Alex Zwanenburg

Power transformations are used to stabilize variance and achieve normality of features, especially in methods assuming normal distributions such as ANOVA and linear discriminant analysis. However, the commonly used Box-Cox and Yeo-Johnson power transformation methods are sensitive to the location, scale, and presence of outliers in the data. Here we present location- and scale-invariant Box-Cox and Yeo-Johnson transformations to mitigate these issues. We derive maximum likelihood estimation criteria for optimizing transformation parameters and propose robust adaptations that reduce the influence of outliers. We also introduce an empirical test for assessing central normality of transformed features. In simulations and real-world datasets, robust location- and scale-invariant transformations outperform conventional variants, resulting in better transformations to central normality. In a machine learning experiment with 231 datasets with numerical features, integrating robust location- and scale-invariant power transformations into an automated data processing and machine learning pipeline did not result in a meaningful improvement or detriment to model performance compared to conventional variants. In conclusion, robust location- and scale-invariant power transformations can replace conventional variants.

Radiomics is a tool for medical imaging analysis that could have a relevant role in precision oncology by offering precise quantitative support for clinical decision-making. The Radiomics Quality Score (RQS) is a tool developed to assess the rigour of radiomics studies that has now been widely adopted by researchers. Although RQS version 1.0 established a benchmark, an updated framework is required to account for evolving knowledge and ensure optimal evaluation of the quality of radiomics studies through the inclusion of fairness, explainability, rigorous quality control and harmonization. In this Review, we introduce the updated RQS 2.0, which maintains the scientific rigour of its predecessor and addresses these contemporary needs, and therefore could potentially accelerate clinical translation. Moreover, we introduce the radiomics readiness levels, inspired by the technology readiness level framework, which are integrated in RQS 2.0 and reflect nine distinct levels of incremental improvement in radiomics research with the ultimate aim of clinical implementation. We also detail anticipated future directions in radiomics, outlining a strategic vision to advance precision oncology, which is the ultimate aim of RQS 2.0.

Background: The risk of postoperative pancreatic fistula (POPF), one of the most dreaded complications after pancreatic surgery, can be predicted from preoperative imaging and tabular clinical routine data. However, existing studies suffer from limited clinical applicability due to a need for manual data annotation and a lack of external validation. We propose AutoFRS (automated fistula risk score software), an externally validated end-to-end prediction tool for POPF risk stratification based on multimodal preoperative data. Materials and methods: We trained AutoFRS on preoperative contrast-enhanced computed tomography imaging and clinical data from 108 patients undergoing pancreatic head resection and validated it on an external cohort of 61 patients. Prediction performance was assessed using the area under the receiver operating characteristic curve (AUC) and balanced accuracy. In addition, model performance was compared to the updated alternative fistula risk score (ua-FRS), the current clinical gold standard method for intraoperative POPF risk stratification. Results: AutoFRS achieved an AUC of 0.81 and a balanced accuracy of 0.72 in internal validation and an AUC of 0.79 and a balanced accuracy of 0.70 in external validation. In a patient subset with documented intraoperative POPF risk factors, AutoFRS (AUC: 0.84 ± 0.05) performed on par with the uaFRS (AUC: 0.85 ± 0.06). The AutoFRS web application facilitates annotation-free prediction of POPF from preoperative imaging and clinical data based on the AutoFRS prediction model. Conclusion: POPF can be predicted from multimodal clinical routine data without human data annotation, automating the risk prediction process. We provide additional evidence of the clinical feasibility of preoperative POPF risk stratification and introduce a software pipeline for future prospective evaluation. Export

Artificial intelligence (AI) systems may personalise radiotherapy by assessing complex and multifaceted patient data and predicting tumour and normal tissue responses to radiotherapy. Here we describe three distinct generations of AI systems, namely personalised radiotherapy based on pretreatment data, response-driven radiotherapy and dynamically optimised radiotherapy. Finally, we discuss the main challenges in clinical translation of AI systems for radiotherapy personalisation.

Medical imaging provides non-invasive anatomical and functional visualisation of the human body. It is used clinically for diagnostic, prognostic, treatment planning and other purposes. Many current uses of medical imaging involve qualitative or semi-quantitive assessment by experts. Radiomics seeks to automate analysis of medical imaging for clinical decision support. At its core, radiomics involves the extraction and machine learning-based analysis of quantitive features from medical images (Lambin et al., 2017). However, very few-if any-radiomics tools have been translated to the clinic (Huang et al., 2022). One of the essential prerequisites for translation is reproducibility and validation in external settings (O'Connor et al., 2017). This can be facilitated through the use of standardised radiomics software. Here we present mirp, a Python package for standardised processing of medical imaging and computation of quantitative features. Researchers can use mirp for their own radiomics analyses or to reproduce and validate radiomics analyses of others.

Agatston scores for coronary artery disease derived from true noncontrast and photon-counting CT angiography virtual noncontrast images (PureCalcium algorithm) showed excellent correlation but limi... Background Coronary artery calcium scoring (CACS) for coronary artery disease requires true noncontrast (TNC) CT alongside contrast-enhanced coronary CT angiography (CCTA). Photon-counting CT provi...

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.