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

Bone-wise rigid registration of femur, tibia, and fibula for the tracking of temporal changes

BACKGROUND

Multiple myeloma (MM) induces temporal alterations in bone structure, such as osteolytic bone lesions, which are challenging to identify through manual image interpretation. The large variation in radiologists' assessments, even at expert centers, further complicates diagnosis. Automatic image analysis methods, including segmentation and registration, can expedite detecting and tracking these bone changes.

PURPOSE

This study presents an automated pipeline for accurately tracking temporal changes in the femurs, tibiae, and fibulae of MM patients using 3D whole-body CT images. The pipeline leverages image segmentation, rigid registration, and temporal subtraction to accelerate disease monitoring and support clinical decision-making.

METHODS

A convolutional neural network (CNN) was trained to segment bones in 3D CT images of 30 MM patients. Nine patients with pre- and post-diagnosis CT scans were used to validate the segmentation and registration process. A two-phase bone-wise rigid registration was applied, followed by temporal subtraction to generate difference images. Segmentation and registration accuracy were assessed using the Dice similarity coefficient (DSC) and mean surface distance (MSD). The proposed method was compared to a non-rigid registration method.

RESULTS

The neural network segmentation resulted in a mean DSC of 0.93 across all bone types and all test data. The registration accuracy measured by the mean DSC across the test data was at least 0.94 for all bone types. The second phase of rigid registration improved the registration fibulae. Metric-wise, the nonrigid method performed better but diminished lesion visibility in difference images.

CONCLUSIONS

An automated pipeline for the longitudinal tracking of bone alterations was presented. Both segmentation and registration demonstrated high accuracy as measured by DSC and MSD. In the difference images produced by temporal subtraction, osteolytic lesions were clearly visible in the femurs. The methodology lays a solid foundation for future improvements, such as inclusion of the axial spine.

Imaging of Multiple Myeloma: Present and Future

Multiple myeloma (MM) is the second most common adult hematologic malignancy, and early intervention increases survival in asymptomatic high-risk patients. Imaging is crucial for the diagnosis and follow-up of MM, as the detection of bone and bone marrow lesions often dictates the decision to start treatment. Low-dose whole-body computed tomography (CT) is the modality of choice for the initial assessment, and dual-energy CT is a developing technique with the potential for detecting non-lytic marrow infiltration and evaluating the response to treatment. Magnetic resonance imaging (MRI) is more sensitive and specific than 18F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) for the detection of small focal lesions and diffuse marrow infiltration. However, FDG-PET/CT is recommended as the modality of choice for follow-up. Recently, diffusion-weighted MRI has become a new technique for the quantitative assessment of disease burden and therapy response. Although not widespread, we address current proposals for structured reporting to promote standardization and diminish variations. This review provides an up-to-date overview of MM imaging, indications, advantages, limitations, and recommended reporting of each technique. We also cover the main differential diagnosis and pitfalls and discuss the ongoing controversies and future directions, such as PET-MRI and artificial intelligence.

Current Status and Future of Artificial Intelligence in MM Imaging: A Systematic Review

Artificial intelligence (AI) has attracted increasing attention as a tool for the detection and management of several medical conditions. Multiple myeloma (MM), a malignancy characterized by uncontrolled proliferation of plasma cells, is one of the most common hematologic malignancies, which relies on imaging for diagnosis and management. We aimed to review the current literature and trends in AI research of MM imaging. This study was performed according to the PRISMA guidelines. Three main concepts were used in the search algorithm, including "artificial intelligence" in "radiologic examinations" of patients with "multiple myeloma". The algorithm was used to search the PubMed, Embase, and Web of Science databases. Articles were screened based on the inclusion and exclusion criteria. In the end, we used the checklist for Artificial Intelligence in Medical Imaging (CLAIM) criteria to evaluate the manuscripts. We provided the percentage of studies that were compliant with each criterion as a measure of the quality of AI research on MM. The initial search yielded 977 results. After reviewing them, 14 final studies were selected. The studies used a wide array of imaging modalities. Radiomics analysis and segmentation tasks were the most popular studies (10/14 studies). The common purposes of radiomics studies included the differentiation of MM bone lesions from other lesions and the prediction of relapse. The goal of the segmentation studies was to develop algorithms for the automatic segmentation of important structures in MM. Dice score was the most common assessment tool in segmentation studies, which ranged from 0.80 to 0.97. These studies show that imaging is a valuable data source for medical AI models and plays an even greater role in the management of MM.

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.

Printability and Shape Fidelity of Bioinks in 3D Bioprinting

Three-dimensional bioprinting uses additive manufacturing techniques for the automated fabrication of hierarchically organized living constructs. The building blocks are often hydrogel-based bioinks, which need to be printed into structures with high shape fidelity to the intended computer-aided design. For optimal cell performance, relatively soft and printable inks are preferred, although these undergo significant deformation during the printing process, which may impair shape fidelity. While the concept of good or poor printability seems rather intuitive, its quantitative definition lacks consensus and depends on multiple rheological and chemical parameters of the ink. This review discusses qualitative and quantitative methodologies to evaluate printability of bioinks for extrusion- and lithography-based bioprinting. The physicochemical parameters influencing shape fidelity are discussed, together with their importance in establishing new models, predictive tools and printing methods that are deemed instrumental for the design of next-generation bioinks, and for reproducible comparison of their structural performance.

CT based quantitative measures of the stability of fractured metastatically involved vertebrae treated with spine stereotactic body radiotherapy

Mechanical instability secondary to vertebral metastases can lead to pathologic vertebral compression fracture (VCF) mechanical pain, neurological compromise, and the need for surgical stabilization. Stereotactic body radiation therapy (SBRT) as a treatment for spinal metastases is effective for pain and local tumor control, it has been associated with an increased risk of VCF. This study quantified computed tomography (CT) based stability measures in metastatic vertebrae with VCF treated with spine SBRT. It was hypothesized that semi-automated quantification of VCF based on CT metrics would be related to clinical outcomes. 128 SBRT treated spinal metastases patients were identified from a prospective database. Of these, 18 vertebral segments were identified with a VCF post-SBRT. A semi-automated system for quantifying VCF was developed based on CT imaging before and after SBRT. The system identified and segmented SBRT treated vertebral bodies, calculated stability metrics at single time points and changes over time. In the vertebrae that developed a new (n = 7) or progressive (n = 11) VCF following SBRT, the median time to VCF/VCF progression was 1.74 months (range 0.53-7.79 months). Fractured thoracolumbar vertebrae that went on to be stabilized (cemented and/or instrumented), had greater fractured vertebral body volume progression over time (12%) compared to those not stabilized (0.4%, p < 0.05). Neither the spinal instability neoplastic score (SINS) or any single timepoint stability metrics in post-hoc analyses correlated with future stabilization. This pilot study presents a quantitative semi-automated method assessing fractured thoracolumbar vertebrae based on CT. Increased fractured vertebral body volume progression post-SBRT was shown to predict those patients who were subsequently stabilized, motivating study of methods that assess temporal radiological changes toward augmenting existing clinical management in the metastatic spine.

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.

Automatic rib unfolding in postmortem computed tomography: diagnostic evaluation of the OpenRib software compared with the autopsy in the detection of rib fractures

OBJECTIVES

The main objective of this study was to evaluate the diagnostic performance of the OpenRib software against the gold standard of autopsy in the detection of rib fractures. The secondary objective was to measure inter-rater agreement between each radiological reader.

MATERIALS AND METHODS

Thirty-six subjects who underwent postmortem CT and autopsy were included in this study. Rib fractures were first assessed during the autopsy by carefully dissecting and examining each rib. They were also independently evaluated by three readers using OpenRib software. This software produces from postmortem CT images a reformat of the rib cage and a display of all ribs in a single plane. Each reader was asked to determine if the rib was fractured and, if so, whether the fracture was single or multiple.

RESULTS

After exclusions, 649 ribs were included in the statistical analysis. The two readers with a similar level of experience showed a satisfactory inter-rater agreement and a sensitivity of 0.73 and 0.83 with a specificity of 0.95 and 0.91. However, the experienced reader diagnosed significantly more fractures than the autopsy and the other two readers (p < 0.001).

CONCLUSION

The use of automatic rib unfolding software in postmortem CT allows an efficient and accurate assessment of rib fractures and enables the diagnosis of fractures that cannot be detected during a standard autopsy. For now, this method seems to be the simplest that can be routinely performed; however, it requires training time in order to be sufficiently effective.