Application of Artificial Intelligence Methods for Imaging of Spinal Metastasis

Development of a natural language processing algorithm for the detection of spinal metastasis based on magnetic resonance imaging reports

Background

Metastasis to the spinal column is a common complication of malignancy, potentially causing pain and neurologic injury. An automated system to identify and refer patients with spinal metastases can help overcome barriers to timely treatment. We describe the training, optimization and validation of a natural language processing algorithm to identify the presence of vertebral metastasis and metastatic epidural cord compression (MECC) from radiology reports of spinal MRIs.

Methods

Reports from patients with spine MRI studies performed between January 1, 2008 and April 14, 2019 were reviewed by a team of radiologists to assess for the presence of cancer and generate a labeled dataset for model training. Using regular expression, impression sections were extracted from the reports and converted to all lower-case letters with all nonalphabetic characters removed. The reports were then tokenized and vectorized using the doc2vec algorithm. These were then used to train a neural network to predict the likelihood of spinal tumor or MECC. For each report, the model provided a number from 0 to 1 corresponding to its impression. We then obtained 111 MRI reports from outside the test set, 92 manually labeled negative and 19 with MECC to test the model's performance.

Results

About 37,579 radiology reports were reviewed. About 36,676 were labeled negative, and 903 with MECC. We chose a cutoff of 0.02 as a positive result to optimize for a low false negative rate. At this threshold we found a 100% sensitivity rate with a low false positive rate of 2.2%.

Conclusions

The NLP model described predicts the presence of spinal tumor and MECC in spine MRI reports with high accuracy. We plan to implement the algorithm into our EMR to allow for faster referral of these patients to appropriate specialists, allowing for reduced morbidity and increased survival.

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.

Evaluate the in vitro effect of anthracycline and alkylating cytophosphane chemotherapeutics on dopaminergic neurons

BACKGROUND

Iatrogenesis is an inevitable global threat to healthcare that drastically increases morbidity and mortality. Cancer is a fatal pathological condition that affects people of different ages, sexes, and races around the world. In addition to the detrimental cancer pathology, one of the most common contraindications and challenges observed in cancer patients is severe adverse drug effects and hypersensitivity reactions induced by chemotherapy. Chemotherapy-induced cognitive neurotoxicity is clinically referred to as Chemotherapy-induced cognitive impairment (CICI), chemobrain, or chemofog. In addition to CICI, chemotherapy also causes neuropsychiatric issues, mental disorders, hyperarousal states, and movement disorders. A synergistic chemotherapy regimen of Doxorubicin (Anthracycline-DOX) and Cyclophosphamide (Alkylating Cytophosphane-CPS) is indicated for the management of various cancers (breast cancer, lymphoma, and leukemia). Nevertheless, there are limited research studies on Doxorubicin and Cyclophosphamide's pharmacodynamic and toxicological effects on dopaminergic neuronal function.

AIM

This study evaluated the dopaminergic neurotoxic effects of Doxorubicin and Cyclophosphamide.

METHODS AND RESULTS

Doxorubicin and Cyclophosphamide were incubated with dopaminergic (N27) neurons. Neuronal viability was assessed using an MTT assay. The effect of Doxorubicin and Cyclophosphamide on various prooxidants, antioxidants, mitochondrial Complex-I & IV activities, and BAX expression were evaluated by Spectroscopic, Fluorometric, and RT-PCR methods, respectively. Prism-V software (La Jolla, CA, USA) was used for statistical analysis. Chemotherapeutics dose-dependently inhibited the proliferation of the dopaminergic neurons. The dopaminergic neurotoxic mechanism of Doxorubicin and Cyclophosphamide was attributed to a significant increase in prooxidants, a decrease in antioxidants, and augmented apoptosis without affecting mitochondrial function.

CONCLUSION

This is one of the first reports that reveal Doxorubicin and Cyclophosphamide induce significant dopaminergic neurotoxicity. Thus, Chemotherapy-induced adverse drug reaction issues substantially persist during and after treatment and sometimes never be completely resolved clinically. Consequently, failure to adopt adequate patient care measures for cancer patients treated with certain chemotherapeutics might substantially raise the incidence of numerous movement disorders.

Practical Applications of Artificial Intelligence in Spine Imaging: A Review

Artificial intelligence (AI), a transformative technology with unprecedented potential in medical imaging, can be applied to various spinal pathologies. AI-based approaches may improve imaging efficiency, diagnostic accuracy, and interpretation, which is essential for positive patient outcomes. This review explores AI algorithms, techniques, and applications in spine imaging, highlighting diagnostic impact and challenges with future directions for integrating AI into spine imaging workflow.

Current Trends in Artificial Intelligence-Assisted Spine Surgery: A Systematic Review

This systematic review summarizes existing evidence and outlines the benefits of artificial intelligence-assisted spine surgery. The popularity of artificial intelligence has grown significantly, demonstrating its benefits in computer-assisted surgery and advancements in spinal treatment. This study adhered to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses), a set of reporting guidelines specifically designed for systematic reviews and meta-analyses. The search strategy used Medical Subject Headings (MeSH) terms, including "MeSH (Artificial intelligence)," "Spine" AND "Spinal" filters, in the last 10 years, and English- from January 1, 2013, to October 31, 2023. In total, 442 articles fulfilled the first screening criteria. A detailed analysis of those articles identified 220 that matched the criteria, of which 11 were considered appropriate for this analysis after applying the complete inclusion and exclusion criteria. In total, 11 studies met the eligibility criteria. Analysis of these studies revealed the types of artificial intelligence-assisted spine surgery. No evidence suggests the superiority of assisted spine surgery with or without artificial intelligence in terms of outcomes. In terms of feasibility, accuracy, safety, and facilitating lower patient radiation exposure compared with standard fluoroscopic guidance, artificial intelligence-assisted spine surgery produced satisfactory and superior outcomes. The incorporation of artificial intelligence with augmented and virtual reality appears promising, with the potential to enhance surgeon proficiency and overall surgical safety.

A deep learning-based technique for the diagnosis of epidural spinal cord compression on thoracolumbar CT

PURPOSE

To develop a deep learning (DL) model for epidural spinal cord compression (ESCC) on CT, which will aid earlier ESCC diagnosis for less experienced clinicians.

METHODS

We retrospectively collected CT and MRI data from adult patients with suspected ESCC at a tertiary referral institute from 2007 till 2020. A total of 183 patients were used for training/validation of the DL model. A separate test set of 40 patients was used for DL model evaluation and comprised 60 staging CT and matched MRI scans performed with an interval of up to 2 months. DL model performance was compared to eight readers: one musculoskeletal radiologist, two body radiologists, one spine surgeon, and four trainee spine surgeons. Diagnostic performance was evaluated using inter-rater agreement, sensitivity, specificity and AUC.

RESULTS

Overall, 3115 axial CT slices were assessed. The DL model showed high kappa of 0.872 for normal, low and high-grade ESCC (trichotomous), which was superior compared to a body radiologist (R4, κ = 0.667) and all four trainee spine surgeons (κ range = 0.625-0.838)(all p < 0.001). In addition, for dichotomous normal versus any grade of ESCC detection, the DL model showed high kappa (κ = 0.879), sensitivity (91.82), specificity (92.01) and AUC (0.919), with the latter AUC superior to all readers (AUC range = 0.732-0.859, all p < 0.001).

CONCLUSION

A deep learning model for the objective assessment of ESCC on CT had comparable or superior performance to radiologists and spine surgeons. Earlier diagnosis of ESCC on CT could reduce treatment delays, which are associated with poor outcomes, increased costs, and reduced survival.

The use of deep learning in medical imaging to improve spine care: A scoping review of current literature and clinical applications

Background

Artificial intelligence is a revolutionary technology that promises to assist clinicians in improving patient care. In radiology, deep learning (DL) is widely used in clinical decision aids due to its ability to analyze complex patterns and images. It allows for rapid, enhanced data, and imaging analysis, from diagnosis to outcome prediction. The purpose of this study was to evaluate the current literature and clinical utilization of DL in spine imaging.

Methods

This study is a scoping review and utilized the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to review the scientific literature from 2012 to 2021. A search in PubMed, Web of Science, Embased, and IEEE Xplore databases with syntax specific for DL and medical imaging in spine care applications was conducted to collect all original publications on the subject. Specific data was extracted from the available literature, including algorithm application, algorithms tested, database type and size, algorithm training method, and outcome of interest.

Results

A total of 365 studies (total sample of 232,394 patients) were included and grouped into 4 general applications: diagnostic tools, clinical decision support tools, automated clinical/instrumentation assessment, and clinical outcome prediction. Notable disparities exist in the selected algorithms and the training across multiple disparate databases. The most frequently used algorithms were U-Net and ResNet. A DL model was developed and validated in 92% of included studies, while a pre-existing DL model was investigated in 8%. Of all developed models, only 15% of them have been externally validated.

Conclusions

Based on this scoping review, DL in spine imaging is used in a broad range of clinical applications, particularly for diagnosing spinal conditions. There is a wide variety of DL algorithms, database characteristics, and training methods. Future studies should focus on external validation of existing models before bringing them into clinical use.

Imaging of Common and Infrequent Extradural Tumors

Spinal extradural tumors, although uncommon, have high morbidity and mortality rates. Radiographs and computed tomography scans are typically used to assess and determine the characteristics of these tumors. However, MR imaging is the preferred method for the evaluation of complications that can increase morbidity, such as spinal cord and nerve compression. Imaging features, such as type of matrix, cortical involvement, and margins, aid in determining the diagnosis. This article discusses common and infrequent extradural spinal tumors, their imaging characteristics, and how age, location, and clinical presentation help in diagnosing these neoplasms.

Identification of Origin for Spinal Metastases from MR Images: Comparison Between Radiomics and Deep Learning Methods

OBJECTIVE

To determine whether spinal metastatic lesions originated from lung cancer or from other cancers based on spinal contrast-enhanced T1 (CET1) magnetic resonance (MR) images analyzed using radiomics (RAD) and deep learning (DL) methods.

METHODS

We recruited and retrospectively reviewed 173 patients diagnosed with spinal metastases at two different centers between July 2018 and June 2021. Of these, 68 involved lung cancer and 105 were other types of cancer. They were assigned to an internal cohort of 149 patients, randomly divided into a training set and a validation set, and to an external cohort of 24 patients. All patients underwent CET1-MR imaging before surgery or biopsy. We developed two predictive algorithms: a DL model and a RAD model. We compared performance between models, and against human radiological assessment, via accuracy (ACC) and receiver operating characteristic (ROC) analyses. Furthermore, we analyzed the correlation between RAD and DL features.

RESULTS

The DL model outperformed RAD model across the board, with ACC/ area under the receiver operating characteristic curve (AUC) values of 0.93/0.94 (DL) versus 0.84/0.93 (RAD) when applied to the training set from the internal cohort, 0.74/0.76 versus 0.72/0.75 when applied to the validation set, and 0.72/0.76 versus 0.69/0.72 when applied to the external test cohort. For the validation set, it also outperformed expert radiological assessment (ACC: 0.65, AUC: 0.68). We only found weak correlations between DL and RAD features.

CONCLUSION

The DL algorithm successfully identified the origin of spinal metastases from pre-operative CET1-MR images, outperforming both RAD models and expert assessment by trained radiologists.

Role of artificial intelligence in oncologic emergencies: a narrative review

Oncologic emergencies are a wide spectrum of oncologic conditions caused directly by malignancies or their treatment. Oncologic emergencies may be classified according to the underlying physiopathology in metabolic, hematologic, and structural conditions. In the latter, radiologists have a pivotal role, through an accurate diagnosis useful to provide optimal patient care. Structural conditions may involve the central nervous system, thorax, or abdomen, and emergency radiologists have to know the characteristics imaging findings of each one of them. The number of oncologic emergencies is growing due to the increased incidence of malignancies in the general population and also to the improved survival of these patients thanks to the advances in cancer treatment. Artificial intelligence (AI) could be a solution to assist emergency radiologists with this rapidly increasing workload. To our knowledge, AI applications in the setting of the oncologic emergency are mostly underexplored, probably due to the relatively low number of oncologic emergencies and the difficulty in training algorithms. However, cancer emergencies are defined by the cause and not by a specific pattern of radiological symptoms and signs. Therefore, it can be expected that AI algorithms developed for the detection of these emergencies in the non-oncological field can be transferred to the clinical setting of oncologic emergency. In this review, a craniocaudal approach was followed and central nervous system, thoracic, and abdominal oncologic emergencies have been addressed regarding the AI applications reported in literature. Among the central nervous system emergencies, AI applications have been reported for brain herniation and spinal cord compression. In the thoracic district the addressed emergencies were pulmonary embolism, cardiac tamponade and pneumothorax. Pneumothorax was the most frequently described application for AI, to improve sensibility and to reduce the time-to-diagnosis. Finally, regarding abdominal emergencies, AI applications for abdominal hemorrhage, intestinal obstruction, intestinal perforation, and intestinal intussusception have been described.

Application of Machine Learning for Differentiating Bone Malignancy on Imaging: A Systematic Review

An accurate diagnosis of bone tumours on imaging is crucial for appropriate and successful treatment. The advent of Artificial intelligence (AI) and machine learning methods to characterize and assess bone tumours on various imaging modalities may assist in the diagnostic workflow. The purpose of this review article is to summarise the most recent evidence for AI techniques using imaging for differentiating benign from malignant lesions, the characterization of various malignant bone lesions, and their potential clinical application. A systematic search through electronic databases (PubMed, MEDLINE, Web of Science, and clinicaltrials.gov) was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 34 articles were retrieved from the databases and the key findings were compiled and summarised. A total of 34 articles reported the use of AI techniques to distinguish between benign vs. malignant bone lesions, of which 12 (35.3%) focused on radiographs, 12 (35.3%) on MRI, 5 (14.7%) on CT and 5 (14.7%) on PET/CT. The overall reported accuracy, sensitivity, and specificity of AI in distinguishing between benign vs. malignant bone lesions ranges from 0.44–0.99, 0.63–1.00, and 0.73–0.96, respectively, with AUCs of 0.73–0.96. In conclusion, the use of AI to discriminate bone lesions on imaging has achieved a relatively good performance in various imaging modalities, with high sensitivity, specificity, and accuracy for distinguishing between benign vs. malignant lesions in several cohort studies. However, further research is necessary to test the clinical performance of these algorithms before they can be facilitated and integrated into routine clinical practice.