Sebro, Ronnie

AbstractThe integration of biomarkers into medical practice has revolutionized the field of radiology, allowing for enhanced diagnostic accuracy, personalized treatment strategies, and improved patient care outcomes. This review offers radiologists a comprehensive understanding of the diverse applications of biomarkers in medicine. By elucidating the fundamental concepts, challenges, and recent advancements in biomarker utilization, it will serve as a bridge between the disciplines of radiology and epidemiology. Through an exploration of various biomarker types, such as imaging biomarkers, molecular biomarkers, and genetic markers, I outline their roles in disease detection, prognosis prediction, and therapeutic monitoring. I also discuss the significance of robust study designs, blinding, power and sample size calculations, performance metrics, and statistical methodologies in biomarker research. By fostering collaboration between radiologists, statisticians, and epidemiologists, I hope to accelerate the translation of biomarker discoveries into clinical practice, ultimately leading to improved patient care.

AbstractIntroductionTranexamic acid (TXA) is an antifibrinolytic drug that has been shown to reduce blood loss following surgery. The use of TXA during orthopedic procedures has gained widespread acceptance, with multiple clinical studies demonstrating no increase in thrombotic complications. While TXA has been shown to be safe and effective for several orthopedic procedures, its use in orthopedic sarcoma surgery is not well established. Cancer‐associated thrombosis remains a significant cause of morbidity and mortality in patients with sarcoma. It is unknown if intraoperative TXA use will increase the risk of developing a postoperative thrombotic complication in this population. This study aimed to compare the risk of postoperative thrombotic complications in patients who received TXA during sarcoma resection to patients who did not receive TXA.MethodsA retrospective review was performed of 1099 patients who underwent resection of a soft tissue or bone sarcoma at our institution between 2010 and 2021. Baseline demographics and postoperative outcomes were compared between patients who did and did not receive intraoperative TXA. We evaluated 90‐day complication rates, including: deep venous thrombosis (DVT), pulmonary embolism (PE), myocardial infarction (MI), cerebrovascular accident (CVA), and mortality.ResultsTXA was used more commonly for bone tumors (p < 0.001), tumors located in the pelvis (p = 0.004), and larger tumors (p < 0.001). Patients who received intraoperative TXA were associated with a significant increase in developing a postoperative DVT (odds ratio [OR]: 2.22, p = 0.036) and PE (OR: 4.62, p < 0.001), but had no increase in CVA, MI, or mortality (all p > 0.05) within 90 days of surgery, following univariate analysis. Multivariable analysis confirmed that TXA was independently associated with developing a postoperative PE (OR: 10.64, 95% confidence interval: 2.23–50.86, p = 0.003). We found no association with DVT, MI, CVA, or mortality within 90 days postoperatively, following intraoperative TXA use.ConclusionOur results demonstrate a higher associated risk of PE following TXA use in sarcoma surgery and caution is warranted with TXA use in this patient population.

Abstract Objective Textual radiology reports contain a wealth of information that may help understand associations among diseases and imaging observations. This study evaluated the ability to detect causal associations among diseases and imaging findings from their co-occurrence in radiology reports. Materials and Methods This IRB-approved and HIPAA-compliant study analyzed 1 702 462 consecutive reports of 1 396 293 patients; patient consent was waived. Reports were analyzed for positive mention of 16 839 entities (disorders and imaging findings) of the Radiology Gamuts Ontology (RGO). Entities that occurred in fewer than 25 patients were excluded. A Bayesian network structure-learning algorithm was applied at P &lt; 0.05 threshold: edges were evaluated as possible causal relationships. RGO and/or physician consensus served as ground truth. Results 2742 of 16 839 RGO entities were included, 53 849 patients (3.9%) had at least one included entity. The algorithm identified 725 pairs of entities as causally related; 634 were confirmed by reference to RGO or physician review (87% precision). As shown by its positive likelihood ratio, the algorithm increased detection of causally associated entities 6876-fold. Discussion Causal relationships among diseases and imaging findings can be detected with high precision from textual radiology reports. Conclusion This approach finds causal relationships among diseases and imaging findings with high precision from textual radiology reports, despite the fact that causally related entities represent only 0.039% of all pairs of entities. Applying this approach to larger report text corpora may help detect unspecified or heretofore unrecognized associations.

Abstract Artificial intelligence (AI) has the potential to be a disruptive technology in healthcare. Recently, there is increased speculation that AI may be used to replace healthcare providers in the future. To answer this question, we reviewed over 21 000 articles published in medical specialty journals between 2019 and 2021 to evaluate whether these AI models were intended to assist or replace healthcare providers. We also evaluated whether all Food and Drug Administration (FDA)-approved AI models were used to assist or replace healthcare providers. We find that most AI models published in this time period were intended to assist rather than replace healthcare providers, and that most of the published AI models performed tasks that could not be done by healthcare providers.

Patients with low bone mineral density (BMD) are at risk for fractures however are often undiagnosed. Therefore, there is a need to opportunistically screen for low BMD in patients who present for other studies. This is a retrospective study of 812 patients aged 50 years or older who had dual-energy X-ray absorptiometry (DXA) and radiographs of the hands within 12 months of each other. This dataset was randomly split into training/validation (n=533) and test (n=136) datasets. A deep learning (DL) framework was used to predict osteoporosis/osteopenia. Correlations between the textural analysis of the bones and DXA measurements were obtained. We found that the DL model had an accuracy of 82.00%, sensitivity of 87.03%, specificity of 61.00% and an area under the curve (AUC) of 74.00% to detect osteoporosis/osteopenia. Our findings show that radiographs of the hand can be used to screen for osteoporosis/osteopenia and identify patients who should get formal DXA evaluation.

Knee CT scans are used for planning for total knee arthroplasties in patients who are often simultaneously at risk for frailty fractures due to low bone mineral density. We retrospectively identified 200 patients (85.5% female) with concurrent CT scans of the knee and Dual energy x-ray absorptiometry (DXA). The mean CT attenuation of the distal femur, proximal tibia and fibula, and patella, were calculated using volumetric 3-dimensional segmentation using 3D Slicer. Data were split randomly into training 80% and test 20% datasets. The optimal CT attenuation threshold for the proximal fibula was obtained in the training dataset and evaluated in the test dataset. A support vector machine (SVM) with radial basis function (RBF) using C-classification was trained and tuned using 5-fold cross-validation in the training dataset and then evaluated in the test dataset. The SVM had a higher area-under-the curve (AUC) of 0.937 and better performance to detect osteoporosis/osteopenia than the CT attenuation of the fibula (AUC of 0.717) (P=0.015). Opportunistic screening for osteoporosis/osteopenia could be accomplished using CT scans of the knee.