Radiomics: The Link Between Radiology and Histology?

CTA-Based Radiomics and Area Change Rate Predict Infrarenal Abdominal Aortic Aneurysms Patients Events: A Multicenter Study

RATIONALE AND OBJECTIVES

Clinical assessment of abdominal aortic aneurysm (AAA) intervention and rupture risk relies primarily on maximum diameter, but studies have shown that sole dependence on diameter has limitations. CTA-based radiomics, aneurysm and lumen area change rates (AACR, LACR) are measured to predict potential AAA events.

MATERIALS AND METHODS

Between January 2017 and November 2022, 260 AAA patients from four centers who underwent two preoperative CTA examinations were included in this retrospective study. The endpoint event is defined as AAA rupture or repair. Patients were categorized into event and no-event groups based on the occurrence of endpoint event during follow-up. AACR and LACR were assessed using baseline and follow-up CTA, with radiomics features extracted from the baseline images. C-statistics and the Kaplan-Meier analysis were used to evaluate the predictive performance.

RESULTS

A total of 193 eligible infrarenal AAA patients were included, 176 (91.2%) were man and 17 (8.8%) were woman. The median follow-up was 33.4 (14.2, 57.4) months. Seven models were constructed, comprising the aneurysm-based Radscore model, lumen-based Radscore model, intraluminal thrombus (ILT)-based Radscore model, AACR model, LACR model, clinical model (including high-density lipoprotein, D-dimer, and baseline aneurysm diameter), and a merged model. On the external validation set, the C-index of seven models were 0.713 (0.574-0.853), 0.642 (0.499-0.786), 0.727 (0.600-0.854), 0.619 (0.484-0.753), 0.680 (0.530-0.830), 0.690 (0.557-0.824) and 0.760 (0.651-0.869), in that order. In the Kaplan-Meier analysis, the merged model was best-divided patients into high/low-risk groups with Log-rank p < 0.0001. The AARC and LARC between non-event and event groups have significant differences (AACR: 1.4 cm2/y vs. 2.3 cm2/y, p < 0.0001; LACR: 0.3 cm2/y vs. 1.1 cm2/y, p < 0.0001).

CONCLUSION

CTA-based radiomics, AACR and LACR have good predictive value for outcome event in infrarenal AAA patients.

Artificial intelligence in healthcare: a primer for medical education in radiomics

The application of artificial intelligence (AI) to healthcare has garnered significant enthusiasm in recent years. Despite the adoption of new analytic approaches, medical education on AI is lacking. We aim to create a usable AI primer for medical education. We discuss how to generate a clinical question involving AI, what data are suitable for AI research, how to prepare a dataset for training and how to determine if the output has clinical utility. To illustrate this process, we focused on an example of how medical imaging is employed in designing a machine learning model. Our proposed medical education curriculum addresses AI's potential and limitations for enhancing clinicians' skills in research, applied statistics and care delivery.

The Usefulness of Radiomics Methodology for Developing Descriptive and Prognostic Image-Based Phenotyping in the Aging Population: Results From a Small Feasibility Study

Background: Radiomics is an emerging field that translates medical images into quantitative data to enable phenotypic profiling of human disease. In this retrospective study, we asked whether it is possible to use image-based phenotyping to describe and determine prognostic factors in the aging population.

Methods: A radiomic frailty cohort with 101 patients was included in the analysis (65 ± 15 years, 55 men). A total of 44 texture features were extracted from the segmented muscle area of the ultrasound images of the anterior thigh. Univariate and multivariate analyses were performed to assess the image data sets and clinical data.

Results: Our results showed that the heterogeneity of muscle was associated with an increased incidence of hearing impairment, stroke, myocardial infarction, dementia/memory loss, and falls in the following two years. Regression analysis revealed a muscle radiomic model with 87.1% correct predictive value with good sensitivity and moderate specificity (p = 0.001).

Conclusion: It is possible to develop and identify image-based phenotypes in the elderly population. The muscle radiomic model needs to further be validated. Future studies correlated with biological data (genomics, transcriptomics, metabolomics, etc.) will give further insights into the biological basis and molecular processes of the developed radiomic model.

CT-based radiomics signature for differentiation between cardiac tumors and a thrombi: a retrospective, multicenter study

The study aimed to develop and validate whether the computed tomography (CT) radiomics analysis is effective in differentiating cardiac tumors and thrombi. For this retrospective study, a radiomics model was developed on the basis of a training dataset of 192 patients (61.9 ± 13.3 years, 90 men) with cardiac masses detected in cardiac CT from January 2010 to September 2019. We constructed three models for discriminating between a cardiac tumor and a thrombus: a radiomics model, a clinical model, which included clinical and conventional CT variables, and a model that combined clinical and radiomics models. In the training dataset, the radiomics model and the combined model yielded significantly higher differentiation performance between cardiac tumors and cardiac thrombi than the clinical model (AUC 0.973 vs 0.870, p < 0.001 and AUC 0.983 vs 0.870, p < 0.001, respectively). In the external validation dataset with 63 patients (59.8 ± 13.2 years, 26 men), the combined model yielded a larger AUC compared to the clinical model (AUC 0.911 vs 0.802, p = 0.037). CT radiomics analysis is effective in differentiating cardiac tumors and thrombi. In conclusion, the combination of clinical, conventional CT, and radiomics features demonstrated an additional benefit in differentiating between cardiac tumor and thrombi compared to clinical data and conventional CT features alone.

Multimodality Advanced Cardiovascular and Molecular Imaging for Early Detection and Monitoring of Cancer Therapy-Associated Cardiotoxicity and the Role of Artificial Intelligence and Big Data

Cancer mortality has improved due to earlier detection via screening, as well as due to novel cancer therapies such as tyrosine kinase inhibitors and immune checkpoint inhibitions. However, similarly to older cancer therapies such as anthracyclines, these therapies have also been documented to cause cardiotoxic events including cardiomyopathy, myocardial infarction, myocarditis, arrhythmia, hypertension, and thrombosis. Imaging modalities such as echocardiography and magnetic resonance imaging (MRI) are critical in monitoring and evaluating for cardiotoxicity from these treatments, as well as in providing information for the assessment of function and wall motion abnormalities. MRI also allows for additional tissue characterization using T1, T2, extracellular volume (ECV), and delayed gadolinium enhancement (DGE) assessment. Furthermore, emerging technologies may be able to assist with these efforts. Nuclear imaging using targeted radiotracers, some of which are already clinically used, may have more specificity and help provide information on the mechanisms of cardiotoxicity, including in anthracycline mediated cardiomyopathy and checkpoint inhibitor myocarditis. Hyperpolarized MRI may be used to evaluate the effects of oncologic therapy on cardiac metabolism. Lastly, artificial intelligence and big data of imaging modalities may help predict and detect early signs of cardiotoxicity and response to cardioprotective medications as well as provide insights on the added value of molecular imaging and correlations with cardiovascular outcomes. In this review, the current imaging modalities used to assess for cardiotoxicity from cancer treatments are discussed, in addition to ongoing research on targeted molecular radiotracers, hyperpolarized MRI, as well as the role of artificial intelligence (AI) and big data in imaging that would help improve the detection and prognostication of cancer-treatment cardiotoxicity.