Radiomics refers to the extraction of mineable data from medical imaging and has been applied within oncology to improve diagnosis, prognostication, and clinical decision support, with the goal of delivering precision medicine. The authors provide a practical approach for successfully implementing a radiomic workflow from planning and conceptualization through manuscript writing. Applications in oncology typically are either classification tasks that involve computing the probability of a sample belonging to a category, such as benign versus malignant, or prediction of clinical events with a time-to-event analysis, such as overall survival. The radiomic workflow is multidisciplinary, involving radiologists and data and imaging scientists, and follows a stepwise process involving tumor segmentation, image preprocessing, feature extraction, model development, and validation. Images are curated and processed before segmentation, which can be performed on tumors, tumor subregions, or peritumoral zones. Extracted features typically describe the distribution of signal intensities and spatial relationship of pixels within a region of interest. To improve model performance and reduce overfitting, redundant and nonreproducible features are removed. Validation is essential to estimate model performance in new data and can be performed iteratively on samples of the dataset (cross-validation) or on a separate hold-out dataset by using internal or external data. A variety of noncommercial and commercial radiomic software applications can be used. Guidelines and artificial intelligence checklists are useful when planning and writing up radiomic studies. Although interest in the field continues to grow, radiologists should be familiar with potential pitfalls to ensure that meaningful conclusions can be drawn.Online supplemental material is available for this article.

Background and ObjectivesColorectal cancer with liver metastases is potentially curable with surgical resection however clinical prognostic factors can insufficiently stratify patients. This study aims to assess whether radiomic features are prognostic and can inform clinical decision making.MethodsThis single‐site retrospective study included 102 patients who underwent colorectal liver metastases resection with preoperative computed tomography (CT), magnetic resonance imaging (MRI) with gadoxetic acid (EOB) and clinical covariates. A lasso‐regularized multivariate Cox proportional hazards model was applied to 114 features (10 clinical, 104 radiomic) to determine association with disease‐free survival (DFS). A prognostic index was derived using the significant Cox regression coefficients and their corresponding input features and a threshold was determined to classify patients into high‐ and low‐risk groups, and DFS compared using log‐rank tests.ResultsFour covariates were significantly associated with DFS; bilobar disease (hazard ratio [HR]= 1.56; P = .0043), complete pathological response (HR= 0.67; P = .025), minimum pixel value (HR= 1.66; P = .00016), and small area emphasis (HR= 0.62; P = .0013) from the EOB‐MRI data. Radiomic CT features were not prognostic. The prognostic index strongly stratified high‐ and low‐risk prognostic groups (HR = 0.31; P = .00068).ConclusionRadiomic MRI features provided meaningful prognostic information above clinical covariates alone. This merits further validation for potential clinical implementation to inform management.

Objective: We determined the sensitivity and specificity of multiparametric magnetic resonance imaging (MP-MRI) in detection of locally recurrent prostate cancer and extra prostatic extension in the post-radical radiotherapy setting. Histopathological reference standard was whole-mount prostatectomy specimens. We also assessed for any added value of the dynamic contrast enhancement (DCE) sequence in detection and staging of local recurrence. Methods: This was a single centre retrospective study. Participants were selected from a database of males treated with salvage prostatectomy for locally recurrent prostate cancer following radiotherapy. All underwent pre-operative prostate-specific antigen assay, positron emission tomography CT, MP-MRI and transperineal template prostate mapping biopsy prior to salvage prostatectomy. MP-MRI performance was assessed using both Prostate Imaging-Reporting and Data System v. 2 and a modified scoring system for the post-treatment setting. Results: 24 patients were enrolled. Using Prostate Imaging-Reporting and Data System v. 2, sensitivity, specificity, positive predictive value and negative predictive value was 64%, 94%, 98% and 36%. MP-MRI under staged recurrent cancer in 63%. A modified scoring system in which DCE was used as a co-dominant sequence resulted in improved diagnostic sensitivity (61%–76%) following subgroup analysis. Conclusion: Our results show MP-MRI has moderate sensitivity (64%) and high specificity (94%) in detecting radio-recurrent intraprostatic disease, though disease tends to be under quantified and under staged. Greater emphasis on dynamic contrast images in overall scoring can improve diagnostic sensitivity. Advances in knowledge: MP-MRI tends to under quantify and under stage radio-recurrent prostate cancer. DCE has a potentially augmented role in detecting recurrent tumour compared with the de novo setting. This has relevance in the event of any future modified MP-MRI scoring system for the irradiated gland.

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