning powered applications, we expect that clinicians can prevent and diagnose diseases more accurately and efficiently.

Artificial intelligence and machine learning in spine research

Artificial intelligence (AI) and machine learning (ML) techniques are revolutionizing several industrial and research fields like computer vision, autonomous driving, natural language processing, and speech recognition. These novel tools are already having a major impact in radiology, diagnostics, and many other fields in which the availability of automated solution may benefit the accuracy and repeatability of the execution of critical tasks. In this narrative review, we first present a brief description of the various techniques that are being developed nowadays, with special focus on those used in spine research. Then, we describe the applications of AI and ML to problems related to the spine which have been published so far, including the localization of vertebrae and discs in radiological images, image segmentation, computer-aided diagnosis, prediction of clinical outcomes and complications, decision support systems, content-based image retrieval, biomechanics, and motion analysis. Finally, we briefly discuss major ethical issues related to the use of AI in healthcare, namely, accountability, risk of biased decisions as well as data privacy and security, which are nowadays being debated in the scientific community and by regulatory agencies.

Automatic inference model construction for computer-aided diagnosis of lung nodule: Explanation adequacy, inference accuracy, and experts' knowledge

We aimed to describe the development of an inference model for computer-aided diagnosis of lung nodules that could provide valid reasoning for any inferences, thereby improving the interpretability and performance of the system. An automatic construction method was used that considered explanation adequacy and inference accuracy. In addition, we evaluated the usefulness of prior experts’ (radiologists’) knowledge while constructing the models. In total, 179 patients with lung nodules were included and divided into 79 and 100 cases for training and test data, respectively. F-measure and accuracy were used to assess explanation adequacy and inference accuracy, respectively. For F-measure, reasons were defined as proper subsets of Evidence that had a strong influence on the inference result. The inference models were automatically constructed using the Bayesian network and Markov chain Monte Carlo methods, selecting only those models that met the predefined criteria. During model constructions, we examined the effect of including radiologist’s knowledge in the initial Bayesian network models. Performance of the best models in terms of F-measure, accuracy, and evaluation metric were as follows: 0.411, 72.0%, and 0.566, respectively, with prior knowledge, and 0.274, 65.0%, and 0.462, respectively, without prior knowledge. The best models with prior knowledge were then subjectively and independently evaluated by two radiologists using a 5-point scale, with 5, 3, and 1 representing beneficial, appropriate, and detrimental, respectively. The average scores by the two radiologists were 3.97 and 3.76 for the test data, indicating that the proposed computer-aided diagnosis system was acceptable to them. In conclusion, the proposed method incorporating radiologists’ knowledge could help in eliminating radiologists’ distrust of computer-aided diagnosis and improving its performance.

Formation and Application of High Reflectivity Controllable Barium Sulfate Microspheres

This paper investigated the influence of reaction conditions on particle morphology. X-ray powder diffraction (XRD), particle size distribution (PSD), and scanning electron microscopy (SEM) were used to characterize the morphology of barium sulfate. The barium sulfate microspheres were synthesized with BaCl2, Na2SO4, and ethylenediaminetetraacetic acid disodium (EDTA·2Na). The reflectivity of the synthesized barium sulfate microspheres was greater than 99% in the range of 400–700 nm, which was characterized by a reflectance spectrometer. The morphology of the barium sulfate particles and their cross-section were observed by SEM. The prepared microspheres were applied to high-density lipoprotein dry tablets due to their high reflectivity, and the results showed that the prepared tablets had high sensitivity and good repeatability.

Deep convolutional neural network-based segmentation and classification of difficult to define metastatic spinal lesions in 3D CT data

This paper aims to address the segmentation and classification of lytic and sclerotic metastatic lesions that are difficult to define by using spinal 3D Computed Tomography (CT) images obtained from highly pathologically affected cases. As the lesions are ill-defined and consequently it is difficult to find relevant image features that would enable detection and classification of lesions by classical methods of texture and shape analysis, the problem is solved by automatic feature extraction provided by a deep Convolutional Neural Network (CNN). Our main contributions are: (i) individual CNN architecture, and pre-processing steps that are dependent on a patient data and a scan protocol - it enables work with different types of CT scans; (ii) medial axis transform (MAT) post-processing for shape simplification of segmented lesion candidates with Random Forest (RF) based meta-analysis; and (iii) usability of the proposed method on whole-spine CTs (cervical, thoracic, lumbar), which is not treated in other published methods (they work with thoracolumbar segments of spine only). Our proposed method has been tested on our own dataset annotated by two mutually independent radiologists and has been compared to other published methods. This work is part of the ongoing complex project dealing with spine analysis and spine lesion longitudinal studies.

The mechanism of thoracolumbar burst fracture may be related to the basivertebral foramen

BACKGROUND CONTEXT

The basivertebral foramen (BF), located in the middle posterior wall of the vertebral body, may induce local weakness and contribute to the formation of a retropulsed bone fragment (RBF) in thoracolumbar burst fracture (TLBF). We hypothesize that the mechanism of TLBF is related to the BF.

PURPOSE

This study aimed to clarify the relationship between RBFs and the BF in TLBFs, and to explain the results using biomechanical experiments and micro-computed tomography (micro-CT).

STUDY DESIGN

A comprehensive research involving clinical radiology, micro-CT, and biomechanical experiments on cadaveric spines was carried out.

PATIENT SAMPLE

A total of 162 consecutive patients diagnosed with TLBF with RBFs, drawn from 256 patients who had reported accidents or injuries to their thoracolumbar spine, comprised the patient sample.

OUTCOME MEASURES

Dimensions and location of the RBFs in relation to the BF were the outcome measures.

MATERIALS AND METHODS

Computed tomography reconstruction imaging was used to measure the dimensions and location of RBFs in 162 patients (length, height, width of RBF and vertebral body). Furthermore, micro-CT scans were obtained of 10 cadaveric spines. Each vertebral body was divided into three layers (superior, middle, and inferior), and each layer was divided further into nine regions (R1-R9). Microarchitecture parameters were calculated from micro-CT scans, including bone volume fraction (BV/TV), connectivity (Conn.D), trabecular number (Tb.N), trabecular thickness (Tb.Th), and bone mineral density (BMD). Differences were analyzed between regions and layers. Burst fractures were simulated on cadaveric spines to explore the fracture line location and test the relationship between RBFs and BF.

RESULTS

Retropulsed bone fragment width was usually one-third of the width of the vertebral body, whereas RBF length and height were approximately half of the corresponding vertebral body dimensions. Measures of trabecular bone quality were generally lowest in those central and superior regions of the vertebral body which are adjacent to the BF and which are most affected by burst fracture. In simulated TLBFs, the fracture line went across the vertex or upper surface of the BF.

CONCLUSIONS

The most vulnerable regions in the vertebral body lie within or just superior to the BF. The central MR2 region in particular is at risk of fracture and RBF formation.

Trabecular Microstructure and Damage Affect Cement Leakage From the Basivertebral Foramen During Vertebral Augmentation

STUDY DESIGN

A prospective study on cadaver specimens.

OBJECTIVE

To explore why cement leakage from basivertebral foramen (BF) easily occurs during vertebral augmentation procedures.

SUMMARY OF BACKGROUND DATA

Type B (through BF, basivertebral foramen) cement leakage is the most common type after vertebral augmentation, but the mechanism of this is still controversial. The contribution of vertebral trabecular bone orientation and trabecular damage during compression fracture to cement leakage is still unknown.

METHODS

In this study, 12 fresh-frozen human lumbar spines (T12-L5) were collected and divided into 24 three-segment units. Mechanical testing was performed to simulate a compression fracture. MicroCT were performed on all segments before and after mechanical testing, and trabecular microstructure of the superior, middle (containing BF), and inferior 1/3 of each vertebral body was analyzed. The diameter variation of intertrabecular space before and after compression fracture was used to quantify trabecular injury. After mechanical testing, vertebral augmentation, and imaging-based diagnosis were used to evaluate cement leakage.

RESULTS

Trabecular bone microstructural parameters in middle region (containing BF) were lower than those of the superior or inferior regions (P < 0.01). After compressive failure, 3D-reconstruction of the vertebral body by MicroCT demonstrated that intertrabecular distance in the middle region was markedly increased. Type B cement leakage was the most common type after vertebral augmentation, as found previously in Wang et al. (Spine J 2014;14: 1551-1558).

CONCLUSION

The presence of the BF and the relative sparsity of trabecular bone make the middle region of the vertebral body the mechanically weakest region. Trabecular bone in middle region suffered the most severe damage during compressive failure of the vertebral body, which resulted in the greatest intervertebral spacing, and subsequently the highest percentage of type B cement leakage. These data suggest specific mechanisms by which cement may leak from the BF, and the contribution of trabecular microstructure and trabecular injury.

LEVEL OF EVIDENCE

4.

Analysis of metastatic involvement in bone using anatomical and functional information from 18F-FDG PET/CT

PURPOSE

Although the overall incidence of bone metastasis is not known, over one-half of the people who die of cancer in the USA every year are thought to have bone involvement. In this study we have developed a method to quantify the metabolic and anatomic changes induced by different types of bone metastases in cancer patients using PET/CT images.

PATIENTS AND MATERIALS

Seventy-three cancer patients with no previous history of chemotherapy or radiotherapy who had definite bone metastases documented by PET/CT and other conventional modalities were selected for this study. PET and computed tomography (CT) images were resampled to the same pixel size. Thereafter, the bone structure was segmented using thresholding. The 50% of the maximum standardized uptake value within the bone mask was used to identify bone lesions in each slice. Using the final regions of interest defined at 70% of the maximum, the lesion characteristics including the mean Hounsfield Units were computed from the PET/CT images. The lesions were subjected to visual confirmation by an experienced physician who also categorized them on the basis of the appearances in CT as lytic, sclerotic, mixed, or no-change type. The lesion characteristics were compared using statistical methods.

RESULTS

In all, 340 bony lesions in 73 patients with different cancer types were analyzed. The lesions were further categorized into four groups on the basis of their anatomical location. The spine hosts the largest number of lesions. The lumbar bones are the most preferential sites within the spine. Statistical comparison of CT values indicated that the difference between no-change and lytic types was significant. Uptake period did not seem to have a significant impact on no-change and sclerotic types. Quantitatively, maximum standardized uptake value for lytic, no change, mixed, and sclerotic lesions were 7.4, 6.1, 8.2, and 7.2, respectively.

CONCLUSION

A quantitative method provides a convenient way that may serve as a useful tool in monitoring and assessing the response to therapy.

Temporal Subtraction of Serial CT Images with Large Deformation Diffeomorphic Metric Mapping in the Identification of Bone Metastases

Purpose To determine the improvement of radiologist efficiency and performance in the detection of bone metastases at serial follow-up computed tomography (CT) by using a temporal subtraction (TS) technique based on an advanced nonrigid image registration algorithm. Materials and Methods This retrospective study was approved by the institutional review board, and informed consent was waived. CT image pairs (previous and current scans of the torso) in 60 patients with cancer (primary lesion location: prostate, n = 14; breast, n = 16; lung, n = 20; liver, n = 10) were included. These consisted of 30 positive cases with a total of 65 bone metastases depicted only on current images and confirmed by two radiologists who had access to additional imaging examinations and clinical courses and 30 matched negative control cases (no bone metastases). Previous CT images were semiautomatically registered to current CT images by the algorithm, and TS images were created. Seven radiologists independently interpreted CT image pairs to identify newly developed bone metastases without and with TS images with an interval of at least 30 days. Jackknife free-response receiver operating characteristics (JAFROC) analysis was conducted to assess observer performance. Reading time was recorded, and usefulness was evaluated with subjective scores of 1-5, with 5 being extremely useful and 1 being useless. Significance of these values was tested with the Wilcoxon signed-rank test. Results The subtraction images depicted various types of bone metastases (osteolytic, n = 28; osteoblastic, n = 26; mixed osteolytic and blastic, n = 11) as temporal changes. The average reading time was significantly reduced (384.3 vs 286.8 seconds; Wilcoxon signed rank test, P = .028). The average figure-of-merit value increased from 0.758 to 0.835; however, this difference was not significant (JAFROC analysis, P = .092). The subjective usefulness survey response showed a median score of 5 for use of the technique (range, 3-5). Conclusion TS images obtained from serial CT scans using nonrigid registration successfully depicted newly developed bone metastases and showed promise for their efficient detection. ^© RSNA, 2017 Online supplemental material is available for this article.

Mixed spine metastasis detection through positron emission tomography/computed tomography synthesis and multiclassifier

Bone metastases are a frequent occurrence with cancer, and early detection can guide the patient's treatment regimen. Metastatic bone disease can present in density extremes as sclerotic (high density) and lytic (low density) or in a continuum with an admixture of both sclerotic and lytic components. We design a framework to detect and characterize the varying spectrum of presentation of spine metastasis on positron emission tomography/computed tomography (PET/CT) data. A technique is proposed to synthesize CT and PET images to enhance the lesion appearance for computer detection. A combination of watershed, graph cut, and level set algorithms is first run to obtain the initial detections. Detections are then sent to multiple classifiers for sclerotic, lytic, and mixed lesions. The system was tested on 44 cases with 225 sclerotic, 139 lytic, and 92 mixed lesions. The results showed that sensitivity (false positive per patient) was 0.81 (2.1), 0.81 (1.3), and 0.76 (2.1) for sclerotic, lytic, and mixed lesions, respectively. It also demonstrates that using PET/CT data significantly improves the computer aided detection performance over using CT alone.

Automated "Bone Subtraction" Image Analysis Software Package for Improved and Faster CT Monitoring of Longitudinal Spine Involvement in Patients with Multiple Myeloma

RATIONALE AND OBJECTIVES

The study aimed to assess the diagnostic benefit of a novel computed tomography (CT) post-processing software generating subtraction maps of longitudinal non-enhanced CT examinations for monitoring the course of myeloma bone disease in the spine.

MATERIALS AND METHODS

The local institutional review board approved the retrospective data evaluation. Included were 82 consecutive myeloma patients (46 male; mean age, 65.08 ± 9.76) who underwent 188 repeated whole-body reduced-dose Multislice Detector Computed Tomography (MDCT) at our institution between December 2013 and January 2016. Lytic bone lesions were categorized as new or enlarging versus stable. Bone subtraction maps were read in combination with corresponding 1-mm source images comparing results to those of standard image reading of 5-mm axial and 2-mm multiplanar reformat reconstructions (MPR) scans and hematologic markers, and classified as either progressive disease (PD) or stable disease (SD or remission). The standard of reference was 1-mm axial CT image reading + hematologic response both confirmed at follow-up. For statistical purposes, we subgrouped the hematologic response categories similarly to those applied for CT imaging (progression vs stable/response).

RESULTS

According to the standard of reference, 16 patients experienced PD and 66 SD at follow-up. Th sensitivity, specificity, and accuracy for axial 5 mm + 2 mm MPR image versus bone subtraction maps in a "lesion-by-lesion" reading were 97.6%, 92.3%, and 97.2% versus 97.8%, 96.7%, and 97.7%, respectively. The use of bone subtraction maps resulted in a change of response classification in 9.7% of the patients (n = 8) versus 5 mm + 2 mm MPR image reading from SD to PD. Bone sclerosis lesions were detected in 52 out of 82 patients (63.4%). The reading time was significantly lower with the software bone subtraction compared to standard reading (P < 0.01) and 1-mm image reading (P < 0.001).

CONCLUSION

Accuracy of bone subtraction maps reading for monitoring multiple myeloma is slightly increased over that of conventional axial + MPR image reading and significantly speeds up the reading time.

A study of computer-aided diagnosis for pulmonary nodule: comparison between classification accuracies using calculated image features and imaging findings annotated by radiologists

PURPOSE

In our previous study, we developed a computer-aided diagnosis (CADx) system using imaging findings annotated by radiologists. The system, however, requires radiologists to input many imaging findings. In order to reduce such an interaction of radiologists, we further developed a CADx system using derived imaging findings based on calculated image features, in which the system only requires few user operations. The purpose of this study is to check whether calculated image features (CFT) or derived imaging findings (DFD) can represent information in imaging findings annotated by radiologists (AFD).

METHODS

We calculate 2282 image features and derive 39 imaging findings by using information on a nodule position and its type (solid or ground-glass). These image features are categorized into shape features, texture features and imaging findings-specific features. Each imaging finding is derived based on each corresponding classifier using random forest. To check whether CFT or DFD can represent information in AFD, under an assumption that the accuracies of classifiers are the same if information included in input is the same, we constructed classifiers by using various types of information (CTT, DFD and AFD) and compared accuracies on an inferred diagnosis of a nodule. We employ SVM with RBF kernel as classifier to infer a diagnosis name.

RESULTS

Accuracies of classifiers using DFD, CFT, AFD and CFT [Formula: see text] AFD were 0.613, 0.577, 0.773 and 0.790, respectively. Concordance rates between DFD and AFD of shape findings, texture findings and surrounding findings were 0.644, 0.871 and 0.768, respectively.

CONCLUSIONS

The results suggest that CFT and AFD are similar information and CFT represent only a portion of AFD. Particularly, CFT did not contain shape information in AFD. In order to decrease an interaction of radiologists, a development of a method which overcomes these problems is necessary.

Progress in Fully Automated Abdominal CT Interpretation

OBJECTIVE

Automated analysis of abdominal CT has advanced markedly over just the last few years. Fully automated assessment of organs, lymph nodes, adipose tissue, muscle, bowel, spine, and tumors are some examples where tremendous progress has been made. Computer-aided detection of lesions has also improved dramatically.

CONCLUSION

This article reviews the progress and provides insights into what is in store in the near future for automated analysis for abdominal CT, ultimately leading to fully automated interpretation.

AUTOMATIC DETECTION AND TRACKING OF LONGITUDINAL CHANGES OF MULTIPLE BONE METASTASES FROM DUAL ENERGY CT

Early detection and the assessment of changes in bone metastatic cancers can enable clinicians to monitor disease progression and modify treatment to help achieve improved results for patients. However, poor contrast makes detection difficult, and multiple disease sites make tracking of their changes over time difficult. We present a method for automatically detecting and tracking the longitudinal changes in multiple sclerotic bone metastases from Dual Energy Computed Tomography (DECT) images. We employ a multi-stage approach involving (i) bone and marrow extraction, (ii) slice-wise lesion candidate detection and volumetric segmentation, and (iii) aggregation of these 3D candidates. The algorithm achieved 78% agreement with radiologist identified lesions from 10 patients. Longitudinal consistency in the lesion detection computed over 26 scans using Williams' index was 1.02 ± 0.23 using DICE and 1.03±0.30 using Hausdorff metrics. We also present preliminary results for analyzing lesion material composition changes by using a novel representation computed from the DECT images, where clear differences between bone metastases and normal marrow can be seen.

Basivertebral foramen could be connected with intravertebral cleft: a potential risk factor of cement leakage in percutaneous kyphoplasty

BACKGROUND CONTEXT

Among different types of cement leakage in percutaneous kyphoplasty (PKP) for osteoporotic vertebral body compression fractures, leaks into the spinal canal are considered to be the most common complication. One potential structure causing this type of cement leakage is the potential connection between the basivertebral foramen and the intravertebral cleft, which is revealed clearly on magnetic resonance (MR) images, but is often ignored in the literature.

PURPOSE

The purpose of this study is to assess the incidence rate of different types of cement leakage in PKP with or without intravertebral clefts and to determine whether the basivertebral foramen could be connected to the intravertebral cleft.

STUDY DESIGN

This study is a retrospective assessment of the presence of an intravertebral cleft in osteoporotic vertebral bodies and the different types of cement leakage after PKP on radiographs, computed tomographic (CT) scans, and MR images.

PATIENT SAMPLE

A total of 164 consecutive patients underwent PKP to treat 204 osteoporotic vertebral compression fractures.

OUTCOME MEASURES

Outcome measures include the occurrence of different types of cement leakage in the groups with an intravertebral cleft and without intravertebral clefts.

METHODS

A total of 204 vertebrae in 164 consecutive patients who underwent PKP to treat osteoporotic vertebral compression fractures were classified into two patterns based on preoperative radiographs, CT scans, and/or MR images of the treated levels: cleft pattern (with an intravertebral cleft in the vertebral body) and trabecular pattern (without intravertebral clefts). When an intravertebral cleft was identified, the investigators examined the basivertebral foramen and looked for a communication between the two structures on three-dimensional CT scans and MR images. On direct postoperative images, the patterns of cement leakage were classified as five types: type A, through a cortical defect into the paraspinal soft tissues; type B, through the basivertebral foramen; type C, via the needle channel; type D, through a cortical defect into the disc space; and type E, via the paravertebral vein. The association of the distribution of the cement leakage and the presence of an intravertebral cleft was analyzed retrospectively. Moreover, the association of type B leakage with the communication between the basivertebral foramen and the intravertebral cleft was also assessed.

RESULTS

The average interobserver kappa values for determining the type of cement leakage and the presence of intravertebral cleft were 0.916 (range, 0.792-1) and 0.935, respectively. In 41 of 204 vertebrae (19.9%), an intravertebral cleft was confirmed on preoperative images. A communication between the intravertebral cleft and the basivertebral foramen was seen in 10 vertebrae (24.4%). Cement leakage was 36.2% in the group with a trabecular pattern and 41.5% in the group with a cleft pattern (p>.05). Leaks through the basivertebral foramen (type B; N=30, 14.7%) and through cortical defects into the disc space (type D; N=14, 6.9%) were more common than other types. Twenty of 163 vertebrae with the trabecular pattern (12.3%) and 10 of 41 vertebrae with the cleft pattern (24.4%) were identified as type B leaks, which reached statistical significance (p<.05). There was no statistical difference between the trabecular pattern and the cleft pattern on other types of leaks.

CONCLUSIONS

Type B leaks are more common in vertebrae with an intravertebral cleft, which supports the presence of a connection between an intravertebral cleft and the basivertebral foramen. Thus, care must be taken when PKP is performed in these patients to avoid direct cement leakage into the spinal canal through the basivertebral foramen.

Cortical shell unwrapping for vertebral body abnormality detection on computed tomography

The vertebral body is the main axial load-bearing structure of the spinal vertebra. Assessment of acute injury and chronic deformity of the vertebral body is difficult to assess accurately and quantitatively by simple visual inspection. We propose a cortical shell unwrapping method to examine the vertebral body for injury such as fractures and degenerative osteophytes. The spine is first segmented and partitioned into vertebrae. Then the cortical shell of the vertebral body is extracted using deformable dual-surface models. The cortical shell is then unwrapped onto a 2D map and the complex 3D detection problem is effectively converted to a pattern recognition problem on a 2D plane. Characteristic features adapted for different applications are computed and sent to a committee of support vector machines for classification. The system was evaluated on two applications, one for fracture detection on trauma CT datasets and the other on degenerative osteophyte assessment on sodium fluoride PET/CT. The fracture CAD achieved 93.6% sensitivity at 3.2 false positive per patient and the degenerative osteophyte CAD achieved 82% sensitivity at 4.7 false positive per patient.

Rapid detection of bone metastasis at thoracoabdominal CT: accuracy and efficiency of a new visualization algorithm

PURPOSE

To retrospectively assess the use of a combination of cancellous bone reconstructions (CBR) and multiplanar reconstructions (MPRs) for the detection of bone metastases at thoracoabdominal computed tomography (CT) compared with the use of MPRs alone.

MATERIALS AND METHODS

The study was approved by the local institutional review board. Included were 156 consecutive patients with confirmed cancer who underwent a whole-body positron emission tomography (PET)/CT examination for clinical purposes (93 male and 63 female patients; mean age ± standard deviation, 59.8 years ± 14.9; range, 11-85 years). Only the CT images were processed with the CBR algorithm, which segments the bones and removes the cortical layer from the images. The PET images served as part of the reference standard. Images from 15 patients were used as a training set. Four radiologists independently evaluated images of half of the remaining 141 patients by using CBRs and MPRs together, and the other half by using MPRs only. Radiologists were blinded to patient names, and patient order was randomized. Results for detection rates and reporting time were recorded and compared with a standard of reference for each patient that was created by one senior radiologist and one nuclear medicine specialist by using all available CT and PET data, CBRs, and follow-up examinations. General estimation equations were used for statistical analysis.

RESULTS

There were 349 lesions found in 103 patients, with 203 classified as malignant. Each patient was assessed by two readers per method, leading to a total of 698 lesions. The detection rate for all bone lesions was 35% (247 of 698) for MPRs and 74% (520 of 698) when CBRs and MPRs were used together, which was significantly higher (P < .001). The average reading time decreased from 85 to 43 seconds (P < .001) when both reconstructions were used.

CONCLUSION

Advanced visualization of cancellous bone significantly increased the detection of bone metastases and reduced the time for interpretation.

Integrating patient digital photographs with medical imaging examinations

We introduce the concept, benefits, and general architecture for acquiring, storing, and displaying digital photographs along with medical imaging examinations. We also discuss a specific implementation built around an Android-based system for simultaneously acquiring digital photographs along with portable radiographs. By an innovative application of radiofrequency identification technology to radiographic cassettes, the system is able to maintain a tight relationship between these photographs and the radiographs within the picture archiving and communications system (PACS) environment. We provide a cost analysis demonstrating the economic feasibility of this technology. Since our architecture naturally integrates with patient identification methods, we also address patient privacy issues.

Automated detection of sclerotic metastases in the thoracolumbar spine at CT

PURPOSE

To design and validate a computer system for automated detection and quantitative characterization of sclerotic metastases of the thoracolumbar spine on computed tomography (CT) images.

MATERIALS AND METHODS

This retrospective study was approved by the institutional review board and was HIPAA compliant; informed consent was waived. The data set consisted of CT examinations in 49 patients (14 female, 35 male patients; mean age, 57.0 years; range, 12-77 years), demonstrating a total of 532 sclerotic lesions of the spine of greater than 0.3 cm(3) in volume, and in 10 control case patients (four women, six men; mean age, 55.2 years; range, 19-70 years) without spinal lesions. CT examinations were divided into training and test sets, and images were analyzed according to prototypical fully-automated computer-aided detection (CAD) software. Free-response receiver operating characteristic analysis was performed.

RESULTS

Lesion detection sensitivity on images in the training set was 90%, relative to reference-standard marked lesions (95% confidence interval [CI]: 83%, 97%), at a false-positive rate (FPR) of 10.8 per patient (95% CI: 6.6, 15.0). For images in the testing set, sensitivity was 79% (95% CI: 74%, 84%), with an FPR of 10.9 per patient (95% CI: 8.5, 13.3). False-negative findings were most commonly (37 [40%] of 93) a result of endplate proximity, with 32 (34% of 93) caused by low CT attenuation. Marginal sclerosis caused by degenerative change (174 [28.1%] of 620 actual detections) was the most common cause of false-positive detections, followed by partial volume averaging with vertebral endplates (173 [27.9%] of 620) and pedicle cortex parallel to the axial imaging plane (121 [19.5%] 620).

CONCLUSION

This CAD system successfully identified and segmented sclerotic lesions in the thoracolumbar spine.

Automatic detection of lytic and blastic thoracolumbar spine metastases on computed tomography

Objective

To evaluate a computer-aided detection (CADe) system for lytic and blastic spinal metastases on computed tomography (CT).

Methods

We retrospectively evaluated the CADe system on 20 consecutive patients with 42 lytic and on 30 consecutive patients with 172 blastic metastases. The CADe system was trained using CT images of 114 subjects with 102 lytic and 308 blastic spinal metastases. Lesions were annotated by experienced radiologists. Detected benign lesions were considered false-positive findings. Detector sensitivity and the number of false-positive findings were calculated as the criteria for detector performance, and free-response receiver operating characteristic (FROC) analysis was conducted. Detailed analysis of false-positive and false-negative findings was performed.

Results

Algorithm runtime is 3 ± 0.5 min per patient. The system achieves a sensitivity of 83 % at 3.5 false positives per patient on average for blastic metastases and a sensitivity of 88 % at 3.7 false positives for lytic metastases. False positives appeared predominantly in the area of degenerative changes in the case of the blastic metastasis detector and in osteoporotic areas in the case of the lytic metastasis detector.

Conclusion

The CADe system reliably detects thoracolumbar spine metastases in real time. An additional study is planned to evaluate how the bone lesion CADe system improves radiologists’ accuracy and efficiency in a clinical setting.

Key Points

• Computer-aided detection (CADe) of bone metastases has been developed for spinal CT.

• The CADe system exhibits high sensitivity with a tolerable false-positive rate.

• Analysis of false-positive detection may further improve the system.

• CADe may reduce the number of missed spinal metastases at CT interpretation.

Automated segmentation method for spinal column based on a dual elliptic column model and its application for virtual spinal straightening

Segmentation of vertebral bones in computed tomographic data is important as a first stage of image-based radiological tasks. However, it is a challenging problem to segment an affected spine correctly. In this study, we propose a new method of segmentation of thoracic and lumbar vertebral bodies from thin-slice computed tomographic images. Especially, we focus on a deformable model-based segmentation scheme to confirm the feasibility in clinical data sets with various bone diseases, such as bone metastases and scoliosis. As an application of this algorithm, virtual straightening of the thoracolumbar spine is also performed. Results on a database of 16 patients indicate the applicability of our method to spines affected by scoliosis and multiple bone metastases.

A prospective analysis of CT density measurements of bone metastases after treatment with zoledronic acid

OBJECTIVE

The objective was to prospectively determine CT density changes in bone metastases, before and after intravenous zoledronic acid for a maximum period of 12 months.

PATIENTS AND METHODS

Twenty-three consecutive patients presented with bone metastases and underwent therapy with zoledronic acid from December 2004. All patients underwent CT of the chest, abdomen, and pelvis. Bone density, measured in Hounsfield units (HU), was determined by segmenting lesions in the same anatomical area of the metastasis sites on the axial images of the sequential series of CT examinations. The effects of zoledronic acid were evaluated by calculating absolute and relative increases in bone density.

RESULTS

The patients presented with multiple metastases in 65% of the cases. When compared with the baseline, all groups demonstrated a significant increase in bone density, which significantly (p < 0.01) correlated with the number of zoledronic acid administrations. There was increased bone density of at least 100% in 57%, and an increase of at least 50% in 87% of the patients. This increase was significant in both lytic and sclerotic metastases after 3 months of therapy. No significant bone density difference was found in normal-appearing bone.

CONCLUSION

Bone density measured by CT increases at metastatic sites after zoledronic acid treatment, regardless of the type of metastasis, in contrast to apparently normal bone.