Radiomic-Based Approaches in the Multi-metastatic Setting: A Quantitative Review.

Background: Radiomics traditionally focuses on analyzing a single lesion within a patient to extract tumor characteristics, yet this process may overlook inter-lesion heterogeneity, particularly in the multi-metastatic setting. There is currently no established method for combining radiomic features in such settings, leading to diverse approaches with varying strengths and limitations. Our quantitative review aims to illuminate these methodologies, assess their replicability, and guide future research toward establishing best practices, offering insights into the challenges of multi-lesion radiomic analysis across diverse datasets. Methods: We conducted a comprehensive literature search to identify methods for integrating data from multiple lesions in radiomic analyses. We replicated these methods using either the author's code or by reconstructing them based on the information provided in the papers. Subsequently, we applied these identified methods to three distinct datasets, each depicting a different metastatic scenario. Results: We compared ten mathematical methods for combining radiomic features across three distinct datasets, encompassing a total of 16,850 lesions in 3,930 patients. Performance of these methods was evaluated using the Cox proportional hazards model and benchmarked against univariable analysis of total tumor volume. We observed variable performance in methods across datasets. However, no single method consistently outperformed others across all datasets. Notably, while some methods surpassed total tumor volume analysis in certain datasets, others did not. Averaging methods showed higher median performance in patients with colorectal liver metastases, and in soft tissue sarcoma, concatenation of radiomic features from different lesions exhibited the highest median performance among tested methods. Conclusions: Radiomic features can be effectively selected or combined to estimate patient-level outcomes in multi-metastatic patients, though the approach varies by metastatic setting. Our study fills a critical gap in radiomics research by examining the challenges of radiomic-based analysis in this setting. Through a comprehensive review and rigorous testing of different methods across diverse datasets representing unique metastatic scenarios, we provide valuable insights into effective radiomic analysis strategies.

Simulated deep CT characterization of liver metastases with high-resolution filtered back projection reconstruction.

Early diagnosis and accurate prognosis of colorectal cancer is critical for determining optimal treatment plans and maximizing patient outcomes, especially as the disease progresses into liver metastases. Computed tomography (CT) is a frontline tool for this task; however, the preservation of predictive radiomic features is highly dependent on the scanning protocol and reconstruction algorithm. We hypothesized that image reconstruction with a high-frequency kernel could result in a better characterization of liver metastases features via deep neural networks. This kernel produces images that appear noisier but preserve more sinogram information. A simulation pipeline was developed to study the effects of imaging parameters on the ability to characterize the features of liver metastases. This pipeline utilizes a fractal approach to generate a diverse population of shapes representing virtual metastases, and then it superimposes them on a realistic CT liver region to perform a virtual CT scan using CatSim. Datasets of 10,000 liver metastases were generated, scanned, and reconstructed using either standard or high-frequency kernels. These data were used to train and validate deep neural networks to recover crafted metastases characteristics, such as internal heterogeneity, edge sharpness, and edge fractal dimension. In the absence of noise, models scored, on average, 12.2% ( α = 0.012 ) and 7.5% ( α = 0.049 ) lower squared error for characterizing edge sharpness and fractal dimension, respectively, when using high-frequency reconstructions compared to standard. However, the differences in performance were statistically insignificant when a typical level of CT noise was simulated in the clinical scan. Our results suggest that high-frequency reconstruction kernels can better preserve information for downstream artificial intelligence-based radiomic characterization, provided that noise is limited. Future work should investigate the information-preserving kernels in datasets with clinical labels.

Mechanisms of Exercise Intolerance Across the Breast Cancer Continuum: A Pooled Analysis of Individual Patient Data.

PURPOSE: The purpose of this study is to evaluate the prevalence of abnormal cardiopulmonary responses to exercise and pathophysiological mechanism(s) underpinning exercise intolerance across the continuum of breast cancer (BC) care from diagnosis to metastatic disease. METHODS: Individual participant data from four randomized trials spanning the BC continuum ([1] prechemotherapy [n = 146], [2] immediately postchemotherapy [n = 48], [3] survivorship [n = 138], and [4] metastatic [n = 47]) were pooled and compared with women at high-risk of BC (BC risk; n = 64). Identical treadmill-based peak cardiopulmonary exercise testing protocols evaluated exercise intolerance (peak oxygen consumption; V̇O2peak) and other resting, submaximal, and peak cardiopulmonary responses. The prevalence of 12 abnormal exercise responses was evaluated. Graphical plots of exercise responses were used to identify oxygen delivery and/or uptake mechanisms contributing to exercise intolerance. Unsupervised, hierarchical cluster analysis was conducted to explore exercise response phenogroups. RESULTS: Mean V̇O2peak was 2.78 ml O2.kg-1·min-1 (95% confidence interval [CI], -3.94, -1.62 mL O2.kg-1·min-1; P < 0.001) lower in the pooled BC cohort (52 ± 11 yr) than BC risk (55 ± 10 yr). Compared with BC risk, the pooled BC cohort had a 2.5-fold increased risk of any abnormal cardiopulmonary response (odds ratio, 2.5; 95% confidence interval, 1.2, 5.3; P = 0.014). Distinct exercise responses in BC reflected impaired oxygen delivery and uptake relative to control, although considerable inter-individual heterogeneity within cohorts was observed. In unsupervised, hierarchical cluster analysis, six phenogroups were identified with marked differences in cardiopulmonary response patterns and unique clinical characteristics. CONCLUSIONS: Abnormal cardiopulmonary response to exercise is common in BC and is related to impairments in oxygen delivery and uptake. The identification of exercise response phenogroups could help improve cardiovascular risk stratification and guide investigation of targeted exercise interventions.

Preoperative CT and survival data for patients undergoing resection of colorectal liver metastases.

The liver is a common site for the development of metastases in colorectal cancer. Treatment selection for patients with colorectal liver metastases (CRLM) is difficult; although hepatic resection will cure a minority of CRLM patients, recurrence is common. Reliable preoperative prediction of recurrence could therefore be a valuable tool for physicians in selecting the best candidates for hepatic resection in the treatment of CRLM. It has been hypothesized that evidence for recurrence could be found via quantitative image analysis on preoperative CT imaging of the future liver remnant before resection. To investigate this hypothesis, we have collected preoperative hepatic CT scans, clinicopathologic data, and recurrence/survival data, from a large, single-institution series of patients (n = 197) who underwent hepatic resection of CRLM. For each patient, we also created segmentations of the liver, vessels, tumors, and future liver remnant. The largest of its kind, this dataset is a resource that may aid in the development of quantitative imaging biomarkers and machine learning models for the prediction of post-resection hepatic recurrence of CRLM.

The RSNA Cervical Spine Fracture CT Dataset.

This dataset is composed of cervical spine CT images with annotations related to fractures; it is available at https://www.kaggle.com/competitions/rsna-2022-cervical-spine-fracture-detection/.

A primer on texture analysis in abdominal radiology.

The number of publications on texture analysis (TA), radiomics, and radiogenomics has been growing exponentially, with abdominal radiologists aiming to build new prognostic or predictive biomarkers for a wide range of clinical applications including the use of oncological imaging to advance the field of precision medicine. TA is specifically concerned with the study of the variation of pixel intensity values in radiological images. Radiologists aim to capture pixel variation in radiological images to deliver new insights into tumor biology that cannot be derived from visual inspection alone. TA remains an active area of investigation and requires further standardization prior to its clinical acceptance and applicability. This review is for radiologists interested in this rapidly evolving field, who are thinking of performing research or want to better interpret results in this arena. We will review the main concepts in TA, workflow processes, and existing challenges and steps to overcome them, as well as look at publications in body imaging with external validation.

Radiomics artificial intelligence modelling for prediction of local control for colorectal liver metastases treated with radiotherapy.

Background and Purpose: Prognostic assessment of local therapies for colorectal liver metastases (CLM) is essential for guiding management in radiation oncology. Computed tomography (CT) contains liver texture information which may be predictive of metastatic environments. To investigate the feasibility of analyzing CT texture, we sought to build an automated model to predict progression-free survival using CT radiomics and artificial intelligence (AI). Materials and Methods: Liver CT scans and outcomes for N = 97 CLM patients treated with radiotherapy were retrospectively obtained. A survival model was built by extracting 108 radiomic features from liver and tumor CT volumes for a random survival forest (RSF) to predict local progression. Accuracies were measured by concordance indices (C-index) and integrated Brier scores (IBS) with 4-fold cross-validation. This was repeated with different liver segmentations and radiotherapy clinical variables as inputs to the RSF. Predictive features were identified by perturbation importances. Results: The AI radiomics model achieved a C-index of 0.68 (CI: 0.62-0.74) and IBS below 0.25 and the most predictive radiomic feature was gray tone difference matrix strength (importance: 1.90 CI: 0.93-2.86) and most predictive treatment feature was maximum dose (importance: 3.83, CI: 1.05-6.62). The clinical data only model achieved a similar C-index of 0.62 (CI: 0.56-0.69), suggesting that predictive signals exist in radiomics and clinical data. Conclusions: The AI model achieved good prediction accuracy for progression-free survival of CLM, providing support that radiomics or clinical data combined with machine learning may aid prognostic assessment and management.

Deformable multimodal registration for navigation in beating-heart cardiac surgery.

PURPOSE: Minimally invasive beating-heart surgery is currently performed using endoscopes and without navigation. Registration of intraoperative ultrasound to a preoperative cardiac CT scan is a valuable step toward image-guided navigation. METHODS: The registration was achieved by first extracting a representative point set from each ultrasound image in the sequence using a deformable registration. A template shape representing the cardiac chambers was deformed through a hierarchy of affine transformations to match each ultrasound image using a generalized expectation maximization algorithm. These extracted point sets were matched to the CT by exhaustively searching over a large number of precomputed slices of 3D geometry. The result is a similarity transformation mapping the intraoperative ultrasound to preoperative CT. RESULTS: Complete data sets were acquired for four patients. Transesophageal echocardiography ultrasound sequences were deformably registered to a model of oriented points with a mean error of 2.3 mm. Ultrasound and CT scans were registered to a mean of 3 mm, which is comparable to the error of 2.8 mm expected by merging ultrasound registration with uncertainty of cardiac CT. CONCLUSION: The proposed algorithm registered 3D CT with dynamic 2D intraoperative imaging. The algorithm aligned the images in both space and time, needing neither dynamic CT imaging nor intraoperative electrocardiograms. The accuracy was sufficient for navigation in thoracoscopically guided beating-heart surgery.