Improving the Post-Operative Prediction of BCR-Free Survival Time with mRNA Variables and Machine Learning.

Predicting the risk of, and time to biochemical recurrence (BCR) in prostate cancer patients post-operatively is critical in patient treatment decision pathways following surgical intervention. This study aimed to investigate the predictive potential of mRNA information to improve upon reference nomograms and clinical-only models, using a dataset of 187 patients that includes over 20,000 features. Several machine learning methodologies were implemented for the analysis of censored patient follow-up information with such high-dimensional genomic data. Our findings demonstrated the potential of inclusion of mRNA information for BCR-free survival prediction. A random survival forest pipeline was found to achieve high predictive performance with respect to discrimination, calibration, and net benefit. Two mRNA variables, namely ESM1 and DHAH8, were identified as consistently strong predictors with this dataset.

Spatially coherent modeling of 3D FDG-PET data for assessment of intratumoral heterogeneity and uptake gradients.

Purpose: Radiomics have become invaluable for non-invasive cancer patient risk prediction, and the community now turns to exogenous assessment, e.g., from genomics, for interpretability of these agnostic analyses. Yet, some opportunities for clinically interpretable modeling of positron emission tomography (PET) imaging data remain unexplored, that could facilitate insightful characterization at voxel level. Approach: Here, we present a novel deformable tubular representation of the distribution of tracer uptake within a volume of interest, and derive interpretable prognostic summaries from it. This data-adaptive strategy yields a 3D-coherent and smooth model fit, and a profile curve describing tracer uptake as a function of voxel location within the volume. Local trends in uptake rates are assessed at each voxel via the calculation of gradients derived from this curve. Intratumoral heterogeneity can also be assessed directly from it. Results: We illustrate the added value of this approach over previous strategies, in terms of volume rendering and coherence of the structural representation of the data. We further demonstrate consistency of the implementation via simulations, and prognostic potential of heterogeneity and statistical summaries of the uptake gradients derived from the model on a clinical cohort of 158 sarcoma patients imaged with F 18 -fluorodeoxyglucose-PET, in multivariate prognostic models of patient survival. Conclusions: The proposed approach captures uptake characteristics consistently at any location, and yields a description of variations in uptake that holds prognostic value complementarily to structural heterogeneity. This creates opportunities for monitoring of local areas of greater interest within a tumor, e.g., to assess therapeutic response in avid locations.

Assessment of the Prognostic Value of Radiomic Features in 18F-FMISO PET Imaging of Hypoxia in Postsurgery Brain Cancer Patients: Secondary Analysis of Imaging Data from a Single-Center Study and the Multicenter ACRIN 6684 Trial.

Hypoxia is associated with resistance to radiotherapy and chemotherapy in malignant gliomas, and it can be imaged by positron emission tomography with 18F-fluoromisonidazole (18F-FMISO). Previous results for patients with brain cancer imaged with 18F-FMISO at a single center before conventional chemoradiotherapy showed that tumor uptake via T/Bmax (tissue SUVmax/blood SUV) and hypoxic volume (HV) was associated with poor survival. However, in a multicenter clinical trial (ACRIN 6684), traditional uptake parameters were not found to be prognostically significant, but tumor SUVpeak did predict survival at 1 year. The present analysis considered both study cohorts to reconcile key differences and examine the potential utility of adding radiomic features as prognostic variables for outcome prediction on the combined cohort of 72 patients with brain cancer (30 University of Washington and 42 ACRIN 6684). We used both 18F-FMISO intensity metrics (T/Bmax, HV, SUV, SUVmax, SUVpeak) and assessed radiomic measures that determined first-order (histogram), second-order, and higher-order radiomic features of 18F-FMISO uptake distributions. A multivariate model was developed that included age, HV, and the intensity of 18F-FMISO uptake. HV and SUVpeak were both independent predictors of outcome for the combined data set (P < .001) and were also found significant in multivariate prognostic models (P < .002 and P < .001, respectively). Further model selection that included radiomic features showed the additional prognostic value for overall survival of specific higher order texture features, leading to an increase in relative risk prediction performance by a further 5%, when added to the multivariate clinical model..

Combining structural and textural assessments of volumetric FDG-PET uptake in NSCLC.

Numerous studies have reported the prognostic utility of texture analyses and the effectiveness of radiomics in PET and PET/CT assessment of non-small cell lung cancer (NSCLC). Here we explore the potential, relative to this methodology, of an alternative model-based approach to tumour characterization, which was successfully applied to sarcoma in previous works. The spatial distribution of 3D FDG-PET uptake is evaluated in the spatial referential determined by the best-fitting ellipsoidal pattern, which provides a univariate uptake profile function of the radial position of intratumoral voxels. A group of structural features is extracted from this fit that include two heterogeneity variables and statistical summaries of local metabolic gradients. We demonstrate that these variables capture aspects of tumour metabolism that are separate to those described by conventional texture features. Prognostic model selection is performed in terms of a number of classifiers, including stepwise selection of logistic models, LASSO, random forests and neural networks with respect to two-year survival status. Our results for a cohort of 93 NSCLC patients show that structural variables have significant prognostic potential, and that they may be used in conjunction with texture features in a traditional radiomics sense, towards improved baseline multivariate models of patient overall survival. The statistical significance of these models also demonstrates the relevance of these machine learning classifiers for prognostic variable selection.

Positron emission tomography-based assessment of metabolic gradient and other prognostic features in sarcoma.

Intratumoral heterogeneity biomarkers derived from positron emission tomography (PET) imaging with fluorodeoxyglucose (FDG) are of interest for a number of cancers, including sarcoma. A range of radiomic texture variables, adapted from general methodologies for image analysis, has shown promise in the setting. In the context of sarcoma, our group introduced an alternative model-based approach to the measurement of heterogeneity. In this approach, the heterogeneity of a tumor is characterized by the extent to which the 3-D FDG uptake pattern deviates from a simple elliptically contoured structure. By using a nonparametric analysis of the uptake profile obtained from this spatial model, a variable assessing the metabolic gradient of the tumor is developed. The work explores the prognostic potential of this new variable in the context of FDG-PET imaging of sarcoma. A mature clinical series involving 197 patients, 88 of whom have complete time-to-death information, is used. Texture variables based on the imaging data are also evaluated in this series and a range of appropriate machine learning methodologies are then used to explore the complementary prognostic roles for structure and texture variables. We conclude that both texture-based and model-based variables can be combined to achieve enhanced prognostic assessments of outcome for patients with sarcoma based on FDG-PET imaging information.