The Image Biomarker Standardization Initiative validated consensus-based reference values for 169 radiomics features, thus enabling calibration and verification of radiomics software. Background Radiomic features may quantify characteristics present in medical imaging. However, the lack of standardized definitions and validated reference values have hampered clinical use. Purpos...

Filters are commonly used to enhance specific structures and patterns in images, such as vessels or peritumoral regions, to enable clinical insights beyond the visible image using radiomics. However, their lack of standardization restricts reproducibility and clinical translation of radiomics decision support tools. In this special report, teams of researchers who developed radiomics software participated in a three-phase study (September 2020 to December 2022) to establish a standardized set of filters. The first two phases focused on finding reference filtered images and reference feature values for commonly used convolutional filters: mean, Laplacian of Gaussian, Laws and Gabor kernels, separable and nonseparable wavelets (including decomposed forms), and Riesz transformations. In the first phase, 15 teams used digital phantoms to establish 33 reference filtered images of 36 filter configurations. In phase 2, 11 teams used a chest CT image to derive reference values for 323 of 396 features computed from filtered images using 22 filter and image processing configurations. Reference filtered images and feature values for Riesz transformations were not established. Reproducibility of standardized convolutional filters was validated on a public data set of multimodal imaging (CT, fluorodeoxyglucose PET, and T1-weighted MRI) in 51 patients with soft-tissue sarcoma. At validation, reproducibility of 486 features computed from filtered images using nine configurations × three imaging modalities was assessed using the lower bounds of 95% CIs of intraclass correlation coefficients. Out of 486 features, 458 were found to be reproducible across nine teams with lower bounds of 95% CIs of intraclass correlation coefficients greater than 0.75. In conclusion, eight filter types were standardized with reference filtered images and reference feature values for verifying and calibrating radiomics software packages. A web-based tool is available for compliance checking.

Purpose The purpose of this educational report is to provide an overview of the present state‐of‐the‐art PET auto‐segmentation (PET‐AS) algorithms and their respective validation, with an emphasis on providing the user with help in understanding the challenges and pitfalls associated with selecting and implementing a PET‐AS algorithm for a particular application. Approach A brief description of the different types of PET‐AS algorithms is provided using a classification based on method complexity and type. The advantages and the limitations of the current PET‐AS algorithms are highlighted based on current publications and existing comparison studies. A review of the available image datasets and contour evaluation metrics in terms of their applicability for establishing a standardized evaluation of PET‐AS algorithms is provided. The performance requirements for the algorithms and their dependence on the application, the radiotracer used and the evaluation criteria are described and discussed. Finally, a procedure for algorithm acceptance and implementation, as well as the complementary role of manual and auto‐segmentation are addressed. Findings A large number of PET‐AS algorithms have been developed within the last 20 years. Many of the proposed algorithms are based on either fixed or adaptively selected thresholds. More recently, numerous papers have proposed the use of more advanced image analysis paradigms to perform semi‐automated delineation of the PET images. However, the level of algorithm validation is variable and for most published algorithms is either insufficient or inconsistent which prevents recommending a single algorithm. This is compounded by the fact that realistic image configurations with low signal‐to‐noise ratios (SNR) and heterogeneous tracer distributions have rarely been used. Large variations in the evaluation methods used in the literature point to the need for a standardized evaluation protocol. Conclusions Available comparison studies suggest that PET‐AS algorithms relying on advanced image analysis paradigms provide generally more accurate segmentation than approaches based on PET activity thresholds, particularly for realistic configurations. However, this may not be the case for simple shape lesions in situations with a narrower range of parameters, where simpler methods may also perform well. Recent algorithms which employ some type of consensus or automatic selection between several PET‐AS methods have potential to overcome the limitations of the individual methods when appropriately trained. In either case, accuracy evaluation is required for each different PET scanner and scanning and image reconstruction protocol. For the simpler, less robust approaches, adaptation to scanning conditions, tumor type, and tumor location by optimization of parameters is necessary. The results from the method evaluation stage can be used to estimate the contouring uncertainty. All PET‐AS contours should be critically verified by a physician. A standard test, i.e., a benchmark dedicated to evaluating both existing and future PET‐AS algorithms needs to be designed, to aid clinicians in evaluating and selecting PET‐AS algorithms and to establish performance limits for their acceptance for clinical use. The initial steps toward designing and building such a standard are undertaken by the task group members.

In this study we investigated the characteristics of a commercial ion chamber array and its performance in the verification of radiotherapy plans. The device was the 2D Array Seven29 model (PTW, Freiburg, Germany). This is a two-dimensional detector array with 729 ionization chambers uniformly arranged in a 27 x 27 matrix with an active area of 27 x 27 cm(2). The detector short-, medium- and long-term reproducibility have been tested through an extensive set of repeated measurements. Short-term reproducibility was well within 0.2%. Medium- and long-term reproducibility were within 1%, including set-up reproducibility errors and linac output fluctuations. Dose linearity was also assessed. The system response to dose was verified to be linear within the range 2-500 MU. Output factors matched very well pinpoint chamber measurements performed in the same experimental conditions with a maximum local percentage difference of 0.4%. Furthermore, the 2D Array sensitivity to millimetric collimator positional changes and to perturbation effect of irradiated area was tested. The comparison with ion chamber data carried out in water was very satisfying. Finally, measurements of wedge-modulated fields and IMRT beam sequence matched very well ion chamber dose profiles acquired in a water tank. The extensive tests performed in this investigation show that the 2D Array Seven29 is a reliable and accurate dosimeter and that it could be a useful tool for the quality assurance and the verification of radiotherapy plans.

Imaging dose in radiation therapy has traditionally been ignored due to its low magnitude and frequency in comparison to therapeutic dose used to treat patients. The advent of modern, volumetric, imaging modalities, often as an integral part of linear accelerators, has facilitated the implementation of image-guided radiation therapy (IGRT), which is often accomplished by daily imaging of patients. Daily imaging results in additional dose delivered to patient that warrants new attention be given to imaging dose. This review summarizes the imaging dose delivered to patients as the result of cone beam computed tomography (CBCT) imaging performed in radiation therapy using current methods and equipment. This review also summarizes methods to calculate the imaging dose, including the use of Monte Carlo (MC) and treatment planning systems (TPS). Peripheral dose from CBCT imaging, dose reduction methods, the use of effective dose in describing imaging dose, and the measurement of CT dose index (CTDI) in CBCT systems are also reviewed.

Access to healthcare data is indispensable for scientific progress and innovation. Sharing healthcare data is time-consuming and notoriously difficult due to privacy and regulatory concerns. The Personal Health Train (PHT) provides a privacy-by-design infrastructure connecting FAIR (Findable, Accessible, Interoperable, Reusable) data sources and allows distributed data analysis and machine learning. Patient data never leaves a healthcare institute. Lung cancer patient-specific databases (tumor staging and post-treatment survival information) of oncology departments were translated according to a FAIR data model and stored locally in a graph database. Software was installed locally to enable deployment of distributed machine learning algorithms via a central server. Algorithms (MATLAB, code and documentation publicly available) are patient privacy-preserving as only summary statistics and regression coefficients are exchanged with the central server. A logistic regression model to predict post-treatment two-year survival was trained and evaluated by receiver operating characteristic curves (ROC), root mean square prediction error (RMSE) and calibration plots. In 4 months, we connected databases with 23 203 patient cases across 8 healthcare institutes in 5 countries (Amsterdam, Cardiff, Maastricht, Manchester, Nijmegen, Rome, Rotterdam, Shanghai) using the PHT. Summary statistics were computed across databases. A distributed logistic regression model predicting post-treatment two-year survival was trained on 14 810 patients treated between 1978 and 2011 and validated on 8 393 patients treated between 2012 and 2015. The PHT infrastructure demonstrably overcomes patient privacy barriers to healthcare data sharing and enables fast data analyses across multiple institutes from different countries with different regulatory regimens. This infrastructure promotes global evidence-based medicine while prioritizing patient privacy.