Multiple Sclerosis: Clinical Update and Clinically-Oriented Radiologic Reporting.

Multiple sclerosis (MS) is a chronic inflammatory disease of the nervous system. MR imaging findings play an integral part in establishing diagnostic hallmarks of the disease during initial diagnosis and evaluating disease status. Multiple iterations of diagnostic criteria and consensus guidelines are put forth by various expert groups incorporating imaging of the brain and spine, and efforts have been made to standardize imaging protocols for MS. Emerging ancillary imaging findings have also attracted increasing interests and should be sought for on radiologic examination. In this paper, the authors review the clinical guidelines and approach to imaging of MS and related disorders, focusing on clinically impactful image interpretation and MR imaging reporting.

Tumor volumes in T3 supraglottic cancers treated with radiotherapy in the modern era: A study of the Canadian Head & Neck Collaborative Research Initiative.

PURPOSE: To evaluate the association of primary tumor volume (TV) with overall survival (OS) and disease-free survival (DFS) in T3 N0-3M0 supraglottic cancers treated with intensity-modulated radiotherapy (IMRT). METHODS: This was a retrospective cohort study involving 239 patients diagnosed with T3 N0-3M0 supraglottic cancers between 2002 and 2018 from seven regional cancer centers in Canada. Clinical data were obtained from the patient records. Supraglottic TV was measured by neuroradiologists on diagnostic imaging. Kaplan-Meier method was used for survival probabilities, and a restricted cubic spline Cox proportional hazards regression analysis was used to analyze TV associations with OS and DFS. RESULTS: Mean (SD) of participants was 65.2 (9.4) years; 176 (73.6%) participants were male. 90 (38%) were N0, and 151 (64%) received concurrent systemic therapy. Mean TV (SD) was 11.37 (12.11) cm3 . With mean follow up (SD) of 3.28 (2.60) years, 2-year OS was 72.7% (95% CI 66.9%-78.9%) and DFS was 53.6% (47.4%-60.6%). Increasing TV was associated (per cm3 increase) with worse OS (HR, 1.01, 95% CI 1.00-1.02, p < 0.01) and DFS (HR, 1.01, 95% CI 1.00-1.02, p = 0.02). CONCLUSIONS: Increasing primary tumor volume is associated with worse OS and DFS in T3 supraglottic cancers treated with IMRT, with no clear threshold. The findings suggest that patients with larger tumors and poor baseline laryngeal function may benefit from upfront laryngectomy with adjuvant radiotherapy.

Medical imaging and multimodal artificial intelligence models for streamlining and enhancing cancer care: opportunities and challenges.

INTRODUCTION: Artificial intelligence (AI) has the potential to transform oncologic care. There have been significant developments in AI applications in medical imaging and increasing interest in multimodal models. These are likely to enable improved oncologic care through more precise diagnosis, increasingly in a more personalized and less invasive manner. In this review, we provide an overview of the current state and challenges that clinicians, administrative personnel and policy makers need to be aware of and mitigate for the technology to reach its full potential. AREAS COVERED: The article provides a brief targeted overview of AI, a high-level review of the current state and future potential AI applications in diagnostic radiology and to a lesser extent digital pathology, focusing on oncologic applications. This is followed by a discussion of emerging approaches, including multimodal models. The article concludes with a discussion of technical, regulatory challenges and infrastructure needs for AI to realize its full potential. EXPERT OPINION: There is a large volume of promising research, and steadily increasing commercially available tools using AI. For the most advanced and promising precision diagnostic applications of AI to be used clinically, robust and comprehensive quality monitoring systems and informatics platforms will likely be required.

Natural Language Processing for the Identification of Incidental Lung Nodules in Computed Tomography Reports: A Quality Control Tool.

PURPOSE: To evaluate the diagnostic performance of a natural language processing (NLP) model in detecting incidental lung nodules (ILNs) in unstructured chest computed tomography (CT) reports. METHODS: All unstructured consecutive reports of chest CT scans performed at a tertiary hospital between 2020 and 2021 were retrospectively reviewed (n = 21,542) to train the NLP tool. Internal validation was performed using reference readings by two radiologists of both CT scans and reports, using a different external cohort of 300 chest CT scans. Second, external validation was performed in a cohort of all random unstructured chest CT reports from 57 different hospitals conducted in May 2022. A review by the same thoracic radiologists was used as the gold standard. The sensitivity, specificity, and accuracy were calculated. RESULTS: Of 21,542 CT reports, 484 mentioned at least one ILN (mean age, 71 ± 17.6 [standard deviation] years; women, 52%) and were included in the training set. In the internal validation (n = 300), the NLP tool detected ILN with a sensitivity of 100.0% (95% CI, 97.6 to 100.0), a specificity of 95.9% (95% CI, 91.3 to 98.5), and an accuracy of 98.0% (95% CI, 95.7 to 99.3). In the external validation (n = 977), the NLP tool yielded a sensitivity of 98.4% (95% CI, 94.5 to 99.8), a specificity of 98.6% (95% CI, 97.5 to 99.3), and an accuracy of 98.6% (95% CI, 97.6 to 99.2). Twelve months after the initial reports, 8 (8.60%) patients had a final diagnosis of lung cancer, among which 2 (2.15%) would have been lost to follow-up without the NLP tool. CONCLUSION: NLP can be used to identify ILNs in unstructured reports with high accuracy, allowing a timely recall of patients and a potential diagnosis of early-stage lung cancer that might have been lost to follow-up.

Promotion of higher rates of early fusion using activated titanium versus polyetheretherketone cages in adults undergoing 1- and 2-level transforaminal lumbar interbody fusion procedures: a randomized controlled trial.

OBJECTIVE: There is ongoing debate on the relative benefits and drawbacks of polyetheretherketone (PEEK) versus titanium (Ti) in generating a bone-to-implant surface microenvironment conducive to osseointegration. Micro- and nanoscale internal and topographic cage modifications have recently been posited to facilitate osseointegration and fusion, but human in vivo confirmation remains lacking. The authors of this study sought to directly compare early radiological outcomes in adults undergoing 1- and 2-level transforaminal lumbar interbody fusion (TLIF) procedures using either PEEK or nano-etched Ti interbody cages with an incorporated microlattice structure. METHODS: Patients were enrolled in a single academic center using a single-blind randomized controlled superiority design. Screening was undertaken from a pool of consecutive patients eligible for TLIF to undergo placement in a 1:1 ratio of either lordotic PEEK or activated Ti cages at each level of 1- or 2-level procedures. An a priori power analysis was performed and a preplanned interim analysis was undertaken once 50 of 70 patients were enrolled. Patient study data were collected perioperatively and uploaded to a Research Electronic Data Capture (REDCap) registry. Interbody fusion was assessed based on 6-month postoperative lumbar dual-energy CT (DECT) studies using the method of Brantigan and Steffee, as modified to describe the Fraser definition of locked pseudarthrosis (Brantigan-Steffee-Fraser [BSF] scale). RESULTS: In the final cohort of 50 patients, 40 interbody levels implanted with PEEK cages were compared with 34 interbody levels with activated Ti cages. The trial was stopped early given the results of an interim analysis with respect to the primary outcome. Surgical parameters including number of levels treated, average cage height, and position were not different between groups. For the PEEK and activated Ti groups, 20.6% versus 84.0% demonstrated BSF grade 3 fusion on 6-month postoperative DECT imaging (p < 0.001). Subsidence at 6 months on DECT was identified in 12 (41.4%) of PEEK levels versus 5 (20.8%) of activated Ti levels (p < 0.001). BSF-3 grading was predictive of segmental stability and numeric rating scale (NRS) leg pain improvement at 1 year postoperatively. Oswestry Disability Index and NRS back and leg pain scores all improved similarly in both cohorts at 1 year postoperatively. CONCLUSIONS: Activated Ti interbody cages mediate early fusion at significantly higher rates with lower rates of subsidence as compared with PEEK cages. These findings support the idea that interbody cage microscale properties, including surface topography, may play a primary role in facilitating osseointegration and fusion.

Introduction to Radiomics and Artificial Intelligence: A Primer for Radiologists.

Health informatics and artificial intelligence (AI) are expected to transform the healthcare enterprise and the future practice of radiology. There is an increasing body of literature on radiomics and deep learning/AI applications in medical imaging. There are also a steadily increasing number of FDA cleared AI applications in radiology. It is therefore essential for radiologists to have a basic understanding of these approaches, whether in academia or private practice. In this article, we will provide an overview of the field and familiarize the readers with the fundamental concepts behind these approaches.

Applications of Artificial Intelligence in the Radiology Roundtrip: Process Streamlining, Workflow Optimization, and Beyond.

There are many impactful applications of artificial intelligence (AI) in the electronic radiology roundtrip and the patient's journey through the healthcare system that go beyond diagnostic applications. These tools have the potential to improve quality and safety, optimize workflow, increase efficiency, and increase patient satisfaction. In this article, we review the role of AI for process improvement and workflow enhancement which includes applications beginning from the time of order entry, scan acquisition, applications supporting the image interpretation task, and applications supporting tasks after image interpretation such as result communication. These non-diagnostic workflow and process optimization tasks are an important part of the arsenal of potential AI tools that can streamline day to day clinical practice and patient care.

Artificial Intelligence for Cardiothoracic Imaging: Overview of Current and Emerging Applications.

Artificial intelligence algorithms can learn by assimilating information from large datasets in order to decipher complex associations, identify previously undiscovered pathophysiological states, and construct prediction models. There has been tremendous interest and increased incorporation of artificial intelligence into various industries, including healthcare. As a result, there has been an exponential rise in the number of research articles and industry participants producing models intended for a variety of applications in medical imaging, which can be challenging to navigate for radiologists. In thoracic imaging, multiple applications are being evaluated for chest radiography and computed tomography and include applications for lung nodule evaluation and cancer imaging, quantifying diffuse lung disorders, and cardiac imaging, to name a few. This review aims to provide an overview of current clinical AI models, focusing on the most common clinical applications of AI in cardiothoracic imaging.

A Practical Guide for AI Algorithm Selection for the Radiology Department.

There is a steadily increasing number of artificial intelligence (AI) tools available and cleared for use in clinical radiological practice. Radiologists will increasingly be faced with options provided by other radiologist colleagues, clinician colleagues, vendors, or other professionals for obtaining and deploying AI algorithms in clinical practice. It is important that radiologists are familiar with basic and practical aspects that need to be considered when assessing an AI tool for use in their practice, so that resources are properly allocated and there is an appropriate return on investment through enhancements in patient quality of care, safety, and/or process efficiency. In this review, we will discuss a potential approach for AI software assessment and practical points that should be considered when considering the acquisition and deployment of an AI tool in the radiology department.

Generalizability of Machine Learning Models: Quantitative Evaluation of Three Methodological Pitfalls.

Purpose: To investigate the impact of the following three methodological pitfalls on model generalizability: (a) violation of the independence assumption, (b) model evaluation with an inappropriate performance indicator or baseline for comparison, and (c) batch effect. Materials and Methods: The authors used retrospective CT, histopathologic analysis, and radiography datasets to develop machine learning models with and without the three methodological pitfalls to quantitatively illustrate their effect on model performance and generalizability. F1 score was used to measure performance, and differences in performance between models developed with and without errors were assessed using the Wilcoxon rank sum test when applicable. Results: Violation of the independence assumption by applying oversampling, feature selection, and data augmentation before splitting data into training, validation, and test sets seemingly improved model F1 scores by 71.2% for predicting local recurrence and 5.0% for predicting 3-year overall survival in head and neck cancer and by 46.0% for distinguishing histopathologic patterns in lung cancer. Randomly distributing data points for a patient across datasets superficially improved the F1 score by 21.8%. High model performance metrics did not indicate high-quality lung segmentation. In the presence of a batch effect, a model built for pneumonia detection had an F1 score of 98.7% but correctly classified only 3.86% of samples from a new dataset of healthy patients. Conclusion: Machine learning models developed with these methodological pitfalls, which are undetectable during internal evaluation, produce inaccurate predictions; thus, understanding and avoiding these pitfalls is necessary for developing generalizable models.Keywords: Random Forest, Diagnosis, Prognosis, Convolutional Neural Network (CNN), Medical Image Analysis, Generalizability, Machine Learning, Deep Learning, Model Evaluation Supplemental material is available for this article. Published under a CC BY 4.0 license.

Predicting histopathology markers of endometrial carcinoma with a quantitative image analysis approach based on spherical harmonics in multiparametric MRI.

PURPOSE: Identifying optimal machine learning pipelines for computer-aided diagnosis is key for the development of robust, reproducible, and clinically relevant imaging biomarkers for endometrial carcinoma. The purpose of this study was to introduce the mathematical development of image descriptors computed from spherical harmonics (SPHARM) decompositions as well as the associated machine learning pipeline, and to evaluate their performance in predicting deep myometrial invasion (MI) and histopathological high-grade in preoperative multiparametric magnetic resonance imaging (MRI). PATIENTS AND METHODS: This retrospective study included 128 women with histopathology-confirmed endometrial carcinomas who underwent 1.5-T MRI before hysterectomy between January 2011 and July 2015. SPHARM descriptors of each tumor were computed on multiparametric MRI images (T2-weighted, diffusion-weighted, dynamic contrast-enhanced-MRI and apparent diffusion coefficient maps). Tensor-based logistic regression was used to classify two-dimensional SPHARM rotationally-invariant descriptors. Head-to-head comparisons with radiomics analyses were performed with DeLong tests with Bonferroni-Holm correction to compare diagnostic performances. RESULTS: With all MRI contrasts, SPHARM analysis resulted in area under the curve, sensitivity, specificity, and balanced accuracy values of 0.94 (95% confidence interval [CI]: 0.85, 1.00), 100% (95% CI: 100, 100), 74% (95% CI: 51, 92), 87% (95% CI: 78, 98), respectively, for predicting deep MI. For predicting high-grade tumor histology, the corresponding values for the same diagnostic metrics were 0.81 (95% CI: 0.64, 0.90), 93% (95% CI: 67, 100), 63% (95% CI: 45, 79) and 78% (95% CI: 64, 86). The corresponding values achieved via radiomics were 0.92 (95% CI: 0.82, 0.95), 82% (95% CI: 65, 93), 80% (95% CI: 51, 94), 81% (95% CI: 70, 91) for deep MI and 0.72 (95% CI: 0.58, 0.83), 93% (95% CI: 65, 100), 55% (95% CI: 41, 69), 74% (95% CI: 52, 88) for high-grade histology. The diagnostic performance of the SPHARM analysis was not significantly different (P = 0.62) from that of radiomics for predicting deep MI but was significantly higher (P = 0.044) for predicting high-grade histology. CONCLUSION: The proposed SPHARM analysis yields similar or higher diagnostic performance than radiomics in identifying deep MI and high-grade status in histology-proven endometrial carcinoma.

Association of Primary Tumor Volume With Survival in Patients With T3 Glottic Cancer Treated With Radiotherapy: A Study of the Canadian Head & Neck Collaborative Research Initiative.

Importance: The association of primary tumor volume with outcomes in T3 glottic cancers treated with radiotherapy with concurrent chemotherapy remains unclear, with some evidence suggesting worse locoregional control in larger tumors. Objective: To evaluate the association of primary tumor volume with oncologic outcomes in patients with T3 N0-N3 M0 glottic cancer treated with primary (chemo)radiotherapy in a large multi-institutional study. Design, Setting, and Participants: This multi-institutional retrospective cohort study involved 7 Canadian cancer centers from 2002 to 2018. Tumor volume was measured by expert neuroradiologists on diagnostic imaging. Clinical and outcome data were extracted from electronic medical records. Overall survival (OS) and disease-free survival (DFS) outcomes were assessed with marginal Cox regression. Laryngectomy-free survival (LFS) was modeled as a secondary analysis. Patients diagnosed with cT3 N0-N3 M0 glottic cancers from 2002 to 2018 and treated with curative intent intensity-modulated radiotherapy (IMRT) with or without chemotherapy. Overall, 319 patients met study inclusion criteria. Exposures: Tumor volume as measured on diagnostic imaging by expert neuroradiologists. Main Outcomes and Measures: Primary outcomes were OS and DFS; LFS was assessed as a secondary analysis, and late toxic effects as an exploratory analysis determined before start of the study. Results: The mean (SD) age of participants was 66 (12) years and 279 (88%) were men. Overall, 268 patients (84%) had N0 disease, and 150 (47%) received concurrent systemic therapy. The mean (SD) tumor volume was 4.04 (3.92) cm3. With a mean (SD) follow-up of 3.85 (3.04) years, there were 91 (29%) local, 35 (11%) regional, and 38 (12%) distant failures. Increasing tumor volume (per 1-cm3 increase) was associated with significantly worse adjusted OS (hazard ratio [HR], 1.07; 95% CI, 1.03-1.11) and DFS (HR, 1.04; 95% CI, 1.01-1.07). A total of 62 patients (19%) underwent laryngectomies with 54 (87%) of these within 800 days after treatment. Concurrent systemic therapy was associated with improved LFS (subdistribution HR, 0.63; 95% CI, 0.53-0.76). Conclusions and Relevance: Increasing tumor volumes in cT3 glottic cancers was associated with worse OS and DFS, and systemic therapy was associated with improved LFS. In absence of randomized clinical trial evidence, patients with poor pretreatment laryngeal function or those ineligible for systemic therapy may be considered for primary surgical resection with postoperative radiotherapy.

DECT-CLUST: Dual-Energy CT Image Clustering and Application to Head and Neck Squamous Cell Carcinoma Segmentation.

Dual-energy computed tomography (DECT) is an advanced CT computed tomography scanning technique enabling material characterization not possible with conventional CT scans. It allows the reconstruction of energy decay curves at each 3D image voxel, representing varied image attenuation at different effective scanning energy levels. In this paper, we develop novel unsupervised learning techniques based on mixture models and functional data analysis models to the clustering of DECT images. We design functional mixture models that integrate spatial image context in mixture weights, with mixture component densities being constructed upon the DECT energy decay curves as functional observations. We develop dedicated expectation-maximization algorithms for the maximum likelihood estimation of the model parameters. To our knowledge, this is the first article to develop statistical functional data analysis and model-based clustering techniques to take advantage of the full spectral information provided by DECT. We evaluate the application of DECT to head and neck squamous cell carcinoma. Current image-based evaluation of these tumors in clinical practice is largely qualitative, based on a visual assessment of tumor anatomic extent and basic one- or two-dimensional tumor size measurements. We evaluate our methods on 91 head and neck cancer DECT scans and compare our unsupervised clustering results to tumor contours traced manually by radiologists, as well as to several baseline algorithms. Given the inter-rater variability even among experts at delineating head and neck tumors, and given the potential importance of tissue reactions surrounding the tumor itself, our proposed methodology has the potential to add value in downstream machine learning applications for clinical outcome prediction based on DECT data in head and neck cancer.

Deep Learning Algorithms for Diagnosis of Lung Cancer: A Systematic Review and Meta-Analysis.

We conducted a systematic review and meta-analysis of the diagnostic performance of current deep learning algorithms for the diagnosis of lung cancer. We searched major databases up to June 2022 to include studies that used artificial intelligence to diagnose lung cancer, using the histopathological analysis of true positive cases as a reference. The quality of the included studies was assessed independently by two authors based on the revised Quality Assessment of Diagnostic Accuracy Studies. Six studies were included in the analysis. The pooled sensitivity and specificity were 0.93 (95% CI 0.85−0.98) and 0.68 (95% CI 0.49−0.84), respectively. Despite the significantly high heterogeneity for sensitivity (I2 = 94%, p < 0.01) and specificity (I2 = 99%, p < 0.01), most of it was attributed to the threshold effect. The pooled SROC curve with a bivariate approach yielded an area under the curve (AUC) of 0.90 (95% CI 0.86 to 0.92). The DOR for the studies was 26.7 (95% CI 19.7−36.2) and heterogeneity was 3% (p = 0.40). In this systematic review and meta-analysis, we found that when using the summary point from the SROC, the pooled sensitivity and specificity of DL algorithms for the diagnosis of lung cancer were 93% and 68%, respectively.

Development and Validation of Multiparametric MRI-based Radiomics Models for Preoperative Risk Stratification of Endometrial Cancer.

Background Stratifying high-risk histopathologic features in endometrial carcinoma is important for treatment planning. Radiomics analysis at preoperative MRI holds potential to identify high-risk phenotypes. Purpose To evaluate the performance of multiparametric MRI three-dimensional radiomics-based machine learning models for differentiating low- from high-risk histopathologic markers-deep myometrial invasion (MI), lymphovascular space invasion (LVSI), and high-grade status-and advanced-stage endometrial carcinoma. Materials and Methods This dual-center retrospective study included women with histologically proven endometrial carcinoma who underwent 1.5-T MRI before hysterectomy between January 2011 and July 2015. Exclusion criteria were tumor diameter less than 1 cm, missing MRI sequences or histopathology reports, neoadjuvant therapy, and malignant neoplasms other than endometrial carcinoma. Three-dimensional radiomics features were extracted after tumor segmentation at MRI (T2-weighted, diffusion-weighted, and dynamic contrast-enhanced MRI). Predictive features were selected in the training set with use of random forest (RF) models for each end point, and trained RF models were applied to the external test set. Five board-certified radiologists conducted MRI-based staging and deep MI assessment in the training set. Areas under the receiver operating characteristic curve (AUCs) were reported with balanced accuracies, and radiologists' readings were compared with radiomics with use of McNemar tests. Results In total, 157 women were included: 94 at the first institution (training set; mean age, 66 years ± 11 [SD]) and 63 at the second institution (test set; 67 years ± 12). RF models dichotomizing deep MI, LVSI, high grade, and International Federation of Gynecology and Obstetrics (FIGO) stage led to AUCs of 0.81 (95% CI: 0.68, 0.88), 0.80 (95% CI: 0.67, 0.93), 0.74 (95% CI: 0.61, 0.86), and 0.84 (95% CI: 0.72, 0.92), respectively, in the test set. In the training set, radiomics provided increased performance compared with radiologists' readings for identifying deep MI (balanced accuracy, 86% vs 79%; P = .03), while no evidence of a difference was observed in performance for advanced FIGO stage (80% vs 78%; P = .27). Conclusion Three-dimensional radiomics can stratify patients by using preoperative MRI according to high-risk histopathologic end points in endometrial carcinoma and provide nonsignificantly different or higher performance than radiologists in identifying advanced stage and deep myometrial invasion, respectively. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Kido and Nishio in this issue.

Importance of sex and gender factors for COVID-19 infection and hospitalisation: a sex-stratified analysis using machine learning in UK Biobank data.

OBJECTIVE: To examine sex and gender roles in COVID-19 test positivity and hospitalisation in sex-stratified predictive models using machine learning. DESIGN: Cross-sectional study. SETTING: UK Biobank prospective cohort. PARTICIPANTS: Participants tested between 16 March 2020 and 18 May 2020 were analysed. MAIN OUTCOME MEASURES: The endpoints of the study were COVID-19 test positivity and hospitalisation. Forty-two individuals' demographics, psychosocial factors and comorbidities were used as likely determinants of outcomes. Gradient boosting machine was used for building prediction models. RESULTS: Of 4510 individuals tested (51.2% female, mean age=68.5±8.9 years), 29.4% tested positive. Males were more likely to be positive than females (31.6% vs 27.3%, p=0.001). In females, living in more deprived areas, lower income, increased low-density lipoprotein (LDL) to high-density lipoprotein (HDL) ratio, working night shifts and living with a greater number of family members were associated with a higher likelihood of COVID-19 positive test. While in males, greater body mass index and LDL to HDL ratio were the factors associated with a positive test. Older age and adverse cardiometabolic characteristics were the most prominent variables associated with hospitalisation of test-positive patients in both overall and sex-stratified models. CONCLUSION: High-risk jobs, crowded living arrangements and living in deprived areas were associated with increased COVID-19 infection in females, while high-risk cardiometabolic characteristics were more influential in males. Gender-related factors have a greater impact on females; hence, they should be considered in identifying priority groups for COVID-19 infection vaccination campaigns.