Refinement of radiomic results and methodologies is required to ensure progression of the field. In this work, we establish a set of safeguards designed to improve and support current radiomic methodologies through detailed analysis of a radiomic signature. Methods: A radiomic model (MW2018) was fitted and externally validated using features extracted from previously reported lung and head and neck (H&N) cancer datasets using gross-tumour-volume contours, as well as from images with randomly permuted voxel index values; i.e. images without meaningful texture. To determine MW2018's added benefit, the prognostic accuracy of tumour volume alone was calculated as a baseline. Results: MW2018 had an external validation concordance index (c-index) of 0.64. However, a similar performance was achieved using features extracted from images with randomized signal intensities (c-index = 0.64 and 0.60 for H&N and lung, respectively). Tumour volume had a c-index = 0.64 and correlated strongly with three of the four model features. It was determined that the signature was a surrogate for tumour volume and that intensity and texture values were not pertinent for prognostication. Conclusion: Our experiments reveal vulnerabilities in radiomic signature development processes and suggest safeguards that can be used to refine methodologies, and ensure productive radiomic development using objective and independent features.

Abstract. Recent works in automated radiotherapy treatment planning have used machine learning based on historical treatment plans to infer the spatial dose distribution for a novel patient directly from the planning image. We present an atlas-based approach which learns a dose prediction model for each patient (atlas) in a training database, and then learns to match novel patients to the most relevant atlases. The method creates a spatial dose objective, which specifies the desired dose-pervoxel, and therefore replaces any requirement for specifying dose-volume objectives for conveying the goals of treatment planning. A probabilistic dose distribution is inferred from the most relevant atlases, and is scalarized using a conditional random field to determine the most likely spatial distribution of dose to yield a specific dose prior (histogram) for relevant regions of interest. Voxel-based dose mimicking then converts the predicted dose distribution to a deliverable treatment plan dose distribution. In this study, we investigated automated planning for right-sided oropharaynx head and neck patients treated with IMRT and VMAT. We compare four versions of our dose prediction pipeline using a database of 54 training and 12 independent testing patients. Our preliminary results are promising; automated planning achieved a higher number of dose evaluation criteria in 7 patients and an equal number in 4 patients compared with clinical. Overall, the relative number of criteria achieved was higher for automated planning versus clinical (17 vs 8) and automated planning demonstrated increased sparing for organs at risk (52 vs 44) and better target coverage/uniformity (41 vs 31). The novel dose prediction method with dose mimicking can generate deliverable treatment plans in 12-13 minutes without any user interaction. The method is a promising approach for fully automated treatment planning and can be readily applied to different treatment sites and modalities.

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a b s t r a c tPurpose: The aims of this study are to evaluate the stability of radiomic features from Apparent Diffusion Coefficient (ADC) maps of cervical cancer with respect to: (1) reproducibility in inter-observer delineation, and (2) image pre-processing (normalization/quantization) prior to feature extraction. Materials and methods: Two observers manually delineated the tumor on ADC maps derived from pretreatment diffusion-weighted Magnetic Resonance imaging of 81 patients with FIGO stage IB-IVA cervical cancer. First-order, shape, and texture features were extracted from the original and filtered images considering 5 different normalizations (four taken from the available literature, and one based on urine ADC) and two different quantization techniques (fixed-bin widths from 0.05 to 25, and fixed-bin count). Stability of radiomic features was assessed using intraclass correlation coefficient (ICC): poor (ICC < 0.75); good (0.75 ICC 0.89), and excellent (ICC ! 0.90). Dependencies of the features with tumor volume were assessed using Spearman's correlation coefficient (q). Results: The approach using urine-normalized values together with a smaller bin width (0.05) was the most reproducible (428/552, 78% features with ICC ! 0.75); the fixed-bin count approach was the least (215/552, 39% with ICC ! 0.75). Without normalization, using a fixed bin width of 25, 348/552 (63%) of features had an ICC ! 0.75. Overall, 26% (range 25-30%) of the features were volume-dependent (q ! 0.6). None of the volume-independent shape features were found to be reproducible. Conclusion: Applying normalization prior to features extraction increases the reproducibility of ADCbased radiomics features. When normalization is applied, a fixed-bin width approach with smaller widths is suggested. Ó 2019 The Author(s). Published by Elsevier B.V. Radiotherapy and Oncology 143 (2020) 88-94 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).Cervical cancer is the fourth most frequent cancer in women, with an estimated 570,000 new cases in 2018, representing 6.6% of all female cancers worldwide. Cervical cancer still represents a significant burden for middle-and low-income countries [1]. Standard treatment for locally advanced (stage IB2-IVA) cervical cancer is concurrent chemoradiation.Computed Tomography (CT) and Magnetic Resonance (MR) are the standard imaging modalities for cervical cancer staging and evaluation of treatment response. Through appropriate choice of pulse sequences MR imaging provides greater soft tissue contrast than CT and enables assessment of physiological parameters and biochemical function. Diffusion-weighted imaging (DWI) in MR enables measurement of water diffusivity via generation of Apparent Diffusion Coefficient (ADC) maps, and ADC is an established biomarker of tumor cell density and related changes posttherapy [2]. DWI is increasing acquired in addition to T2weighted MRI to detect cervical tumor [3], and pre-treatment tumor ADC has been shown to be p...

The results of this pilot study suggest that Perk Tutor provides an improved training environment for US-guided facet joint injections on a synthetic model.

Quantitative imaging features (radiomics) extracted from apparent diffusion coefficient (ADC) maps of rectal cancer patients can provide additional information to support treatment decision. Most available radiomic computational packages allow extraction of hundreds to thousands of features. However, two major factors can influence the reproducibility of radiomic features: interobserver variability, and imaging filtering applied prior to features extraction. In this exploratory study we seek to determine to what extent various commonly-used features are reproducible with regards to the mentioned factors using ADC maps from two different clinics (56 patients). Features derived from intensity distribution histograms are less sensitive to manual tumour delineation differences, noise in ADC images, pixel size resampling and intensity discretization. Shape features appear to be strongly affected by delineation quality. On the whole, textural features appear to be poorly or moderately reproducible with respect to the image pre-processing perturbations we reproduced.

Highlighting the risk of biases in radiomics-based models will help improve their quality and increase usage as decision support systems in the clinic. In this study we use machine learning-based methods to identify the presence of volume-confounding effects in radiomics features. Methods 841 radiomics features were extracted from two retrospective publicly available datasets of lung and head neck cancers using open source software. Unsupervised hierarchical clustering and principal component analysis (PCA) identified relations between radiomics and clinical outcomes (overall survival). Bootstrapping techniques with logistic regression verified features' prognostic power and robustness. Results Over 80% of the features had large pairwise correlations. Nearly 30% of the features presented strong correlations with tumor volume. Using volume-independent features for clustering and PCA did not allow risk stratification of patients. Clinical predictors outperformed radiomics features in bootstrapping and logistic regression. Conclusions The adoption of safeguards in radiomics is imperative to improve the quality of radiomics studies. We proposed machine learning (ML)based methods for robust radiomics signatures development.

Needle guidance with TUSS improves the success rate and time efficiency in spinal facet joint injections. This technique readily translates also to other spinal needle placement applications.